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Supporting Farmworkers Through Regulatory Processes
|
Supporting Farmworkers Through Regulatory Processes
|
https://www.theregreview.org
|
[
"The Regulatory Review",
"Sadie Basila",
"Michael Z. Green",
"Hugh Rennie"
] |
Lerae Kroon emphasizes the importance of staying up to date with changing regulations in her work with farmworkers. She shares how workers and their employers ...
|
Lerae Kroon explains how direct legal services providers interact with regulations protecting farmworkers.
In a conversation with The Regulatory Review, Lerae Kroon, supervising attorney of the Pennsylvania Farmworker Project at Philadelphia Legal Assistance, discusses how non-profit organizations participate in the regulatory process and how she interacts with government agencies to support farmworkers in accessing justice.
The H-2A program enables some domestic employers to bring foreign nationals to the United States to work in temporary agricultural positions. In December 2024, the U.S. Citizenship and Immigration Service revised the program to strengthen worker protections for agricultural workers.
President Donald Trump has prioritized immigration as a policy issue in each of his presidential campaigns. Since his inauguration in January, he has issued multiple executive orders to decrease immigration and increase deportations. Reportedly, in light of these executive actions, farmworkers are fearful that they could face deportation or other legal consequences.
Lerae Kroon emphasizes the importance of staying up to date with changing regulations in her work with farmworkers. She shares how workers and their employers are impacted by developments in immigration policy. Kroon explains how non-profits working with clients affected by agency regulations can have a mutually beneficial relationship with government actors. Moreover, she discusses the gaps in protections for farmworkers and addresses how legal services providers can help enforce legal protections for farmworkers who may not know their rights.
Kroon has worked as an attorney advocating for tenants, farmworkers, and immigrant laborers throughout her career. Through her work at the Pennsylvania Farmworker Project, she aims to assist farmworkers with legal issues and advocates workplace safety, better health standards, and fairer wages. Kroon graduated from Harvard Law School, where she was recognized for her contributions to clinical work and from which she received a post-graduation fellowship to provide humanitarian immigration legal services.
The Regulatory Review is pleased to share the following interview with Lerae Kroon.
The Regulatory Review: How do non-profits engaged in legal advocacy generally work with government agencies?
Kroon: I currently work at a non-profit focused on providing direct services and representation to low-income individuals and groups who would otherwise struggle to access justice. At times, we represent individual clients before government agencies such as the U.S. Social Security Administration or state offices of unemployment compensation. As a result, non-profit providers of direct legal services become knowledgeable “repeat players” in systems, with the ability to view and identify trends and gaps in protections for their clients.
Some government agencies value the perspective that non-profit legal advocates can bring and seek their input when creating policy, both informally and in formal rulemaking. This provides advocates with an opportunity to weigh in on the effect of regulations on our clients and raise real-life stories to agencies creating policy that will affect our future clients.
Other types of legal non-profits have the ability and resources to engage in more direct policy advocacy with government agencies by taking an active role in pushing for improvements to law and policy.
TRR: How have you interacted with federal or state agencies while working in farmworker advocacy?
Kroon: In addition to the activities I have already noted, I strive to build strong relationships with labor enforcement agencies at the state and federal level. One reason is capacity: Legal services non-profits consistently encounter more people that need assistance accessing justice than they have the resources to serve. I cannot litigate every case—nor is every case well-suited for litigation. When it comes to issues such as wage theft and health and safety conditions, for example, the goals of labor agencies overlap with worker goals, and an agency complaint is a viable route to a resolution.
In the past, I have engaged in very productive partnerships with agencies through “community enforcement” efforts. In these partnerships, I have been able to work closely with agencies such as the U.S. Department of Labor to investigate and resolve labor violations by connecting them with workers who may otherwise be hesitant to come forward. For example, H-2A temporary agricultural visa workers have particular vulnerabilities related to isolation and the connection of their immigration status with their employment. Some of these workers fear the impact that reporting labor abuses may have on their ability to obtain work visas in the future. I am able to provide context and information about agency processes to help make an informed decision about whether to bring a complaint.
TRR: Do political changes, including President Trump’s inauguration and accompanying changes in regulatory priorities, affect your strategy as an advocate for farmworkers?
Kroon: A new administration means a shift in priorities and resource allocation, which will change how advocates interact with federal agencies. Many priorities and positions are already shifting. Part of my job is to stay up-to-date on changing interpretations of the law and legal concepts, so that I can assess cases and advise my clients accordingly. We will be watching closely in the months ahead to understand the shifting legal landscape for farmworkers. If new regulations are issued, we fully expect to weigh in when requested.
During his first term, the Trump Administration, has suggested that it may support an increase in usage of the H-2A temporary agricultural worker program, possibly including its expansion of eligible employment from seasonal work to year-round work. If that were to occur, there would be significant changes to the nature of agricultural work in Pennsylvania and thus to our work as farmworker advocates. For example, there may be an increased need for enforcement of the H-2A program protections for U.S. workers to ensure that they are not displaced by H-2A workers and that wages and conditions for U.S. workers are not negatively affected by H-2A labor.
TRR: Are there gaps in federal and state protections for farmworkers? If so, what are some examples of these gaps?
Kroon: Many federal and state employment and labor laws carve out exceptions for farmworkers from protections such as minimum wage and overtime protections. Passed in 1938 as part of New Deal policies, the Fair Labor Standards Act excluded farmworker and domestic workers. Employers were not required to pay farmworkers the federal minimum wage until 1966, and farmworkers are still excluded from federal overtime requirements. Most states followed the federal government’s path by excluding farmworkers from state employment laws, including those providing for the right to organize or join unions and key health and safety protections.
Studies by the National Center for Farmworker Health report that farmworkers “represent some of the most socially and economically disadvantaged people in the U.S.” The National Agricultural Workers Survey found that, during 2021 and 2022, the mean and median personal annual income of crop workers was between $20,000 and $24,000, and that 21 percent of workers have family incomes below the poverty level.
TRR: U.S. Customs and Immigration Enforcement has been in the news after reports of recent immigration raids in major cities. How are farmworkers, whether authorized to work in the United States or not, and their employers impacted by these developments?
Kroon: Farmworkers have a variety of immigration statuses, commonly including U.S.-born and naturalized citizenship, lawful permanent residence, work visas, and no status. The National Agricultural Workers Survey found that approximately 68 percent of the U.S. agricultural workforce is made up of individuals born outside of the United States.
In my experience interacting with farmworkers over the past few months, many workers actively fear the effect of immigration raids on their families and communities regardless of their work authorization status. It remains to be seen whether there will be an increase in worksite Immigration and Customs Enforcement raids, but many folks are preparing themselves and their families for the worst possible scenarios. Here in Pennsylvania, we are starting to see reports that agricultural employers, which have historically relied on immigrant labor, are struggling to find enough workers. Farmworkers I speak with are assessing how much risk they face simply by showing up each day to work in an industry staffed primarily by immigrant workers.
TRR: In December 2024, U.S. Citizenship and Immigration Services issued a final rule modifying the H-2A visa program to strengthen worker protections. How do governmental and non-governmental actors alert farmworkers of their legal protections?
Kroon: Ideally, H-2A workers should receive information about their rights at various stages of their employment. H-2A program regulations require employers to include certain information in their job offers, including details about wage and hours, housing, and transportation. Employers must also include assurances committing to abide by H-2A program requirements as part of their job offers. As a result, literate workers who are given a copy of their job offer and contract before beginning employment have information about their legal protections. Unfortunately, we know that some recruiters and employers do not share all required information at the time of hire and that some common program violations—such as charging recruitment fees—can take place before workers ever see a contract.
Non-governmental actors have a huge part to play in informing H-2A workers not only of their rights but also how to enforce them. Because many H-2A workers live in isolated areas and have limited ability to access resources outside their labor camp, farmworker advocacy programs do robust in-person outreach to H-2A workers. We check in, provide information about their rights, and offer support if needed. This season, we are adjusting our know-your-rights trainings and materials to incorporate the updated H-2A rule. In a moment of regulations-in-action, I now get to explain the new protections to the workers they cover, empowering them with information about their rights and inviting them to action if employers are not in compliance.
| 2025-06-29T00:00:00 |
2025/06/29
|
https://www.theregreview.org/2025/06/29/spotlight-supporting-farmworkers-through-regulatory-processes/
|
[
{
"date": "2025/06/29",
"position": 91,
"query": "technology regulation worker rights"
}
] |
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] |
“Temporary” Tyranny: Senate is Voting on The 10-Year AI ...
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“Temporary” Tyranny: Senate is Voting on The 10-Year AI Moratorium That Silences States and Artists
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https://musictechpolicy.com
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That's a decade-long federal gag order—one that blocks states from enforcing critical protections for privacy, civil rights, and creative workers. And in any ...
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Congress is floating a “temporary pause” on state and local regulation of artificial intelligence solely to benefit Big Tech under the One Big Beautiful Bill Act. But here’s the trick: it lasts ten years. That’s not a pause. That’s a decade-long federal gag order—one that blocks states from enforcing critical protections for privacy, civil rights, and creative workers. And in any regulation that touches Big Tech, ten years may as well be eternity.
Under this amendment, any state, county, city, or other political subdivision like many school districts that takes federal broadband funds (called “BEAD” funding) would be prohibited from regulating AI, automated decision systems, or data-driven tools if they touch interstate commerce. In plain English: everything important. Blatant coercion.
Crushing State Protections for Artists, Journalists and Citizens
This provision directly threatens emerging state laws protecting creators and personal identity.
Tennessee’s ELVIS Act , signed into law in March 2024, created the nation’s strongest protections against AI-based impersonation of performers and public figures—criminalizing unauthorized voice and likeness cloning. Under the moratorium, enforcement of that law could be blocked for a full decade if Tennessee or any subdivision accepts broadband infrastructure funding.
, signed into law in March 2024, created the nation’s strongest protections against AI-based impersonation of performers and public figures—criminalizing unauthorized voice and likeness cloning. Under the moratorium, enforcement of that law could be blocked for a full decade if Tennessee or any subdivision accepts broadband infrastructure funding. Biometric privacy laws like Illinois’ Biometric Information Privacy Act (BIPA) , which empowers individuals to sue over unauthorized face and voice data collection, could also be challenged or preempted.
like , which empowers individuals to sue over unauthorized face and voice data collection, could also be challenged or preempted. Laws passed or proposed in California, New York, Texas, and Washington to govern generative AI, deepfakes, synthetic media labeling, and AI in employment and housing could all be frozen under this amendment.
A federal ban would also devastate local journalism. Without the ability to enforce state-level protections, AI systems could ingest news articles, headlines, and investigations to train models or answer queries—without attribution, permission, or compensation. That strips value from reporting and erodes the already fragile business model of ad-supported journalism.
The result? A brand new safe harbor for the most powerful AI platforms many of whom are the richest corporations in commercial history—and total exposure for artists, journalists, consumers, and working people.
And Then There’s that Old Constitutional Thing
The AI safe harbor’s constitutional weaknesses are serious and given who has lined up against it will almost certainly be challenged:
Spending Clause Violation – Under NFIB v. Sebelius, Congress cannot coerce states into federal compliance by threatening to withhold unrelated funds. Conditioning broadband money on banning AI regulation is blatantly coercive crosses that line. Tenth Amendment Violation – It effectively “commandeers” state policy, telling legislatures and school boards what they cannot regulate in violation of the Constitutional “anti-commandeering” doctrine. Federalism Breakdown – States have sovereign police powers to protect civil rights, consumer welfare, labor conditions, and artistic expression. This amendment tries to nullify those powers, see 10th Amendment. Equal Protection & Civil Liberties – It prevents governments from responding to known discriminatory harms in AI systems, especially against insurance redlining, minors, gig workers, and creators of color.
Gagging Local School Districts
Buried in the fine print is a direct blow to local educational control. If a school district receives broadband funds—even passively through a state grant—it would be barred from adopting policies governing AI use in classrooms. After Skrmetti v. U.S., this is exceptionally bizarre.
No restrictions on ChatGPT-style tools during exams.
No rules on surveillance tech, predictive analytics, or automated grading.
No transparency or accountability for third-party platforms scraping student work or personal data.
This is an attack on school board authority and parent-led policy development. It reduces districts to passive recipients of private AI experimentation—at taxpayer expense.
The Backlash Has Already Begun
Some members of Congress are pushing this under the radar, but opposition is mounting:
Over 40 state attorneys general have raised concerns about federal interference in state AI and privacy policy.
have raised concerns about federal interference in state AI and privacy policy. At least 11 state legislatures have introduced or passed laws to protect against AI harms.
have introduced or passed laws to protect against AI harms. 6 governors have issued executive orders or vetoed legislation in defense of state control over AI systems and data usage.
Opposing the Moratorium
Even bipartisan voices in Congress are beginning to push back against sweeping preemption language masquerading as innovation policy.
Marsha Blackburn (R‑TN) — Criticized the moratorium for eroding states’ rights and undermining privacy laws
— Criticized the moratorium for eroding states’ rights and undermining privacy laws Josh Hawley (R‑MO) — Joined fellow Republicans in opposing the measure on grounds of federal overreach .
— Joined fellow Republicans in opposing the measure on grounds of federal overreach . Marjorie Taylor Greene (R‑GA) — Opposed it after realizing the bill included AI restrictions, calling out the lack of transparency
— Opposed it after realizing the bill included AI restrictions, calling out the lack of transparency Maria Cantwell (D‑WA) — A leading Senate Democrat who pushed amendments to strip the moratorium, warning it would “hold the BEAD funding hostage”
— A leading Senate Democrat who pushed amendments to strip the moratorium, warning it would “hold the BEAD funding hostage” Edward Markey (D‑MA) — Filed formal amendments to remove the ban, citing overreach and consumer protection concerns
Vote counters may notice that there are 3 republicans and four senators who have openly opposed the bill–which with a 53 vote majority in the Senate and given that the OBBBA passed by one vote in the House is not looking good for this must-pass, triple whip signature legislation for President Trump. That Senate cloak room is going to look like a hog auction for prize pork.
What is to be Done?
This so-called “pause” is no pause at all. It is a decade-long moratorium on democratic governance, shielding AI companies from state accountability and freezing laws designed to protect real people from real harms—whether artists facing deepfake impersonation, journalists getting plagiarized, school districts trying to preserve educational standards, or citizens caught in biased algorithms by weapons of math destruction.
A better future with AI doesn’t require a federal muzzle on 50 states and 13,000 school districts. It requires collaboration—not coercion—and guardrails that respect local needs, creative rights, and constitutional structure.
So you know the drill–time to call Congress and help give them some spine. They need to oppose the AI moratorium in the “One Big Beautiful Bill” because tying BEAD funding to a ban on state AI laws undercuts our local protections and oversteps constitutional authority. Bipartisan state leaders and legislators strongly disagree with this measure. They need to support bipartisan efforts to strip it from the bill.
You can use the “5 Calls” page already devoted to the AI moratorium available here, or you can call the House of Representatives (202) 225-3121 or the Senate (202) 224-3121 (Senate phone list here)
| 2025-06-29T00:00:00 |
2025/06/29
|
https://musictechpolicy.com/2025/06/29/temporary-tyranny-senate-is-voting-on-the-10-year-ai-moratorium-that-silences-states-and-artists/
|
[
{
"date": "2025/06/29",
"position": 12,
"query": "technology regulation creative workers"
}
] |
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Cultural workers gear up for AI battles ahead
|
Cultural workers gear up for AI battles ahead
|
https://www.cpbml.org.uk
|
[] |
By using their power through trade unions, creative workers are beginning to find that they can prevent even the largest corporations using AI to more ...
|
The ruling class will inevitably seek to use AI against workers, as they have done with every step forward in technology. Creative workers are among the first to feel this – and they are beginning to respond.
Trade unions representing workers in the arts and professions where creativity has a big role to play have cooperated over the past year, advising members about intensified exploitation and the danger to livelihoods.
It is a complex issue, with no single definition of AI. One useful description is “technologies with the ability to perform tasks that would otherwise require human intelligence”. Research by Equity, Musicians’ Union, National Union of Journalists (NUJ) and the Writers’ Guild has resulted in clear analysis of the subject and detailed advice for each sector.
Equity (representing mainly actors, singers and media workers) explains that AI aims to produce a close approximation to how humans learn, reason, carry out tasks and make decisions. It does so by training itself on vast amounts of data.
AI encompasses a wide range of technologies, including machine learning, deep learning, neural networks, expert systems, and robotics. But of most relevance to creative workers is generative AI, which can create new content, such as text, images, audio, or computer code.
Generative AI output can resemble human-created content or synthetic media, but cannot replicate the creativity, emotion and authenticity performers bring. AI can help the creative process, but has already started to replicate aspects of performers’ roles.
Replacement
The trend of trying to replace human decisions is likely to increase as the technology develops. Equity emphasises the importance of being a trade union member as the best way to deal with these threats. But it also points out possible benefits too, if controlled by performers.
“If AI is applied ethically and responsibly in collaboration with workers and their trade union, it has the potential to positively impact performers,” the union says
For example AI could help performers appear in multiple productions across a single period and improve income; increase accessibility for deaf and disabled performers; enhance safety for stunt performers; and aid dubbing and automated dialogue replacement.
UK Music & Musicians’ Union protest on 7 May as Parliament passed the Data Bill without safeguards against generative AI. Photo courtesy MU.
Equity spells out the dangers to all aspects of work, dangers that are similar across the sector – and echoed by the other unions. Job loss is the most obvious: 65 per cent of performers surveyed thought that AI technology poses a threat to employment. Notably, over 90 per cent of audio artists felt AI would affect their opportunities.
Pay is another obvious area of concern. One-off payments to performers for AI work often do not reflect that their contribution may be used forever, on thousands of occasions.
Agreements for AI performance are inadequate and poorly understood. Performers don’t even know where their work is being used. And 80 per cent of those surveyed didn’t have a full understanding of their rights before signing a contract for AI work.
Poor employment practices are commonplace in the arts. Adding AI into the mix can only make this worse. Equity and other unions try to control this through collective bargaining. But the new technology is challenging across the globe as it would not be covered in historic collective agreements.
Creative expression is at risk too. Technologies such as the creation and use of digital replicas remove agency from the performer. This cuts opportunities to perform in person and removes incentives for people to train and enter artistic work.
Stop stealing the show
By using their power through trade unions, creative workers are beginning to find that they can prevent even the largest corporations using AI to more intensively exploit them. Due to their innovative campaign, “Stop AI Stealing the Show”, Equity members have achieved a huge victory, with the government abandoning a damaging data mining exemption.
Equity’s next priority is, “…to integrate ethical terms and conditions for AI work into working practices through collective bargaining.” The union believes the strength of the industry is built on strong and equitable collectively bargained agreements with engagers, including the BBC, ITV, Sky, Netflix, Disney+, and Apple+, but they need strengthening. The union is also exploring new partnerships with digital cloning companies presently operating outside of agreements.
All the unions advocate for an artist-centred approach to safeguard their members. For example, Equity’s AI Vision Statement outlines core principles for the industry to adopt when engaging artists for performance cloning. The aim is that performers have the contractual right to informed consent, control, fair compensation and transparency.
Protecting copyright
Many generative AI platforms create new material using original creative work without a licence. This infringes workers’ intellectual property and legal rights, which remain poorly protected under British law against AI abuse.
The government says it wants to make Britain a global AI superpower. But rather than introducing regulation, they propose a voluntary code of practice for the industry. Technology companies developing AI are generally resistant to challenges; their business model seems to rely on free use of material generated by others.
Creative sector unions are alert to the need to tackle the dangers to members and their copyright work in the training of generative AI. Equity, for example, is seeking “…a constructive dialogue with producers…to ensure…informed consent, transparency and compensation” for all use of its members’ work.
Most script writers, music composers and authors join their professional bodies, the Mechanical Copyright Protection Society (MCPS) and the Performing Rights Society (PRS). They work in a coordinated way to collect and distribute royalties on original works.
Both societies are analysing and making policy on the effects of AI. MCPS has updated its membership agreement, disallowing registration of 100 per cent AI-generated works. In other words, only the human contribution would be recognised.
‘Intellectual property rights are poorly protected against AI abuse…’
A report launched on 4 June revealed how generative AI is already having profound impacts on Britain’s creative economy. Compiled by Queen Mary University of London, The Institute for the Future of Work and The Turing Institute the report called for urgent action to protect creative workers. The Musicians’ Union is already campaigning on those lines. It held a protest on 7 May as the Data Bill was passed without safeguards against generative AI. But along with others it continues to press for urgent action.
Journalists are in jeopardy from AI too. The NUJ’s campaign calls for urgent regulatory oversight that includes promoting ethical approaches that safeguard the work of its members. It is calling for engagement with journalists and those whose work is used in AI technology.
The NUJ cited breaches of intellectual property rights, inaccurate data used in AI-generated stories, false attributions to journalists, and more. These “…directly threaten journalism and risks the reputation of every journalist abiding by ethical standards to ensure accuracy and honesty through their work.”
Some final words include those of the authors Val McDermid and Chris Brookmyre. Chris discovered that twelve of his books had been recognisably plundered by AI, angrily pointing out that he “had given permission for precisely zero to be used to train AI” while Val urged fellow authors to use the search function and find out if their works had been used. The campaign continues to expand, bringing in the Writers’ Guild and the Society of Playwrights.
A major struggle is under way to prevent the productivity screws being tightened on workers by stealing and exploiting their creative and original talents.
| 2025-06-29T00:00:00 |
https://www.cpbml.org.uk/news/cultural-workers-gear-ai-battles-ahead
|
[
{
"date": "2025/06/29",
"position": 14,
"query": "technology regulation creative workers"
}
] |
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|
There's Still A Chance Of Recession In 2025. What To Watch
|
There's Still A Chance Of Recession In 2025. What To Watch
|
https://www.investopedia.com
|
[
"Diccon Hyatt",
"Learn About Our",
"Editorial Policies"
] |
So far, the unemployment rate remains at a relatively low 4.2%, whereas a sharp jump would indicate a brewing recession. The data has shown a slowdown in the ...
|
Key Takeaways The U.S. economy is likely to avoid a recession in the second half of the year, forecasters said in their mid-year outlooks.
President Donald Trump has walked back some of his most extreme tariff proposals, reducing the risks to the economy.
A downturn is still possible, especially if war in the Middle East escalates or tariffs bite harder than expected, economists said.
The chances of an economic downturn have diminished, but aren't entirely gone, several economists said in mid-year forecasts.
Anxiety among experts about a possible recession peaked in April, after President Donald Trump announced higher-than-expected "reciprocal" tariffs on numerous trading partners. At the time, forecasters were concerned that the rapidly escalating trade war would push up inflation and drag down the economy. Some economists even said a recession was more likely than not.
Since then, however, Trump has paused or rolled back some of his most extreme tariff announcements, easing fears of a sharp downturn. Still, many tariffs remain in place, including a 10% tariff on most countries, 50% tariffs on steel and aluminum and 25% duties on foreign cars. Several economists said those tariffs will likely slow the economy but not drag it into an outright recession.
However, the ongoing uncertainty surrounding the tariff policy is also a major drag on the economy. Because tariff rates aren't finalized, it's difficult for businesses and individuals to make financial plans for the future, delaying purchases and investments.
Uncertainty has fallen since April but poses a lingering risk, according to forecasters. The conflict in the Middle East also risks causing a downturn, especially if it escalates to the point where oil supplies are disrupted.
"The tariff and trade uncertainty will not result in the U.S. economy entering a recession, Sean Snaith, a professor of economics at the University of Central Florida, wrote. "It may, however, cause growth to be somewhat lower than it would have been in the absence of the disorder."
What Are The Odds?
Forecasters at Oxford Economics pegged the chances of a recession in the next year at 35%, still higher than the baseline 15% recession chance in any given year.
So far, the unemployment rate remains at a relatively low 4.2%, whereas a sharp jump would indicate a brewing recession. The data has shown a slowdown in the job market, with job openings trending lower in recent months, partly due to the firing of federal workers by the DOGE cost-cutting task force.
"The labor market is definitely slowing in 2025 as concerns about tariffs and federal spending cuts take hold," PNC economists led by Chief Economist Gus Faucher wrote. "The most likely outcome is weaker job growth and a slightly higher unemployment rate through the rest of this year, but not a recession. Still, the labor market could turn quickly south in the months ahead if employers lose confidence because of tariffs."
What Business Leaders Say About The Risks
Although the hard data like unemployment hasn't shown many red flags, "soft" data such as surveys of business leaders indicate they are pessimistic.
Goldman Sachs economists Ronnie Walker and David Mericle said the C-suite chatter shows leaders are downbeat about the economy, but not so much that a recession seems inevitable. Goldman does expect unemployment to tick up and the annual inflation rate to rise to more than 3% from its current mid-2% levels, but doesn't forecast a recession.
"Overall, company commentary appears roughly consistent with our forecast that tariffs will have a visible effect that will leave the U.S. economy with slower hiring and a slightly higher unemployment rate, little growth in investment spending this year, below-potential GDP growth but not a recession, and a meaningful but one-time inflation rebound to the mid-3s," Mericle and Walker wrote.
Robert Fry, an independent forecaster, is slightly more concerned.
"Even if a recession is avoided, consumer spending on durable goods and business investment in equipment, which were pulled forward in anticipation of tariffs, are likely to decline in coming months," he wrote. "And if the war between Israel and Iran pushes oil prices significantly higher, a recession could work its way back into the forecast."
| 2025-06-29T00:00:00 |
https://www.investopedia.com/will-there-be-a-recession-in-the-second-half-of-2025-11761215
|
[
{
"date": "2025/06/29",
"position": 5,
"query": "financial crisis stock exchange employment rate"
}
] |
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] |
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I. Sustaining stability amid uncertainty and fragmentation
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I. Sustaining stability amid uncertainty and fragmentation
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https://www.bis.org
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... economic uncertainty to levels typically associated with crises and sparked high volatility in financial markets. As a result, the outlook for global growth ...
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Key takeaways The outlook for global growth deteriorated amid heightened uncertainty and the fraying of long-established economic ties. Financial markets experienced significant volatility in response to frequent, unpredictable trade policy announcements.
The baseline outlook of soft economic growth and moderate inflation is clouded by heightened policy uncertainty, while existing vulnerabilities in the real economy and financial system have the potential to amplify the negative impact of shocks and adverse shifts in policy.
Policymakers must act as a stabilising force by ensuring fiscal positions are sustainable, enhancing macro-financial resilience through a level playing field across different types of financial intermediation and prioritising price stability.
Introduction
The prospects for the global economy have become much more uncertain and unpredictable in recent months, marking a notable departure from the relative optimism of the previous year when a soft landing was in sight. Trade disruptions now threaten to reshape the global economic landscape, as long-standing political and economic relationships are being questioned. The new US trade policy, with its unknown eventual scope and impact, has elevated measures of economic uncertainty to levels typically associated with crises and sparked high volatility in financial markets. As a result, the outlook for global growth has been downgraded, with mixed implications for inflation across economies.
These developments are unfolding in a world already grappling with significant vulnerabilities. Trade-related challenges are likely to reinforce pre-existing shifts towards greater economic fragmentation and protectionism, further exacerbating the decade-long decline in economic and productivity growth across many economies. Coupled with ageing populations and emerging labour shortages, trade fragmentation could further reduce supply flexibility, leaving economies more prone to inflation pressures. Meanwhile, in several jurisdictions, high public debt makes the financial system vulnerable to interest rate rises, while reducing governments' ability to respond to adverse developments. Adding to these challenges, the shift of financial intermediation from banks to non-bank financial institutions (NBFIs) and towards financing public debt has heightened liquidity risks in bond markets, raising the potential for financial stability risks to emerge outside traditional banking systems.
Addressing these challenges requires efforts by policymakers on multiple fronts. Structural policy needs to address low productivity growth and improve the ability of the economy to scale up production and reallocate resources. The removal of barriers to trade, both within and across borders, would help offset the damage from the ongoing trade conflict. In addition to supporting structural reforms, fiscal policy needs to adjust to ensure debt sustainability and restore the space for supporting the economy when needed. The regulation and supervision of the financial system must take into account the shifting nature of financial risks arising from the structural changes in the financial system described in Chapter II of this report. In this context, there is a need for a consistent regulatory framework for banks and other financial intermediaries that pose similar risks to financial stability. Finally, for monetary policy, the experience of recent years has been a forceful reminder of the primacy of price stability as a cornerstone for sustainable growth. In an era of heightened uncertainty, preserving this anchor is more important than ever.
To succeed in these challenges, policy needs to be conducted with credible frameworks that deliver reasonable outcomes in a broad range of scenarios. Policymakers must set clear targets against which their policies can be assessed and select appropriate tools to achieve them. They need to clearly explain their actions and decisions to the public and be held accountable when things do not go to plan. Periodic reviews aimed at strengthening these frameworks can help to ensure they remain fit for purpose in the face of a changing environment. This steadfast commitment to the pursuit of their goals will foster society's trust in policymakers and institutions, ultimately enhancing the effectiveness of measures taken.
From soft landing to turbulence and uncertainty
The outlook for the global economy, which until early 2025 seemed on track for a soft landing, has been overshadowed by heightened uncertainty. In the second half of 2024, inflation was projected to converge to central bank targets alongside ongoing moderate economic growth. However, the global environment is now characterised by disruptions to trade and rising geopolitical tensions punctuated by periods of heightened financial market volatility.
The global economy was growing at a moderate rate ahead of the disruptions that have defined recent months. Global GDP growth was just over 3% in 2024, broadly in line with mid-2024 expectations (Graph 1).
Graph 1 Close all
The stable aggregate outcome for global growth in 2024 masked significant differences across economies. The United States stood out as the strong performer among advanced economies (AEs), with economic growth in 2024 once again surprising to the upside. By contrast, growth in Europe and Japan was weak.
A key factor behind these differences was the resilience of the US consumer (Graph 2.A). Household consumption was above its pre-pandemic trend in the United States with a lower saving rate, while households in most other AEs were much more cautious, with saving rates typically well above their pre-pandemic level (Graph 2.B).
Economic trends in emerging market economies (EMEs) were also varied. In many East Asian economies, solid growth in export volumes helped offset weak domestic demand (Graph 2.C). In China, strong expansion in manufacturing output and exports enabled the country to achieve the authorities' GDP growth target of 5% for 2024, despite the ongoing adjustment in the property sector. Meanwhile, growth in India slowed, following an exceptionally strong performance after the pandemic. In Latin America, economic activity was generally subdued, except in Brazil where strong domestic demand was supported by a tight labour market and fiscal transfers.
Labour market conditions had largely normalised by the second half of 2024. While unemployment rates had increased relative to the very low levels reached in the immediate aftermath of the pandemic, they generally remained below pre-pandemic norms. As labour markets rebalanced, nominal wage growth generally eased but remained firmer than before the pandemic. One exception was Japan, where the growth rate of nominal wages continued to rise, reaching its highest level in decades.
Inflation continued to ease, reaching or approaching central bank targets in most economies (Graph 3). However, progress towards target slowed in some Latin American economies, such as Brazil, Chile and Colombia, due to domestic drivers, including private demand, adjustments in regulated prices and exchange rate depreciations. By contrast, inflation was generally at or below target in East Asia, highlighted by the persistent very low rates of inflation in China.
Graph 2 Close all
Graph 3 Close all
With inflation reaching or approaching targets, most central banks eased monetary policy over the past year to support economic growth (Graph 4.A). Similarly, the People's Bank of China adjusted its stance from "prudent" to "moderately loose". Two key exceptions to this broad easing trend were the Central Bank of Brazil, which raised rates rapidly in response to evidence that inflation expectations were de-anchoring amid high inflation, and the Bank of Japan, which lifted its policy interest rate to 0.5%, a level last seen about two decades ago.
Graph 4 Close all
In addition to reducing policy rates, several AE central banks continued to shrink their balance sheets (Graph 4.B). This was primarily done by contracting their government bond portfolios, largely through the passive roll-off of maturing securities. This process has generally proceeded smoothly.
As the soft landing for the economy came into view, financial markets performed strongly through 2024 and early 2025. In the United States, equities rallied on the back of solid growth throughout 2024 as well as expectations of looser regulation and tax cuts from the incoming administration. Similarly, European equity markets surged in late 2024, driven by growing optimism, partly related to the improvement in banks' earnings. This optimism was further bolstered by news that Germany eased its constitutional debt brake for defence-related expenditure and also committed to spending €500 billion – more than 10% of its GDP – on infrastructure over the next 12 years. Consistent with the favourable backdrop for markets, credit spreads generally remained compressed by historical standards. Meanwhile, the US dollar appreciated against most currencies in the second half of 2024, reflecting the relatively strong economic fundamentals of the US economy. Only the muted outlook for EMEs and the sustained gold price rally were out of sync.
Trade policy takes centre stage
The relatively favourable global outlook of early 2025 was overshadowed by major policy shifts and heightened financial market volatility. The announcement of significant US tariffs on Canada and Mexico in January came as a surprise to many and was followed by a series of product- and country-specific tariff proposals over subsequent months, culminating in the announcement of broad-based tariffs on US imports from all trading partners in early April. This marked a watershed moment for the global economy, with the potential to weaken demand, disrupt global supply chains and destabilise the global trading system. The April proposals included "reciprocal" tariffs of at least 10% on almost all countries, with significantly higher rates targeting those with large bilateral trade surpluses. These measures raised fears of retaliation, which materialised as a cycle of escalation between the United States and China. Tariffs peaked at 145% on most US imports from China and 125% on most Chinese imports from the United States.
Subsequent developments saw the US administration scale back the size and scope of its tariff proposals, introducing temporary pauses, carve-outs for certain goods and modest trade deals with select partners. US–China tensions eased as tariff rates were temporarily reduced to allow for further negotiations, calming financial market fears and reducing downside risks to the global economy. Later, at the end of May, a US court ruling that struck down a substantial share of the tariffs imposed by the US administration under existing law cast new doubts on how tariffs would eventually be implemented. Given the frequent reversals, the risk of further escalation and legal challenges, as well as the lack of historical precedents for shifts of this magnitude, the broader impact of these policy changes remains uncertain.
Tariffs were accompanied by several other major policy changes in the United States that heightened concerns about policy direction and stability. The administration also introduced significant shifts in immigration, regulatory and fiscal policy, while doubts were raised about its commitment to central bank independence. Beyond the policy measures themselves, the repeated cycle of announcements, adjustments and reversals has fostered an atmosphere of uncertainty and volatility, compounding the challenges for the global economy.
Common metrics of uncertainty have risen sharply. Trade policy uncertainty spiked to a record high in the first half of 2025 (Graph 5.A) and broader economic policy uncertainty also rose, albeit unevenly across regions (Graph 5.B). Moreover, the geopolitical landscape, with active conflicts on multiple continents, remains fertile ground for unpredictable events that could compound challenges from the ongoing trade tensions.
Graph 5 Close all
Frequent policy changes led to pronounced swings in financial markets
Global financial markets saw a dramatic escalation of volatility when the reciprocal tariffs announced in early April exceeded market expectations. With markets already on edge due to greater policy uncertainty, gauges of volatility surged to levels not seen since the pandemic, as investors scrambled to reduce risky exposures (Graph 6.A). Stock markets across the globe plunged and corporate credit spreads across the rating spectrum soared. As investors curbed US dollar exposures in April, safe haven flows accrued to gold and other major currency markets instead (Graph 6.B). Short-term yields in the euro area, Japan and the United Kingdom dropped swiftly, also driven in part by investors' anticipation of easing monetary policy (blue line). Long-term yields were less responsive, as investors appeared reluctant to increase duration risk exposure. This was particularly evident in US Treasury markets, with large price swings and an increase in long-term yields. Unusually for a risk-off episode, the US dollar depreciated against many currencies, particularly the euro, the yen and the Swiss franc, as investors proceeded to cover some of the currency risk of their US asset holdings (Graph 6.C).
As the larger proposed tariffs were walked back, financial markets stabilised and recovered. Measures of market volatility returned to the range observed in the second half of 2024. Equity markets rebounded, reaching fresh year-to-date highs in some jurisdictions. Credit spreads compressed again, particularly in the high-yield segment. Industrial metals rebounded to the range predating the tariff announcement. Oil prices initially stayed lower, weighed by large increases in supply, but subsequently increased as geopolitical tensions intensified in June. The US dollar, however, traded at the bottom of the range observed since the start of the tightening period in early 2022. And long-term yields in the United States and some other major markets have trended higher over recent months, consistent with growing investor concerns about fiscal sustainability.
Graph 6 Close all
The economic impact of tariffs and uncertainty was initially evident in a range of timely survey data, showing a significant deterioration in household and business sentiment, particularly in the United States. By contrast, the effect on conventional statistics – or hard data – has taken more time to emerge and has provided a mixed reading on the economy to date. Some indicators were boosted by the pull forward of spending to beat the imposition of tariffs. The associated variability in these hard data, highlighted by examples such as the contraction in US GDP in the first quarter, made the underlying trend in many economies difficult to distil. Lagging indicators, such as those from the labour market, generally remained firm.
Tariffs and uncertainty have overshadowed the economic outlook
The effects of recent policy developments are expected to have a material impact on the economic outlook. In assessing the economic effects of tariffs several channels are key, from the direct impact of the tariffs on the economy and the implications for supply chains, to the indirect effects associated with the reaction of financial markets and other policy responses. Yet assessing the strength of these channels is difficult, not only because of continuous shifts in policy but also because of the practical and legal complexity of enforcing them, as well as the lack of recent precedents to assess their effects. The related effects of policy uncertainty may add to the drag on the economy, even if trade negotiations are successful.
Consider first the impact of tariffs and their key transmission channels. Increased tariffs are a negative supply shock in imposing countries, reducing output and increasing prices. By making imported goods more expensive, tariffs erode real incomes, in turn reducing demand for imported and domestic goods. While higher import prices may encourage substitution towards domestically produced goods, potentially boosting output in protected industries, empirical evidence suggests that this effect is limited. Importantly, this substitution often comes at the cost of efficiency, as capital and labour are redirected towards less competitive firms and sectors. Furthermore, while tariffs lead to changes in gross import and export flows, they barely affect net trade flows (see Box A).
Tariffs tend to act as a negative demand shock for targeted countries, primarily by lowering exports. Their impact on activity will depend on the strength of trade links, along with the scope to redirect exports to other markets. At the same time, their disinflationary impact will hinge not only on domestic slack but also on the potential increased supply of goods diverted from tariff-imposing countries. In the case of US tariffs, economic output in countries such as Mexico and Canada is likely to be among the largest affected, while the effects on European economies are expected to be smaller.
Further to the direct effects on individual economies, the introduction of higher tariffs could impose adjustment costs on the global economy as trade overall becomes less efficient. As supply chains adjust, there is the potential for significant disruptions to trade and temporary shortages of some goods. As witnessed during the pandemic, such disruptions can have significant and long-lasting ramifications for production and prices across the economy. Additionally, the effects of tariffs will be greatly influenced by the adjustment in monetary and fiscal policies as well as shifts in financial market conditions, including changes in nominal exchange rates. Such effects could be further amplified by existing vulnerabilities (discussed below).
Until greater clarity emerges regarding the scope and coverage of the new tariffs, uncertainty is likely to remain one of the dominant factors shaping the near-term outlook. Evidence suggests that some firms anticipated the potential turbulence ahead, increasing credit lines towards the end of 2024 (Graph 7.A). The increase was most pronounced for firms in sectors exposed to trade policy changes.
Graph 7 Close all
The effects of uncertainty alone could have a major impact on the near-term economic outlook through several channels. For one, heightened uncertainty may lead firms to adopt a more cautious approach to investment and hiring because these decisions could be very costly to reverse. Likewise, consumers may choose to postpone durable goods purchases and increase savings as a precautionary measure. At the same time, elevated uncertainty might also increase the cost of external finance because of greater caution by lenders, further discouraging investment. Consistent with these channels, increases in measures of uncertainty have typically been followed by weaker economic activity and particularly business investment. Estimates suggest a substantial negative contribution of the recent increase in uncertainty on business investment in 2025 and 2026 (Graph 7.B).1
Policymakers have responded to the shifting economic and geopolitical landscape. For example, the Canadian government has introduced a range of economic support programmes to help businesses and workers directly impacted by US tariffs. Authorities in China have announced fiscal support aimed at boosting consumer demand and infrastructure investment. Meanwhile, the European Central Bank, the People's Bank of China, the Bank of Mexico and the Bank of Thailand were among many central banks that cut their policy rates in recent months, citing growth risks from trade and uncertainty. Market pricing suggests central banks will provide additional support in the period ahead. Among the major economy central banks, further easing is expected by the Federal Reserve and European Central Bank. The Bank of Japan is expected to lift rates more gradually than previously projected.
Consensus forecasts for GDP growth have been revised lower as the direct and indirect impacts of the trade conflict have been factored in. Global growth is now expected to be 2.7% in 2025 and only a little firmer in 2026. These forecasts are around a quarter percentage point lower than expectations at the start of 2025. The most significant downward revisions have been observed in North America and parts of East Asia. Growth in the United States was downgraded by around 1 percentage point relative to expectations at the beginning of 2025 along with similar downgrades to growth in Mexico and Canada, which both have the United States as their largest trading partner. Elsewhere, the near-term outlook for GDP growth is generally somewhat weaker relative to expectations at the start of 2025.
The net effects of lower growth and trade policy changes on inflation vary by economy, particularly in the near term. In the United States, the cumulative effect of broad-based tariffs is expected to lead to a large increase in the price level over coming months. However, inflation is projected to ease in 2026, as weakness in the domestic economy weighs on price growth. The inflationary impact of the US tariffs for many other economies is generally expected to be small but will depend on the interaction of trade policy responses such as retaliatory measures, the redirection of trade flows and currency movements. Combined with the associated slowdown in global growth due to the trade frictions, the outlook for inflation has been revised a little lower for many economies.
Vulnerabilities on the path ahead
The existence of multiple vulnerabilities magnifies the risks to the global outlook. These fall into three broad categories. The first pertains to the real economy. Potential growth has been steadily declining for decades in many economies, while supply capacity is also becoming less flexible. Moreover, the pandemic-era surge in inflation may have heightened households' and firms' sensitivity to future inflation. Second, public debt has reached unprecedented levels in many countries. As a result, risks to inflation and financial stability can more easily originate from or be propagated through stress in sovereign bond markets. Finally, credit and liquidity risks have grown in parts of the non-bank financial sector, with implications for banks as well as the functioning of key financial markets. This section considers each of these vulnerabilities in turn.
Real vulnerabilities
The global economy faces a combination of long-standing and emerging structural problems. A major issue is the decades-long decline in economic growth, a trend evident in many AEs and, more recently, in several EMEs (Graph 8.A). Slower growth not only limits improvements in living standards but also delays recovery from recessions, undermines debt sustainability and heightens risks to macroeconomic and financial stability. In addition, with population ageing and globalisation slowing, many economies are now beginning to grapple with a less responsive supply, which could make inflation more sensitive to output changes.
Consider lower trend economic growth first. In many AEs, productivity growth had been on a downward trend for decades.2 This decline partly reflects diminishing productivity gains from new technologies. At the same time, the timeline for realising the full benefits of emerging technologies, including generative artificial intelligence (AI), remains uncertain. Adding to these challenges, population ageing means that most AEs can no longer count on the domestic labour force expanding (Graph 8.B).
A similar growth slowdown is now also evident in many EMEs. After benefiting from catch-up growth, convergence with their AE counterparts has slowed, with many EMEs now beginning to face challenges similar to those of AEs, including ageing populations. Additionally, several of these EMEs remain highly reliant on foreign direct investment (FDI) for technology diffusion and, especially in Asia, on export-led growth.
That said, policy retains a crucial role in shaping long-term growth. Across many economies, including the most dynamic ones, the slowdown in productivity growth has been accompanied by weaker business dynamism – as shown in several countries by declining rates of business formation, expansion and exit – and lower allocative efficiency, as seen in greater dispersion of firms' productivity outcomes.3 These declines stem from multiple causes that vary by country but often include insufficient competition, regulatory complexity, rigid labour markets, and high energy and logistics costs from inadequate infrastructure.4,5
Against this backdrop, globalisation has been a vital force in sustaining income growth, countering other factors that have weighed on it. The integration of trade and capital flows has fostered greater specialisation, improved capital allocation and enhanced competition. It has also greatly facilitated technological diffusion through FDI, especially among EMEs.6,7
Graph 8 Close all
Yet these benefits are increasingly under threat from escalating trade and geopolitical tensions. Even before recent US tariff announcements, growth in global trade had slowed considerably and FDI had been falling following the Great Financial Crisis (GFC) (Graph 8.C; see Box B). To be sure, these trends partly reflect the maturity of global value chains, as well as the growth slowdown in China and other major EMEs. The benefits from earlier reductions in tariffs had waned, while non-tariff barriers and industrial policy measures had also been increasing, especially in AEs (Graphs 9.A and 9.B). This has coincided with a rise in protectionist sentiment, especially in countries with persistent trade deficits, fuelled by concerns over rising income inequality and job losses (Graph 9.C). The imposition of substantial trade tariffs by the United States marked a further intensification of these trends.
Broad-based import tariffs are, however, unlikely to address these concerns effectively. In countries imposing them, such tariffs risk lowering overall living standards and reducing employment, even in sectors competing with imports.8 Instead, if the objective is to reduce trade imbalances, a more effective approach involves the adoption of appropriate fiscal and structural policies, including policies that help displaced workers to find jobs in other sectors (see Box A).
Recent structural developments not only exacerbate long-standing factors that limit trend growth but also reduce the economy's supply flexibility in responding to shocks. For instance, demographic shifts are contributing to labour shortages in several economies and a more inelastic labour supply. In many cases, reliance on foreign-born workers has been the only factor preventing outright declines in labour forces as older workers retire (see Graph 8.B). In less globally integrated goods markets, firms may face higher costs when expanding output, constrained by limited access to intermediate products, weaker competition and more rigid supply chains. Market rigidities that hinder resource allocation may further constrain firms' ability to adapt to shifts in demand. As a result, many economies might today face a steeper Phillips curve than in recent decades, with changes in output translating into larger changes in inflation.9
Graph 9 Close all
In addition, inflation expectations may have become less stable. Having been caught by surprise by the inflation surge during the pandemic recovery, business and household expectations of inflation are likely to react more quickly to future price increases. Indeed, recent surveys show that households' inflation expectations for the year ahead are closely related to their perception of the increase in the price level over the past five years. This link is stronger today than it was pre-pandemic (see Box C). As a result, the risk that inflation expectations could become unmoored seems greater post-pandemic.10,11
Compounding these challenges is the increased likelihood that the economic landscape will face more frequent, intense and persistent adverse supply shocks. Key drivers of these shocks include climate change, with its extreme weather events, and geopolitical tensions, with their potential to disrupt commodity markets and value chains. Overall, inflation may be subject to more frequent and persistent deviations from targets in the future as economic activity suffers, making the conduct of monetary policy more challenging.
Fiscal vulnerabilities
The GFC and the pandemic have left many economies with higher public debt and large fiscal deficits, with debt levels in many cases reaching historic peacetime highs (Graph 10.A). These levels are projected to rise further, as deficits – which were 6–7% of GDP in several major economies in 2024 – are expected to close only partially or at a slow pace in the coming years. Debt levels will also face upward pressure from population ageing, which drives up pension and healthcare costs, as well as from emerging demands such as infrastructure investment, the transition to greener energy and growing defence needs. Furthermore, limited tolerance for further erosion in real incomes among households could lead to more immediate demands for compensation via fiscal support in the event of future shocks.
While higher public debt levels can be sustainable in the presence of strong income growth and low interest rates, current and future conditions look less favourable. As noted above, economic growth is expected to remain subdued for the foreseeable future. Moreover, interest rates may not return to the low levels observed in the pre-pandemic decade. Indeed, current interest rates are already putting pressure on fiscal accounts. For instance, among Organisation for Economic Co-operation and Development (OECD) countries with relatively high interest payments, average payments have risen from 3% of GDP in 2021 to more than 4% in 2024 (Graph 10.B), and they are projected to increase further, even if rates remain unchanged, as several countries are set to refinance up to half their public debt stock within the next two years (Graph 10.C). A significant risk for debt sustainability is that bond yields could rise even higher, especially if inflation were to prove more volatile and persistent or if governments delay tackling large fiscal deficits.
These risks could be compounded by shifts in the investor base. In major economies, the reduction in central bank holdings of government securities – as central banks unwind their quantitative easing programmes – tilts the supply-demand imbalance in sovereign markets, putting upward pressure on yields through term premia. Such reduction has already led to some widening in term premia from the lows seen in the aftermath of the GFC and the pandemic, as private investors stepped in to fill the gap (Graph 11.A). For example, the Federal Reserve's $1½ trillion reduction in Treasury holdings since mid-2022 is estimated to have increased long-term yields by around 80 basis points, reversing around two thirds of the estimated impact of quantitative easing during the pandemic.12
Graph 10 Close all
Graph 11 Close all
At the same time, there are signs of some weakening in investor appetite for government bonds and rising intermediation challenges. One indicator is the recent decline of interest rate swap spreads – the difference between the fixed-rate leg of an interest rate swap and government bond yields of the same tenor – into negative territory for Japanese yen and German euro swaps, following in the footsteps of comparable US instruments (Graph 11.B).13 Meanwhile, the rising correlation between stocks and bonds signalled a reduction in the traditional hedging properties of government bonds for investment portfolios, amid higher inflation expectations and rising concerns about inflation uncertainty. This is also reflected in lower convenience yields on US Treasuries – the premium investors place on holding these securities for their safety and liquidity (Graph 11.C).14
High public debt levels, if coupled with rising interest rates and sluggish economic growth, raise the chance of adverse scenarios that could compromise price and financial stability. For instance, growing concerns about fiscal sustainability could give rise to refinancing challenges and potentially dislodge inflation expectations, complicating the conduct of monetary policy. These challenges could be compounded by currency depreciation, as capital flees the country, amplifying inflationary pressures. That said, these risks are significantly mitigated by central bank independence and strong institutional safeguards, which shield monetary authorities from political interference and enable them to adhere strictly to their mandates. This reinforces public trust, anchoring inflation expectations even in periods of heightened concerns about fiscal sustainability.15
High public debt also makes the financial system vulnerable to lower asset values when interest rates increase, especially when it is accompanied by high leverage in the financial sector.16 Whether it reflects changes in sovereign or inflation risk, or an unexpected but necessary tightening of monetary policy, a repricing of government securities can lead to substantial losses for banks and NBFIs with significant sovereign debt holdings. Such losses could, in turn, potentially trigger deleveraging or even insolvencies and ultimately tighten financial conditions for the broader economy. The resulting economic slowdown can further erode sovereign creditworthiness, creating a feedback loop between fiscal and financial vulnerabilities that exacerbates risks to both sectors.
Recent events have highlighted how these vulnerabilities can play out in case of a sharp increase in perceived fiscal risks. For instance, bond losses were central to the turmoil in the UK gilt markets following the announcement of the mini-budget in September 2022. This episode also triggered a crisis in liability-driven investment (LDI) strategies used by UK pension funds, which faced sudden margin calls as gilt yields surged, forcing them to sell assets in a self-reinforcing spiral that further destabilised the market. Similarly, the rapid increase in US policy rates to combat inflation played a pivotal role in the March 2023 US regional banking crisis. Unrealised losses on government bonds became a critical issue when some banks faced sudden and substantial deposit withdrawals. In the future, the stress could be much more severe if the creditworthiness of some sovereigns is questioned.
Macro-financial vulnerabilities
Macro-financial vulnerabilities have the potential to amplify economic developments, including the anticipated slowdown triggered by the shift in trade policy and heightened uncertainty. While private sector debt levels have remained relatively stable in most countries since the GFC and throughout the pandemic, they remain historically high among non-financial corporations in several jurisdictions and households in several smaller economies (Graph 12.A).
At the same time, major changes in the financial system since the GFC have introduced new risks. Debt issuance by the government in the form of sovereign bonds has outpaced the growth of claims on the private sector, while financial intermediation has increasingly shifted from banks to NBFIs. Private credit funds now play a larger role in providing credit to private firms, while asset managers and hedge funds have a larger footprint in sovereign bond markets and cross-border flows, supported by the expansion of short-term funding and hedging markets. As a result, financial conditions as well as financial stability risks are increasingly influenced by players outside traditional banking systems.17
Graph 12 Close all
Consider first the risks posed by high debt in the household and non-financial corporate sectors. One concern is that elevated debt could amplify any economic downturn by leading to wider credit spreads, rising defaults and reduced credit availability. In the household sector, debt service ratios suggest that risks in most countries remain contained. But a deterioration in labour market conditions, coupled with declining house prices, could slow consumer spending and pose significant challenges in countries with high household debt. In the corporate sector, the potential deflationary impact of recent economic events could worsen the credit worthiness of firms in some economies. Furthermore, non-financial corporations – particularly in EMEs – will continue to face substantial debt rollovers in the coming years (Graph 12.B). If benchmark rates increase further and spreads widen from today's compressed levels, the cost for firms of refinancing maturing debt could rise substantially, putting additional pressure on debt service ratios, which are already elevated in some jurisdictions (Graph 12.C).
In this context, the rapid growth of private credit markets in recent years could represent an emerging pocket of risk. A growing share of the long-term credit to small or medium-sized and highly indebted companies is provided by private credit funds, which are, in turn, typically funded by pension funds and insurance companies.18 Compared with bank loans, this form of credit is less exposed to maturity transformation and liquidity risks but is also notoriously opaque.19 Since these assets are not regularly marked to market, they are less likely to amplify reductions in credit supply during a downturn, but they may allow bad credit to accumulate in the corporate sector.20 Moreover, recent trends such as efforts to attract retail investors and the offer of frequent redemption windows may reintroduce liquidity risks. Ultimately, the resilience of this relatively young sector to a sizeable downturn in the credit cycle remains largely untested.
While private credit providers present emerging risks, traditional banks are also exposed to credit risk through the support they provide to private markets. For example, banks supply loans to finance leveraged buyouts or warehouse portfolios of loans before they are securitised – that is, sold to private investors. As long as these loans are on banks' balance sheets, banks remain exposed to the associated credit and market risks.21 When conditions for securitisation deteriorate, as occurred in 2022, banks' balance sheets may become encumbered with those loans, crowding out new lending.22
Aside from loan markets, the shifts in financial intermediation activity towards NBFIs has increased the likelihood that financial instability could originate or be amplified by liquidity stresses. For instance, with broker-dealer balance sheets having smaller heft in the financial system post-GFC, liquidity in sovereign bond markets increasingly relies on open-ended mutual funds, hedge funds and other asset managers. These entities often face significant liquidity mismatches, rely on short-term funding backed by government securities as collateral or are either frequently highly leveraged or exhibit leverage-like behaviour. As a result, their liquidity provision is less stable and more likely to evaporate during periods of market stress.
Hedge funds, in particular, have increasingly become a significant source of procyclical liquidity, especially in government bond markets. These investors actively pursue relative value trading strategies that seek to exploit small price differences between related financial instruments.23 To boost the returns on these small price differences they heavily leverage their positions. One method often used is to pledge government securities as collateral in the repo market to borrow more cash with which to purchase additional government securities. This practice has further evolved in recent years, with investors borrowing amounts equal to or higher than the market value of the collateral provided – that is, without any discount, or haircut, protecting the cash lender from market risk.24 In turn, that means that the borrower can obtain more leverage, leaving the overall market exposed to dislocations if haircuts are increased even slightly, leading to forced selling. The increased heft of hedge funds is reflected, for instance, in their growing US Treasury gross exposure – now exceeding 10% of the outstanding free float (Graph 13.A, red line) – and the expansion of the US repo market segment catering to leveraged investors (blue line).25
Hedge funds' relative value strategies are highly vulnerable to adverse shocks in funding, cash or derivative markets, as evidenced by some recent episodes. During the market turmoil of March 2020, for instance, margin calls in Treasury futures markets triggered fire sales, resulting in destabilising deleveraging spirals.26 More recently, a more orderly unwinding of relative value trades – this time tied to interest rate swap markets, where investors had bet on a narrowing in spreads due to potential deregulation – seems to have contributed to the heightened volatility observed in Treasury markets in early April 2025.
Another potential source of liquidity risk stems from the growing presence of stablecoins in the Treasury market. Although relatively small in terms of aggregate capitalisation, some major issuers, such as Tether and Circle, hold significant reserves in US Treasuries and provide substantial repo market funding through dedicated money market funds (Graph 13.B). Their increasing heft raises financial stability concerns, as it exposes traditional finance to the ebbs and flows of the crypto ecosystem. On the one hand, as stablecoins grow, they will absorb an increasing share of safe assets that traditional financial institutions could otherwise use. On the other hand, negative shocks in the crypto market could lead to sizeable sell-offs which could disrupt the orderly functioning of Treasury markets (see Chapter III).
Graph 13 Close all
NBFIs have also significantly expanded their role in cross-border financial transactions, mainly through portfolio investment in bonds. Many rely on short-term dollar funding and hedging markets through repos and foreign exchange (FX) swaps to finance positions and manage currency exposures. Notably, non-US pension funds and insurers hold substantial US assets hedged with short-maturity FX derivatives that are continuously rolled over. By end-2024, dollar borrowing through FX swaps, forwards and currency swaps accounted for 90% of the $111 trillion in these instruments globally outstanding (Graph 13.C). While these tools facilitate funding and hedging of large cross-border positions, they also expose NBFIs to significant short-term rollover risks and funding squeezes, as highlighted by the sharp volatility in early August 2024 (see Chapter II).
Policies to deal with a more uncertain and fragmented world
Economic policy should strive to promote sustainable economic growth while preserving economic and financial stability. Its success depends not only on the specific measures chosen but also on how they are implemented. Central to effective policymaking is building and maintaining society's trust, defined as the expectation that policymakers will act predictably in the pursuit of legitimate and predefined goals and that they will achieve these goals over time. For this, policymakers must set clear targets against which their policies can be assessed and select appropriate tools to achieve them. They must remain steadfast in pursuing them, addressing deviations promptly and transparently, while clearly explaining their actions and decisions to the public.
When trust exists, the public aligns its behaviour with policymakers' actions and is more willing to accept short-term costs for long-term gains. This not only enhances the effectiveness of policy but also strengthens its ability to adapt to changing circumstances – a trait that becomes even more vital in periods of sudden disruptions and heightened uncertainty. Trust and policy effectiveness reinforce each other, forming a virtuous circle. But the dynamic can also work in reverse. From this viewpoint, threats to central bank independence or drastic changes in international policies risk not only failing to achieve their stated objectives but also undermining the effectiveness of future policy measures. Thus, policymakers should continuously strive to strengthen their policy frameworks, ensuring they remain fit for purpose in the face of evolving challenges.27
Against this backdrop, the rest of this section discusses the main policy priorities needed to support long-term growth and preserve monetary and financial stability in the face of the vulnerabilities described earlier.
Structural policy
Structural reforms are key to addressing the persistent challenges of low economic and productivity growth experienced by many economies in recent decades, as well as the growing rigidity of the supply side of these economies.
Despite their importance, structural reforms have often lagged behind. Yet the need to implement them has now become more pressing. For one, if higher tariffs and other trade barriers become permanent, or if the fragmentation of the global economy along geopolitical lines continues, the global economy might see significant reconfigurations of supply chains, changes in the patterns of capital flows and shifts in the final destinations of goods and services. This puts a premium on improving the capacity of domestic markets to reallocate resources efficiently across firms and sectors. In addition, with higher public debt and still elevated private sector debt, raising potential growth is crucial for improving debt sustainability and ensuring broader macroeconomic stability.28 Finally, structural reforms that strengthen the supply side are the only means to achieving sustainable economic growth. Neither expansionary monetary policy nor expansionary fiscal policy can act as a lasting driver of long-term growth. At the same time, a stronger and more flexible supply side also improves the economy's resilience to adverse shocks, easing the trade-offs faced by central banks.
Structural policies should focus on at least three key interrelated areas, with a balance that depends on the specific needs and priorities of each country.
One is to reduce market rigidities and strengthen administrative capacity. In tight labour markets, reforms to fiscal incentives, pension systems and immigration policies are crucial to expand the labour supply. Business dynamism could be enhanced by reducing bureaucracy, streamlining administrative processes and offering targeted tax relief, among other measures.29 To lessen these rigidities, an efficient administration is also key. This may require attracting top talent, changing staff incentives and reviewing planning laws and regulations to make regular government spending and major public investment projects more efficient.
Another area of reform is to remove barriers to trade, both within and across borders, which could help offset the loss of trade from the ongoing trade conflicts. Remarkably, internal barriers can be quite large. For instance, estimates indicate that intra-EU trade barriers amount to tariffs of 45% on manufacturing and 110% on services trade, while provincial trade barriers in Canada resemble a 7% tariff.30 Reducing these barriers, including those preventing the full integration of internal capital markets, can foster business growth and improve credit flow to new investment projects.
Given ongoing trade tensions, revamping existing regional trade arrangements or striking new ones has gained greater urgency. For example, increasing the breadth of the Southern Common Market (Mercosur/Mercosul) in Latin America and the Regional Comprehensive Economic Partnership in the Asia-Pacific region has the potential to offset the current drop in cross-border investments. Moreover, the trade conflict may accelerate the conclusion of long-stalled trade agreements (eg EU-Mercosur) and the full ratification of repeatedly delayed treaties (eg Canada-EU).
Finally, greater public investment is essential in many countries not only to address low trend growth and supply rigidities but also to tackle emerging challenges such as the impact of climate change and increased defence needs. It may also prove key to helping the economy adapt to shifting trade patterns in the future. Importantly, public investment also serves as a catalyst for private business investment. By improving infrastructure and supporting research in areas like energy, biomedicine and AI, public investment can reduce costs, create new markets and stimulate private sector activity.31 Furthermore, the additional demand generated by public investment can amplify private investment through acceleration effects.
Unfortunately, the share of public investment as a share of public expenditure has been on a declining trend in many economies, contributing in part to the persistent slowdown in productivity growth.32 The German government's recent announcement of a large infrastructure fund is a step towards reversing this trend, and similar initiatives may follow in other countries with sufficient fiscal space.33 However, limited capacity to complete large projects on time and within budget poses significant challenges in some countries, while others may struggle to fully utilise available funds, as evidenced by the low uptake of the European Union's Recovery and Resilience Facility in certain cases. Moreover, without addressing supply side rigidities, substantial public investment risks fuelling higher inflation. To succeed, large investment plans need to be complemented by structural reforms in many countries.
Fiscal policy
A key priority for fiscal policy is to ensure debt sustainability and the rebuilding of fiscal buffers. This reduces the risk of destabilising scenarios that threaten price and financial stability, while creating the fiscal space needed to accommodate future increases in essential expenditures, such as increased public investment or support for broader structural reforms.
In practice, this means that countries facing large fiscal deficits and limited fiscal space must pursue fiscal consolidation. Research shows that fiscal consolidation is more likely to succeed when certain conditions are met.34 For a start, consolidation should be gradual to minimise short-term output costs, as long as markets have not raised concerns about fiscal sustainability. A large negative output impact would make it harder to stabilise debt ratios, making a gradual approach more effective.
Equally important is the quality of the fiscal adjustment, as the composition of spending and tax changes must be carefully designed to not only achieve the fiscal targets but also minimise short-run costs and enhance long-term supply potential. In countries with already high levels of spending and taxation, relying heavily on tax-based consolidation could hinder growth further. Adjustments to the composition of taxes and spending are part of structural reforms needed to boost sustainable growth and are better carried out with other structural measures, as discussed above.35
A strong fiscal framework – including well designed budget rules to guide short-term decisions, fiscal rules to meet longer-term objectives and an independent fiscal council – helps reinforce the credibility of fiscal plans, thereby allowing for gradual consolidation and smaller output costs. An added benefit of strong fiscal institutions is that they tend to be associated with lower risk premia and hence lower financing costs for the broader economy, even outside periods of consolidation.36 These are clear examples of how trust in policy institutions strengthens policy effectiveness.
Fiscal consolidation also tends to be more successful when implemented during periods of robust economic growth and stable global financial conditions. From this perspective, the recent deterioration of the global economic outlook risks complicating consolidation efforts. Should a downturn materialise, the pace of consolidation would have to be carefully calibrated to avoid deepening the slowdown, yet without endangering investors' confidence. For governments with limited fiscal space, a prudent approach could focus on stabilising the structural balance while allowing automatic stabilisers to operate fully. Countries that have robust fiscal institutions and are already implementing needed structural reforms would be in a better position to do so.
Regulation and supervision
Key risks to financial stability include a growing range of potential sources of liquidity stress in core financial markets, such as those for government bonds. At the same time, the myriad linkages between banks and non-banks have expanded the way stress in the NBFI sector could spill over to the banking sector.
Two key priorities for prudential policies and financial regulation will help address growing vulnerabilities in the financial system: first, ensuring the consistent implementation of Basel III across jurisdictions; and second, adopting a more holistic approach by applying consistent regulatory frameworks to financial intermediaries that pose similar risks to financial stability.
Some jurisdictions have considered delaying the implementation of Basel III or deregulating their financial sector in an attempt to balance growth objectives with financial stability risks. Proponents argue that easing regulations can stimulate credit supply and economic activity. However, history demonstrates that any short-term gains come at the cost of long-term instability. Adequate capital and liquidity regulation, together with supervision and the appropriate macroprudential overlay, have been shown to reign in excessive risk-taking by financial institutions, preventing financial crises that could have jeopardised the broader economy.37 Moreover, higher capital ratios have been associated with higher loan growth, highlighting the importance of bank capital as a robust funding source for credit supply.38
The timely and consistent adoption of Basel III across regions remains central to addressing regulatory shortcomings revealed by the GFC. Moreover, the insights on interest rate and liquidity risk management gained from the 2023 banking turmoil should also be used to inform and adapt national and international standards.39
Further, regulators need to be mindful of the risks and vulnerabilities that have migrated to the NBFI sector. As explained above, two key developments are the migration of credit risk from well regulated to less regulated entities and rising liquidity risks, especially in government bond markets. Examples include strong growth and rising leverage in private credit,40 the increasing presence of internationally active NBFIs and stablecoins' growing ties to traditional finance. Left unaddressed, these developments could amplify shocks and pose financial stability risks, for example when margin calls force fire sales and trigger deleveraging spirals. At the same time, such vulnerabilities have become harder to assess because more of the activity is conducted by less regulated entities, often with high levels of leverage.
This requires regulatory frameworks that apply similar stringency to financial intermediaries posing similar risks to financial stability, regardless of legal form or business model. A holistic approach, or "congruent regulation", would ensure that even if traditional banking activities migrate to the NBFI sector, they are subject to similar regulation, in turn reducing the potential for dangerous build-ups of systemic risk.41 Examples could include requiring minimum haircuts in all securities financing transactions and appropriately calibrated margin requirements even in transactions that are not centrally cleared. Such changes could improve the functioning and stability of bond markets in stress events and reduce the risks of liquidity spirals. Similarly, regulation of stablecoins and cryptoassets should follow a technology-neutral, "same activities, same risk, same regulatory outcomes" approach. The most pressing issues relate to integrity and financial crime – including adherence to anti-money laundering/countering the financing of terrorism reporting requirements – and the backing of stablecoins.
Such a holistic approach to regulation would ensure that regulatory adjustments are made with careful consideration of systemic consequences, avoiding the unintended effect of merely diluting existing safeguards.42
Monetary policy
The post-pandemic experience and recent trade tensions demonstrated that broad-based inflationary pressures could emerge from multiple sources, not just strong aggregate demand. Structural shifts and the growing rigidity of the supply side of many economies might translate into shocks having a larger impact on inflation than in the past. Added to that, the scars from the post-pandemic inflation surge might leave a lasting imprint on inflation expectations with implications for the outlook for inflation (see Box C). In such an environment, central banks' role as anchors of price stability becomes even more important.
For some central banks, including that of the United States and those of countries that retaliate strongly to US tariffs, these developments are expected to resemble a supply shock. They therefore present a difficult trade-off for monetary policy. In choosing the extent and pace of ongoing monetary easing – or whether to pause it altogether – central banks must carefully balance the need to cushion the impact on output and employment against the risk that the expected one-off rise in prices morphs into persistently higher inflation. Assessing this trade-off is particularly challenging given a lack of clarity surrounding the combined effect of the tariffs so far implemented and uncertainty regarding potential future measures. The initial impact on the price level will depend on the extent to which tariffs could be absorbed by firms' margins and on the response of exchange rates, both of which are hard to predict.
This uncertainty is further compounded by the interplay of forces that could either amplify or mitigate the initial impact on inflation and its subsequent trajectory. On one hand, wage- and price-setters may have become more responsive to new inflation shocks. Households, in particular, may show less tolerance for real wage declines following the sharp rise in living costs after the pandemic. On the other hand, a large negative impact of tariffs and uncertainty on domestic output and unemployment could put downward pressure on inflation. Additional disinflationary forces may arise from declining commodity prices, particularly in the event of a pronounced global economic slowdown, as well as from trade diversion and lower prices from tariff-affected countries like China and other surplus economies. These mitigating factors are likely to be especially relevant for economies other than the United States.
In countries that have not imposed tariffs or retaliatory measures, the trade shock is likely to be more akin to an adverse demand shock. As a result, the disinflationary effects in these economies are likely to dominate, including from lower prices for manufactured goods and lower commodities prices. Economies in this group, particularly Asian EMEs experiencing inflation at or below target, may therefore have greater room to continue supporting growth with monetary easing.
Whatever course of action they take, central banks should continue to foster a stable economic environment with a medium-term orientation for policy. By adhering firmly to their mandates, especially against political pressures, and communicating their decisions with clarity and consistency, they can build trust and anchor expectations, providing a sense of predictability in an otherwise uncertain environment. Importantly, central banks should protect their independence and credibility by remaining grounded in realistic views of what monetary policy can and cannot deliver, focusing on the objectives they are well equipped to achieve, namely price and financial stability.
Looking ahead, it is important that monetary policy frameworks remain fit for purpose in a rapidly evolving economic landscape characterised by a wider range of supply shocks and increased uncertainty and unpredictability. Periodic reviews of these frameworks, as currently undertaken by some major central banks, serve this purpose and are informed by three key lessons from recent experience.
First, the experience of the post-pandemic inflation surge and the rising prevalence of supply shocks highlights the need for a symmetric approach to inflation targeting. Such an approach allows central banks to respond forcefully not only to the risks of inflation undershooting the target and interest rates hitting the effective lower bound but also to the risks of inflation surges.
Second, in the face of high and rising uncertainty central banks should remain agile and responsive, continuously adapting their tools to evolving economic conditions. Flexible policy tools and exit strategies should be prioritised to address the potential for abrupt macroeconomic changes. The policy rate should remain the primary instrument, while the use of balance sheet instruments outside crisis periods should be approached with caution. Large-scale asset purchases, in particular, tend to exhibit diminishing returns and are difficult to reverse. Similarly, forward guidance should be employed judiciously, along with clear communication emphasising its state-contingent nature and the dependence of future policy paths on economic developments.
Tools for restoring market functioning should be carefully designed with a clear exit strategy to alleviate moral hazard concerns and to ensure that they do not interfere with the appropriate monetary policy stance.43 Instead, macroprudential tools should play a pre-emptive, complementary role, containing the build-up of vulnerabilities and increasing the resilience of the financial system in the face of shocks, thereby reducing the likelihood of financial crises throughout the interest rate cycle.44 Central banks, in particular in EMEs, would continue to benefit from frameworks that combine inflation targeting and greater exchange rate flexibility with judicious use of FX intervention to shield against destabilising external developments.
Finally, policymakers must be humble in the face of high uncertainty, acknowledging the inherent limits of economic forecasting. Relying too heavily on a single baseline outlook may hinder policymakers' ability to effectively communicate the risks to the outlook and their potential responses to alternative outcomes. By incorporating information such as sensitivity analyses and alternative scenarios, central banks can provide greater transparency around their decisions. While this approach in communication is more complex, as it can dilute the message or overemphasise unlikely outcomes, it can offer the public a more nuanced understanding of the risks to the outlook and the trade-offs the central bank is facing.
Endnotes
Additional notes to graphs
Graph 1.A: Actual figures, Consensus Economics estimates and forecasts. For IN, for fiscal years beginning in April. For the regions, GDP-PPP weighted averages of seven other AEs, eight other Asian EMEs and five other Latin American economies. Global is the GDP-PPP weighted average of 65 economies, based on data availability.
Graph 1.B: Actual figures, Consensus Economics estimates and forecasts as of May 2025. GDP-PPP weighted averages. The sample consists of 65 economies, based on data availability. Asia & Oceania = AM, AU, AZ, CN, GE, HK, ID, IL, IN, JP, KR, KZ, MY, NZ, PH, SA, SG, TH, TM, TR, UZ and VN; Americas = AR, BO, BR, CA, CL, CO, CR, DO, EC, GT, HN, MX, NI, PA, PE, PY, SV, US, UY and VE; Europe = AL, BA, BG, BY, CH, CZ, DK, EA, GB, HU, MD, ME, MK, NO, PL, RO, RS, RU, SE and UA; Africa = EG, NG and ZA. For IN, for fiscal years beginning in April.
Graph 2.A: Real private consumption per capita; seasonally adjusted series. The trend is based on Q1 2015–Q4 2019 data. Other AEs includes 10 economies.
Graph 2.B: Households and non-profit institutions serving households (NPISHs), or closest based on data availability. Definitions differ among economies; seasonally adjusted series. Latest: for CA, SE and US, Q1 2025; Q4 2024 otherwise. Pre-pandemic average: for NZ, Q2 2016–Q4 2019; Q1 2015–Q4 2019 otherwise.
Graph 2.C: GDP-PPP weighted average of eight other Asian EMEs and six Latin American economies. For CN, annual series from IMF, World Economic Outlook; seasonally adjusted quarterly national series otherwise.
Graph 3: Latest: for AU and NZ, Q1 2025; April 2025 otherwise. Inflation targets/tolerance intervals are official point targets, target bands, tolerance ranges or unofficial objectives announced by authorities.
Graph 4.A: The sample covers 11 AEs, nine Asian EMEs, six Latin American economies and 13 other EMEs.
Graph 4.B: Median across AT, AU, BE, CA, CH, DE, DK, ES, FI, FR, GB, GR, IE, IT, JP, LU, NL, NO, NZ, PT, SE and US, where data are available. End-year central bank assets and annual average policy rates. The real policy rate is calculated by adjusting the nominal rate for headline inflation. For policy rates in 2025, averages of available data.
Graph 5.A: Trade policy uncertainty index refers to the number of articles related to trade policy uncertainty as a share of the total number of articles in seven newspapers each month (data downloaded from www.matteoiacoviello.com/tpu.htm). Economic policy uncertainty index refers to the number of articles related to economic policy uncertainty as a share of the total number of own-country articles in each month; GDP-PPP weighted average of 21 economies (data downloaded from www.policyuncertainty.com). Geopolitical risk index refers to the number of articles related to adverse geopolitical events as a share of the total number of articles in 10 newspapers each month (data downloaded from www.matteoiacoviello.com/gpr.htm). Series are demeaned and divided by their standard deviation based on the period starting in January 2000.
Graph 5.B: Economic policy uncertainty index refers to the number of articles related to economic policy uncertainty as a share of the total number of own-country articles in each month (data downloaded from www.policyuncertainty.com). For global, GDP-PPP weighted average of 21 economies. For CN, index based on mainland newspapers; DE, ES, FR, GB and IT represent Europe. Series are demeaned and divided by their standard deviation based on the period starting in January 2000. Latest: for global, March 2025; for CN, April 2025; for Europe, JP and US, May 2025.
Graph 7.A: Seasonally adjusted medians. The sample covers AR, AT, AU, BE, BR, CA, CH, CL, CN, CO, CY, CZ, DE, DK, ES, FI, FR, GB, GR, HK, HU, ID, IE, IL, IN, IT, JP, KR, LU, MT, MX, MY, NL, NO, NZ, PH, PL, PT, RU, SA, SE, SG, SI, TH, TR, US and ZA. See Banerjee et al (2025).
Graph 7.B: The uncertainty impact on business (non-residential private) investment is derived from conditional forecasts using a BVAR model running the following country-specific regressions at quarter t: , where is an vector of endogenous variables, is an intercept vector, are coefficient matrices and is an vector of Gaussian exogenous shocks with zero mean and variance-covariance matrix encompasses a range of key economic indicators, including non-residential private investment, labour productivity (measured as real GDP per hour worked), public investment, real GDP, consumer price index (CPI), term premium (ie 10-year minus three-month yield), shadow rate and economic policy uncertainty index (EPU) by Baker et al (2016) (data downloaded from www.policyuncertainty.com). The model includes four lags and is estimated using the Minnesota prior with a hierarchical approach to prior selection (Giannone et al (2015)). The uncertainty impact is measured as the difference between a forecast conditioned on Q2 2025 uncertainty levels and an unconditional forecast. Specifically, the uncertainty conditions for Q2 2025 are an average over the EPUs for April and May 2025. Conditional forecasts are computed based on a range of structural shocks (monetary policy shock, uncertainty shock, fiscal policy shock, supply shock, demand shock and other shocks). See the online annex of Burgert et al (2025) for further details.
Graph 8.A: Hodrick-Prescott filtered trend of year-on-year real GDP growth rates. For the regions, GDP-PPP weighted averages of 10 AEs; nine Asian EMEs and six Latin American economies.
Graph 8.B: Latest observations: for US, April 2025; for CA, average of February to April 2025; for GB, Q1 2025 and 2024 otherwise. Monthly and quarterly series are seasonally adjusted. JP shows end-October values.
Graph 8.C: World trade and foreign direct investment (FDI) from World Bank.
Graph 9.A: Based on trade-weighted averages of all products for each country. For the regions, median across economies: AEs = AU, CA, CH, GB, JP, NO and NZ; EMEs = AE, AR, BR, CL, CN, CO, DZ, ID, IL, IN, KR, KW, MA, MX, MY, PE, PH, RU, SA, SG, TH, TR, VN and ZA; where data are available. The US total effective tariff rate is defined by Fitch as the total duties as a percentage of total imports and changes with shifts in import share by country of origin and product mix.
Graph 9.B: The panel shows net interventions, which are defined as all harmful interventions minus all liberalising interventions as defined by the Global Trade Alert. The number of net interventions for each bilateral country relationship are weighted using the bilateral goods imports as a share of sample country goods imports. Non-tariff interventions include all harmful trade interventions except tariff interventions. The timing is based on the implementation date of an intervention.
Graph 9.C: Trade balance = exports minus imports. For the periods, simple averages are used.
Graph 10.A: The sample covers AR, AT, AU, BE, CA, CH, CO, DE, DK, ES, FI, FR, GB, GR, IE, IN, IT, JP, KR, MX, NL, NO, NZ, PH, PT, SE, TH, US and ZA, where data are available. Definitions may vary across time and countries. Estimates, if actual is not available.
Graph 10.B: The sample covers AT, AU, BE, CA, CH, CZ, DE, DK, ES, FI, FR, GB, GR, HU, IE, IT, JP, KR, LU, NL, NO, NZ, PL, PT, SE and US. Gross general government interest payments. OECD estimates, if actual is not available.
Graph 11.A: Based on data from the US Treasury International Capital (TIC) System. Sovereign investor base estimates by Arslanalp and Tsuda (2014); last updated on 2 May 2025.
Graph 11.C: Stock-bond correlation = 10-year rolling correlation between the three-month log returns of bonds and equity. For bonds, total return index of the 10-year US Treasury; for equity, S&P 500. Convenience yield = 10-year TIPS yield + 10-year inflation swap rate – 10-year Treasury yield.
Graph 12.A: Households = households and NPISHs.
Graph 12.B: Annual data, 2024 if available, 2023 otherwise.
Graph 12.C: Each series starts in 1980 or with the earliest available observation; demeaned and divided by the standard deviation.
Graph 13.A: Gross exposure represents the sum of long and short positions. The US government debt asset class includes debt issued by US federal, state and local entities, such as cash Treasury securities, Treasury bond futures, other Treasury security derivatives, municipal bonds and obligations issued by government-sponsored entities. CC repo market includes the tri-party market, the Fixed Income Clearing Corporation's (FICC) DvP service and FICC's General Collateral Financing (GCF) Repo service. Hedge funds' gross exposure: quarterly data. Repo data correspond to quarterly averages of daily values.
Graph 13.B: Tether's and Circle's reported holdings of Treasuries including indirect (eg via repo), compared with US and EA government money market funds (MMFs). Full fund size (including non-US treasuries) for EA MMFs.
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| 2025-06-29T00:00:00 |
https://www.bis.org/publ/arpdf/ar2025e1.htm
|
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{
"date": "2025/06/29",
"position": 76,
"query": "financial crisis stock exchange healthcare workers"
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] |
|
STOP Taking Random AI Courses — Read These Books Instead
|
STOP Taking Random AI Courses — Read These Books Instead
|
https://medium.com
|
[
"Egor Howell"
] |
Deep learning is where all these generative AI algorithms came from, so ... Most AI jobs are so-called AI engineers, and it's closer to ...
|
STOP Taking Random AI Courses — Read These Books Instead Egor Howell 7 min read · Jun 28, 2025 -- 34 Share
A comprehensive guide to the books and courses that helped me learn AI
Photo by Kimberly Farmer on Unsplash
After working in AI and machine learning for four years, I want to share all the resources that helped me on my journey.
As there are quite a few, I am going to break them down into the following categories:
| 2025-06-28T00:00:00 |
2025/06/28
|
https://medium.com/data-science-collective/stop-taking-random-ai-courses-read-these-books-instead-352bca9fa303
|
[
{
"date": "2025/06/27",
"position": 68,
"query": "generative AI jobs"
},
{
"date": "2025/06/27",
"position": 78,
"query": "generative AI jobs"
}
] |
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From Hiring to Privacy: How AI Is Reshaping California Workplaces
|
From Hiring to Privacy: How AI Is Reshaping California Workplaces
|
https://www.californiaemploymentlawreport.com
|
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1. AI Is Already in the Workplace—and Use Is Expanding · 2. California Is Leading with New AI Employment Regulations · 3. Employers Are Still ...
|
AI is no longer just a buzzword—it’s actively transforming the workplace. Whether employers are aware of it or not, AI tools are being embedded into daily operations across industries. With California pushing forward with proposed regulations that could take effect as early as July 1, 2025, employers must begin understanding the implications now. Here are five essential points to keep in mind:
1. AI Is Already in the Workplace—and Use Is Expanding
Many California employers are already using AI—even if only in limited ways. One of the most common use cases is resume screening. Large employers facing thousands of applications use AI to quickly sort resumes and identify the most qualified candidates. AI is also being used to:
Draft employee communications : HR teams are using tools like ChatGPT to write performance reviews, disciplinary notices, or coaching emails.
: HR teams are using tools like ChatGPT to write performance reviews, disciplinary notices, or coaching emails. Assist in onboarding : Chatbots can guide new hires through paperwork, benefits enrollment, and training schedules.
: Chatbots can guide new hires through paperwork, benefits enrollment, and training schedules. Enhance recruiting: Some available tools use video analysis and natural language processing to assess applicant responses.
Even managers are using AI to brainstorm better ways to give feedback or handle difficult conversations.
Example: An HR manager struggling to coach an underperforming employee may use ChatGPT to draft a constructive and empathetic email, saving time and ensuring the message is clear and legally sound.
2. California Is Leading with New AI Employment Regulations
California’s proposed Automated Decision Systems (ADS) regulations aim to ensure that AI tools used in employment decisions are fair, transparent, and compliant with anti-discrimination laws (see our prior article on the proposed regulations here). These rules would:
Require notice to applicants when AI tools are used
when AI tools are used Mandate anti-bias testing and corrections
Impose a four-year recordkeeping requirement
Apply to vendors and hold employers legally responsible for third-party tools
The regulations are designed to prevent unintentional discrimination—such as AI tools screening out candidates with gaps in employment (which may affect women or caregivers more).
Example: If your company uses AI to rank candidates and the system deprioritizes people who’ve taken career breaks, that could result in a discriminatory impact. Under the new rules, you’d need to detect and fix that.
3. Employers Are Still Liable—Even When AI Makes the Decision
You can’t delegate legal responsibility to an algorithm. If an AI screening tool inadvertently excludes protected classes (e.g., based on age, race, or disability), your business is still liable under existing discrimination laws.
A current case in California, Mobley v. Workday, involves a 40+ year-old Black man who alleges he was repeatedly rejected by AI tools used by employers. The case is moving forward under current federal and state anti-discrimination laws.
Example: Even if a third-party software provider made the AI, if it screens out applicants unfairly, and your business uses it, your company can be sued under FEHA or Title VII.
4. AI May Actually Help Reduce Bias—When Used Correctly
There’s hope for AI to help level the playing field. A study by researchers at Stanford and USC found that AI-led hiring processes were more successful at identifying qualified candidates—particularly younger, less experienced, and female applicants—than traditional resume reviews and human interviews.
AI can promote a more “blind” evaluation process, reducing the potential for human bias related to names, photos, age, or education pedigree.
Example: A company replaces its initial resume screening with a structured AI interview system. The result? A more diverse candidate pool, selected based on skills and responses, not superficial traits.
5. Wearables and AI Tools Raise Serious Privacy Concerns
With tools like AI-powered glasses (like Google’s new Gemini glasses) and Zoom note-takers, privacy risks are growing. These tools can record conversations, analyze speech, and even recognize faces. California is a two-party consent state—recording without consent is illegal and could lead to criminal and civil penalties.
Example: An employee shows up wearing smart glasses that record everything they see and hear. If they record private conversations without the other party’s consent, it could violate California Penal Code section 632 and create liability for the employer.
Employers should start preparing policies or training on the use of wearables and AI tools in the workplace—even if a formal “AI governance policy” feels premature.
Final Thoughts
AI can help your business stay compliant, attract better candidates, and operate more efficiently—but only if you understand the risks and responsibilities. Think of it like a powerful new employee: it needs supervision, training, and accountability. Don’t wait until your competitors—and the regulators—are ahead of you.
Start small. Use AI to help HR draft communications or streamline workflows. Just be sure you pair every tool with legal oversight and human judgment.
| 2025-06-28T00:00:00 |
2025/06/28
|
https://www.californiaemploymentlawreport.com/2025/06/from-hiring-to-privacy-how-ai-is-reshaping-california-workplaces/
|
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{
"date": "2025/06/27",
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},
{
"date": "2025/06/27",
"position": 14,
"query": "AI employers"
},
{
"date": "2025/06/27",
"position": 45,
"query": "ChatGPT employment impact"
},
{
"date": "2025/06/27",
"position": 49,
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AI 'could reshape essential function of journalism ' - Gulf Times
|
AI 'could reshape essential function of journalism '
|
https://www.gulf-times.com
|
[
"Joseph Varghese"
] |
Artificial Intelligence ( AI), in the coming years, could reshape journalism's essential function, noted a professor from one of the Qatar ...
|
Artificial Intelligence ( AI), in the coming years, could reshape journalism’s essential function, noted a professor from one of the Qatar Foundation partner universities while highlighting some of the substantial risks of AI integration in journalism.“As AI becomes more involved in content creation, it could begin to reshape journalism’s essential function in helping the public understand the world and make informed decisions. This is particularly critical in regions like the Global South, where AI systems may not recognise or value local perspectives,” Prof Eddy Borges-Rey, associate professor in residence, Northwestern University in Qatar told Gulf Times.Prof Borges-Rey said that currently journalists are cautiously experimenting with AI for specific tasks such as transcription, translation, summarisation, and initial drafts.“If the current trends continue, we may see a newsroom workflow where human editors are managing AI-generated drafts, optimising content for algorithms rather than audiences. The most radical shift will not be technical, it will be conceptual,” he explained.However, the professor cautioned that these experiments are occurring amid growing concerns and involve substantial risks.“Tools like ChatGPT and Gemini can produce plausible-sounding but inaccurate content, and generative outputs often lack context and editorial judgment. The BBC recently conducted a study asking major AI platforms to summarise 100 news stories; 51% of the results contained significant issues. These findings reflect a broader trend: while journalists explore automation, the risks remain substantial,” he said.The academic said that AI is rather a transformative force that challenges people’s assumptions about knowledge, authority, and responsibility. He stated : “If journalism is to remain a meaningful institution, we must be proactive in shaping how AI is adopted. That means investing in education, building internal safeguards, and centering voices from the Global South in global debates. We should be cautiously optimistic, but more importantly, we must move away from the rhetoric of ‘falling behind’.”Prof Borges-Rey noted the media faces several profound challenges in integrating AI. “First, there is the problem of bias — most large language models are trained on English-language data from the Global North, which can marginalise other cultures and viewpoints,” he said. “Second, the lack of transparency in how AI makes decisions raises questions about accountability.“Third, editorial authority is being blurred: when a machine suggests a headline or summary, who is ultimately responsible for its accuracy or ethical framing? And finally, there is mounting pressure to adopt AI quickly, often without adequate safeguards in place.”Prof Borges-Rey also highlighted some of the advantages and risk factors of integrating AI into journalism.“It can reduce the burden of repetitive tasks, improve accessibility through automated translations and voiceovers, and help identify patterns in large datasets for investigative reporting. It can also support local journalism by generating summaries or story alerts for underserved communities. But these benefits must be weighed against the ethical and professional risks. Used wisely, AI can support journalism’s mission. Used blindly, it can undermine the very trust it is meant to foster,” he added.
| 2025-06-28T00:00:00 |
2025/06/28
|
https://www.gulf-times.com/article/706699/qatar/ai-could-reshape-essential-function-of-journalism-
|
[
{
"date": "2025/06/28",
"position": 98,
"query": "AI journalism"
}
] |
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Anthropic's AI Economic Program: A Catalyst for Crypto and DeFi ...
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Anthropic’s AI Economic Program: A Catalyst for Crypto and DeFi Disruption?
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https://openexo.com
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[] |
Crypto Relevance: AI-driven disruption might accelerate DeFi adoption and blockchain innovation, offering decentralized tools for a shifting ...
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Anthropic’s Economic Futures Program: Can AI Fuel a Decentralized Crypto Economy?
Anthropic, a heavyweight in AI research, has rolled out the Economic Futures Program, an initiative to dissect how artificial intelligence could reshape economies and labor markets. As advocates for decentralization and disruptive tech here at “Let’s Talk, Bitcoin,” we’re diving into this program not just for its AI focus, but for how its findings might ripple into the crypto space—potentially amplifying Bitcoin’s role as a financial hedge or boosting blockchain solutions like decentralized finance (DeFi) amid economic upheaval.
Core Mission: Anthropic’s program studies AI’s economic impact through research grants, policy forums, and data tracking, aiming to balance growth prospects with job displacement risks.
Anthropic’s program studies AI’s economic impact through research grants, policy forums, and data tracking, aiming to balance growth prospects with job displacement risks. Dire Warnings: CEO Dario Amodei predicts AI could slash 50% of entry-level white-collar jobs, pushing unemployment to 20% within five years.
CEO Dario Amodei predicts AI could slash 50% of entry-level white-collar jobs, pushing unemployment to 20% within five years. Crypto Relevance: AI-driven disruption might accelerate DeFi adoption and blockchain innovation, offering decentralized tools for a shifting economic landscape.
Decoding Anthropic’s Economic Futures Program
Let’s get straight to the nuts and bolts. Anthropic’s Economic Futures Program is a calculated effort to understand AI’s influence on economies, focusing on hard evidence over wild speculation. AI, for the uninitiated, refers to computer systems that mimic human intelligence—think algorithms that analyze data, make decisions, or even automate entire workflows. This initiative, detailed in their recent announcement of the Economic Futures Program, zeroes in on three pillars: dishing out rapid grants of $10,000 to $50,000 for researchers to study AI’s effects on labor and productivity (with results expected in six months), hosting symposia this fall in Washington D.C. and Europe to craft evidence-based policy proposals, and building datasets to track AI’s real-world economic footprint. They’re even providing access to their Claude API—credits worth $5,000—to help researchers crunch data using Anthropic’s own tech. This builds on their earlier Economic Index project from February, which used open-source data to start mapping AI’s long-term effects.
The program’s leadership isn’t shy about the stakes. Sarah Heck, Anthropic’s Head of Policy Programs, is adamant about grounding discussions in reality rather than knee-jerk reactions to AI’s promise or peril. Her goal? Figure out what’s truly happening before crafting solutions, whether that’s mitigating job losses or capitalizing on economic booms.
“It’s really important to root these conversations in evidence and not have predetermined outcomes or views on what’s going to [happen],” Heck stated.
This no-BS stance is refreshing in a field often drowned in hype—much like the crypto space where shillers peddle absurd price targets. If AI triggers massive job cuts, Heck wants a collective of sharp minds to brainstorm mitigation. If it sparks a GDP surge, policymakers need to be ready to channel that growth. It’s not a one-track narrative, and that complexity is key to understanding the road ahead, as highlighted in recent coverage of Anthropic’s efforts to track AI’s economic impact.
Labor Shakeup: AI as a Job Killer or Creator?
Here’s where the rubber meets the road. Anthropic’s CEO, Dario Amodei, dropped a bombshell earlier this year, warning that AI could wipe out half of all entry-level white-collar jobs—think data entry, basic accounting, or customer support roles—and drive unemployment to a staggering 20% within five years. To put that in perspective, that’s roughly double the peak unemployment rate during the 2008 financial crisis. We’re talking about a potential economic earthquake that could hit just as the next Bitcoin halving reshapes crypto markets. For deeper insights into these warnings, check out this analysis of Amodei’s job loss predictions.
“AI could eliminate half of all entry-level white-collar jobs and increase unemployment to as high as 20% in the next five years,” Amodei predicted.
But it’s not just about slashing jobs. Heck and her team are also peering into how AI reshapes workflows—how tasks are done in entirely new ways—and whether it spawns job categories we can’t yet fathom. Will some skills remain invaluable while others become obsolete overnight? Beyond labor, they’re exploring fiscal policy changes (how governments adjust taxing and spending in response to AI’s economic waves) and how businesses redefine value when machines handle more of the heavy lifting, a topic further explored in Anthropic’s labor market research findings.
Still, let’s pump the brakes on the doomscrolling. Not everyone agrees AI is an unstoppable job-killer. Take Klarna, a fintech giant that swapped out 700 customer service agents for AI in 2024, only to backpedal in 2025 when customers demanded human interaction. Turns out, people still value the messy, empathetic touch of a real person over a chatbot’s cold efficiency. Other experts, like Cisco’s Field CTO Andy Thurai, caution against blind cost-cutting with AI, pointing to mixed results when firms ditch seasoned pros for AI-augmented rookies. Automation without a damn good strategy, as LeapXpert CEO Dima Gutzeit puts it, can backfire—especially in regulated industries where human oversight isn’t just nice, it’s mandatory. Sounds a bit like the crypto space, doesn’t it? Tech can’t solve everything if the human element gets ignored.
AI Meets Blockchain: A Crypto Lifeline?
Now, let’s talk our turf. AI’s economic turbulence could send shockwaves through the crypto world, and not just because trading bots are already outpacing human investors on Bitcoin and Ethereum exchanges. These bots execute trades with ruthless precision—faster than a day trader hopped up on Red Bull during a bull run. But if Amodei’s 20% unemployment forecast plays out, could we see a flood of displaced workers turning to decentralized finance as a financial escape hatch? DeFi, for those new to the game, is a blockchain-based system where lending, borrowing, and trading happen without traditional banks, often powered by smart contracts on networks like Ethereum. For broader perspectives on this intersection, explore this research on AI and DeFi economic disruption.
Imagine millions sidelined by AI automation, seeking income through yield farming or staking in DeFi protocols as a hedge against economic uncertainty. Bitcoin, with its fixed supply and decentralized ethos, could become a go-to store of value for those burned by centralized systems failing to adapt. Hell, blockchain itself might offer solutions Anthropic could use—think transparent, decentralized datasets to track AI’s economic impact, leveraging projects like Chainlink for reliable data oracles. And if unemployment spikes, fringe ideas like blockchain-based universal basic income—where tokens are distributed on-chain as a safety net—could shift from Reddit threads to real-world experiments. Past attempts, like small-scale basic income tokens on Ethereum, show it’s not pure fantasy, even if scalability remains a hurdle. Community discussions on platforms like Reddit about AI’s economic effects echo similar concerns and ideas.
But let’s not get carried away with the utopian daydreams. Just as we roast baseless crypto price predictions, we’ve got to scrutinize AI-blockchain synergies with a sharp eye. AI can centralize power as easily as it decentralizes if big tech or governments weaponize it—much like how some altcoin projects promise freedom but end up as glorified Ponzi schemes. And while Bitcoin remains the gold standard of decentralization in our view, we can’t ignore Ethereum’s utility in marrying AI with smart contracts. Altcoins and other blockchains often fill niches Bitcoin doesn’t touch, and that diversity might be crucial if AI reshapes economic fundamentals.
Skepticism and Reality: AI Isn’t a Silver Bullet
Anthropic’s push for evidence over sensationalism mirrors our own disdain for crypto hype machines. Their symposia events could steer how governments tackle tech regulation, and that’s a double-edged sword for our space. If AI-driven economic shifts force a reckoning, might lawmakers revisit digital asset frameworks—especially if displaced workers flock to Bitcoin or DeFi for independence? Or will AI’s centralization risks—think mega-corporations hoarding data and power—spill over into tighter crypto oversight? If AI kills 20% of jobs, will bureaucrats ban it before they target Bitcoin again? Don’t bet on it. For additional context on Anthropic’s broader mission, their Wikipedia page offers a useful overview.
Playing devil’s advocate, let’s question whether AI’s economic threat is overblown. Beyond Klarna’s reversal, industries like telecom show AI’s limits—Oculeus CEO Arnd Baranowski notes that while AI handles scale, it lacks the human imagination to outsmart evolving fraud tactics. Cut human teams too deep, and you’re screwed. This hybrid reality could temper AI’s job-killing potential, much like how blockchain hasn’t fully displaced traditional finance despite two decades of disruption. AI and crypto share a truth: they’re tools of freedom and efficiency, but only if wielded with intent over blind faith. Similar discussions on AI’s future impact can be found in this Quora thread on AI and economic changes, alongside other relevant studies on AI’s economic influence.
Key Takeaways and Questions
What is the goal of Anthropic’s Economic Futures Program?
It aims to analyze AI’s impact on economies and labor through research grants, policy forums in D.C. and Europe, and data tracking, seeking to balance potential GDP growth with risks like massive job displacement.
It aims to analyze AI’s impact on economies and labor through research grants, policy forums in D.C. and Europe, and data tracking, seeking to balance potential GDP growth with risks like massive job displacement. How could AI disrupt the job market, per Anthropic’s leadership?
CEO Dario Amodei warns AI might eliminate 50% of entry-level white-collar jobs, driving unemployment to 20% in five years—a seismic shift that could rival past economic crises.
CEO Dario Amodei warns AI might eliminate 50% of entry-level white-collar jobs, driving unemployment to 20% in five years—a seismic shift that could rival past economic crises. What does AI’s economic impact mean for Bitcoin and crypto?
It could drive adoption of Bitcoin as a store of value and DeFi as a financial alternative for displaced workers, while blockchain might offer decentralized tools to track AI’s effects transparently.
It could drive adoption of Bitcoin as a store of value and DeFi as a financial alternative for displaced workers, while blockchain might offer decentralized tools to track AI’s effects transparently. Why focus on evidence over speculation in AI’s economic analysis?
Hard data ensures realistic policies, avoiding overreactions to AI’s hyped benefits or fears—much like we reject baseless crypto price predictions peddled by shillers.
Hard data ensures realistic policies, avoiding overreactions to AI’s hyped benefits or fears—much like we reject baseless crypto price predictions peddled by shillers. Are there counterpoints to AI’s job-killing narrative?
Yes, cases like Klarna’s reversal show humans are still preferred in some roles, and experts warn automation without strategy fails in regulated spaces, suggesting AI’s impact might be less apocalyptic than feared.
AI and blockchain are two sides of a disruptive coin—both challenge centralized systems, promising freedom and efficiency while risking misuse or overreach. Anthropic’s Economic Futures Program may not name-drop Bitcoin, but its focus on labor shifts, policy, and economic value hits home for anyone betting on decentralized tech to upend the status quo. We’re keeping tabs on whether this sparks ideas to fuse AI’s raw power with blockchain’s transparent ethos. Until then, hold your private keys tight and your skepticism tighter—disruption doesn’t wait for permission.
| 2025-06-28T00:00:00 |
2025/06/28
|
https://openexo.com/l/acaa509d
|
[
{
"date": "2025/06/28",
"position": 88,
"query": "AI economic disruption"
}
] |
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] |
The AI Backlash Keeps Growing Stronger - WIRED
|
The AI Backlash Keeps Growing Stronger
|
https://www.wired.com
|
[
"Reece Rogers",
"Will Knight",
"David Gilbert",
"Kate Knibbs",
"Matt Burgess",
"Zoë Schiffer",
"Caroline Haskins",
"Kylie Robison"
] |
Still, the potential threat of bosses attempting to replace human workers with AI ... worker-replacing technology. “AI companies have ...
|
Before Duolingo wiped its videos from TikTok and Instagram in mid-May, social media engagement was one of the language-learning app’s most recognizable qualities. Its green owl mascot had gone viral multiple times and was well known to younger users—a success story other marketers envied.
But, when news got out that Duolingo was making the switch to become an “AI-first” company, planning to replace contractors who work on tasks generative AI could automate, public perception of the brand soured.
Young people started posting on social media about how they were outraged at Duolingo as they performatively deleted the app—even if it meant losing the precious streak awards they earned through continued, daily usage. The comments on Duolingo’s TikTok posts in the days after the announcement were filled with rage, primarily focused on a single aspect: workers being replaced with automation.
The negative response online is indicative of a larger trend: Right now, though a growing number of Americans use ChatGPT, many people are sick of AI’s encroachment into their lives and are ready to fight back.
When reached for comment, Duolingo spokesperson Sam Dalsimer stressed that “AI isn’t replacing our staff” and said all AI-generated content on the platform would be created “under the direction and guidance of our learning experts.” The company's plan is still to reduce its use of non-staff contractors for tasks that can be automated using generative AI.
Duolingo’s embrace of workplace automation is part of a broad shift within the tech industry. Leaders at Klarna, a buy now, pay later service, and Salesforce, a software company, have also made sweeping statements about AI reducing the need for new hires in roles like customer service and engineering. These decisions were being made at the same time as developers sold “agents,” which are designed to automate software tasks, as a way to reduce the amount of workers needed to complete certain tasks.
Still, the potential threat of bosses attempting to replace human workers with AI agents is just one of many compounding reasons people are critical of generative AI. Add that to the error-ridden outputs, the environmental damage, the potential mental health impacts for users, and the concerns about copyright violations when AI tools are trained on existing works.
Many people were initially in awe of ChatGPT and other generative AI tools when they first arrived in late 2022. You could make a cartoon of a duck riding a motorcycle! But soon artists started speaking out, noting that their visual and textual works were being scraped to train these systems. The pushback from the creative community ramped up during the 2023 Hollywood writer's strike, and continued to accelerate through the current wave of copyright lawsuits brought by publishers, creatives, and Hollywood studios.
Right now, the general vibe aligns even more with the side of impacted workers. “I think there is a new sort of ambient animosity towards the AI systems,” says Brian Merchant, former WIRED contributor and author of Blood in the Machine, a book about the Luddites rebelling against worker-replacing technology. “AI companies have speedrun the Silicon Valley trajectory.”
| 2025-06-28T00:00:00 |
2025/06/28
|
https://www.wired.com/story/generative-ai-backlash/
|
[
{
"date": "2025/06/28",
"position": 87,
"query": "AI replacing workers"
}
] |
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Government's Role: Policy Frameworks to Support Workers in an AI ...
|
Government’s Role: Policy Frameworks to Support Workers in an AI-Dominated Economy
|
https://www.linkedin.com
|
[] |
This includes revising unemployment benefits, healthcare access, and other social protections to better fit the realities of an AI-influenced ...
|
As artificial intelligence increasingly transforms the workforce, governments face the critical responsibility of ensuring that economic progress does not come at the expense of social stability. The rapid automation of tasks and jobs across sectors demands proactive policy frameworks designed to protect workers, foster innovation, and promote inclusive growth.
Navigating the Challenges of Workforce Automation
AI-driven automation threatens to disrupt traditional employment models by displacing workers in roles susceptible to mechanization. However, it also creates new opportunities in emerging fields that require a different set of skills. This dual nature requires governments to carefully balance regulatory oversight with incentives for innovation. Effective policies must address worker displacement while encouraging businesses to adopt AI responsibly.
Reskilling and Lifelong Learning as Cornerstones
One of the most significant roles for government is to enable workforce adaptability through investment in reskilling and lifelong learning initiatives. Public funding for training programs, partnerships with educational institutions, and collaboration with the private sector can equip workers with the skills necessary to thrive in AI-augmented jobs. Policies that prioritize accessible, affordable, and relevant education will be key to minimizing unemployment and underemployment caused by AI.
Strengthening Social Safety Nets
Alongside upskilling efforts, robust social safety nets must be in place to support workers during periods of transition. This includes revising unemployment benefits, healthcare access, and other social protections to better fit the realities of an AI-influenced labor market. Additionally, governments can promote job placement services and entrepreneurship programs to help displaced workers find new employment paths.
Ethical Standards and AI Governance
Building trust in AI technologies requires governments to implement ethical standards and regulatory frameworks that safeguard privacy, ensure fairness, and prevent bias. Transparent and accountable AI governance will not only protect workers but also encourage responsible AI development and deployment across industries.
International Collaboration and Inclusive Growth
Given the global nature of AI development, governments must also collaborate internationally to share best practices and address cross-border workforce challenges. Coordinated efforts can help harmonize standards, avoid a regulatory race to the bottom, and promote equitable distribution of AI’s economic benefits.
Governments that take an active, forward-looking approach to policy design will be instrumental in shaping an AI-driven economy where workers are empowered, businesses innovate responsibly, and society benefits broadly.
#FutureOfWork #AIandJobs #WorkforceAutomation #ReskillingRevolution #InclusiveGrowth #AIPolicy #LifelongLearning #SocialSafetyNets #EthicalAI #AIGovernance
| 2025-06-28T00:00:00 |
https://www.linkedin.com/pulse/governments-role-policy-frameworks-support-dwtac
|
[
{
"date": "2025/06/28",
"position": 7,
"query": "government AI workforce policy"
}
] |
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|
Top 8 Artificial Intelligence Start-Ups to Watch in 2025
|
Top 8 AI Start-ups in 2025
|
https://www.fool.com
|
[
"Jeremy Bowman"
] |
In this discussion on AI start-ups, we'll review some of the hottest privately held companies working on artificial intelligence today.
|
Investor interest is surging in all things AI, so it shouldn't come as a surprise that artificial intelligence (AI) start-ups are also popping up on investors' radars. Ever since the launch of ChatGPT, the potential for AI has crystallized across the business world and among investors, and the implications for the new technology are vast. Among them is a new race among start-ups to challenge ChatGPT and, separately, to come up with the next big thing in generative AI.
Image source: Getty Images.
Not surprisingly, money from venture capital firms and other investors has poured into AI start-ups, and the industry is booming. In this discussion on AI start-ups, we'll review some of the hottest privately held companies working on artificial intelligence today, analyze the pros and cons of investing in AI start-ups, and answer some commonly asked questions about AI start-ups.
Definition Icon Artificial Intelligence Artificial intelligence is the use of machines to mimic human intelligence.
Best AI start-ups Best AI start-ups to watch this year 1. OpenAI 1. OpenAI As you might expect, ChatGPT owner OpenAI has received much of the attention in AI start-ups this year, and continues to set the pace in its cohort. OpenAI has developed other renowned products, including its image-generating AI, DALL-E, and text-to-video AI, Sora. Its success with ChatGPT and its high-profile partnership with Microsoft (MSFT -0.01%) makes it the premier AI start-up these days. OpenAI was founded in 2015 with several co-founders, including Elon Musk, though Musk eventually left the company to work on Tesla (TSLA 1.14%) and SpaceX.
Definition Icon AutoGPT AutoGPT is an artificial intelligence (AI) agent that can complete complex tasks without human intervention.
In March of 2025, Open AI raised $40 billion at a valuation of $300 billion, making it the largest private tech funding round on record. The round was led by SoftBank (SFTBF 0.31%), the prolific tech investor, which invested $30 billion; Microsoft and venture capital firms made up the remaining $10 billion. The Stargate Project, a joint venture between Softbank, OpenAI, and Oracle that was announced at the White House in January, is expected to receive $18 billion. Stargate aims to invest as much as $500 billion in AI infrastructure in the U.S. Key facts: Founded in 2015
Headquartered in San Francisco
Industries served: Technology, education, retail, manufacturing, healthcare, and others.
Services provided: Foundation models including GPT and DALL, chatbots, enterprise services, and an API platform.
2. Anthropic 2. Anthropic Anthropic is rapidly emerging as the chief start-up rival to OpenAI. Founded in 2021 by former OpenAI researchers, Anthropic counts Google and Amazon (AMZN 0.29%) as major investors. The Alphabet (GOOG 0.85%) (GOOGL 0.79%) subsidiary took a 10% stake in 2023, and Amazon has invested $8 billion in Anthropic. After its most recent funding round in March, it's valued at $61.5 billion, rising $3.5 billion at the time. Anthropic also counts Salesforce (CRM 0.53%) and Zoom (ZM 1.92%) as investors. Anthropic is best known for its generative AI chatbot, Claude, which it launched in March 2023. Some users think Claude is friendlier than ChatGPT, though they say that ChatGPT is better for analyzing and summarizing documents. It's also said to be more versatile than Claude.
According to some reviews, Claude gets better results with things like creative tasks, following instructions, and answering trivia questions. Anthropic describes Claude as being "much less likely to produce harmful outputs, easier to converse with, and more steerable" than other AI chatbots. In other words, Claude seems designed to avoid some of the problems that have plagued ChatGPT and Google's AI chatbots. Key facts: Founded in 2021
Headquartered in San Francisco
Industries served: Technology, healthcare, customer support, finance, and others
Services provided: Claude AI models, focus on AI safety, enterprise services, cloud partnerships.
3. Perplexity AI 3. Perplexity AI Valued at $14 billion as of May 2025, Perplexity AI is another promising AI start-up. Perplexity is backed by Softbank's Vision Fund, the prolific start-up investors known for backing companies like Uber (UBER -1.58%), DoorDash (DASH 1.0%), Arm Holdings (ARM -0.96%), and WeWork. Perplexity has also raised money from Nvidia (NVDA -0.46%), Amazon founder Jeff Bezos, and Shopify (SHOP 4.16%) CEO Tobi Lutke. Perplexity has differentiated itself from other AI chatbots with a focus on search tools that feature sources and citations. Some see Perplexity as the best AI alternative to a traditional Google search. In fact, both Apple (AAPL -1.12%) and Samsung (SSNL.F 9.01%) are considering integrating its search engine into their hardware. Perplexity said it crossed $100 million in annualized revenue March with 6.3x growth in Perplexity, its paid subscription tier. Key facts Founded in 2022
Headquartered in San Francisco
Industries served: Education, healthcare, media and entertainment, travel, and others
Services provided: Real-time search, citations, conversational interface, file uploads, and others.
4. xAI 4. xAI Elon Musk has had his hand in a wide range of emerging technologies, including electric vehicles, renewable energy, spacecraft and rockets, and satellite internet, so it shouldn't come as a surprise that he also has an AI start-up. (Musk was even a co-founder of OpenAI, although he left that company in 2018.) xAI, his AI start-up, is one of the most valuable today, and in June 2025 was aiming for a funding round with the help of Morgan Stanley (MS 1.19%) that would value the company at more than $100 billion in a deal that includes $5 billion debt and $20 billion in equity. xAI is the creator of Grok, the chatbot featured on the social media platform X, which Musk also owns. That gives xAI something of an advantage since most AI start-ups don't have an allied platform like X that can help draw users to the product. Some users think Grok is less censored than ChatGPT. Key facts: Founded in 2023
Headquartered in San Francisco
Industries served: Technology, finance, healthcare, autonomous vehicles, manufacturing, and others.
Services provided: Grok chatbot, models for developers, and enterprise services.
5. Mistral AI 5. Mistral AI Mistral AI is one of the youngest AI start-ups to make a splash in the industry. The company was able to easily raise money since it was founded by execs from Meta Platforms (META 0.46%) and Google. After a funding round in June 2024, the start-up is now valued at $6.2 billion, raising $640 million from the likes of Andreessen Horowitz, Nvidia, and Samsung. Mistral is unique among AI start-ups. It's based in France and has received a strong endorsement from the French government. It takes an open-source approach to AI, meaning its code is available to anyone who wants to use it. It's chatbot is called Le Chat and is known for being strong in a wide range of languages. Key facts: Founded in 2023
Headquartered in Paris
Industries served: Financial services, healthcare, customer support, education, and others
Services provided: Large language models, API access, open-source models, cloud-based access.
6. Scale AI 6. Scale AI Scale AI is different from other companies on this list because it doesn't make a chatbot or a generative AI interface. Instead, the company helps prepare data for AI training, a process called data labeling. Scale AI's technology is impressive enough that Meta Platforms just spent $14.3 billion on a 49% stake in the company, hoping it will accelerate its ambitions in AI. As part of the deal, Scale's 28-year-old CEO Alexandr Wang will join Meta, and will reportedly lead a research lab at the tech giant. Given that Meta didn't take a majority stake, Wang's agreement to leave is a bit odd, but the relationship could favor Scale in the long run. Key facts: Founded in 2016
Headquartered in San Francisco
Industries served: Autonomous vehicles, healthcare, e-commerce, generative AI, robotics, and others
Services provided: Data annotation/labeling, model training, human-in-the-loop labeling, and tools for building custom LLMs.
7. Databricks 7. Databricks Databricks is also unlike most of the AI start-ups on this list. It's older than the ones focused generative AI as it was founded in 2013, and its focus is primarily on data analytics. Its AI platform offers machine learning and generative AI applications to help its customers deploy AI models. As of December 2024, the company was valued at $62 billion after raising $10 billion. Databricks has been one of the more anticipated IPOs for a while, but the company has not given any indication of when it will go public. Key facts: Founded in 2013
Headquartered in San Francisco
Industries served: Financial services, healthcare, retail, media, and others.
Services provided: Data lakehouse, machine learning platform, analytics platform.
8. Cohere 8. Cohere Founded in 2019 by ex-Google AI researchers, Cohere is an enterprise AI platform focused on large language models and natural language processing. Cohere allows developers and businesses to build their chatbots and virtual assistants, and the company also provides services like data analysis and search. The company aims to provide higher performance and accuracy with a specific focus on the enterprise market. Cohere reached an annualized revenue of $100 million in May 2025, and its valuation was $5.5 billion as of June 2025. It counts Nvidia and Cisco (CSCO -0.19%) among its backers. Key facts: Founded in 2019
Headquartered in Toronto
Industries served: Financial services, healthcare, manufacturing, retail, and others
Services provided: LLMs, API access, text generation, summarization and data extraction.
Related investing topics
Should you invest? Should you invest in AI start-ups? Start-ups, by definition, are privately held, which means it's difficult for retail investors to invest in them. However, there are ways to get exposure to AI start-ups. The easiest way is by investing in a publicly traded company that has a stake in an AI start-up. For instance, you could buy shares of Microsoft, which would give you indirect ownership of OpenAI. Similarly, you could buy stock in Alphabet to get exposure to Anthropic. Investors should remember that start-ups tend to be riskier than publicly traded companies, and many start-ups won't succeed. However, if you find an AI start-up that looks promising, it's worth investigating if any publicly traded companies have a stake in it. Investors looking for opportunities in artificial intelligence can also consider investing in AI stocks or AI ETFs.
FAQ
AI start-up FAQ What are AI start-ups? angle-down angle-up Artificial intelligence (AI) start-ups are early-stage companies that are developing AI technologies or using them in their products and generally raising money from venture capital firms. Those could include everything from natural language processing like chatbots to robotics to machine learning. How do AI start-ups make money? angle-down angle-up Some AI start-ups make money by selling their products to customers, but most AI start-ups are unprofitable. They raise money from venture capitalists and others to fund research, develop new products, and bring them to market. What are the five big ideas in AI? angle-down angle-up The five big ideas in AI are: Perception: Computers need to sense the world through seeing and hearing like humans do. Representation and reasoning: Computers use data to construct representations to help them develop reasoning algorithms. Learning: Machine learning algorithms that use statistical inference from large amounts are a foundational concept in AI. Natural interaction: AI agents need to be able to converse in human languages and read facial expressions and emotions. Societal impact: AI is a powerful technology, and it's important for industry leaders to consider the potential for harm, as well as the other ethical implications of their products. What is the success rate of AI start-ups? angle-down angle-up Estimates vary, but according to AI4SP, around 90% of AI start-ups fail. AI is highly competitive, and while billions of dollars are flowing into AI start-ups, those are typically for a select few companies with pedigreed founders and known products. Why do most AI start-ups fail? angle-down angle-up AI start-ups face competition for investment dollars and attention from users, plus there's only room for so many chatbots on the market. It's also difficult for these companies to make a profit right now, so raising cash is crucial. What are the risks of AI start-ups? angle-down angle-up Any start-up is risky, but AI start-ups face a number of risks, including an inability to raise cash, differentiate its product, and find a customer base.
| 2025-06-28T00:00:00 |
https://www.fool.com/investing/stock-market/market-sectors/information-technology/ai-stocks/ai-startups/
|
[
{
"date": "2025/06/28",
"position": 88,
"query": "AI employers"
}
] |
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Salesforce says AI does 50% of company's work as ...
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S.F.’s largest employer, which cut 1,000 jobs this year, says AI does 50% of the work
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https://www.sfchronicle.com
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[
"Rachel Swan"
] |
Salesforce, San Francisco's largest employer, says AI bots do 50% of the company's work. Experts see this as the start of a wave of workforce change.
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The CEO of Salesforce, which resides in Salesforce Tower, said 50% of the company’s work is done by AI bots. Stephen Lam/The Chronicle
As one of San Francisco’s more outspoken tech leaders, Marc Benioff is no stranger to media firestorms, provoked by his unflinching stances on homelessness or surprise cheerleading for President Donald Trump.
The Salesforce CEO ignited a new controversy on Thursday, when he said during an episode of “The Circuit with Emily Chang” that artificial intelligence does “30 to 50% of the work” at his cloud computing company.
Long a happy warrior for AI, Benioff tried to put a positive spin on its encroachment into his company’s workforce culture. With bots taking over labor once performed by humans, he said, the human employees can “move on to do higher value work.”
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But given that Salesforce has cut 1,000 positions this year, Benioff’s remark stung. Salesforce is extremely influential, both as the city’s top employer and a proponent of returning to the office. Benioff is also a highly visible public figure. His championing of robot labor comes at a moment of eviscerating layoffs across the tech industry. Although most companies haven’t blamed automation for these cuts, they coincide with a major industry push toward AI.
Salesforce CEO Marc Benioff is speaking publicly in support of using AI tools in the workforce. Analysts say he may have no choice but to do so. Brontë Wittpenn/S.F. Chronicle
After Thursday’s segment aired, immediately drawing more than 100,000 views, people pushed back on social media. A few self-identified Salesforce employees insisted the CEO had exaggerated, and that the human workers at Salesforce are too valuable to be replaced by software.
To industry observers, Benioff is just being realistic.
“There’s no doubt that AI agents are replacing, and will replace, a substantial chunk of the workforce,” said Professor Saikat Chaudhuri, faculty director of the Management, Entrepreneurship & Technology Program at UC Berkeley’s Haas School of Business.
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Chaudhuri describes this moment as having a similar tectonic effect as the Internet Revolution, which caused pain for print media and brick-and-mortar stores, before opening a new world of possibilities. It became “something that people had to acknowledge,” Chaudhuri said, making some jobs obsolete but creating new ones.
He offered an example from the travel sector. In the old days, people made appointments with human travel agents to plan their vacations, find flights and handle other logistics. By today’s standards, the agents were expensive and inefficient, and became anachronisms once online booking took hold. At that point, consumers could plan trips on their own, by doing internet searches and entering their information into web pages that had to be built by human engineers.
Looking ahead, the travel agent could be revived, but not in human form. The new iteration would be a concierge-style AI bot that listens to the consumer’s requests and culls flight schedules instantaneously. With booking web pages no longer needed, the engineers are, theoretically, freed up for more creative pursuits.
Data gathered by economists at Stanford University corroborates that view, suggesting that Benioff, while bullish on AI deployment, isn’t necessarily an outlier. Rather, he’s pulling back the curtain on what’s actually a widespread practice. Usage spiked over the past two quarters, with 40% of firms across the country now saying they use generative AI at work — up from 30% last December, according to a survey called “The Labor Market Effects of Generative Artificial Intelligence.”
“It’s increasing very rapidly, even surprisingly,” said Jon Hartley, a policy fellow at Stanford’s Hoover Institution and lead author of the survey. Silicon Valley executives, in turn, have become more unapologetic about gambling in the technology. During a recent call with shareholders, Nvidia CEO Jensen Huang envisioned a future society with “billions of robots.”
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At some level, corporations have an incentive to put out these messages, said Jeff Hancock, a professor of communication at Stanford and founding director of the Stanford Social Media Lab. Over the past few years, many businesses have invested heavily in AI programs and most are seeking some kind of return. They at least need to convey to shareholders that the bet was worthwhile.
Zeal from the C-suite doesn’t necessarily mean the employees are on board. Some might feel intimidated and reject the technology altogether. Others will simply program the software to do their jobs, viewing it as a substitute rather than a helper. Many will find this new phase of work to be discouraging and dehumanizing.
Two schools of thought have emerged around artificial intelligence, Hancock said. The first is a “pilot” mindset, which casts AI as a tool to augment performance and help people achieve their goals. Hancock describes the second mindset as that of a “passenger,” or someone who feels a loss of power and control when interacting with AI.
By many measures, Benioff seems to fit in the first camp. He embraced a concept called “agentic AI,” which refers to technology so sophisticated, it can accomplish tasks without human supervision. At its best, this class of AI should broaden possibilities for workers and create an opening for projects that previously would have been too burdensome. This is the AI that reads and summarizes your emails, manages your calendar, handles the customer service call with that person who wants to return shoes.
Benioff is so enamored of this model that he refers to Salesforce’s AI bots as an army of digital “agents.” According to Chang, he spent more than $20 million to license physicist Albert Einstein’s likeness for Salesforce’s AI branding. Salesforce seeks to have 1 billion active agents by the end of the year.
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Whether Salesforce workers accept this new “AgentForce” depends largely on how they interpret Benioff’s attitude toward AI. And his remarks this past week could serve as an inflection point.
Because the technology is so new and could so profoundly change labor, executives are uniquely positioned to shape the mentality at their companies, Hancock said. If Benioff portrays AI as productive and liberating, then his optimism will flow down from managers to lower-level employees. Hancock calls this a “leadership cascade.”
On the flip side, if Salesforce employees construe Benioff’s comments as a warning that he plans to lay off half the staff, then people will feel degraded and overwhelmed, as though they’re being whip-sawed by new machinery.
That creates problems for employee retention, Hancock said. As the bots take over, the humans will leave. The question is where they go next.
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Chaudhuri remains hopeful.
| 2025-06-28T00:00:00 |
2025/06/28
|
https://www.sfchronicle.com/sf/article/ai-work-employee-salesforce-20396370.php
|
[
{
"date": "2025/06/28",
"position": 99,
"query": "AI employers"
},
{
"date": "2025/06/28",
"position": 58,
"query": "AI layoffs"
}
] |
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I Asked ChatGPT If AI Could Really Replace Our Day Jobs
|
I Asked ChatGPT If AI Could Really Replace Our Day Jobs — Here’s What It Said
|
https://www.yahoo.com
|
[] |
ChatGPT's final summation is that, “Yes, AI can replace or automate many traditional job functions. Fully replacing a day job with AI is possible, but usually ...
|
Many people are concerned that as AI technology advances, it could potentially take over roles that are traditionally filled by humans, putting people out of work for good. Others are actually looking forward to the mundane and monotonous tasks of their labor being replaced by AI. No matter how you view the scenario, is any of it true or even possible?
Read More: I Asked ChatGPT How Much Money I’ll Need To Retire in 5 Years
Explore Next: How Much Money Is Needed To Be Considered Middle Class in Your State?
GOBankingRates decided to ask AI itself, posing questions to ChatGPT about whether or not artificial intelligence is poised to replace your day job. Here is what it said:
Jobs That Are Already Being Augmented or Replaced by AI
ChatGPT responded, “You can replace some or even most aspects of a day job with artificial intelligence — but whether you can fully replace your specific job depends on several factors.”
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The first factor is the type of job you are working. ChatGPT noted that types of assignments that are “Repetitive/Rule-Based” such as data entry, transcription and scheduling can be done by an AI. Information processing, like market research, report writing and analysis, are also able to be taken care of by AI.
Additionally, creative tasks including social media posting and customer support, are already being handled in numerous industries by artificial intelligence.
I’m a Self-Made Millionaire: Here’s How I Use ChatGPT To Make a Lot of Money
Goals for Having AI Replace Your Job
Next, ChatGPT asked what is the desired outcome of having AI fill the role or do the work of a human? “You want to replace income,” it stated, “you can build AI-based side hustles or businesses that may replace your salary.”
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As some examples, ChatGPT listed selling AI-generated content (ebooks, art, templates), running a dropshipping or print-on-demand store with AI marketing, and offering AI-enhanced services (e.g., resume writing, website copy) as ways AI could aid in replacing your salary without you doing much heavy lifting.
If you want to replace your workload, ChatGPT outlined that “you can offload a huge chunk of repetitive or time-consuming tasks using AI, reducing your working hours. This is more about augmentation than replacement.”
Limitations and Considerations
In ChatGPT’s estimations, human judgment and creativity are still hard for AI to replicate at a deep level. On top of that, it pointed to legal and ethical concerns, including but not limited to violating intellectual property agreements and creating AI-generated misinformation. Even if artificial intelligence did take your job, ChatGPT predicted companies would probably require some sort of human oversight or hybrid roles to ensure the AI was doing the work correctly and on time.
Conclusion
ChatGPT’s final summation is that, “Yes, AI can replace or automate many traditional job functions. Fully replacing a day job with AI is possible, but usually requires building income streams or freelance-style work using AI tools. The more you understand how to use AI, the more you can reduce dependency on traditional nine to five roles.”
More From GOBankingRates
This article originally appeared on GOBankingRates.com: I Asked ChatGPT If AI Could Really Replace Our Day Jobs — Here’s What It Said
| 2025-06-28T00:00:00 |
https://www.yahoo.com/lifestyle/articles/asked-chatgpt-ai-could-really-110953213.html
|
[
{
"date": "2025/06/28",
"position": 19,
"query": "ChatGPT employment impact"
}
] |
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|
AI's Rapid Workplace Shift: How to Secure Your Job in the ...
|
AI's Rapid Workplace Shift: How to Secure Your Job in the Age of Automation
|
https://www.investopedia.com
|
[
"Tobi Opeyemi Amure",
"Learn About Our",
"Editorial Policies",
"Peter Gratton",
"M.A.P.P.",
"Ph.D.",
"Is A New Orleans-Based Editor",
"Professor With Over Years Of Experience In Investing",
"Risk Management",
"Public Policy. Peter Began Covering Markets At Multex"
] |
AI automation is expected to threaten 300 million jobs globally by 2030. Learn which roles are most at risk and discover five expert-backed strategies to ...
|
The rapid rise of artificial intelligence (AI) is reshaping the workplace faster than most people realize. What started with automating back-office tasks and customer service roles has now expanded into programming, legal research, financial analysis, and even creative fields such as writing and design. Experts predict that by 2030, up to 30% of U.S. jobs could be automated, with as many as 300 million jobs globally at risk because of AI and related technologies.
As AI tools become smarter and more accessible, the line between human and machine work is blurring—and the pressure to adapt is mounting. If you’ve noticed your workflow getting “smarter” or your company talking more about efficiency than expertise, you’re not imagining things. The age of AI-driven disruption has arrived, and it’s rewriting the rules of the workplace worldwide.
Key Takeaways AI is rapidly automating roles in customer service, data entry, programming, content creation, and analysis-heavy jobs across finance, law, and medicine.
The most at-risk jobs are those with repetitive, rules-based, or entry-level tasks.
Human-centric skills like judgment, empathy, and creativity remain in demand.
Which Jobs Are Most At Risk from AI?
The first wave of AI automation swept through customer service, data entry, and routine administrative work, said Dima Gutzeit, CEO of LeapXpert, a New York-based tech vendor that provides modern business communication tools with AI capabilities.
Now, he said, even roles in software development, content creation, finance, law, and medicine are being reshaped by code-writing engines, AI copywriters, and data-crunching models. Entry-level and repetitive positions are especially vulnerable, as AI excels at handling foundational tasks that once helped early-career professionals gain a foothold.
A June 2025 study by the Federal Reserve Bank of Dallas argued that most claims for what AI will do are "speculative" at this point. Indeed, many—including the World Economic Forum—have argued that the jobs AI produces will far outnumber those it renders redundant—170 million versus 90 million, respectively.
Nevertheless, the jobs most at risk from language-modeling AI include clerks, administrative assistants, and certain teaching positions. The telltale signs your job could be next? Your daily workflow starts to feel more software-driven, tools gain “AI-powered” features, and management talks about “co-pilots” and “automated insights.” If your responsibilities are becoming more about overseeing software than applying your unique skills, it’s time to take action.
While AI is rapidly transforming the workplace, experts agree that the best way to stay relevant is to focus on the qualities that make us uniquely human.
Here are some strategies to avoid being replaced by AI:
1. Demonstrate Your Humanity
AI can process data, but it can’t replicate judgment, empathy, or ethical decision-making. “What sets you apart isn’t your ability to process data—it’s your ability to interpret it, communicate it, and act on it,” Gutzeit told Investopedia. Employers are increasingly valuing creativity and abilities that remain stubbornly human, like relationship-building and nuanced communication.
2. Become an AI Power User
Don’t just fear the new tools, master them. Learn how to use AI platforms relevant to your field, from prompt engineering in content creation to AI-driven analytics in finance. The fastest learners today will be tomorrow’s leaders. Experiment with AI, critique its output, and figure out how to make it work for you.
3. Automate the Repetitive, Focus on the Unique
Identify the mechanical parts of your job and automate them, freeing up time for higher-value work.
“Strip the mechanical from your day so you can invest in the interpersonal-relationships, storytelling, negotiation,” Gutzeit said. The more you focus on tasks AI can’t do, the more secure your position becomes.
4. Upskill Continuously
Stay ahead by regularly updating your technical and soft skills. Pair AI literacy with human-centric strengths: Combine analytics with storytelling, or prompt engineering with leadership. The best opportunities will go to those who can bridge the gap between algorithmic speed and human nuance.
5. Watch Industry Trends and Pivot Early
Monitor which roles and industries are being automated, and be proactive about moving into areas where human expertise is still essential. Look for companies that use AI to amplify, and not replace, human value.
“Professionals who understand that partnership create more value than either humans or machines can deliver alone," Gutzeit said.
The Bottom Line
AI isn’t just coming for your job; it’s already transforming the workforce. But the future belongs to those who adapt early, master new tools, and double down on the skills that make us human. It’s important to stay curious, proactive, and relentlessly focused on value. You can turn the AI revolution into an opportunity instead of a threat.
| 2025-06-28T00:00:00 |
https://www.investopedia.com/protect-your-job-from-ai-11755710
|
[
{
"date": "2025/06/28",
"position": 22,
"query": "future of work AI"
}
] |
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|
AI Fears Overblown? People Using Artificial Intelligence At Work Are ...
|
AI Fears Overblown? People Using Artificial Intelligence At Work Are Happier, Healthier
|
https://studyfinds.org
|
[
"Studyfinds Analysis",
"Research Led Osea Giuntella",
"University Of Pittsburgh",
"Luca Stella",
"University Of Milan",
"Johannes Konig",
"German Ministry Of Finance",
"About Studyfinds Analysis"
] |
Workers in AI-heavy jobs reported better health, unchanged job satisfaction, and no increase in anxiety about their futures.
|
(© chinnarach - stock.adobe.com)
In a nutshell Workers exposed to AI reported better physical health and higher satisfaction with their well-being, without increased anxiety, depression, or job insecurity.
The study found no evidence that AI harms workers’ mental health or job satisfaction, and even showed a small reduction in working hours without impacting wages.
Germany’s gradual AI adoption and strong labor protections may have helped ensure that AI complemented human jobs instead of replacing them.
PITTSBURGH — Today’s headlines are riddled with warnings about AI stealing jobs and making workers miserable. But international researchers who actually followed real people for 20 years found the opposite. Workers in AI-heavy jobs reported better health, unchanged job satisfaction, and no increase in anxiety about their futures. The robots, it turns out, might be helping more than hurting.
Research published in Scientific Reports tracked nearly 18,500 workers from 2000 to 2020, comparing those in AI-heavy jobs with workers in traditional roles. People working alongside AI reported better physical health and greater satisfaction with their well-being, while showing no increase in anxiety, depression, or job insecurity.
Americans are increasingly worried about AI’s impact on their livelihoods, from ChatGPT writing emails to robots manning factory floors. This research suggests our fears might be overblown. Instead of replacing human workers or making them miserable, AI appears to be making physically demanding jobs easier and potentially safer.
For example, instead of a warehouse worker lifting heavy boxes all day, AI-powered machinery handles the heavy lifting while the human focuses on oversight and decision-making. Rather than a nurse spending hours on paperwork, AI tools streamline administrative tasks, leaving more time for patient care.
AI could help with tedious administrative tasks to free up time for employees. (feeling lucky/Shutterstock)
The study followed workers through Germany’s gradual adoption of AI technology, which accelerated significantly after 2010. Researchers used an objective analysis that identified which jobs were most susceptible to AI automation, and self-reported data from workers about their actual use of AI technologies.
Unlike smaller studies that capture just a moment in time, this analysis tracked the same people over two decades, watching how their health and job satisfaction changed as AI became more common in their workplaces. Researchers controlled for factors that could skew results, including age, education, industry, and regional differences.
Germany proved an ideal testing ground because of the country’s strong labor protections, extensive unionization, and comprehensive employment legislation. These create conditions where technological change tends to complement rather than replace workers. AI adoption among German firms grew significantly from virtually zero before 2010 to substantial levels by the 2020s.
Workers reported their life satisfaction, job satisfaction, and various health metrics on standardized scales. Researchers also tracked concerns about job security and personal economic situations. Results consistently showed either neutral or positive effects across nearly all measures.
Workers in AI-exposed occupations reported better overall health status and greater satisfaction with their health. Researchers believe this stems from AI’s ability to reduce physically demanding and hazardous tasks—the kind of repetitive, back-breaking work that damages bodies over time.
Additionally, workers in AI-heavy jobs saw a modest reduction in working hours, about 30 minutes per week on average, without any negative impact on wages or employment rates. This suggests AI might be creating more humane working conditions rather than eliminating jobs entirely.
As long as AI is thoughtfully implemented into jobs, it should be helpful instead of harmful. (sdecoret/Shutterstock)
Workers in eastern Germany, where economic conditions have historically been more challenging, showed increased anxiety related to AI exposure. This regional difference highlights how institutional protections and economic stability influence workers’ experiences of technological change.
Germany’s strong labor protections and gradual AI adoption might not reflect what happens in countries with weaker worker safeguards. The study also focused on workers who entered the job market before 2010, meaning it doesn’t capture how younger workers might fare differently.
We’re also still in the early stages of the AI revolution. Technologies examined in this study represent the first wave of workplace AI adoption. More sophisticated systems, particularly recent generative AI tools like ChatGPT, might have different effects that haven’t yet been measured.
AI’s employment impact might be more nuanced than the “jobs versus robots” narrative dominating headlines. Rather than wholesale job replacement, AI appears to be reshaping work in ways that could reduce physical strain and workplace hazards while maintaining employment levels.
Twenty years of real-world data beats a thousand think pieces about AI’s impact on work. German workers showed us that with the right protections and thoughtful implementation, artificial intelligence can make jobs less physically punishing while keeping paychecks steady. Now the challenge is making sure that positive experience isn’t exclusive to Germany.
| 2025-06-28T00:00:00 |
2025/06/28
|
https://studyfinds.org/ai-not-office-enemy-workers-feel-better/
|
[
{
"date": "2025/06/28",
"position": 55,
"query": "artificial intelligence worker well-being"
},
{
"date": "2025/06/28",
"position": 62,
"query": "artificial intelligence worker well-being"
},
{
"date": "2025/06/28",
"position": 71,
"query": "artificial intelligence worker well-being"
}
] |
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] |
Will AI completely replace humans in the creative professions?
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Will AI completely replace humans in the creative professions?
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https://www.valterosa.com
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Far from eliminating creative jobs, AI is creating new opportunities: Experts in prompt engineering (the art of giving effective instructions to AI) ...
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Introduction
In recent years, we have witnessed impressive advances in artificial intelligence, especially in the creative field. Tools such as Midjourney, DALL-E, ChatGPT, and others have demonstrated capabilities that seemed impossible just a few years ago. But this leads us to a fundamental question: will these technologies one day completely replace human creative professionals?
As someone who works in the creative field developing websites and helping companies build their digital presence, I have been closely following this evolution. And I am not alone in this curiosity (or concern): many creative professionals wonder if their work will be at risk in the near future.
So I will try to explore this question further: will AI completely replace humans in creative professions such as design, writing, music, and other areas?
The current capabilities of creative AI
Antes de mais, vamos ver o que a IA já consegue fazer hoje nas diferentes áreas criativas.
Graphic and Visual Design
Tools like Midjourney and DALL-E already do this:
Generate impressive images from textual descriptions
Create illustrations in various artistic styles
Produce design concepts for products, logos, and advertising materials
In 2022, an image generated by Midjourney called “Théâtre D'Opéra Spatial” won an art contest in the US, beating human artists. This event raised important questions about the role of AI in art and sparked a heated debate in the art community.
Popular platforms such as Canva have already incorporated AI features (Magic Studio) that automatically generate layouts and designs, further democratizing access to design.
Music and Audio
In the field of music, AI already:
Composes melodies and arrangements in different styles
Generates personalized music for videos
Imitates singers' voices (sometimes with alarming realism)
One notorious case was a fake song created with synthetic voices of Drake and The Weekend, which went viral and racked up millions of plays before being taken down. The quality was so convincing that many listeners believed it to be a genuine collaboration between the artists.
Writing and Content
In the field of writing, models such as ChatGPT and Claude already:
They write articles, blog posts, and social media posts.
They create slogans and advertising copy.
They help brainstorm ideas and scripts.
They produce content in different styles and tones of voice.
Major brands such as Heinz and Nestlé have already used AI-generated content in their marketing campaigns. According to Harvard Business Review, these tools already produce advertising copy that is close in quality to that of human writers.
Video and Multimedia
AI is also advancing in the field of video:
Runway ML allows you to create and edit videos from text descriptions
Synthesia generates videos with virtual presenters
Tools such as Descript allow you to edit videos simply by editing the transcribed text
AI tools that are transforming creative work
AI tools continue to emerge and evolve. Some of the most relevant ones you can explore are:
For images and design:
Midjourney - Generates high-quality artistic images
DALL-E 3 - Creates images from detailed textual descriptions
Adobe Firefly - Integrates image generation into the Adobe suite
Canva Magic Studio - Automates design tasks
Luma AI - Creates realistic 3D models from photos
For music and audio:
Suno AI and Soundraw - Generate personalized music
AIVA - Compose more complex instrumental pieces
ElevenLabs - Create realistic synthetic voices with emotion
For text and content:
ChatGPT and Claude - Versatile writing assistants
Jasper and Copy.ai - Focused on marketing content
Notion AI - Integrates text generation into the workplace
For video:
Runway ML - AI-powered video generation and editing
Synthesia - Create videos with virtual presenters
Descript - Edit videos by editing text
Why AI will not (yet) completely replace creatives
Despite all these impressive capabilities, there are several reasons why a total replacement of humans in creative professions is unlikely, at least in the near future.
1. Technical limitations and genuine creativity
Current AIs do not “create” in the same sense that humans do. They analyze enormous amounts of data and generate variations based on patterns they find. This means that:
They lack true originality and disruptive innovation
They do not truly understand the world, only statistical patterns
They lack the ability to discern which ideas have genuine artistic or cultural value
A human designer who has grown up in a particular culture, traveled the world and lived through unique experiences brings perspectives to their work that no AI can replicate. Human creativity is born from unexpected connections between memories, emotions and diverse knowledge.
2. Emotional intelligence and empathy
Effective art and design often depend on a deep understanding of human emotions and psychology. AIs don't:
Feel emotions or fully understand their impact
Have personal experiences that inform emotional work
Capture cultural and emotional subtleties that resonate on a deep level
An example: when I design a website for a social project, my own empathy for the problem the project aims to solve informs subtle decisions in the design that can make the difference between a page that merely informs and one that truly motivates action.
3. Cultural context and adaptability
Human creatives are immersed in culture and that's why:
They understand cultural nuances, local humor, and current references
They adapt quickly to new contexts and emerging trends
They capture the “zeitgeist”—the spirit of the times—in ways that AI cannot
A designer living in Portugal naturally understands certain visual elements, linguistic expressions, and cultural references that resonate specifically with the Portuguese audience—something that global AI may not capture with the same authenticity.
4. Strategic vision and purpose
Creativity with purpose goes beyond simply producing aesthetically pleasing content:
Sets clear goals for communication or experience
Aligns with brand values and strategic positioning
Considers the long-term impact of creation
When I work with clients on website projects, my role is not just to create something beautiful—it's to understand the business in depth, identify what sets it apart from the competition, and translate that into a digital experience that communicates the right values and leads to concrete results.
5. Ethical issues and preference for human touch
There is also an ethical dimension and social preference:
Many consumers explicitly value work created by humans
Copyright and intellectual property issues remain unresolved
There is intense ethical debate about the use of human creations to train AIs
In the music world, for example, recording studios and artists have reacted strongly against AI imitations. Universal Music Group condemned a fake song that imitated Drake, questioning whether we want a world that values “human creative expression or deepfakes and fraud.”
6. Legal and organizational barriers
Various institutional protections also limit total substitution:
Copyright laws in many countries do not recognize works generated entirely by AI
Creative unions are negotiating specific protections (such as the Writers Guild of America)
Regulations on transparency in the use of AI are emerging
In 2023, the Hollywood writers' union secured a contract stipulating that studios cannot use AI to write or rewrite scripts, nor force human writers to use these tools.
How AI is transforming (not replacing) creative work
Rather than replacement, we are witnessing a transformation in the way creatives work. The emerging relationship between humans and AI is more symbiotic than competitive.
AI as an amplifier of human creativity
For many creative professionals, AI is becoming:
A brainstorming tool that generates hundreds of initial ideas
An assistant that automates repetitive technical tasks
A means to quickly explore variations and possibilities
A practical example: when I am working on a new website, I can use AI to quickly generate different layout concepts, freeing up the time I would otherwise spend on this task to focus on refining the user experience and ensuring that the site perfectly reflects the client's identity.
New emerging roles
Far from eliminating creative jobs, AI is creating new opportunities:
Experts in prompt engineering (the art of giving effective instructions to AI)
Curators and editors of AI-generated content
Designers of hybrid human-AI experiences
Ethical consultants for the responsible use of AI in creativity
Adobe reported that by 2024, 83% of creative professionals were already using generative AI tools to streamline their workflow, not to replace their work, but to make it more efficient.
Increasing the value of the “human touch”
As AI becomes more prevalent, paradoxically:
The value of human creative direction increases
Curation and discernment become more valued skills
Genuinely human originality stands out even more
Human creatives are evolving to become “technology orchestrators,” combining their innate talent with the power of machines.
The economic and labor market impact
Like any technological revolution, generative AI is reshaping the creative labor market.
Transformation rather than elimination
Studies indicate that AI tends to:
Automate specific tasks within jobs, not entire jobs
Increase the productivity of professionals who adopt it
Create new job categories while transforming others
A McKinsey report predicts that by 2030, technologies could automate around 30% of the hours worked in various occupations, but AI tends to enhance the work of creative professionals rather than render them obsolete.
Possible market adjustments
Some changes are simply inevitable:
Junior and assistant positions may decline, as a single person with AI can perform work that previously required a small team
Professionals who resist adopting AI may encounter more difficulties
The value of “average” creative content may decline due to the abundance generated by AI
On the other hand, there is room for new roles and specializations that combine human creativity with technical knowledge of AI.
Democratization of Creation
One positive effect is broader access to creative tools:
Small businesses and freelancers can now compete with large agencies
The global volume of creative content is increasing drastically
Technical barriers to creation are lowering, allowing more people to express their creativity
Human Skills That Will Remain Irreplaceable
Certain human qualities will continue to be our “safe harbor” in the face of automation:
🌟 Genuine Originality
The ability to have truly new insights, inspired by unique life experiences and unexpected connections between ideas.
🤝 Empathy and Emotional Connection
Deeply understanding people’s needs, desires, and emotions—creating work that resonates on a visceral level.
🧠 Strategic Thinking
Defining the “why” behind the creation and aligning the creative work with broader business or communication goals.
🎨 Refined Aesthetic Sense
A trained eye that spots the detail that makes all the difference—shaped by years of experience and cultural sensitivity.
💡 Authentic Storytelling
The ability to tell stories that genuinely reflect the human experience, with all its complexities and contradictions.
⚖️ Ethical Judgment
Knowing when something could be problematic or insensitive to certain groups—something that requires social awareness.
How Creative Professionals Can Thrive in the Age of AI
For those working in creative fields, the strategy isn’t to resist change—but to adapt to it:
1. Embrace AI as a Tool, Not a Threat
Try out the available tools and discover how they can enhance your creative process.
2. Invest in Your Uniquely Human Skills
Develop what sets you apart: your unique vision, deep empathy, strategic thinking, and cultural knowledge.
3. Learn How to Give Effective Instructions to AI
Mastering the art of prompt engineering is a valuable skill—it allows you to get the best out of AI tools. It may seem simple, but one word can completely change the context.
4. Position Yourself as a Curator and Creative Director
Value is increasingly in selection, refinement, and strategic direction, not just technical execution.
5. Stay Updated with Technology
AI evolves rapidly - keeping up with the latest tools and techniques is essential to remain relevant. New technologies and tools emerge daily, and while it may be nearly impossible to follow everything, try to stay on top of the most impactful innovations.
Conclusion: The Future is Collaborative
The question shouldn't be whether AI will completely replace humans in creative professions, but how we can build a symbiotic relationship where each brings their strengths to the table.
Machines excel at:
Processing massive amounts of information
Generating variations and alternatives quickly
Executing technical tasks with precision
Humans shine at:
Giving meaning and purpose
Understanding emotions and motivations
Innovating in truly original ways
The most likely future is one where human creatives work side by side with AI—using it to amplify creativity and productivity, while maintaining control over the vision, direction, and meaning of the work.
As one expert at the World Economic Forum said:
"Artificial intelligence should serve human creativity, not replace it."
This quote perfectly captures how we should approach the future—not with fear, but with a clear vision of how humans and machines can build something greater together.
And what about you?
Are you already using AI tools in your creative work?
How do you see the future of your profession with the rise of this technology?
Share your experience with me.
If you need help adapting your business to the AI era or creating a digital presence that highlights your human authenticity, get in touch, I’d love to explore how I can support you in navigating this new world.
| 2025-06-28T00:00:00 |
https://www.valterosa.com/blog/will-ai-completely-replace-humans-in-the-creative-professions
|
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{
"date": "2025/06/28",
"position": 13,
"query": "artificial intelligence creative workers"
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] |
|
Course: What creative professionals need to know about AI
|
Course: What creative professionals need to know about AI
|
https://digitalskillseducation.com
|
[] |
This course is for people working in the culture sector as creative professionals. This includes visual artists, illustrators, designers, makers and crafters, ...
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Everyone’s talking about Artificial Intelligence – but what does it mean for you as an artist or creative?
AI is already having an impact on the creative industries. We think every creative professional should have the opportunity to learn and explore AI tools in a creative and safe environment.
Is it possible to use AI tools in an ethical way?
And how can you protect your own work from being exploited.
We’ll explore the ethical issues of using AI, covering topics like intellectual property, environmental impact, and possible misuse.
| 2025-06-28T00:00:00 |
https://digitalskillseducation.com/course/what-creative-professionals-need-to-know-about-ai/
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[
{
"date": "2025/06/28",
"position": 93,
"query": "artificial intelligence creative workers"
}
] |
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] |
|
Head of Strategy & Transformation - Civil Service Jobs - GOV.UK
|
Civil Service Jobs
|
https://www.civilservicejobs.service.gov.uk
|
[] |
As Head of Strategy & Transformation, you will work closely with the Chief Digital and Information Officer (CDIO) to support the delivery of Digital & Data ...
|
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| 2025-06-28T00:00:00 |
https://www.civilservicejobs.service.gov.uk/csr/jobs.cgi?jcode=1957754
|
[
{
"date": "2025/06/28",
"position": 60,
"query": "digital transformation wage distribution"
}
] |
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] |
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18 Africa in the World
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Africa in the World
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https://futures.issafrica.org
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[] |
... income distribution. A closed, rigidly divided world (the World at War and ... Our analysis started by presenting the recent history and likely forecasts of the ...
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Africa’s development potential depends on transformative domestic governance, effective institutions, enhanced security, and economic and human development progress—key areas within its domestic policy space. The website’s geographic and thematic analyses explore these priorities in detail.
However, Africa’s trajectory is not shaped in isolation, but it is deeply intertwined with global dynamics. This theme examines how shifts in global power and influence shape Africa’s development prospects.
The analysis incorporates results from modelling the global distribution of power and influence using three indices embedded in the International Futures forecasting platform (University of Denver) that assist in understanding the likely evolution of a business-as-usual forecast (Current Path):
See the About section on this website for more details on IFs.
The first two indices measure power potential, a complex and challenging exercise. The third, FBIC, assesses the bilateral influence of one country over another, reflecting the intricate dynamics of global relationships.
Building on these measurements, we frame Africa’s development within four alternative global scenarios: a Sustainable World, a Growth World, a Divided World, and a World at War. Each scenario explores the implications for Africa, highlighting how globalisation, sustainability and geopolitical shifts might shape the continent’s future. The analysis also considers the disruptive potential of wildcards, including a great power implosion in the US, China or Russia, major regional developments in Europe or Asia, extreme impacts from climate change, and the unintended effects of rapidly advancing artificial intelligence.
The theme concludes with actionable policy recommendations to foster a stable global environment that enables Africa’s growth while safeguarding its autonomy in an increasingly competitive international system.
Africa's position in global development Download to pdf
With some exceptions since independence, Africa has not been able to narrow the gulf between itself and averages for the rest of the world on key well-being indicators, such as infant mortality rates and life expectancy. Measures of income have done even worse. Using a crude measure such as Gross Domestic Product per capita in purchasing power parity (GDP PPP), the gap between Africa and the average for the rest of the world has steadily increased. Some countries, such as Seychelles, Mauritius and Botswana have done well, with Ethiopia and Rwanda experiencing some of the fastest economic expansions in the world, with an average of more than 7.5% per year in recent decades. Nevertheless, most African countries have stagnated or fallen further behind in key development indicators, even when compared to other developing regions such as South Asia and South America. Chart 1 depicts this gap in average GDP per capita between Africa, South Asia and South America from 2000, with a forecast to 2043.
So what would the future look like?
Our forecasts indicate that, on its current development trajectory, the growing divergence between Africa and the rest of the world, including other developing regions, is likely to increase when considering GDP per capita. Things are improving in Africa but more slowly than elsewhere, with large country-to-country variations. Generally, we refer to this as the Current Path elsewhere on this website.
Six recent successive shocks have accelerated that trend: the impact of the 2008/09 global financial crisis, the COVID-19 pandemic, Russia’s invasion of Ukraine, the deteriorating relations between China and the West, the resurgence of hostilities in the Middle East following events in Israel and Gaza, and the rise of populism in the US and Europe, including the recent re-election of Donald Trump and his subsequent assault on the US's Western allies. The world is entering uncharted territory with the accelerated effects of climate change, which is a structural driver of scarcity and migration, and the disruptive impact of generative artificial intelligence.
Given its dependence on imported food, Africa is especially at risk of food insecurity. The threat of a simultaneous harvest failure in the six global breadbaskets that produce 60% of the world’s corn, rice, soy and wheat crops is rapidly increasing, among other risks. Thus, the UN Secretary-General’s report, Our Common Agenda, notes that ‘we are at an inflexion point in history’ facing a stark choice between ‘breakdown’ and ‘breakthrough’.
To what extent will the growing divergence between averages for Africa and the rest of the world impact global sustainability and stability, including increased migration to Europe and within the continent? Is it possible to envisage a stable world so starkly divided between Europe and Africa in wealth and quality of life? For example, the average GDP per capita in Africa is only 11% of that in the European Union (EU), with a 14-year gap in average life expectancy. We expect these differences to only reduce marginally over the next two decades.
Fixing these alarming discrepancies will require better governance in Africa, characterised by more robust, more capacitated states with fit-for-purpose institutions, more security, and improvements across various economic and human development sectors, much of which is within Africa’s domestic policy space. We examine many of these considerations elsewhere in the website's geographic and thematic analyses.
Rapid development in Africa will also require a facilitating global environment — the subject of this theme. Therefore, we ask what is the impact of geopolitics on Africa's development. To explore the potential implications, we model Africa's development in four future global scenarios further below.
A rising China but still a dominant West Download to pdf
The rise of China and surrounding Asia as the most important source of economic growth globally and the associated shift of economic heft eastward is well established and widely reported. Given its growing population and deepening ties with China, Africa is well-positioned to benefit from this trend. The result is a commensurate reduction in the West's relative economic and political weight and the rise of additional sources of capital and influence, such as from the Middle East (particularly the UAE and Saudi Arabia), reflecting an associated trend towards regionalism that reinforces global fracturing. At the height of globalisation, global trade grew as a portion of the global economy. It is now stable - but trade within politically aligned blocs now grows faster than trade between them, reflecting a splintering into more deeply integrated blocks. Today, Asia’s economy is less dependent on trade with other regions and is increasingly more integrated and self-sufficient. China is at the heart of Asia, but Asia is much more than China, as the region includes many dynamic economies and large powers such as India and Japan — both of whom are at odds with China. Africa too is intending to expand intra-regional trade, though for different reasons.
For over a century, the US has been the most powerful country in the world (both in hard and soft power terms). It has successfully presented a narrative that equates global development, stability and progress with American interests. The close connection that it has established between democracy, the free market and so-called Western values is, however, starting to fray and is accelerating as Trump rails against friend and foe alike. Eventually the US and China need to decide - to pursue profit and partnership or competition and potentially conflict.
The US has derived considerable advantage from its dominant global position. Globalisation allowed it substantial advantages, attracting investment and allowing the US to manipulate the international system to its benefit. The status of the US dollar as the global reserve currency allows America to consistently run a more significant current account deficit than other countries. That advantage will fade in the years ahead as the use of national currencies in some regions, including in Africa, gains momentum. The assault by the 2025 US administration on aid, multilateralism and upon its Western alliance partners will accelerate this trend.
The fracking revolution in the US, together with the fallout from the war in Ukraine, means that China has become the most significant fossil energy market for the billions of dollars of oil exports from the Middle East, Russia, Venezuela and others. A new world energy order is taking shape that will eventually transact in various currencies, not only in the dollar. That shift will set the scene for the steady diminution of the dollar-based financial system and the benefits that flowed from it for the US. While China powers ahead, expanding its relations in the region through the Belt and Road Initiative and other efforts, the US is in danger of withdrawing from the world. Globalisation is now less popular than previously, with the belief that it allows ‘others’ to steal US intellectual property and catch up in a remarkable display of historical amnesia for a country that has gained enourmous advantage from the rules based system that it helped to create after the Second World War. The reaction to globalisation across rural America and the rise of domestic populism has translated into a resurgence of nationalism and isolationism that is detracting from US soft power.
The first presidency of Donald Trump and the US’s lack of consistency on global matters — ranging from its vacillations on the utility of NATO (Trump), stepping away from membership of the International Criminal Court (that predates Trump) to a lack of support for the World Trade Organization — was already deeply damaging to the US. It undermined its global appeal and the trust that others have in the US as a dependable ally, notably where foreign policy approaches may diverge significantly between presidents, such as views on Ukraine, North Korea, Russia and Taiwan when comparing the first Trump administration with those of Obama or Biden. The second Trump presidency could even lay the foundation for a global economic partnership between the US and China given the complementary and interdependent nature of these two giants and the extent to which US foreign policy interests are likely to focus on bilateral trade and economic benefits rather than democracy, human rights and good governance.
Given economic growth in Asia and elsewhere, the US's hard and soft power advantage is declining. Its reaction to the September 11, 2001, terrorist attacks, its subsequent military operations in Afghanistan and, in 2003, the disastrous invasion of Iraq that rescued Al Qaeda from the jaws of defeat and that ignited ISIS have affected perceptions of US hard power capabilities and fueled domestic debates regarding its future role globally. More recently, the impunity that the US has provided to Israel for the oppression, repression and war that Israeli Prime Minister Benjamin Netanyahu is pursuing in the wake of the terror attacks by Hamas of 7 October 2023 has reinforced views amongst the global South that different standards apply for people generally considered part of the 'civilised West' versus the rest.
The trend in the decline in the soft power of attraction enjoyed by the US predates these events. Most damaging was the image of a mob violently occupying Capitol Hill at the instigation of Donald Trump as he sought to overturn the presidential election results of 2020. His 2025 return as US president has significant ramifications for the US and the West. Eventually, the West may fracture as Trump's tariff war is directed at America's allies rather than others given the natural trade synergies that it has with countries that are nominally part of the expanded BRICS group (consisting of Brazil, Russia, India, China, South Africa, Iran, Egypt, Ethiopia and the United Arab Emirates).
The inevitable power transition between the US and China is confirmed by work published early in 2022 by our colleagues at the Frederick S Pardee Institute for International Futures. Using 29 alternative scenarios in its Diplomacy, Military and Economy (DiME) Index about the future diplomatic, military and economic capabilities of the US and China (including forecasts of nuclear weapon stockpiles), the Pardee Institute concludes that Chinese capabilities surpass the United States in 26 scenarios before 2060, with the most frequent period of power transition being the early 2040s. Chart 2 presents one scenario result consisting of the per cent of the global power of the EU28 (including the UK given its alignment as part of the West), the US, India and China from 2000 with a forecast to 2063, this time using the Global Power Index (GPI). Under Trump as the 47th president as of January 2025, the key question is if the US will pursue its traditional partnership with the EU (the West), or if Trump will prioritise the economic opportunities globally possible with China as part of a grand bargain?
The EU, with an economy comparable in size to China and the US, sets the global ‘ethical’ standard among larger powers on many issues, ranging from antitrust activities to Internet privacy, membership of international organisations, pursuit of democracy and support for human rights. Still, it suffers from an acute deficit in hard power capabilities compared to the US and China. Its social-democratic or more egalitarian model of development, particularly that of the Nordic countries, stands in sharp contrast to the raw capitalism in the US and the denial of individual rights in China although that model is steadily in question with the rise of populist parties in key member states including Germany, France, Italy and others. For the time being, European soft power is more significant than any other group of countries although its stance on intellectual property waiver for COVID-19 vaccines, rising xenophobia and its lacklustre responses on Ukraine and Palestine is steadily eroding that advantage. Similar to the US, nationalist populism is driving politics in Europe and it will negatively affect its relations with Africa, particularly regarding concerns around migration, culture and religion.
Given its history of colonialism, it is not surprising that even in 2000, the combined US/EU group had eight times more influence in Africa than the combined influence of China and Russia, using the FBIC index mentioned previously. But by 2023, the ratio had declined to only three times more influence, and it is declining.[1]
Also, Western countries evidence a trend of power diffusion away from the state, which is today less central in many people’s lives as additional patterns of interaction emerge. New technologies, the internet, and lately artificial intelligence, detract from social cohesion, infusing hate speech, extremism, and conspiracy theories into echo chambers on social media that serve to divide rather than cohere society. These technologies detract from the ability of democracies to offer freedom of speech and access to information in a balanced and unbiased manner, both of which are now increasingly manipulated, whilst strengthening the ability of centralised systems of authoritarian countries such as China and Russia to monitor and control their populations. The power of generative Artificial Intelligence in today’s connected yet divided world is such that the World Economic Forum’s Global Risks Report 2024 identified misinformation and disinformation as the number one global risk in 2024 and 2025, given the number of elections scheduled in these years.
A range of non-state actors, including from the private sector, civil society, and social media influencers, now serve as alternative sources of authority and reference, complicating and crowding the space previously occupied by traditional media, bureaucracies and elected representatives.
The flow of materials from Africa to China and Beijing's investments in infrastructure and trade with Africa are shifting relationships. Using FBIC (see Chart 3), China has increased its influence in 53 African countries, and FBIC only calculates a slight decline in one country, Tunisia. The UK has seen a decline in its influence in most African countries. Taking 2000 as a baseline, by 2019, the UK had increased its influence in 18 African countries but experienced a decline of double that number. The recent surge in anti-French sentiment in its former African colonies speaks for itself, as it has now been expelled by military governments in Niger, Mali, Burkina Faso, and Chad, as well as democratic Senegal.
The picture for the US is mixed, but its interest in Africa has waned over many years, reflected in declining investment. Following less Russian influence for much of the 1990s, Moscow has made a concerted effort to benefit from the decline in support for the West in the Sahel, particularly with the provision of security and arms to several military governments in West Africa in the wake of its invasion of Ukraine. With limited economic leverage in Africa, however, Russia's proxy war with the West has its limits. With an economy that is the size of Brazil, Russia lags significantly behind great powers such as the US and China.
Measures of influence are at an early stage of development but are bound to become more popular over time.
In summary, should we consider the West (North America, Europe, Japan, South Korea and others) as one group and juxtapose it with countries aligned with China, the relative power and influence of the West is declining as a portion of the global total but remains dominant globally as the wealth and technology of its citizens and states significantly outpaces that of others. Despite the rise of China and Asia, the GDP per capita gap between North America, Europe, and Japan, compared to China, has remained constant and will likely increase. The comparative advantage of citizens and countries in the West vs China and its few true partners, such as North Korea, Cambodia and others, remains large across the forecast horizon.
Although China will become hugely influential within our forecast period, its rise does not translate into globally dominant power capabilities in any of the four scenarios. Even a combination of 17 countries from the Global South, including China, Russia, Iran and various others, does not compare in power potential and influence to a combined West. Current indications point to China becoming more influential in Africa than any other single country and the continent's future increasingly tied to Asia, particularly when including ties to countries such as India, South Korea and Indonesia. Rather than a new uni- or even bipolar order, the global trend is towards a complex, multipolar global power configuration.
However, entirely different scenarios emerge when considering a US retreat from the international system and increased trade competition with the EU. The US and China collectively account for 41% of the global economy, which are growing more rapidly than the EU. In the face of the better trade synergies between the US and China and competition between the EU and the US on labour and energy costs (crucial inputs into manufacturing), the expectation that the US should continue to subsidise Europe's security while running a trade deficit with the EU is a ready sticking point in the US-EU relations in spite of the fact that the US regularly runs a much larger trade deficit with China than with the EU.
The GPI and DiME indices clarify the actual state of power in the international system using a set of carefully curated measures and weights. In 2023, a combined West[1Consisting of Argentina, Australia, Austria, Belgium, Canada, Denmark, Egypt, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, South Korea, Luxembourg, Morocco, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, Singapore, Spain, Sweden, Ukraine, UK and the US.] constituted 58% of total power in GPI, declining to 46% in 2050.
In considering these findings, it is essential to recognise the benefits that accrue to the US (and Europe) as the historical ‘system makers’ and, therefore, ‘privilege takers’ of the current global order. This group of industrialised, rich countries shares several core values (such as a market-based economy, democracy and human rights) and various cultural traits. The North Atlantic powers have dominated world affairs since the Industrial Revolution and have shaped today's rules, norms and values to determine inter-state relations in their interests.
However, in these times of upheaval and change, the past is not an inevitable guide to the future.
For example, the geo-political orientation of Japan, South Korea, Australia and New Zealand could shift in China’s favour as leaders position themselves to benefit from China’s rise. A Chinese decision to incorporate Taiwan and continued internal repression and human rights issues concerning communities such as the Uyghurs in China could exacerbate current regional divisions. The unification of the two Koreas would likely shift that country into a non-aligned grouping or China's orbit, given the proximity impact and the accompanying trade-offs. Similar to the problematic history that the US has in its relations in Central and South America, China's giantism is encouraging efforts at resisting its domination in Asia, particularly by the six or so countries that have territorial disputes with it.
New alliances may also appear between India and Japan, but none would be as consequential as a US withdrawal from its previous role of international security commitments, leaving space for others, China in particular.
In the wake of successive failed adventures in Afghanistan, Iraq and elsewhere, the second Trump administration clearly favours a more restrained approach to international engagements, prioritising domestic concerns and avoiding costly foreign entanglements. This includes a shift away from multilateralism towards trade protectionism and a clear transactional approach to international relations that steps away from efforts to promote free trade, democracy and human rights. The implications could be far-reaching. For example, a US decision to take Taiwan tensions off the table by respecting China's red lines could see a shift in relations that could eventually unlock a de facto economic partnership between the US and China should Trump's transactional foreign policy orientations set the scene for his successor in the Oval Office. The result will inevitably embolden others such as Russia, Iran, the UAE and Turkey also to pursue their regional interests.
The current configuration already comes with significant penalties for those on the margins of the system makers, including for Africa, practically reflected in limited voting rights in essential institutions such as the IMF and the World Bank, in the UN Security Council and punitive risk ratings from Western-based credit agencies, among others. Given its historical scepticism of multilateralism, reform such as a revision of voting rights within the key institutions at the heart of the international financial system or expansion of the UN Security Council to allow for the inclusion of African countries as permanent members with a populist US president in the White House is unlikely.
At the heart of geopolitical change is Asia's rise, reshaping global relations. Beyond its growing economic heft, East and South-East Asia has a larger population than any other region. It is increasingly interconnected through efforts such as China’s Belt and Road Initiative. Still, the region does not have the associated political superstructure, alliances or shared values to translate its growing economic heft into coherent power potential.
Views on China's global ambitions differ. Parag Khanna argues that China’s primary interests are ‘foreign resources and markets, not foreign colonies.’ He argues that its grand strategy of building global infrastructure and engagement in regions such as Africa aims to reduce its dependence on the West, bind its neighbourhood more closely together and reduce its reliance on foreign suppliers such as minerals from Australia and high technology from the US. In this view, China’s ambition is regional, not global dominance, but it does require a facilitating international context. Students of Xi Jinping, such as Steve Tsang and Olivia Cheung, differ. Their book, The Political Thought of Xi Jinping makes a compelling case of huge ambition, to 'make China great again'. The successive release by President Xi of China's proposal on building a community with a shared future for humanity (in 2013), its Global Development Initiative (at the UN General Assembly in 2021), a Global Security Initiative (in 2022), in 2023, its Global Civilisation Initiative and, in September the same year, its proposal on Reform and Development of Global Governance, present a comprehensive alternative framework to the Western rules-based system including on views on national sovereignty, national development, human rights, climate change, green and low-carbon development, globalisation, the role of the private sector, and more. It presents a model of domestic and foreign relations where regional powers are likely to dominate, free from interference from elsewhere, and where governments pursue national policies without the constraints of a free media, foreign interference or the need to balance the executive, judiciary and legislature - all subject to the requirement to pay due homage in a Sino-centric world.
Other regions and groups, such as Latin America and the Caribbean, Oceania, and the Middle East (including Turkiye), each constitute less than 5% of global GDP, except for Latin America and the Caribbean, which have a share of almost 7%. However, that portion will decline to below 5% by 2043. Many countries may consider themselves bishops or castles (rooks) in the global chess game, but most are plodding pawns.
Whereas the West will continue to dominate in many aspects of material and soft power calculations, a growing divide is opening up with the Global South and within the Global North as bickering and trade friction between the US and the EU are likely to increase.
Briefly
Against the background outlined above, we next consider Africa’s place and power in the international arena.
Recent History
After independence, Africa’s standing was elevated, with East and West competing for influence until the collapse of the Berlin Wall in 1989, which robbed it of that strategic value. Until then, important states on the continent were courted with money and arms as part of the Cold War competition between Washington and Moscow. Democracy and human rights considerations were generally trumped by loyalty, although sections within the development assistance community in some Western countries pushed back against this crude division.
After that, Africa’s oil exports and location in the US war against terrorism briefly elevated its status at different times. Violent, political Islam spread from Afghanistan and Syria to North-West Africa and East Africa, primarily driven by the displacement effect of US military interventions in Asia and the Middle East. The period coincided with a brief unipolar moment during which the US achieved peak power and influence in the absence of a rival.
Africa’s importance again dissipated thereafter, allowing for a brief period during which development priorities rose in international prominence. That period culminated with the agreement on the Millennium Development Goals in 2000 and, in 2015, on the Sustainable Development Goals. The boom in hydraulic fracturing for oil and gas in the US that started in these years effectively ended its dependence on imported fossil fuels and hence concerns about stability in the Middle East as well as in key African states such as Angola and Nigeria, and the support that it had provided to oil-rich autocracies.
European–African relations have been deeper and more enduring than relations with the US, not surprising given colonialism. Europe shares the same time zones and key languages, and the two are geographically proximate. Europe is Africa’s largest trading partner and it has the most extensive stock of foreign direct investment in Africa. However, the growing popularity of right-wing parties in Sweden, Germany, France and Italy is the single most consequential domestic trend in several decades and it is increasingly determining Europe's foreign and international relations with Africa. At its extreme were responses by the UK and Italy that planned to send African asylum seekers to Rwanda and Albania on a one-way ticket. Other measures include the hardening of borders such as by Spain and large grants and loans to countries such as Egypt in return for efforts by the government of President Abdel Fattah El-Sisi to clamp down on the transit of migrants to Europe. Structurally, the tensions are fuelled by globalisation and the extent to which blue-collar jobs in manufacturing have moved to Asia, the rural/urban political divides in rich countries, and feed off deep-seated historical, religious and cultural divisions and fears that go back centuries.
In contrast to the declining relations with the US and Europe, China’s footprint and influence in Africa has become more important each year. ‘No other country comes near the breadth and depth of China’s engagement in Africa,’ wrote The Economist in an in-depth study of the relationship in May 2022.
During the 1960s and 1970s, as the Cold War intensified, China–Africa relations were political and ideological to the extent that, in 1971, when the UN voted for China to replace Taiwan, 26 African countries voted in favour. Itself a poverty-stricken country, China provided military support and aid to the African continent. The construction of the Tazara railway line in support of the frontline states in their conflict with apartheid South Africa, then primarily supported by the West, serves as the most prominent showpiece.
China’s relationship with Africa changed during the 1990s as it increased in economic and political importance. A booming China needed oil and metals and eventually found an outlet for its sizable current account surplus and work for its construction companies that had built its roads, railway lines and ports, perfectly matching Africa’s need for investment and infrastructure. However, China’s annual loans to Africa shrank with the onset of the COVID-19 pandemic, despite the steady reductions in its current account surplus. Trade and return on investment are now more critical for China, even as it continues to buy favours in Africa, such as the recent gift of a building (and bugging) the tower block that hosts the offices of the African Union Commission in Addis Ababa, and building modern parliament buildings for Zimbabwe and Malawi. Zimbabwe, which has an external debt of US$21 billion, is heavily indebted to China, which is still the only country willing to extend loans to Harare due to its deficient domestic investment environment, poor governance and dismal repayment record.
After a COVID-19-induced decline in 2020, trade between China and Africa rose by 35% to US$245 billion in 2021 and to US$295 billion by 2024. China is Africa’s largest bilateral creditor (as a group, Western private banks have a larger share) and a crucial infrastructure construction and investment source. Its projects are also concluded more rapidly. The average infrastructure project in the Belt and Road Initiative takes 2.8 years, roughly a third of the time needed by the World Bank or the African Development Bank, in part because environmental impact studies and other regulations are sometimes bypassed. Whereas the West provides aid and, through its various agencies, concessional loans, Chinese development finance generally takes the form of loans at near market rates, much of it for infrastructure.
China’s hard-nosed practice is quite different from its benevolent ‘win-win’ rhetoric. Contracts include strict confidentiality clauses, requirements that China be repaid ahead of others, the use of escrow accounts and specific identification of which revenues would be required to pay back loans. Because Chinese creditors are numerous and fragmented, keeping track is complex. More than one newly elected African leader (most recently President Hakainde Hichilema of Zambia) has found the amounts that his country owed to China to be much higher than initially thought once so-called hidden debts were also counted.
China’s role in Africa is expanding beyond trade and loans. Already, Chinese firms account for an estimated one-eighth of the continent's industrial output. Its digital infrastructure is critical to Africa’s communication, much of which was built by Huawei, a company under US sanctions. The result is that political, military and cultural ties are all becoming closer. African views about China are now more favourable than those of the US. However, a majority of Africans still list the US significantly ahead of China as a preferred future model, given its more open system of governance.
Relations between China and Africa have, therefore, deepened. In April 2022, for example, China’s special envoy to the Horn of Africa, Xue Bing, offered to help ‘silence the guns’ in the Horn of Africa — a pragmatic move by an influential investor. China has about 400 construction and manufacturing projects worth over US$4 billion in Ethiopia alone. Since 2020 and until the Pretoria peace agreement in December 2022, Ethiopia was mired in a vicious ethnic conflict with the federal government in Addis Ababa pitted against rebel forces in the northern Tigray region. In July 2024, Tanzania hosted the largest-ever Chinese military deployment to sub-Saharan Africa for Exercise Peace Unity-2024, which included troops from neighbouring Mozambique.
The response in the West to China’s growing influence in Africa has been alarmist with recent efforts to counter the Belt and Road Initiative in Africa and elsewhere. In 2021, the Biden administration launched its Build Back Better World, and the EU launched its Global Gateway; in 2022, the G7 club announced its Partnership for Global Infrastructure and Investment (PGII) to mobilise US$600 billion in infrastructure projects over the next five years and with a particular focus on Africa. And for a brief few years under the Biden presidency, senior US and European diplomats visit the continent.
Africa’s prospects
Despite its large population, Africa is a small global player, with its combined influence diminished by the number of its constituent countries and its current lack of economic and political integration. Russia accounts for almost half of Africa’s arms imports. It is a major supplier (along with Ukraine) of Africa’s cereal imports, which was severely disrupted by the war between the two. Russia accounts for only 1% of the continent's foreign direct investment (FDI). This is minute compared to the stock of Europe's FDI in Africa, and recent FDI flows from China, Africa’s largest trading partner.
The large number of African states and their relative weakness mean that they offer numerous opportunities for Russia (and others) to pursue a proxy war with the West. For example, they could provide military support (through the Wagner group, now known as the Africa Corps) and protection to coup makers in the Sahel, leading to the ouster of French and US forces from the region.
Without a supranational authority such as the EU Commission and its various structures and much deeper economic integration, the calculation of Africa’s power potential is inevitably less than the sum of its more than 50 members, which, using GPI, constituted a mere 2.2% of global power in 2023. Because of the increased weighting of technology within modern indices of power (as opposed to historical measures that placed considerable weight on population size), by 2050, Africa will only increase its portion of global power to 3.9% while then constituting 27% of the world's population. Unlike the EU, the Commission of the AU is essentially an intergovernmental secretariat with limited and circumscribed policy latitude. The continent still has to register practical progress on trade integration.
However, there is also a flip side to Africa’s large number of constituent states, illustrated by Russia’s recent charm offensive that followed its invasion of Ukraine and subsequent sanctions and ostracism from the West.
Africa’s economic growth and population increase will steadily increase its power potential but more slowly than most analysts think.
An important reason is that Africa’s labour force is relatively small in relation to its dependants (children and the elderly), although it is increasing rapidly, while workers suffer from low levels of education, with some exceptions, and poor health. Africa is rapidly approaching a double burden of disease as the rates of non-communicable diseases are increasing rapidly. The result is that Africa’s labour productivity is about one-fifth the average of the rest of the world, and together with high poverty levels and low incomes, the capital per working-age person is even less.
Although it receives relatively large amounts of capital through remittance inflows and aid, Africa loses substantial amounts due to corruption and illicit financial outflows. Because of extreme poverty levels, unemployment, high levels of inequality and limited government revenues to improve basic services delivery, key African countries, including Nigeria, South Africa and others, experience high crime levels and poor governance. However, perceptions of instability are often generalised, not distinguishing between developing and stable countries and those in conflict. Finally, much of Africa’s physical capital, such as roads, rail, water, electricity and other essential infrastructures, is still of a colonial-era vintage. Still, it is being improved mainly due to recent investments by African governments and China in railways, ports, and associated infrastructure.
Manufacturing and services will expand rapidly on the continent – although much of this growth will initially be at the lower end of the value-add curve due to Africa's dependence on commodity exports. Until recently, the evolution of complex global supply chains meant that the location of least-cost manufacturing tended to gravitate towards the region with the cheapest labour, domestic stability, policy certainty, and access to a large market, typically Asia. Given reductions in input costs, the push towards reshoring and diversification and the need to reduce carbon emissions provide incentives to locate manufacturers closer to the future market in which Africa features prominently. Sub-Saharan Africa will increasingly feature as a location where industry can thrive. However, this depends upon the rapid integration of its fragmented markets, infrastructure provision, investment in improving its human capital endowment and better governance.
Eventually, regional economic communities with common currencies, freedom of movement of labour and capital across borders, and standard import and export tariffs will increase Africa’s attraction as a location for manufacturing. Indeed, Africa took a big step towards this goal when its members ratified the African Continental Free Trade Agreement (AfCFTA).
These prospects do not hide that Africa has effectively been an instrument of global power competition since independence, reflected in the preceding analysis, although health and humanitarian relief considerations have also been prominent. Its limited influence is hardly surprising since Africa's entire economy is only 3% of the world economy. On current expectations, it will increase to 5% of the world economy by mid-century.
Rather than a more productive economy, the increase in Africa’s power potential results from the continent’s rapidly growing population. Nigeria, Africa’s largest economy, constitutes a mere 0.5% of the global economy and will increase that portion by only 0.1 percentage points to 0.6% by 2050, reflecting its lack of international economic significance despite its large population, but not the extent to which it dominates within Africa, as its economy accounts for 16% of the continent's total.
Against that background, the following section presents four global scenarios and then examines their impact on Africa.
Logic and modelling
We frame Africa’s development within four alternative global scenarios based on the above-mentioned trends and measurements. We use two key dimensions that represent fundamental and highly uncertain forces shaping the international system, each with significant implications for development, geopolitics, and environmental resilience, depicted in Chart 4. The extent of globalisation (vertical axis) captures the degree to which countries and regions remain interconnected through trade, technology, and cooperation—or shift towards fragmentation and regionalism. The pursuit of sustainability (horizontal axis) reflects the prioritisation of equitable growth, environmental stewardship, and long-term resource management versus unchecked exploitation and short-term gains.
These dimensions form a framework with four quadrants, each representing a possible future scenario based on how these uncertainties might unfold: a Sustainable World, a Divided World, a World at War and a Growth World.
Chart 4: Scenario framework Click to enlarge
No scenario seeks to present the current global trajectory, which would inevitably be somewhere between these ideal types but currently most akin to the Divided World scenario.
The first scenario, a Sustainable World scenario, prioritises sustainability, equity, and pursuing the objectives set out in the UN’s Sustainable Development Goals. Rapid and ambitious regional integration lies at the heart of this scenario, successively translating into an African continental free trade area, then a customs union and eventually a common market that allows the free movement of capital, labour, and services amongst most countries. In this world, Africa can achieve the development outcomes modelled as the combined scenario on this website, which is associated with steady progress in accountability, democracy, and stability. The Sustainable World maximises economic growth with a 2043 global economy of US$170 trillion (compared to US$94 trillion in 2023), improves income, and reduces poverty. However, it is the most difficult to attain given its focus on multilateralism, environmental sustainability and equity.
In a second Growth World scenario, countries focus on the rapid improvements in income and returns on investment, eschewing environmental concerns, and do so within the context of a grand bargain between the US and China. Commercial and trade interests dominate, and by 2043, the world economy will be similar in size to the forecast in the Sustainable World scenario. Still, the Growth World will release 11% less CO 2 from fossil fuels, one indicator of the differences between these two futures. The Growth World leads to excellent economic results but to the detriment of equality and efforts to contain global greenhouse gases, resulting in negative climate change impacts, slow reductions in extreme poverty and an increase in inequality.
Instead of the continental integration characteristic of the Sustainable World, in the Growth World, African countries and regions link up with Europe, the Persian Gulf, the North American Free Trade Agreement (NAFTA) and China, signing preferential agreements with external partners. This is still, nominally, a rules-based context, although considerations regarding human rights and democracy play a relatively minor role. The AU is eventually marginal with countries and groups of countries pursuing the advantages of partnering with Europe (North Africa), the Persian Gulf (the Horn), the US (West Africa) and China (Southern, East and Central Africa). More importantly, the Growth World will have 192 million more extremely poor people than the Sustainable World, most of whom will be in sub-Saharan Africa.
The third indicative scenario is of a Divided World with a future characterised by a sense of global fracturing, populism, nationalism and a retreat from globalisation — effectively, the fraying of the rules-based system as we know it, with its complex lattice of norms and institutions. Everyone seems to be angry, selfish and unhappy and xenophobia and anti-migrant sentiment increase as rates of migration accelerate. National and competitive interests dominate in Africa as bickering and beggar thy neighbour policies escalate, although there is limited trade integration at the sub-regional level. Economic growth is tepid, and poverty reduction is slow. In addition to constant interference in the domestic affairs of their neighbours and domestic oppression, Africans again allow themselves to be instrumentalised by external partners.
The fourth scenario is a World at War, where competition within and between the US and China dominates all aspects of the global economy, politics and relations with violent outcomes. In addition to fragmentation in Libya, Sudan and the Sahel, more African states threaten to fracture as competing armed groups vie for power, including large countries such as the DR Congo. Shut out of the prospects for more rapid development, Africans suffer. Poverty increases as economic growth is insufficient to improve incomes, given more rapid population growth typically associated with instability. The World at War scenario is the worst case for everyone, as overall gains are below any other with successive wars between major powers. Autocracy increases everywhere, and those African countries that avoid fracturing grow slowly based on their small domestic market without the advantages of trade integration. The World at War scenario results in a much smaller world economy at US$132 trillion in 2043, with Africa growing particularly slowly despite its much larger population.
Chart 5 summarises the key characteristics of the four scenarios.
Chart 5: Summary feature of four global scenarios Click to enlarge
The scenario currently closest to the global trajectory is the Divided World scenario, with 724 million extremely poor people in 2043, significantly more than in the Sustainable (385 million) or Growth World scenarios (563 million). Extreme poverty in the World at War scenario will be 695 million, most of which will be in sub-Saharan Africa.
Because it includes determined efforts at an energy transition to renewables, a global carbon tax and carbon sequestration through the protection and regeneration of forests, the Sustainable World scenario has the lowest carbon emissions from fossil fuels. By contrast, economic growth in the Growth World, which is marginally higher than in the Sustainable World scenario, comes at a huge cost: carbon emissions from fossil fuels peak in 2039 (at 37.7 billion tons of CO 2 equivalent), six years later and much higher than in the Divided World scenario which has the second highest carbon emissions from fossil fuels. The result is more severe global warming associated with the Growth World than any other scenario.
Because of slow economic growth, the growth of carbon emissions from fossil fuels in the World at War scenario is lower than in other scenarios. Still, in the long term (beyond 2044), carbon emissions in the Sustainable World scenario are the lowest.
Divided World
The Divided World scenario reflects the acceleration of current trends towards a more fragmented global order, an associated retreat from the Western rules-based system, and China's rapid rise to become globally dominant towards the end of the forecast horizon.
Populist party successes in Germany, France, Italy, Finland, Netherlands, Sweden, Denmark and Austria followed the election of Donald Trump as US president in November 2024. Instead of pulling together, pursuing its narrow national interests rather than a common approach to China and on matters such as support to Ukraine is to the detriment of the West. Nationalist populism eventually undermines the Western rules-based system, which is, by 2040, hardly recognisable from its current form with with the US and the EU constantly bickering and pulling in different directions, providing the opportunity for others, most prominently China and Russia, to exploit these differences to their advantage.
Having been driven closer due to European sanctions on Russian oil and gas, the rapprochement between China and Russia proceeded apace. The latter eventually becomes entirely dependent upon China for oil and gas exports, in addition to its exports of agriculture and minerals to countries in the Global South.
Although the EU avoids another Brexit moment, it is consumed by bickering in the wake of the end of the war in Ukraine in 2027 with the ceding of Crimea and some eastern provinces to Russia. Since the US and the EU do not act as one in this scenario, China overtakes the EU in 2024 and the US in 2038, using the Global Power Index, although not using DiME, which applies heavier weightings to technological sophistication.
On this trajectory, the steady loss in legitimacy, influence and salience of the UN proceeds apace. Non-permanent members eventually do not attend Security Council sessions in protest against the right to veto still held by permanent members and the lack of structural reform. Local solutions, including industrial subsidies and hard border control, dominate. The free movement of labour, knowledge and eventually capital is restricted. Uncertainty and insecurity mean that the number of nuclear-armed states increases as efforts to contain proliferation have long collapsed.
Nominally, an isolationist US, a bickering EU, China, India and others are constantly at odds with one another. Smaller countries in Africa, from South America and Asia try and stay out of the fight. Globally efforts at new alliances and partnerships regularly appear as countries seek the best partners to pursue their interests, but none last.
India pursues its interests and alliances, including with Africa, while continuing to challenge China in Asia. Still, it struggles to gain traction for its traditional independent stance, given its worsening relations with Pakistan after New Delhi’s unilateral decision in 2019 to alter Kashmir’s constitutional status. In this scenario, armed confrontation along their shared borders becomes endemic.
The Asian region is particularly tense, with the Chinese invasion of Taiwan more likely given public pronouncements by US President Trump in 2025 that US soldiers would not die in defence of the island. As a result, China's subsequent occupation of Taiwan proceeds relatively quickly and with relatively limited damage, although extensive financial and other sanctions follow.
The degree to which Russia disregarded the UN Charter with its invasion of Ukraine and Israeli defiance of the UN Security Council in 2024/5 in its war on Gaza emboldens others. In addition to the US, countries such as Turkey, Iran, India, Pakistan, Myanmar, North Korea, Saudi Arabia, UAE, Malaysia, Rwanda, Egypt and others regularly violate previously sacrosanct international norms of behaviour. Domestic priorities regularly trump the global good, including the fraying of humanitarian practices, regressive legislation and practices in respect of the widespread reintroduction of the death penalty, rolling back of progress with women's rights and even the reintroduction of practices such as female genital mutilation in Africa. The Gambia first proposed a bill to this effect in 2024, subsequently also legislated in Uganda, Somalia, and several other countries.
Perceptions in this scenario reinforce long-standing caricatures of Africa (corrupt, poor, suffering) and the West (unequal, selfish, exploitative). Chinese efforts towards an alternative global order (non-interference, mutual respect, and social order based on domestic surveillance and control) gain traction. Rather than pulling together, the African Union is divided, and an African voice is generally absent from discussions about global futures. Some countries try to remain non-aligned; others align with the West or China. There is no solid African voice or position on crucial issues ranging from peace and security to climate change and development, and more countries pivot away from democracy to pursue their version of an autocratic development future. A lack of coherence in decision-making on crucial development policies means that Africa falls further behind average development indicators compared to averages for the rest of the world.
Attitudes harden. This world is more crowded, angry and fearful, with slow growth in the Global South propelling a substantial illegal migrant movement that drives populist politics and xenophobia in Europe and North America, reducing the ability of these countries and regions to play a positive role in international politics. Africa’s colonial legacy transforms into a decidedly anti-Western sentiment, and the continent is again a theatre for proxy wars between Russia and the West, and between the EU, the US and China. By 2026, France, the UK, and the US have effectively been ejected from Africa, losing access to all their previous military bases and some embassies. Whereas the US had 29 military bases in 15 African countries in 2019, by 2026, it is down to five, and by 2030, only Camp Lemonnier in Djibouti remained. Illegal migration to the EU becomes a big problem and regularly overwhelms border arrangements with violent clashes and many deaths.
In the Divided World, relations between most African and European countries deteriorate significantly, and the once close partnership between the EU and the AU is eventually a distant memory. China gains the most in this scenario with the determined pursuit of the ambitions of Xi Jinping and his vision to 'make China great again', a globally respected leader in high technology manufacturing, particularly as regards the green economy.
There is little appetite for follow-on Sustainable Development Goals (SDGs) in a Divided World. Sustainable development solutions are regional and scattered. Efforts to contain carbon emissions and combat climate change proceed apace, but they are weaker and less effective than the Sustainable World scenario, given the lack of coordinated international action. China powers ahead, however, and within a decade, it emerges as the undisputed global leader in sustainable economic space, cementing its role as the dominant supplier of batteries, wind and solar solutions, and associated technologies.
However, African countries are adept at playing China, Europe, and the US off against one another, as they have done for several decades. African subregions, such as the Southern African Development Community (SADC), deepen existing levels of economic integration, but progress with the AfCFTA stalls. Conflicts are complex, with the number of actors involved constantly increasing, frustrating African efforts at mediation. The effects of climate change are evident across Africa but most visible in the Sahel and the Horn of Africa. With its large, youthful and poor population, instability in Africa increases.
In summary, the Divided World scenario predicts a future marked by global fragmentation, where rising populism and geopolitical tensions diminish Western influence and pave the way for China's dominance. This division undermines global cooperation and international norms, leading to a world where regional interests trump collective efforts, exacerbating geopolitical tensions, particularly in Asia, and increasing nuclear proliferation that sees Australia, Japan, Iran, South Korea, Ukraine, Poland and Germany all develop national nuclear forces. It is eventually this balance of terror that staves off mutually assured destruction at a global level. Sustainable development is sidelined in a Divided World, global governance weakens, and Africa faces heightened instability and challenges in economic integration, reflecting the broader global shift towards prioritising national over collective interests.
World at War
Hard power competition dominates in the World at War scenario, which consists of successive large-scale wars and significant smaller conflicts that eventually engulf all leading and regional economies. On several occassions the use of nuclear weapons is only averted at the last minute in a context that has seen a degree of proliferation. Efforts to review the Treaty on the Non-Proliferation of Nuclear Weapons (NPT) collapsed and, in 2026, the New START treaty finally lapsed in 2026, having been on life-support since the February 2023 State of the Nation address by Russian President Vladimir Putin that his country would suspend participation.
The first large-scale conflict is the escalation of Russia’s war on Ukraine into a broader military confrontation with NATO. To stave off a Ukranian defeat and to counter the deployment of more significant numbers of troops from North Korea, France and the UK deployed combat troops into the country. Russian retaliation included attacks on logistic bases in Poland and elsewhere, setting the scene for the geographic expansion of the conflict. The war in Ukraine has already pushed Russia closer to China as the primary destination for its oil and gas exports. In April 2024, the two countries pledged to cooperate more closely to maintain international industrial supply chain stability. 'China and Russia will be more active in pursuing the convergence of their interests . . . and work together to maintain international industrial supply chain stability,' a ministry statement quoted Chinese Foreign Ministry Wang Yi as saying. Under full sanctions from the West, Russia has no other outlet for its fossil fuel exports upon which its economy depends.
Others that extend the China–Russia military cooperation include Iran (which has a long-standing aggrievement with the West), North Korea, Pakistan, and eventually Vietnam and Cambodia. For China, importing gas and oil from Russia bolsters its efforts to reduce its reliance on strategic resources from Western suppliers. Still, it remains dependent on oil and gas from the Middle East.
Russia and China do not enter a formal military alliance to oppose NATO. Still, military cooperation is close, building on the statement by Chinese President Xi Jinping and Russian President Vladimir Putin in February 2022 that their partnership has ‘no limits’ as the two vowed to deepen cooperation on various fronts. Already, in 2022, NATO added China to its perceived threat environment.
China bides its time and readies for an invasion of Taiwan but eventually desists following the announcement that the renegade island had developed an own nuclear capacity. Previous efforts towards such a capacity in the 1970s and 1980s were stopped by the US, but a third attempt now succeeds with covert support from the US.
A second trigger for the World at War scenario originates in the Middle East. Following the horrific terror attacks by Hamas on Israel on 7 October 2023, the Jewish state lashed out in Gaza, the West Bank, Lebanon, Syria, Yemen, the Red Sea, the Arabian Sea and eventually Iran. The US, which had tried to resist the growing regional conflagration, going so far as to support UN Security Council decisions that sought to constrain and eventually condemn Israel, is drawn into steady escalating military support for its ally, eventually alienating long-standing partners such as Saudi Arabia and Egypt where popular sentiment force both countries to abandon their efforts at moderation.
The first indication of what is to come is the collapse of the Abraham Accords of 2020, which briefly normalised relations between Israel, the United Arab Emirates, and Bahrain. Eventually, it is separate deadly attacks by Houthis and the Islamic State in the region, including clear evidence of the extent of military support to Iran from Russia, that tip the scales into a bloody confrontation - opening up a front extending from Ukraine to the Middle East with the US too stretched to ensure Israel's security. After a decade of war and with a weakened US, Israel eventually agreed to a two-state solution along the lines of the 1967 delimitations having come perilliously close to the use of its nucear arsenal on several occassions.
A third trigger (or successive regional war) is border conflict, which eventually leads to a war between India and China. Although India will continue to have significantly fewer power capabilities than China across the forecast horizon, the two are increasingly regional and global power competitors with a shared long border. The significant disparities in their material power capabilities make this only likely towards the end of our forecast horizon.
In addition to direct conflict, the often violent rivalry between Chinese-supported, nuclear-armed Pakistan and India over Kashmir could also trigger conflict between China and India, particularly if the two look to balance their relations with Washington and Beijing. India already fought a brief war with China in 1962, and India and Pakistan have had numerous border skirmishes and military stand-offs.
In this world, India’s alarm at Chinese assertion and aggression, particularly in the South China Sea, sees it align itself more closely with the West to balance Islamabad’s close relations with Beijing. In a starkly bipolar world, there would be less space for India’s traditional non-aligned orientation.
Complicating matters is New Delhi’s cordial but guarded relations with Russia, from which it buys most of its weapons.
The defining characteristic of the World at War scenario is the division of the globe into two poles with little space for others — a return of global relations to a bipolar era reminiscent of the height of the Cold War but on steroids. The US House of Representatives passed the Countering Malign Russian Activities in Africa Act on 27 April 2022 as a clear sign of where things could go. After approval by the Senate, it was signed into law by President Trump in 2026, placing African countries that trade with Russia and are perceived to have close relations with it, such as South Africa, under sanctions. China is in a different league from the former USSR, however. In 2024, China was already the largest trading partner for more than 120 countries and regions, including the US and the EU. Its economy is already more significant than the US’s, using purchasing power parity, and in this scenario the Chinese economy surpasses the US in 2037 at market exchange rates. By 2043, the Chinese economy will be 7% larger than the US economy in market exchange rates and nearly 31% larger in terms of purchasing power parity.
The intense competition and even conflict between a declining US and rising China in this scenario will affect every country and region in the world, even as struggles for self-determination and independence intensify, such as efforts by the Kurds to establish their homeland, the ongoing struggle of the Palestinians to escape the yoke of Israeli repression and occupation, and in regions such as the Sahel in Africa.
Africa becomes a key area of strategic and sometimes violent competition for control of its strategic mineral resources in the World at War scenario. Still, it remains unable to benefit from its beneficiation. China has been a first mover in securing a supply of the strategic minerals required to transition to a renewables-based future, including lithium, nickel, cobalt, manganese and palladium. Chinese companies were the only ones willing to invest in a country like the DR Congo for years. As a result, by 2021, Chinese companies controlled 60% of global cobalt reserves and 80% of the world’s cobalt refining capacity, which helped China secure a significant lead as an electronic vehicle battery maker to the extent that a single Chinese company, Contemporary Amperex Technology Co., Limited (CATL), controls one-third of the entire global battery market.
Chart 7 depicts world military expenditure. Whereas it averaged 2.3% of GDP in 2023, by 2043, it will have increased to 5.3% in the World at War scenario and 2.6% in the Divided World scenario. Instead of spending US$2.8 trillion on the military in 2043 (in the Sustainable World scenario), the world will spend more than double that amount at US$7 trillion. From a low base military expenditure in Africa increases more than fivefold from US$53 billion in 2023 to US$264 billion by 2043.
As arms purchases and the number of arms increase, Africa is again flooded by surplus weaponry, older stocks are replaced with more modern armaments, and countries upgrade and replace their systems, as happened at the end of the Cold War. The ready supply of weapons threatens the rupture of several additional African countries on top of the divisions in Libya, Sudan, Nigeria, Ethiopia and Cameroon.
Democracy declines globally and Africans are pressured to choose sides to the extent that problems emerge in the interoperability of the Internet, which is now segmented into regional fiefdoms. The momentum towards the AfCFTA and trade integration at the subregional level fails. Each country does the best that it can, on its own.
Instead of African states being able to secure their territories and borders, in the World at War scenario, the Islamic State further spreads its influence to establish the caliphate's future after being defeated and driven out of Syria and Iraq. Already, in 2022, at least 20 countries directly experienced the group’s activities, with more than 20 others used for logistics and to mobilise funds and other resources. In this scenario, Iran and Russia play an important role in funding, supporting and expanding terror in Western-aligned African countries.
In summary, the World at War scenario predicts a future defined by intensified global military conflicts, including significant wars among major economies and nuclear exchanges. The breakdown of nuclear non-proliferation treaties and escalating tensions, particularly between NATO and Russia and between China and India, underscores a shift towards a bipolar global division reminiscent of the Cold War but much more intense. This era of hard power competition sees the US and China as central figures, with Africa becoming a strategic battleground for mineral resources and military influence, leading to a dramatic increase in military expenditures and armament. The scenario highlights the dire consequences of global divisions, emphasizing the need for strategic alliances and the significant impact of leadership decisions on global stability and regional conflicts.
Growth World
Neoliberal, trickle-down economics and increased corporate concentration characterise the Growth World with little care for the environment. This high-growth, unequal world would see slow reductions in extreme poverty and a rise of the power and influence of private capital. The rich get richer and the poor suffer. Efforts to introduce minimum tax rates for corporations, which started in 2021 when 136 countries agreed to implement a 15% global minimum rate, do not get off the ground. Instead, large corporations, originally particularly characteristic of the US, increase their power and profits everywhere including in the EU that was previously a bastion of anti-trust legislation. All profits flow back to the corporate head offices of a handful of global behemoths.
The US and the EU both step away from anti-trust efforts that could reign in anticompetitive behaviour and through mergers, major industries consolidate their presence in the services, finance and manufacturing sectors. Lack of competitiveness lets companies lower wages, increase prices and dilute the quality of their products. The practice of tax avoidance through profit shifting to low-tax jurisdictions effectively leads to a race to the bottom as countries compete to attract foreign direct investment. Developing countries suffer in the process as monies are drained away to tax havens and least-cost locations. Unemployment reaches unprecedented high levels in poor countries with large pools of low-skilled labour.
Chart 8 shows the rapid growth in world GDP in the Growth World scenario, passing US$170 trillion by 2043 and the simultaneous rise of carbon emissions to the end of the forecast horizon. In contrast to the Sustainable World scenario, income growth comes at the cost of a rapidly deteriorating environment and growing inequality.
Competition between China, the US and Europe changes. The unexpected death of Chinese President Xi Jinping in 2026 reverses the tightening of central social, political and economic control from Bejing, and his successor relaxes the harsh clampdown on economic and political freedoms that had come to characterise Xi Jinping's third five-year term as the nation's president. China is rewarded by massive inward investment when the US enters into a Grand Bargain with its former foe in the final year of the second Trump presidency. The restrictions on ownership and investment in Western countries disappear almost overnight as business leaders scramble to cash in on the largest global market. Instead of contracting, global value chains expand, and the period of reshoring and friendshoring manufacturing in 2022/5 is, by 2030, a distant memory. The lowest-cost considerations again drive the location of factories and manufacturing. Instead of moving from China to surrounding countries with lower labour costs, the extent of state subsidies, excellent infrastructure, and numerous incentives see China strengthen its position as the world's high-technology factory, most prominently regarding generative artificial intelligence.
Skilled migration to China resumes, and other countries that provide quality of life, security for investment, and the required information technology attract the best and brightest. Companies now compete in an unregulated global market to provide high-end services without establishing a legal presence or paying taxes elsewhere. The Chinese economy grows more rapidly in the Growth World than in any other scenario. However, as incomes rise, domestic pressure for more freedom and growing inequality make China’s future more unpredictable.
With a focus on maximising profit and the extraction of rents, the saliency of the United Nations, its Security Council and various agencies radily decline in this scenario. Rich countries adapt to the impact of climate change, but the developing world suffers. Instead of the AfCFTA, African subregions link up externally, such as North Africa with the EU, several West African countries enter into agreements with the US, countries in the Horn of Africa with the Middle East, and those in East and Southern Africa with China. Central Africa trails behind. More significant migration flows inevitably follow. Peacekeeping is a lucrative business in this world, now outsourced to private companies and developing countries with large populations. Instead of through the UN, peacekeeping is funded bilaterally with richer countries keen to secure their investments in unstable areas.
In summary, the Growth World scenario illustrates a future dominated by neoliberal economics and corporate power, leading to significant global GDP growth and environmental degradation, with large corporations bypassing efforts to implement global minimum tax rates and anti-trust legislation, further exacerbating inequality and unemployment. This world sees China's economic and political landscape transform following Xi Jinping's unexpected death, resulting in massive inward investment and a shift towards less regulated global markets. In contrast, the developing world, including Africa, faces the increased effects of rapid climate change and external economic dependencies.
Sustainable World
In the Sustainable World scenario, the international community balances growth and distribution by reducing overall consumption and constraining greenhouse gas emissions through, amongst others, a differentiated global carbon tax (examined in our theme on Energy) that contributes to Africa's energy transition. Collaboration and norm development extend across multiple sectors, including a resurgence in the role of the World Trade Organization (WTO), the introduction and steady increase of a global minimum corporate tax rate, which stands at 20% by 2043.
This future is most likely to emerge from a crisis, such as the impact of the World at War scenario, the rapid acceleration of the effects of climate change and repeat global pandemics that forces a reluctant world to adopt a collective response. Already, in June 2023, UN Secretary-General Guterres warned that the world would hit 1.5 degrees Celsius warming above preindustrial levels by early 2030 and that the Earth was on a trajectory towards two to three degrees Celsius by 2100.
A Sustainable World could even originate in the US due to the corporate profiteering and excesses evident during the second Trump presidency. In this scenario, US politics swing violently against the corrosive power of money during the 2029 presidential elections with the election of a progressive independent who restores democracy. In subsequent years, the US pursued aggressive antitrust policies to increase competition and rein in the damaging effect of private monies on domestic politics.
In terms of great power competition, a Sustainable World scenario is likely associated with an expanded and more influential EU in addition to constraints on the role of money in US politics. Different from the US and China, the EU has limited hard power and prioritises its role as an advocate of a global rules-based system, reflected in its approach to digital sovereignty, harmonised rules on fair access and use of data that protects individual rights and democratic freedoms, amongst others.
The Sustainable World scenario is the most difficult to achieve. Unlike the other three scenarios, leaders with little in common must take bold steps to realise a better world that will inevitably run into significant domestic resistance. It is only possible with the realisation within the EU and the US, working with countries such as China and India, that a future rules-based system will eventually require rethinking all aspects of global collaboration and governance, including the role and decisions at the International Financial Institutions and an overhaul of the UN Charter including the composition and workings of the UN Security Council.
Under the auspices of the UN, this scenario would see countries craft and agree on an ambitious set of follow-on Sustainable Development Goals to eliminate extreme poverty in the most affected region, sub-Saharan Africa, which is also under significant threat from climate change. These follow-on goals and targets merge climate mitigation and adaptation ambitions into an overarching and comprehensive Global Sustainability Framework 2050 (GSF) 2050. Part and parcel of the GSF 2050 is a new push for aid to low and low-middle-income African countries. Whereas aid to Africa amounted to US$64 billion in 2023 (2.4% of Africa’s GDP), by 2043, it has increased to US$104 billion, although now accounting for only 1.2% of Africa’s much larger GDP.
Commitments of this nature mean the world can sustainably pursue poverty alleviation, reduce carbon emissions and advance environmental protection. Chart 9 shows the dual impact of the Sustainable World scenario on global poverty and carbon emissions. Extreme poverty in Africa falls from 437 million (29% of the population) in 2023 to 225 million (10%) in 2043. Globally, carbon emissions from fossil fuels peak in 2032 at 10 billion tons of carbon (36.6 billion tons of CO 2 equivalent) and, by 2043, have declined to 9.1 billion tons (33.4 billion tons of CO 2 equivalent).
In this world, an expanded G20+ has eclipsed the G7 and the BRICS and coordinates responses to global turbulence, such as recurring pandemics that are a feature of a 2043 world with 9.3 billion people. This is a rules-based future that eventually includes a revision of voting rights in both the World Bank and the International Monetary Fund, and an end to permanent membership and the veto within the UN Security Council. By 2043, a reformed Council consists of powerful countries with increased voting rights, groups like the EU, and a rotational system of other members. However, it remains a council of states that does not include non-state actors such as civil society or business.
Nuclear disarmament also proceeds apace. In July 2022, 122 countries adopted an international treaty banning nuclear weapons. The treaty makes it illegal for signatories to develop, test, produce, manufacture, acquire, possess, stockpile, transfer, use or threaten nuclear weapon use or to encourage anyone to engage in these activities. In the Sustainable World scenario, the UK and France, then the US, China, Russia, India and Pakistan eventually signed and ratified the treaty, followed by others, including Israel and Korea, the latter shortly after unification.
In retrospect, the momentum for the nuclear-armed states to join started in 2022 with the review conference on the Treaty on the Non-Proliferation of Nuclear Weapons (NPT). It followed the extension of the New START treaty between the US and Russia, initially set to expire in 2026.
The AfCFTA is fully implemented in Africa by 2033 and discussions on deeper economic and political integration follow. By 2043, Africa has progressed significantly towards establishing a continental customs union, with subregions such as the Southern African Customs Union (SACU) having even found a common market. All countries are democratic, and regular elections see a steady turnover of leadership as Africans hold their leadership to account.
In summary, the Sustainable World scenario envisions a future where global cooperation leads to balanced growth, reduced consumption, and significant progress towards environmental sustainability, including a strengthened role for the WTO and an increased global minimum corporate tax rate. This scenario, emerging from crises like climate change and global pandemics, would necessitate ambitious global governance reforms, including the implementation of a new compact (the GSF 2050) aimed at eliminating extreme poverty and significantly reducing carbon emissions. It foresees an era of enhanced global cooperation, with the EU playing a pivotal role in advocating for a rules-based system, and a fully implemented AfCFTA that propels Africa towards greater economic and political integration, supported by increased aid and a commitment to nuclear disarmament.
The primary driver of the divergent outcomes for African countries in the four scenarios is the success or failure of integration efforts such as the AfCFTA. But before proceeding with the differences, it is important to recognise the continent's complexity.
Most African countries have small populations and economies. In 2023, only seven countries had economies larger than US$100 billion, while 18 were less than US$10 billion. While a few countries, such as Nigeria, have large populations, 21 African countries had fewer than 10 million people in 2023. Africa is also divided politically, and the imprint of Belgian, German, Spanish, Portuguese, British and French colonialism continues to affect domestic and foreign policies as well as the poor governance often characteristic of its elderly ruling elites. Because the various regional organisations such as ECOWAS and the African Union have no supranational powers, Africa seldom speaks with one voice on international issues.
In 2023, Africa constituted less than 3% of the world economy in market exchange terms. Despite rapid population growth, Africa will only constitute 5.1% in 2043 in the Growth World scenarios, which, together with the Sustainable World, deliver the most rapid economic growth. Meanwhile, its population will increase from 18% to 24% of the global population. The size of the world economy will increase to US$170.1 trillion by 2043 in the Growth World scenario, closely followed by the Sustainable World scenario at US$169.6 trillion, but with significant differences in the number of extremely poor people, the majority of whom will be in sub-Saharan Africa. A Divided World would result in global economic output of US$151 trillion in 2043. A World at War scenario results in a world economy producing a combined GDP of only US$132 trillion in 2043, with Africa growing particularly slowly despite its much larger population.
The key characteristics associated with the four global scenarios for Africa are summarised in Chart 10.
Chart 10: African scenario characteristics associated with each global scenario Click to enlarge
Africa gains most in the Sustainable World, both in terms of the size of its economy (see Chart 11) and of its average GDP per capita (see Chart 12).
The nature of this type of ‘big picture’ analysis inevitably glosses over the different impacts that each scenario has on individual African countries. For example, the countries that get the most significant income increase—in the Sustainable World scenario—are Seychelles, Eswatini, Mauritius, Botswana, and Djibouti, experiencing more than a US$5 000 increase in GDP per capita in 2043 compared to Africa’s current development forecast. By 2043, nineteen African countries have extreme poverty rates below 3% of their population compared to only seven in 2023.
Carbon emissions from fossil fuel use in Africa will increase in all four scenarios, given Africa's current low levels of energy availability and growing population. Because the rate of economic growth in the Sustainable World scenario is significantly more rapid in Africa than in the Growth World scenario. Carbon emissions from fossil fuels in Africa are lowest in the World at War scenario which has much lower economic growth rates and higher poverty than other scenarios.
The climate and energy themes provide additional analyses of carbon emissions and Africa's energy transition.
The differences in economic well-being are particularly evident in Sub-Saharan Africa, the region with the largest poverty burden globally at 434 million people in 2023, equivalent to 34% of its population. The extreme poverty rate in Sub-Saharan Africa will decline rapidly to 12% in the Sustainable World and be highest in the World at War scenario at 24%, equivalent to 224 and 479 million people, respectively. Note that Charts 14 are for Africa, not Sub-Saharan Africa.
In the Sustainable World scenario, inequality in Africa decreases by six per cent between 2023 and 2043 despite rapid economic growth that, under normal circumstances, tends to increase inequality. The reason is that for Africa the Sustainable World scenario increases a large social grant programme as well as gender empowerment and greater participation of women in the workforce, leading to better outcomes on indices that reflect income distribution.
A closed, rigidly divided world (the World at War and even the Divided World) will not benefit Africa. Africa did not do well during the Cold War, and instability increased significantly in the years leading up to the collapse of the Berlin Wall in 1989. The pattern could likely repeat itself in these two scenarios, which is worrying as the current global trajectory is closest to the Divided World scenario.
Russia’s invasion of Ukraine and events in Gaza have accelerated global divisions. In the former, a permanent member of the UN Security Council that is mandated to ensure international peace and security has invaded another and in the latter, Israel violates international law and ignores Council decisions. Views differ, however. Westerners typically support Israel and are typically more pessimistic than people from Asia, which is understandable since power and economic weight will continue to shift eastward in all four scenarios. Still, a lot depends on what happens between the US and the EU, the choices made by India, the extent to which China and Russia cement a potential alliance, and who joins them.
Furthermore, the choices made in areas such as digital sovereignty will have a long-term impact. Does Africa pursue an approach with an emphasis on the rights of the individual on the one hand, as is the case in Europe, or the prioritisation of the collective interests of the state on the other, as is the case for China? Or does Africa allow for private sector competition that drives costs down and allows the continent to pursue the least-cost solution (such as the approach of the US), but which leaves little room for Africa’s private sector without clear policies on domestic investment? For example, procurement choices on digital infrastructure could create a path dependency with geopolitical implications. African governments with low state capacity, large youthful populations and characterised by insecurity may, for instance, prefer the state-centred stability approach of China over the EU’s orientation towards individual rights and democratic freedoms.
Africa will significantly close the gap with other regions in the Sustainable World scenario regarding electricity access, literacy rates, the human development index (HDI) and malnourishment. Higher life expectancy, better literacy rates and education follow. However, given its historical deficits, it will still rank at the bottom compared to other regions. In contrast to the Growth World, the Sustainable World balances significant economic gains with greater equality and a smaller carbon footprint.
Given its marginal position in the global economy, a large and growing population and the impact of climate change on Africa, the Growth World scenario and its associated high carbon emissions are disadvantageous to the continent. The World at War is a lose-lose scenario for all countries, and the current trajectory towards a Divided World constrains Africa’s growth and development. For example, our analysis indicates that Africa’s ‘development potential’ in the Divided World scenario is only 76% of that in the Sustainable World, using GDP per capita as an indicator.
In material power terms (using GPI or the DiME Index), Africa remains a minor player in international affairs, regardless of the scenario — and the reader is reminded about the large number of African countries that constitute the continent, meaning that the effective exercise of ‘African’ power is invariably lower than the sum of all its countries. For example, the tripling of Africa’s GDP in the Sustainable World scenario from its 2023 size means Africa will see its share of global power increase by a mere two percentage points from its 2023 level to 6.6% of the worldwide total in 2043, using the DiME Index and 4% using GPI.
Still, as a collective, the African continent could carry more weight towards the middle of the 21st century in the Sustainable World scenario. If the African Union could speak with one voice globally and maintain that unity, Africa would become a swing region with significant soft and discourse power mainly because of its large population numbers and the collective voice of its 55 member states.
Meanwhile, the current trajectory towards a Divided World places a cap on Africa’s development potential. Compared to the Sustainable World, GDP per capita in Africa will be 24% lower (US$5 907 vs US$7 738), and extreme poverty in Africa will be 91% higher (429 vs 225 million), reflected in Chart 16.
Chart 16: Comparing scenario impact on economic size, GDP PC and extreme poverty Click to enlarge
Wildcards and Megatrends: Great power implosion, technology and climate change Download to pdf
The four scenarios presented in this study and the associated African scenarios could be upended by any number of developments, particularly the impact of a great power implosion, regional developments in Europe or Asia, and technology, generative artificial intelligence in particular.
In How Civil Wars Start: And How to Stop Them, Barbara F Walter presents internal conflict and competition within big powers as one of the gravest threats to global peace, particularly the likelihood of a second civil war in the US. Academic reviews of her work indicate that she has exaggerated the threat posed by political polarisation in the US. For example, Moyer, Matthews and others find that wealthier, politically consolidated states (all of which are democratic) typically have more civil conflict onset associated with minority repression, not evident in the US, than any other cluster of motivated variables.
The US is already starkly divided and increasingly violent, as seen in January 2021 with the effort at an unconstitutional power grab after Donald Trump lost the presidential elections. The re-election of Trump as the 47th president in 2024 means that the West is in deep trouble, as seen from the perspective of a partnership amongst liberal democracies to constrain China’s growing power and influence. Already evident early in 2025, Trump’s approach to the rest of the world will strengthen the hand of autocrats everywhere, undermine the global rules-based system and eventually accelerate the relative loss of power and influence of the US. It will accelerate the trend toward a Divided World or even the World at War scenario but could open up the door towards a Growth World should the US pursue a Grand Bargain with China.
A variant of great power implosion in China and Russia would likely follow democratisation in either or both. In the case of Russia, which will experience a steady diminution of its material power base in all scenarios, disruptive change is almost sure to follow the end of the presidency of Vladimir Putin, who has ruled since 1999. While change in both countries, also in North Korea, and the democratisation of oil-rich Middle Eastern countries may be desirable from a liberal and normative perspective, all will likely come with considerable negative economic consequences. In the case of China, these will be global.
The correlation between income and democracy is contested, but, according to a significant body of work, democracy becomes very likely beyond certain income levels. If that theory holds, the Chinese Communist Party and many countries in the Middle East will soon be in trouble. The democratisation of China (i.e., replacing the current authoritarian model with a political system that provides for greater individual choice and freedoms) will almost inevitably be violent and economically disruptive. Chinese growth rates differ per scenario, although steadily declining in all four as its economy matures and its population ages. It is highest in the Sustainable World, averaging 4% per annum from 2024 to 2043, implying that this scenario would present China with the earliest associated challenge.
Regional developments could also alter the world in substantive ways, such as the breakup of the EU or a radical reduction in its membership. The UK has already left the EU. At regular intervals, others threatened the same, although Russia's war on Ukraine and the damage that Brexit did to the UK has reduced threats of similar endeavours by others. China's economy is already larger than the EU27 in terms of purchasing power parity and market exchange rates. Given reasonable expectations of Chinese vs EU27 economic growth rates, this gap will increase in future, although the West remains significantly larger as a single group than others. The EU further serves as an essential advocate of a rules-based international order on matters ranging from consumer protection to human rights. A breakup of the EU will have significant adverse effects globally. On the contrary, substantial enlargement with the UK rejoining the EU, including other countries such as Türkiye or a democratic Russia, could unlock positive opportunities.
Should Asians set their differences aside, strengthen their political cooperation and overcome the deep divisions between countries such as Japan, Korea, India and China, Asia would become globally dominant, and a vastly different international order may emerge. However, that is unlikely, given the tensions between China, India and Japan. Historically, two adjacent large powers (China and India) compete rather than cooperate, and relations between these two are tense. More likely, Japan and others will enter closer ties with India as a bulwark against China.
The complex and unpredictable ways that environmental changes are intertwined with development and security illustrate how the biosphere crisis can only be successfully addressed through international cooperation. Europe recently experienced its worst drought in 500 years, much of Pakistan was under water, and portions of Africa are facing long-term distress with massive suffering and loss of life. Beyond Africa's needs, the window for global action to respond to these challenges is closing.
The accelerated impact of environmental change is undoubtedly the most significant threat to humanity, particularly in poorer regions with less capacity to cope. Catastrophic climate-change ‘tipping points’, for example, are nearing — from surging sea levels as polar ice melts to spiking temperatures as methane escapes from permafrost — that would drive an unstoppable cycle of higher global temperatures and more melting. Africa is particularly vulnerable due to its weak adaptive capacity, increased dependence on ecosystems for livelihoods, and rain-fed agriculture. Currently, the continent is warming faster than the rest of the world, on average, and scientists warn that large portions of Africa may become uninhabitable by mid-century or earlier. Variations in rainfall are significant, and extreme weather events are becoming more regular. Globally, the accelerated impact of climate change is the most likely driver that could unlock the current geo-political impasse towards a Sustainable World. We examine these trends in a separate Climate theme.
Technological innovation is a structural driver of change, but the advent of generative artificial intelligence and its potential unintended consequences could become a wildcard. Particularly in the Divided World, World at War, and Growth World scenarios, aggressive state or private sector competition could lead to a race to the bottom, with the rapid development of autonomous killer systems and a lack of regulation and control. Even in the Sustainable World, the extent to which a comprehensive regulatory regime could be pursued is debatable, given the technology's rapid evolution.
The challenge is twofold. On the one hand, rather than creating jobs and opportunities for the majority of the population in poor and middle-income countries, the current path of AI is raising the demand for capital, for highly skilled production workers, and even higher-skilled services, such as management-consulting and technology companies.
On the other hand, digital tools are used for massive data collection and processing - potent assets in both autocratic governments and private companies alike that are interested in surveillance and the dependence upon big data for social control and their business model, respectively. The power of generative Artificial Intelligence in today’s connected yet dividing world is such that the World Economic Forum’s Global Risks Report 2024 identifies misinformation and disinformation as the number one global risk, given the number of elections that will occur.
It is important to underline how much a more crowded, interdependent world is changing interstate relations. The split between an apparent self-centred West and the Global South gained substantive impetus when, at the height of the devastating COVID-19 pandemic, European vaccine manufacturers blocked efforts to relax patent rights whilst simultaneously hoarding available vaccines for domestic use. In this changing world, Western countries underestimate how deeply intertwined their future is with the Global South regarding stability, growth, and environmental change. The divide was illustrated by the Global South's ambivalence in a forum such as the UN General Assembly on Russia's invasion of Ukraine, the decision in September 2023 to expand the original group of Brazil, Russia, India, China and South Africa (BRICS) to include six additional countries with more to follow and the very different responses to events in Gaza when comparing that in the West with reactions from the Global South.
Conclusion: For Africa — politics for development Download to pdf
Scenarios are not predictions. They are tools that help to frame alternative futures systematically and to enable a coherent discussion and analysis. Scenarios done using the traditional two-dimensional analysis - in this case, the extent of global fracturing versus efforts at sustainability - present four divergent alternatives that assist in the associated modelling and conceptualisation. Reality will be much more complex and typically ends up somewhere in between. Done well, however, scenarios surface structural trends and the outcomes or effects of different pathways.
Our analysis started by presenting the recent history and likely forecasts of the global distribution of material power and influence, focusing on China and the US. In line with the more detailed research done by our colleagues at the Frederick S Pardee Institute for International Futures at the University of Denver, we find that a power transition that would see China overtake the US as the most powerful country in the world will likely occur before mid-century. However, a larger group of rich countries that share a commitment to individual (as opposed to collective) aspirations (democracy, individual human rights, etc.), aka the West, will continue to dominate globally and maintain a technological, wealth and power advantage even over a global axis led by Chinese and Russian concepts for subsequent decades. These divisions are, however, set to become less stark and decline in importance with the emergence of regionalism. From that perspective, what happens within the West, between the US and the EU, is therefore very important.
The emergence of China as the most powerful country globally will inevitably reshape global relations. However, it will occur in a more crowded and complex world where the ability to exercise power is constrained. Given the combined material power of the West and China’s relative international isolation (it inspires fear rather than attraction and has tense relations in its neighbourhood), this trend will eventually deliver a global order that will evolve quite differently from the so-called liberal international order even as Asia partly ‘decouples’ from the rest of the world. At the same time, there are many examples of Chinese integration with the existing rules and norms, such as the extent to which its actions on debt relief for Africa today are to collaborate with international financial institutions rather than operate outside them and the steady adoption of best project standards in expanded infrastructure in Africa and elsewhere. In that future, Africa will geogr
| 2025-06-28T00:00:00 |
https://futures.issafrica.org/thematic/18-africa-in-the-world/
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|
The biggest AI companies you should know - Yahoo Finance
|
The biggest AI companies you should know
|
https://finance.yahoo.com
|
[
"Fri",
"Jun",
"Min Read"
] |
Microsoft (MSFT), Google (GOOG, GOOGL), Meta (META), and Amazon (AMZN) continue to debut new AI-powered software capabilities while leaders from ...
|
AI continues to be the hottest trend in tech, and it doesn't appear to be going away anytime soon. Microsoft (MSFT), Google (GOOG, GOOGL), Meta (META), and Amazon (AMZN) continue to debut new AI-powered software capabilities while leaders from other AI firms split off to form their own startups.
But the furious pace of change also makes it difficult to keep track of the various players in the AI space. With that in mind, we're breaking down what you need to know about the biggest names in AI and what they do.
From OpenAI (OPAI.PVT) to Perplexity (PEAI.PVT), these are the AI companies you should be following.
OpenAI/Microsoft
Microsoft-backed OpenAI helped put generative AI technology on the map. The company's ChatGPT bot, released in late 2022, quickly became one of the most downloaded apps in the world. Since then, the company has launched its own search engine, 4o image generator, a video generator, and a file uploader that allows you to ask the bot to summarize the content of your documents, as well as access to specialized first- and third-party GPT bots.
Microsoft uses OpenAI's various large language models (LLM) in its Copilot and other services. Apple (AAPL) also offers access to ChatGPT as part of its Apple Intelligence and Visual Intelligence services.
Open AI CEO Sam Altman, left, appears onstage with Microsoft CEO Satya Nadella at OpenAI DevDay on Nov. 6, 2023, in San Francisco. (AP Photo/Barbara Ortutay) · ASSOCIATED PRESS
But there's drama behind the scenes. OpenAI is working to restructure its business into a public benefit corporation overseen by its nonprofit arm, which will allow it to raise more capital. To do that, it needs Microsoft's sign-off, but the two sides are at loggerheads over the details of the plan and what it means for each company.
In the meantime, both OpenAI and Microsoft are reportedly working on products that will compete with each other's existing offerings. Microsoft offers its own AI models, and OpenAI is developing a productivity service, according to The Information.
Still, the pairing has been lucrative for both tech firms. During its most recent quarterly earnings call, Microsoft said AI revenue was above expectations and contributed 16 percentage points of growth for the company’s Azure cloud business. OpenAI, meanwhile, saw its annualized revenue run rate balloon to $10 billion as of June, according to Reuters. That's up from $5.5 billion in Dec. 2024.
OpenAI offers a limited free version of its ChatGPT bot, as well as ChatGPT Plus, which costs $20 per month, and enterprise versions of the app.
Google Gemini
Google's Gemini offers search functionality using the company's Gemini 2.5 family of AI models. You can choose between using Gemini Flash for quick searches or Gemini Pro, which is meant for deep research and coding.
| 2025-06-27T00:00:00 |
https://finance.yahoo.com/news/the-biggest-ai-companies-you-should-know-165407928.html
|
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|
AI Boom Triggers Massive Layoffs: US Job Cuts Surge 80% As Tech ...
|
AI Boom Triggers Massive Layoffs: US Job Cuts Surge 80% As Tech Giants Slash Workforce
|
https://www.freepressjournal.in
|
[
"Tasneem Kanchwala"
] |
AI Boom Triggers Massive Layoffs: US Job Cuts Surge 80% As Tech Giants Slash Workforce · The US job market is facing turmoil with 696,309 layoffs ...
|
Canva
The US jobs market is going through a tough time. According to the latest data from Challenger, Gray & Christmas, employers in US have announced 696,309 job cuts, an increase of 80 percent from the 385,859 announced in the first five months of last year. The report suggests that it is 65,049 cuts away from matching the entire year’s total for 2024.
This rise in layoffs is attributed to the advent of AI which has emerged as a big disruptor in tech employment. Many firms including Amazon and Google have announced that layoffs are inevitable.
The report suggests that technology continues to be the leading job-cut sector as it faces mounting and rapidly changing disruptions. Technology companies announced 10,598 job cuts in May for a total of 74,716 cuts in 2025. This is up 35 percent from the 55,207 cuts announced during the same period last year.
“Technological Updates, including those related to AI implementation, led to 20,000 job cuts so far in 2025,” the report adds.
Amazon CEO Andy Jassy recently hinted at layoffs given the AI boom that the world has seen in the last few years. Jassy confirmed that the company will need fewer people as Amazon pushes for greater efficiency with the help of Generative AI.
Microsoft is reported to have laid off around 6,000 people in May, and has also done two rounds of layoffs in June as well. According to a report by The Information, the Google TV team laid off 25 percent of its staff in April.
| 2025-06-27T00:00:00 |
https://www.freepressjournal.in/tech/ai-boom-triggers-massive-layoffs-us-job-cuts-surge-80-as-tech-giants-slash-workforce
|
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K-12 Office Helps Build Tomorrow's AI/ML Workforce - Research
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K-12 Office Helps Build Tomorrow’s AI/ML Workforce
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https://research.lbl.gov
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Artificial intelligence (AI) and machine learning (ML) are critical skills for today's and tomorrow's scientists. · At Berkeley Lab, the K-12 ...
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Artificial intelligence (AI) and machine learning (ML) are critical skills for today’s and tomorrow’s scientists. So much so that the White House recently announced a presidential action in support of AI education for American youth. The Department of Energy is also focused on building an AI-ready workforce.
At Berkeley Lab, the K-12 team has already been providing data science and AI/ML training for the past five years. Since 2020, they have provided this training to more than 100 students (mostly 10th and 11th graders), building skills and encouraging interest. In fact, among the program’s alumni, a number of students are minoring in data science, computer science, and statistics, because of their experience at Berkeley Lab.
K-12 STEM Education Program Director Faith Dukes, stresses the importance of AI/ML skills. “Our program helps to develop skill sets that students need in order to get an internship at Berkeley Lab,” said Faith. “AI and ML are being integrated into so much of our research now, that it is a must-have skill set.”
The K-12 training program initially focused more on data science, but over the years AI and machine learning have become increasingly important, and Faith and Content and Instruction Manager Alisa Bettale have continued to iterate on the curriculum. Last year, Energy Technologies Area researchers Vi Rapp and Ana Comesana guided interns through the process of analyzing spectral data from chemical compounds and fuel samples. They applied machine learning techniques to this data to predict how potential bio-derived jet fuels might perform, contributing to the development of more alternative aviation fuels.
Computing Sciences Area senior scientist Dani Ushizima worked with students to use computer vision for image enhancement, and machine learning to detect and characterize plant leaves in EcoFAB plant growth devices. Finally, students used data from the Materials Project to train a machine learning model to predict the hardness of materials based only on their densities.
This summer, when the Lab welcomes the sixth cohort of the Berkeley Lab Director’s Apprenticeship Program (BLDAP), AI/ML training is taking center stage. BLDAP, also known as Interdisciplinary Pathways in Machine Learning and Data Science, is an Office of Science Workforce Development for Teachers and Scientists (WDTS) Pathways Summer School Program. About 20 students who may have little to no experience coding will complete an intro to Python/data science/machine learning course as part of the program. The students will then apply their knowledge on data sets from various Berkeley Lab research projects, and also work on machine learning applications. In addition to the BLDAP program, an additional 80 students at various summer internships throughout the Lab will also get exposure to AI/ML skills for science.
Said Faith, “Ultimately, we are exposing our students to the skills and tools needed for scientific research – the foundations for an internship at the Lab or at other national labs. These skills include data science, AI/ML, wet lab, fabrication and engineering skills.” The K-12 team is also working with the Quantum Systems Accelerator to run a quantum computing camp, also a DOE Pathways Summer School program, called QCaMP for educators and high school students this summer. At QCaMP, participants will get a primer on computing fundamentals, learn hands-on about quantum physics, and apply those phenomena to solve computing problems in new ways. The camp will help the teachers better expose their students to the opportunities related to quantum computing.
Sign up to be a Teaching Assistant this Summer
For this summer, the K-12 team is still seeking teaching assistants. Interested researchers can apply online or contact Faith or Alisa.
“The content for our training programs is based on research going on at the Lab. It’s what makes our program special and authentic,” said Faith. “I’m extremely appreciative of our scientists, especially postdocs. Our K-12 program provides the scaffolding and infrastructure, while the researchers provide the content and the real lab experiences.”
K-12 and Workforce Development and Education (WD&E) Offices to be Combined this Fall
To better support the Lab’s commitment to developing the next generation of scientists, engineers, and STEM professionals, the Lab is consolidating the K-12 programs and the WD&E Office within the Lab Directorate. Faith will serve as the office’s director. These changes will be in phases, with the new office fully operational in the fall. This new consolidated structure will improve efficiency and provide a more consistent experience for everyone involved; it will coordinate how the Lab supports interns across the Lab and also strengthen the relationships with teachers, faculty, and those in technical and operations roles.
For more information about the Lab’s efforts to help develop the next generation of scientists, visit the K-12 website and the WD&E Office website.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://research.lbl.gov/2025/06/27/k-12-office-helps-build-tomorrows-ai-ml-workforce/
|
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Exploring the Challenges and Opportunities of Reskilling ... - Simbo AI
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Exploring the Challenges and Opportunities of Reskilling Employees to Adapt to AI Integration in the Modern Workplace
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https://www.simbo.ai
|
[] |
AI and Workflow Automation: Enhancing Healthcare Operations with Skilled Staff. AI plays a big role in automating work in healthcare ...
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Artificial intelligence (AI) is now a normal part of many workplaces, including medical offices in the United States. AI can do simple, repetitive tasks, make decisions faster, and help work run more smoothly. Because of this, medical practice leaders and IT managers have to figure out how their workers can work well with AI. This means teaching employees new skills so they don’t get left behind.
Studies show that by 2025, about half of workers in many fields, including healthcare, will need to learn new skills to keep up with new technology. Many of the skills people use today will change. One-third of future skills will focus on technology, which many workers don’t know about now.
Healthcare leaders should know that AI will not take over all jobs but will change what people do. For example, AI can handle tasks like scheduling appointments, answering billing questions, managing records, and even initial patient checks. These jobs are important but take a lot of time. Letting AI do them means staff can spend time on harder tasks that need human thinking, caring, and medical skill.
This shift means employees must learn how to use AI tools and know the steps of how AI works. Skills like understanding AI, analyzing data, thinking critically, and working with AI systems are becoming more important. Experts say human understanding is still very important because people know context better than AI does. Workers also need to learn how to give good instructions to AI and make AI work efficiently.
Teaching staff new skills makes sure AI helps their work instead of causing problems or worries about jobs.
The Challenges Employers Face in Reskilling for AI
Many medical offices in the U.S. have a hard time teaching workers new AI skills.
One big problem is that employees often do not trust AI. Many worry AI might replace their jobs. This fear is real and based on economic and social issues.
There is also a gap between the skills workers have and the new skills they need. Many workers are trained only in clinical or office tasks and don’t have technical skills related to AI. They also need to improve soft skills like communication, solving problems, and being flexible.
Training is not easy to provide for all. Smaller medical offices often do not have enough money or links with schools to offer continuous AI training for workers.
Legal rules are another challenge. HR managers must follow strict laws when using AI in hiring, firing, and other work decisions. New laws, like one in Colorado, require fairness and clear explanations when AI is used. This makes HR’s job harder.
Training inequality can cause problems. If some workers or offices get good AI training while others do not, it can create a gap, cause workers to leave, and make healthcare less stable.
Opportunities Presented by Reskilling in AI Workplaces
Even with these challenges, there are good chances for medical offices that train their workers in new skills.
As AI takes over simple tasks, employees can do more meaningful work. For example, in fields like radiology, AI can help analyze images, so humans can focus on making decisions and analyzing information instead of just doing routine checks.
Training helps workers combine medical knowledge with AI skills. Learning basic AI, programming, and data can improve patient care because workers understand AI advice better and can help improve AI programs.
Studies show workers who get training feel happier at work. One study showed that 71% of people who learned new skills said their job satisfaction improved. This helps keep workers and reduce burnout problems.
AI-based personalized training is also useful. Some platforms can find what skills each worker needs and offer small learning lessons that fit their jobs. This allows busy healthcare workers to learn without leaving their work for long periods.
A 2024 survey found that 70% of workers felt ongoing learning made them closer to their workplaces. Medical practice leaders who support training may see better worker involvement, fewer mistakes, and happier patients.
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AI and Workflow Automation: Enhancing Healthcare Operations with Skilled Staff
AI plays a big role in automating work in healthcare, especially in tasks done at the front desk, which usually take a lot of staff time.
AI can answer phone calls, book appointments, send reminders, and give routine info without help from humans. This reduces the workload of receptionists and office staff. They can then spend more time helping patients with kindness and good judgment.
Automation helps work run more smoothly by lowering mistakes in scheduling and admin tasks. It also helps keep patient data safe and follow rules.
But AI automation needs workers who can manage and watch over these systems. IT managers in medical offices must teach staff how to look after AI tools, understand AI reports, fix technical problems, and watch out for errors or biases in the AI.
Besides front-office tasks, AI helps with clinical tasks like first patient diagnosis, writing clinical notes, and billing codes. This means healthcare workers need to know about ethical use of AI, data privacy, and rules.
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Legal and Ethical Considerations in AI Implementation
Leaders in medical offices must focus on using AI in ethical ways. AI systems should be clear, fair, and protect patient privacy. This is very important because AI decisions can affect patient health, trust, and confidential information.
Training programs should teach workers about AI ethics and how to manage AI responsibly. HR teams must make sure AI use follows new laws like Colorado’s AI law, which requires human supervision in important AI decisions.
Employers should also plan for workers who might lose some jobs because of automation. Good communication and retraining can help these workers move to new roles in the same workplace.
Preparing a Future-Ready Healthcare Workforce
Healthcare leaders need to keep training workers continuously to stay up-to-date and meet rules.
Healthcare is changing like other industries. Workers need new tech skills like AI, cloud computing, data analysis, and cybersecurity.
Building a culture where workers keep learning is key. Medical offices can use AI learning platforms that give short lessons and real projects, like creating AI chatbots or planning data use. This teaches practical skills.
Continuous learning helps workers and improves how healthcare offices work. With better AI skills, workers can focus on important projects like patient care and communication, while AI handles simple office jobs.
Final Thoughts for Medical Practice Administrators, Owners, and IT Managers in the U.S.
Using AI in healthcare brings both problems and chances for training workers.
Medical offices cannot treat AI as a separate tool. Instead, they have to see AI as a partner that works with human workers.
Teaching workers new skills is key to making this partnership work. It is important to deal with worker fears, give everyone good training chances, and use AI fairly and openly. Learning AI basics, data skills, and people skills helps improve work, job happiness, and patient care.
Healthcare leaders in the U.S. should make training programs that fit their workers’ needs. As AI changes job roles, supporting workers during this change is important for having a strong and ready workforce.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://www.simbo.ai/blog/exploring-the-challenges-and-opportunities-of-reskilling-employees-to-adapt-to-ai-integration-in-the-modern-workplace-624027/
|
[
{
"date": "2025/06/27",
"position": 86,
"query": "reskilling AI automation"
}
] |
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] |
Reskilling Revolution: Preparing the Current Workforce for AI's ...
|
Reskilling Revolution: Preparing the Current Workforce for AI's Demands
|
https://www.linkedin.com
|
[] |
... AI demands. The Urgency of Reskilling in an AI World. AI-powered automation is already displacing routine and repetitive tasks across industries ...
|
As artificial intelligence reshapes the labor market at an unprecedented pace, the question is no longer if jobs will change, but how workers will adapt. The future belongs to those who embrace the reskilling revolution a massive, urgent effort to equip today’s workforce with the skills AI demands.
The Urgency of Reskilling in an AI World
AI-powered automation is already displacing routine and repetitive tasks across industries. From manufacturing floors to finance departments, roles once considered secure are evolving or disappearing altogether. For millions of workers, especially in middle-skill jobs, this shift is a clear call to action: reskill or risk obsolescence.
Unlike past technological disruptions, AI requires a broader range of skills not only technical literacy but also problem-solving, creativity, emotional intelligence, and adaptability. Preparing the workforce for these demands is an enormous challenge that governments, businesses, and educational institutions must tackle collaboratively.
Who Needs Reskilling?
While it’s easy to assume that only workers in manufacturing or low-skill jobs will be affected, AI’s reach is much wider. White-collar roles in accounting, legal services, marketing, and even healthcare are experiencing automation of core tasks. This means professionals across the spectrum must upgrade their skills to remain relevant.
Reskilling initiatives should therefore target a broad audience: frontline workers, mid-career professionals, and even executives who must understand AI’s strategic implications. The goal is to build a versatile workforce capable of complementing AI technologies rather than competing against them.
Effective Reskilling Models: What Works?
Successful reskilling programs combine accessibility, relevance, and flexibility. Online learning platforms, micro-credentialing, and modular courses allow workers to acquire new skills without leaving their jobs. Governments and corporations alike are investing heavily in these initiatives, recognizing that time is of the essence.
Moreover, mentorship and hands-on training accelerate learning by providing real-world context. Pairing AI tools with human expertise creates a powerful synergy, and companies that prioritize continuous learning often see higher employee retention and innovation rates.
The Role of Employers and Policymakers
Employers must view reskilling as a strategic imperative rather than a cost. Proactive workforce development can reduce turnover, improve productivity, and future-proof organizations. Forward-thinking companies are already partnering with educational institutions to co-design curricula that meet evolving industry needs.
Policymakers, meanwhile, must create supportive ecosystems through funding, incentives, and regulatory frameworks. Public-private partnerships can scale reskilling efforts and ensure equitable access across socioeconomic groups.
Overcoming Barriers to Reskilling
Despite growing awareness, several barriers slow reskilling progress. Financial constraints, limited access to training resources, and fear of change often discourage workers from engaging in upskilling efforts. Addressing these challenges requires comprehensive support systems including subsidies, flexible learning schedules, and career counseling.
Cultural attitudes toward lifelong learning must also evolve. Reskilling is not a one-time event but an ongoing journey that requires motivation, encouragement, and a growth mindset.
The Promise of a Reskilled Workforce
The reskilling revolution offers a path toward a more resilient, inclusive labor market. Workers empowered with new skills can navigate the AI era with confidence, contributing to innovation and economic growth. As AI transforms the nature of work, reskilling isn’t just a solution — it’s a necessity. Investing in people is the most effective way to ensure that technology enhances human potential rather than diminishes it. The time to act is now. The future of work depends on how well we prepare today’s workforce for the AI-driven world.
#Reskilling #FutureOfWork #AI #WorkforceDevelopment #LifelongLearning #Upskilling #DigitalTransformation
| 2025-06-27T00:00:00 |
https://www.linkedin.com/pulse/reskilling-revolution-preparing-current-workforce-yucwc
|
[
{
"date": "2025/06/27",
"position": 16,
"query": "reskilling AI automation"
}
] |
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'The biggest risk is doing nothing': insights from early ...
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‘The biggest risk is doing nothing’: insights from early adopters of artificial intelligence in schools and further education colleges
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https://www.gov.uk
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AI in education use is still a new and developing area and is largely experimental. AI can certainly save teachers time, and there is increasing evidence of its ...
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Executive summary
The launch of ChatGPT in November 2022 dramatically increased public access to generative artificial intelligence ( AI ) and brought it into the mainstream. The availability of publicly-available AI tools like Microsoft Copilot and ChatGPT have also sparked widespread interest and discussion about the role that generative AI can play in education.
The UK government is ambitious for AI and views it as a fundamental part of its mission to break down barriers to opportunity for children and young people.[footnote 1] The Department for Education ( DfE ) has stated that:
If used safely, effectively and with the right infrastructure in place, AI can ensure that every child and young person, regardless of their background, is able to achieve at school or college and develop the knowledge and skills they need for life.[footnote 2]
The UK government’s AI Opportunities Action Plan sets out expectations from the Department for Science, Innovation and Technology for AI to improve education and ensure that regulation supports innovation. Regulators, including Ofsted, will be required to publish annually how they have enabled AI innovation in their sector.[footnote 3]
Generative AI can be used to streamline administrative tasks, plan lessons and support assessment.[footnote 4] This has made it particularly attractive for reducing teacher workload, so that teachers can focus on delivering high-quality teaching and on working directly with pupils. Although the UK government has identified education as an area that can benefit from AI , adopting it in schools and further education ( FE ) colleges faces considerable challenges. Half of teachers in England responding to a DfE survey now use generative AI tools.[footnote 5] However, of those who do not use them, 64% say they do not know enough about AI to use it in their role and 35% are concerned about the risks, particularly around data privacy, bias and safeguarding, and users’ ethical and responsible use of AI .[footnote 6]
AI in education use is still a new and developing area and is largely experimental.[footnote 7] AI can certainly save teachers time, and there is increasing evidence of its impact on the process of teaching and learning.[footnote 8] However, there is no conclusive, reliable evidence about its benefits and limitations, particularly its ability to lead to gains in knowledge.[footnote 9] A recent analysis of 143 literature reviews of AI in education concluded that:
The effectiveness of AIED to improve learning outcomes remains far from conclusive, especially in the long-term. As we currently stand, most studies are explorative, short-term and in limited domains [for example] language and academic writing.[footnote 10]
There is also no clear guidance yet about how to measure the impact of AI in education to show whether it is effective at improving educational outcomes or what the best measures of success would be.[footnote 11]
The DfE , therefore, commissioned Ofsted to carry out a study on AI in education to investigate how ‘early adopter’ schools and FE colleges are embedding AI to manage risks, support teaching and learning, and streamline administrative tasks and processes. By ‘early adopter’, we mean ‘someone who is one of the first people to start using a new product, especially a new piece of technology’.[footnote 12]
Our study was carried out in 2 stages. The first stage included having conversations with experts and reviewing the emerging evidence on AI use in education. This helped inform the research questions for the second stage. This stage involved online interviews with senior leaders and those leading AI adoption from 21 schools and FE colleges already invested in using AI in England.[footnote 13] The evidence we collected from the interviews provides insights into leaders’ reasons for adopting AI and shows how they have navigated and overcome some of the obstacles. This information may help other schools and FE colleges when considering their own approaches to using AI .
Our findings show that adoption of AI is complex and has many aspects to it. We can see that, in deciding to adopt AI , providers take different factors into account, depending on the ways they want to use it. These can range from using AI to streamline administrative tasks to allowing pupils use it in a direct and interactive way. Other factors such as differences in adoption readiness, existing experience of digital technology and the availability of resources also affect AI use. For example, schools already committed to using digital technology as part of teaching and learning will have staff expertise and experience of using EdTech and the hardware needed to support the integration of AI .[footnote 14]
It is important to distinguish between these different aspects of AI and how, when and why they are used. Doing so helps us understand how AI is used in different educational settings. It can also suggest how future policies and support mechanisms might be tailored to address the unique challenges and opportunities these different aspects of AI offer.
All the leaders we spoke to were curious and cautious in their adoption of AI . The AI landscape is still changing rapidly, and they need to balance innovation and risk. Typically, leaders saw beyond AI as a shiny new product, and none viewed it as a cure-all for education. Leaders had found ways to integrate AI into processes that they felt were likely to be beneficial for staff, learners and pupils in their college or school.
Most schools and colleges had an AI champion who was instrumental in getting senior leaders to embrace AI and bringing staff on board. In most cases, the champion was a teacher who had previous relevant experience or expertise in technology and AI . In other cases, it was someone who simply had a keen interest that was further fuelled by the release of publicly-available AI tools such as ChatGPT. AI champions typically created a ‘buzz’ around AI and played a vital role in demystifying it so that staff began to understand what it was and how they could use it. They used their expertise to address staff anxieties and build confidence so that staff did not feel overwhelmed. AI champions could determine what help teachers needed and show how AI could be used for this purpose, rather than using AI more generically. In larger schools and FE colleges, and multi-academy trusts ( MATs ), AI leadership brought together their data management teams, IT systems managers and curriculum leads.[footnote 15] This kind of structure recognised that, unlike other forms of technology, AI requires skills and knowledge across more than one department.
Senior leaders made sure there was a clear vision for AI which prioritised safe and ethical use by staff and pupils. They tended to take initial small steps to explore the potential of AI before adopting it across their school, college or MAT . Leaders frequently made sure that teachers had the space and time to experiment and learn how AI could support and enhance their own practice. This created a culture of openness and trust that encouraged innovation.
The use of AI tools in these providers was divided between those who told us the initial reason for introducing AI was to reduce workload, and those who wanted to use it to directly support pupil and student learning. However, we also found that the use of AI often shifted with time. Leaders were rarely prescriptive about the tools teachers could have, and many had a list of approved AI tools teachers could use. Interestingly, a few leaders had already developed, and were testing, their own AI chatbot, while others were in the process of doing so.[footnote 16] Several leaders also highlighted how AI allowed teachers to personalise and adapt resources, activities and teaching for different groups of pupils including, in a couple of instances, young carers and refugee children with English as an additional language.
Commonly, the leaders we spoke to were clear about the risks of AI around bias, personal data, misinformation and safety. They had different mechanisms and procedures to address these. Some had a separate AI policy, while others had added AI to relevant existing policies including those for safeguarding, data protection, staff conduct, and teaching and learning. The pace of change meant that many leaders were updating their AI policies as often as monthly. Importantly, these leaders encouraged regular and open discussion about AI between staff and with pupils to mitigate some of the risks associated with AI . This included developing their curriculum to teach pupils about the advantages and disadvantages of AI and how to use it safely.
Despite some of the perceived strengths of AI , leaders were also clear about several areas that they were looking to develop further. For instance, leaders regularly described their vision for AI to enhance learning or reduce teacher workload, but most were at the early stages of developing a longer-term strategy for AI that set out how it was integrated into their curriculum. Leaders had not yet thought systematically about integrating AI with pedagogy because of the rapid pace of change and because there are still not many AI tools tailored to individual school or college contexts. Some leaders had yet to think strategically about what success with AI looked like or how to evaluate its impact.
These findings provide insights into how early adopters have navigated the challenges of AI and what they see as the benefits of using it. They have helped to inform Ofsted’s own position on AI during inspection. Our statement ‘How Ofsted looks at AI during inspection and regulation’ makes it clear that AI is not a stand-alone part of Ofsted’s inspection and regulation practice. Inspectors will not directly evaluate the use of AI , nor any AI tool. However, when they come across the use of AI , inspectors will consider not only how it is used by providers, but also how it is used in a provider’s setting by others (including staff, parents, pupils and learners). We will also use the findings of this research to develop training for inspectors, to ensure that they are able to account for the use of AI when considering experiences and outcomes in this way.
This small-scale research has been an important first step to enable AI innovation in the sector. However, the study does not reflect the majority of schools, MATs and FE colleges that are yet to adopt AI . We only spoke to leaders and do not know the views and experiences of teachers, pupils and learners. Further research with a wider group of stakeholders could provide more in-depth understanding of the way AI is used, the impact it can make, and the implications for our inspection and regulation practices.
Introduction
Since the release of open-source AI , its adoption in schools and colleges has been met with both enthusiasm and concern by education experts, policymakers and practitioners. Generative AI has been perceived as having the potential to enhance learning and reduce administrative burdens.[footnote 17] However, it is also seen as a risk to academic integrity and cybersecurity.[footnote 18] Consequently, the DfE ’s call for evidence on the use of generative AI highlighted some of the ways in which schools and FE colleges currently use AI , the challenges they face and the impact its use has on them. The DfE has subsequently provided guidance on AI use in education. The guidance emphasises the importance of curriculum and responsible AI integration. It also lays out the relevant legal responsibilities.[footnote 19]
The UK government’s AI Opportunities Action Plan has identified education as an area where AI could have a positive impact. Recent initiatives have further highlighted the growing importance of AI in education. In August 2024, the DfE announced a £4 million investment to develop a set of AI tools for different ages and subjects to help manage the burden on teachers for marking and assessment.[footnote 20] The DfE has also funded development of Oak National Academy’s ‘Aila’ ( AI lesson assistant) and developed training materials on AI with the Chiltern Learning Trust and the Chartered College of Teaching. These are in addition to a new EdTech Evidence Board project funded by the DfE and led by the Chartered College of Teaching. This will evaluate available evidence submitted by AI developers about the effectiveness and impact of their products, as well as whether the products are ethical and comply with data protection laws.
Despite the hype surrounding it, AI adoption in schools is still at an early stage and adopters remain in the minority. For instance, 69% of UK teachers who responded to a 2024 Bett survey said their school has not implemented AI , and 32% of school and college leaders in England are not considering any changes to account for AI .[footnote 21] However, a survey by the National Literacy Trust found the proportion of 13- to 18-year-olds who say they have used generative AI has risen from 37% in 2023 to 77% in 2024. The survey also found that, although nearly half (47.7%) of pupils using AI usually added their own thoughts to what AI showed them, most (79%) did not do this when they used it for homework.[footnote 22] These surveys suggest that, although there is enthusiasm for AI among individual teachers and students, its adoption by school and college leaders is not keeping pace with teacher, pupil and learner use.
As the use of AI in education increases, we need to better understand how schools and colleges are using this technology and managing the risks it poses for pupils, learners and staff. We have to know what impact it is having on a range of outcomes. The DfE , therefore, commissioned Ofsted to carry out research on AI in education. This is intended to highlight the practice of early adopter schools and FE colleges in embedding safe, ethical and responsible AI use, which other educational settings can refer to, if and when they choose to develop their own AI practice.
Ofsted is gathering evidence about how AI is being used in schools and FE colleges. This research will add to Ofsted’s understanding of the mechanisms that support the adoption of AI . It will fill gaps in our knowledge around the use of AI and its impact on pupils and staff. Ultimately, it will inform our inspection and regulation practice, as well as the guidance and training we develop for inspectors in this rapidly evolving field.
The terms of reference provide further details on the aims and objectives of the study. The research covers the following areas:
How are schools and colleges using AI in administrative processes and to support teaching and learning?
in administrative processes and to support teaching and learning? What role do leaders play in embedding and supporting the use of AI ?
? How have schools and colleges approached introducing and using AI , and what have been the challenges, barriers, successes and benefits?
, and what have been the challenges, barriers, successes and benefits? How are schools and colleges monitoring the intended and unintended impacts of AI ?
? How are schools and colleges governing the use of AI and managing the risks to staff, pupils and learners?
Research methods
This was a small-scale qualitative study in 2 phases. In the first phase, we carried out a rapid review of the emerging literature on generative AI to identify its potential benefits in education and the main barriers and challenges schools and colleges may need to overcome. We also reviewed international legislation, policies and guidance related to AI in education, and spoke to international inspectorates and academics with knowledge of AI in education.
The literature review and conversations with experts helped inform the questions for the second stage of the study. This involved online interviews, in the spring term 2025, with leaders of ‘early adopter’ MATs , schools and FE colleges who had responsibility for the adoption of AI . These included leaders from maintained schools and academies and independent schools.
The leaders we interviewed were all enthusiastic about the benefits of AI and had decided to pilot generative AI in their school or college soon after ChatGPT was made publicly available. The interviews allowed us to capture their AI journey and, informed by the literature review, develop our understanding of the following:
leadership of AI : the role of leaders in enabling and embedding AI use
: the role of leaders in enabling and embedding use governance of AI : how leaders make sure staff, pupils and learners use AI ethically, safely and responsibly
: how leaders make sure staff, pupils and learners use ethically, safely and responsibly use of AI : how staff use AI inside and outside the classroom
Please see Appendix B for more details on the research methods and literature review.
Research literature
This section of the report outlines what we know from the literature about the potential benefits and disadvantages of AI in education as well the barriers and challenges to embedding and using AI tools. It also reports on how other countries are approaching AI and responding to some of these challenges.
What is artificial intelligence?
AI is not a new concept. The term was first used in the 1950s to describe machines capable of performing tasks that require human intelligence.[footnote 23] What is new is the introduction of generative AI and the public availability of generative AI tools such as Microsoft Copilot and ChatGPT.
AI is not a single thing. It is an umbrella term that describes a range of technologies and methods such as machine learning, natural language processing, data mining, neural networks and algorithms.[footnote 24] The UK government defines AI systems as ‘products and services that are “adaptable” and “autonomous”’.[footnote 25]
Generative AI refers to AI platforms designed to create new, unique responses to users’ requests. It uses a vast collection of works and other sources to create text, images and videos that can be indistinguishable from those created by humans.[footnote 26] However, generative AI does not understand anything. Unlike traditional search engines such as Safari and Google, it is a predictive tool. It generates an answer based on patterns in data and knowledge that it has learned, rather than by simply retrieving existing information. AI ’s ability to create new responses makes it susceptible to generating inaccurate or misleading outputs presented as fact. These ‘hallucinations’ can happen when AI misinterprets data, has difficulty responding to ambiguous prompts, lacks sufficient context or uses biased data.[footnote 27] For example, if AI has learned that 85% of employees in a company are male, it will predict that the perfect new employee for the company should also be male.
Generative AI includes chatbots and virtual assistants. It has been developed to answer test questions, write essays, translate and summarise texts, provide formative feedback, and even generate lesson plans tailored to individual pupil needs. It can also provide individual teaching that adapts to pupils’ pace of learning and helps understand how learning happens.[footnote 28]
Potential benefits and risks of AI in education
One of the most frequently cited benefits of AI in education is that it reduces teachers’ workload. By automating typical teacher tasks such as lesson planning, marking and resource creation, AI can give teachers more time to concentrate on aspects of teaching that have the most direct impact on pupil engagement and learning, such as producing high-quality resources and learning materials.[footnote 29] A recent trial studied the impact on teacher workload of using ChatGPT for lesson planning and resource creation. The study found that AI could save the time teachers spent on planning and administration by 31%, which was equivalent to 25 minutes per week.[footnote 30]
AI -driven platforms have also shown that they can support formative assessment and feedback.[footnote 31] These systems mark pupils’ and learners’ work and give detailed individual feedback that helps them to understand their progress and areas for improvement. Evidence also suggests that intelligent tutoring systems and chatbots can analyse real-time data on pupil performance and engagement to tailor resources and adjust teaching to individual needs.[footnote 32] AI chatbots respond to users’ questions by generating verbal instructions similar to those a teacher might give to help solve a problem. Pupils and learners can use the real-time feedback to improve their work.[footnote 33] When they use generative AI for personalised learning, it increases their motivation and improves their critical thinking skills.[footnote 34] For example, pupils using chatbots can prompt generative AI with questions and then evaluate the different viewpoints and insights provided. Instead of giving pupils the answer, the AI will use pupils’ prompts to clarify, expand, elaborate, verify and put their knowledge into context.[footnote 35]
However, there are concerns about how valid it is to use AI for setting, marking and assessing exams. Two of the main concerns often raised are that there is a narrow range of acceptable answers, and that AI markers are not able to give reasons for decisions.[footnote 36] Research also shows that AI tools can replicate human marking bias and may struggle to identify high- and low-scoring essays.[footnote 37] AI is also particularly bad at identifying exceptional and original work and may not be able to correctly assess nuance and creativity.
The literature suggests that AI could have a positive impact on teaching and learning. However, there is currently little independent academic evidence of its actual impact on outcomes.[footnote 38] This is particularly the case with generative AI tools as their newness means most of the available information comes from developers themselves.[footnote 39] Systematic reviews of academic literature and research with teachers indicate that AI can enhance personalised learning and provide immediate feedback.[footnote 40] However, these sources also suggest that relying too much on it may make it harder for pupils and learners to develop essential skills, such as essay writing, and higher-order processes such as problem-solving, creativity and critical thinking.[footnote 41] Overreliance on AI -generated responses may also weaken pupils’ capacity to engage deeply with learning material and hinder their ability to retain knowledge in the long term.[footnote 42] It may also create an illusion of learning where pupils and learners produce more but understand less.[footnote 43]
Teachers have also indicated that they are concerned about how AI use may affect them. Using AI for tasks such as marking, resource creation and lesson planning could deskill them.[footnote 44] Teachers may find that AI tools produce similar outputs regardless of their students’ specific needs or the local curriculum goals.[footnote 45] Academic research also suggests that AI may be biased towards particular pedagogical approaches.[footnote 46]
The impact of AI is also felt beyond education. For example, the literature does not give enough attention to the environmental and ecological impact of AI ’s high energy use.[footnote 47] There are also concerns about the lack of transparency around the algorithms and datasets that commercial AI tools use.[footnote 48]
The overarching conclusion is that despite literature indicating there is potential for AI to have a positive impact on teaching and learning, there is currently little reliable evidence available of its actual impact on outcomes.[footnote 49]
Barriers to AI adoption
The literature indicates that adopting AI into educational systems involves navigating a range of barriers and significant challenges. For instance, the use of AI by staff and pupils needs robust safeguarding and governance frameworks. This is because generative AI systems collect and analyse large data sets and there is significant potential for breaches of data privacy. Guidelines from the UK’s Information Commissioner’s Office stress the importance of ensuring that AI tools fully comply with data protection regulations such as the General Data Protection Regulation ( GDPR ).[footnote 50]
A further concern is the risk of AI perpetuating or even amplifying existing biases. AI systems rely on algorithms trained on historical data, which may reflect stereotypical or outdated attitudes.[footnote 51] Education providers need strong governance mechanisms to reassure parents, staff and pupils that AI tools have been evaluated for bias, data security and suitability for different demographics, before they are adopted in practice.[footnote 52]
Schools and colleges also need to be transparent about their use of AI . This includes informing parents, particularly if pupils are using AI . The DfE ’s current position is that schools and colleges should establish clear accountability and transparency around the AI systems they use.[footnote 53] Surveys indicate that parental views about AI play a crucial role in its adoption in education. For example, although parents recognise that AI can help teachers and are happy for them to use it, they are uneasy about pupils using AI , particularly outside school.[footnote 54] Engaging parents in discussions about AI use in schools and increasing their understanding of how it can be used would help overcome some of these concerns.[footnote 55]
AI ’s ability to adapt and personalise resources and learning material to meet the needs of individual pupils and learners can provide significant opportunities for schools and colleges to address educational inequalities. However, the ‘digital divide’ between those who have ready access to digital technologies at home and/or at their school or college or school remains a major barrier in realising this potential. Many schools, particularly those in deprived areas, do not yet have high-speed broadband and/or adequate access to digital devices needed to support AI .[footnote 56]
Bridging this divide would require considerable investment in digital resources and training to ensure that all pupils and learners benefit equally from AI . This is particularly important for those who may be doubly disadvantaged in not having access to AI at home or school/college. There is also the possibility of a digital divide between pupils and learners in schools and FE colleges where AI is embedded in teaching and learning and those in providers yet to adopt AI . Pupils and learners in schools and FE colleges yet to adopt AI may miss out on the benefits described earlier in the section ‘Potential benefits and risks of AI in education’.
Importance of professional development
There are several factors that influence the adoption of AI in education. These include the technological infrastructure in schools and colleges, and the types of support leaders put in place to address teachers’ concerns and increase their trust in AI outputs. Successive surveys of teachers by the DfE show that, although more teachers are using AI , they are concerned about the risks.[footnote 57] There are key distinctions between the risks associated with generative AI used in education and those related to EdTech use more widely, as discussed in the ‘Potential benefits and risks of AI in education’ section. These surveys indicate that teachers need a stronger emphasis on ethical considerations alongside an understanding of how AI tools work and how they can be used safely and responsibly. Professional development needs to address both the technical and pedagogical aspects of AI , so that teachers know how to critically evaluate AI -generated outputs and manage the ethical challenges associated with AI use.[footnote 58]
Decisions to use digital technology are often influenced by what teachers believe about its benefits and how confident they are in integrating technology into their practice.[footnote 59] In the case of AI , these beliefs are more complex because of teachers’ specific fears and misconceptions about the risks of AI .[footnote 60] Along with factors directly related to AI tools, teachers have indicated that things for schools and FE colleges to consider when introducing AI include:
not increasing teacher workload or anxiety
having support mechanisms in place
building teachers’ knowledge of how to use AI
addressing misconceptions about AI and its risks[footnote 61]
Teachers’ acceptance of AI is fundamental to its successful use. Evidence suggests that teachers need to trust AI and perceive its value before they are willing to incorporate it into their practice.[footnote 62] Teachers’ willingness to use AI is also related to how easy they think it is to use. Seeing practical examples of high-quality AI in use and the impact on pupils and learners before using it themselves can make teachers more enthusiastic. This may cause them to challenge their own digital practice and what constitutes effective use of AI in classrooms.[footnote 63] As teachers become more familiar with AI and see its impact, they become significantly more willing to embed it into their practice.[footnote 64]
Teachers’ beliefs about EdTech and its impact on learning also affect whether or not they use AI in the classroom, and how they incorporate it into their pedagogy. Teachers with first-hand evidence of how it enhances teaching and learning are more likely to experiment with AI and use it in the classroom.[footnote 65] On the other hand, teachers who are sceptical about its capabilities and concerned about potential negative consequences (such as decreased teacher–pupil interaction or a loss of critical skills) can be less willing to embrace it and may delay their adoption of AI . Schools and colleges with a supportive infrastructure that gives staff the time and space as well as the agency to explore the benefits of AI in their particular context are more likely to embrace AI tools effectively.[footnote 66]
One way to think about how teachers use AI is to apply the SAMR (Substitution, Augmentation, Modification and Redefinition) model.[footnote 67] This model has 4 levels of technology use – substitution, augmentation, modification and redefinition – which start from using technology-enhancing teaching methods and end with transforming the way pupils learn. When using educational technology early on, teachers often focus on the first 2 levels (substitution and augmentation), which involve replacing traditional materials with digital ones. Examples of this are lesson planning and adapting resources online, or recording lectures on video and making them available for asynchronous learning. At the last 2 levels, technology is used for tasks not previously possible, such as live interviews of AI -generated historical figures. Evidence from researchers and teachers suggests that AI is not yet being widely used to redefine teaching and learning. It tends to be used to automate existing practices rather than to develop new practices that only AI can support.
Two of the main reasons teachers give for not using AI are that they do not know enough about how to use generative AI tools, and that they are concerned about the risks.[footnote 68] Teachers must, therefore, receive comprehensive professional development so they have the guidance and support they need to use AI tools effectively and responsibly. This should address both the technical and pedagogical aspects of AI , so that teachers know how to critically evaluate AI -generated outputs and manage the ethical challenges associated with AI use.
International context for AI in education
International approaches to regulation and governance of AI in education vary considerably. Some countries have introduced new legislation to address the risks around AI use while others, such as the USA, rely on voluntary compliance with guidelines and self-regulation. Different approaches to regulation are driven by how governments believe AI development and innovation is best supported and encouraged.
In Europe, the AI Act is the first legal framework to establish rules for anyone using or developing AI tools for education. All AI in education is classed as high risk and regulated. Schools must show how they ensure that AI is used appropriately, monitor and record how they use it, and report serious incidents. EU legislation also emphasises transparency for staff, pupils and parents. If schools use AI to write reports or grade work, they must tell parents and pupils and explain the processes.
Singapore’s approach to AI regulation combines mandatory regulations with voluntary guidelines. There are no specific laws or regulations that directly regulate AI but regulators have set out compliance requirements for data, accountability, reporting incidents, security, transparency, research and testing that apply to education use. The focus of regulation is schools’ awareness of acceptable risk levels and how they address them. This approach to regulation offers both structure and flexibility.
The DfE has adopted a less prescriptive stance to AI regulation than the EU by building on existing frameworks and guidance for schools around data protection, safeguarding and intellectual property.[footnote 69] This aligns with the Department for Science, Innovation and Technology’s guidance for regulators and is designed to encourage innovation while making sure that schools and FE colleges still have robust governance structures.[footnote 70]
Estonia’s approach to regulating AI in education contrasts sharply with the EU. Use of AI in education relies on self-regulation. Ministry of Education guidance for schools on how to use AI is described as ‘suggestions’ rather than regulations. Safe, ethical and responsible use of AI is included in the digital competencies that are part of the Estonian national curriculum for all children from 4 to 18 years of age. The competencies cover all aspects of digital technology use including copyright, digital security, protection of personal data, privacy, and the environmental impact of digital technology. Schools are expected to teach and assess the competencies as part of their own curriculum.
Evidence from provider visits
The following section reports our findings from 21 online interviews with leaders who have embedded the use of AI across their MAT , school or FE college. Their views highlight the actions and decisions they took that have formed critical parts of their school or college AI journey.
Leadership of AI
Two aspects of leadership were suggested in all the interviews. First, senior leaders were committed to enabling AI adoption at a strategic level. Second, they had an ‘ AI champion’ who had a passion for AI as well as the expertise and seniority to communicate the benefits of AI to other staff and senior leaders. One digital lead from a MAT summed this up as follows:
What you really need is someone with leadership responsibility. Someone who really has knowledge about what’s going on in AI in education. And then someone who can speak “human” as well, rather than “techie”. And if you’ve got those 3 ingredients, which some schools have, they’re the schools that are driving forward with this. But if you miss out the knowledge of what’s going on with AI , you start to increase the risk, or you don’t know the benefits of it. If you haven’t got leadership responsibility, you can’t drive it.
These 2 aspects of leadership created the foundation for AI adoption and also a culture that balanced safe and ethical use of AI with innovation. In some providers, these 2 roles were performed by 1 person. In others, often larger colleges and MATs , this role fell to more than 1 person.
The influence of an AI champion
The introduction and uptake of AI in many settings was driven not by strategic leadership, but by a teacher or leader who championed the use of AI . We often found that these individuals played a vital role in influencing leadership and inspiring staff to embrace AI in their practice. Importantly, the AI champions we spoke to had the essential knowledge and understanding of generative AI needed to convince senior leaders, including governors and trust CEOs, and staff to adopt AI . One champion, who was the director of digital transformation in an FE college, described their impact on governors and trust CEOs as follows:
I think the biggest change was when [I was] invited to show SLT [senior leadership team] and the governors how to use it. That was the turning point, where we all could recognise that this was going to have a big impact on teacher workload, but also on how AI could impact on teaching and learning.
This description is typical of how others described the relationship between the AI champion and senior leaders. They worked in tandem to implement AI and develop an AI mindset among staff.
Case study 1 – The role of the AI champion
This FE college has always had a technology-enhanced learning environment. A few months after the launch of ChatGPT, the lead practitioner for e-learning established themselves as the AI champion and sat down with the college principal to show what AI could do. The meeting was only booked for 30 minutes, and I was in there for an hour and a half, just demonstrating the functionality and key support that AI can provide in the learning environment. One of the first things the principal said was, ‘This is an employability skill I need students to have.’ While some nearby FE colleges had decided to ban AI , the principal felt differently and was cautiously optimistic about exploring how it aligned with what the college already had in place as a Microsoft college.[footnote 71] The AI champion gave staff the confidence and skills to understand how to use AI effectively. This includes understanding different curriculum areas and teaching staff ‘prompt engineering’.[footnote 72] As the champion moved around the different curriculum areas and teams, they tailored their training to individual staff, depending on the needs of skills and assessment methods for the learners. For instance, the champion would adapt their examples of what generative AI can do for learners going into healthcare, by teaching staff how it can support the learners with writing or adapting care plans for their patients. Teaching the staff the importance of knowing how to correctly prompt ChatGPT, and what knowledge staff needed to share with their learners, was a critical part of their role. As the AI champion told us: “If you put junk in, you’ll get junk out.”
Laying the groundwork for AI
Commonly, leaders made sure that they had secured staff buy-in for AI before introducing it across all the schools in a MAT or in individual schools and colleges. This meant having a strategy for addressing staff anxiety and fears about and for dispelling some of the myths associated with AI around job losses. They also invested in raising staff awareness about the risks and challenges of AI and its potential benefits.
AI champions were frequently leading on this. As teachers, rather than IT specialists, they understood the structures and mechanisms that would support staff to use AI effectively, especially what its capabilities were in relation to teaching and learning. For instance, they were well positioned to match AI tools to what teachers actually wanted to use AI for. Often, the starting point was determining what teachers needed help with and showing them how AI could be used for this purpose, rather than using AI more generically.
Furthermore, these champions created a ‘buzz’ around AI through their own enthusiasm and passion. They played a vital role in helping to demystify AI and demonstrating what it could do. They were able to create an ‘ AI mindset’ where it became the norm for teachers to use it, rather than to see AI as a shortcut. As one college head of teaching and learning standards described, they made the use of AI contagious:
What we wanted is staff in the staff room to be able to go, ‘You know I’ve just created that on [our AI tool] and it’s brilliant’… and people will start discussing it with others and… they’ll go, ‘Right. I need to have a good look at this because I’m not using this [tool].’ That is what is starting to happen now and that’s working… we’ve got to allay fears around this.
Taking risks to innovate
Nearly all the leaders we spoke to had begun to research and learn about the potential benefits and risks of AI not long after ChatGPT became available to the public in November 2022. This meant they had a particular view on AI and where it sat in the current educational context. One headteacher from an independent school told us:
We’re only just at the start of this AI era and the biggest risk is doing nothing and assuming that you can just continue as is.
The speed of AI development after the launch of ChatGPT, and inconclusive evidence about its impact, meant these leaders typically took their time to research and understand AI and the different tools available. As one MAT academies director described it, their role was to make sure they saw beyond the hype of AI to decide what was right for their needs:
When products first came out, it’s like they’re sprayed in glitter and they look shiny, sparkly and wonderful, don’t they? So, everybody’s drawn to them… So, I guess it’s our job to make sure that we don’t fly over to the flashy or shiny, sparkly products.
Another MAT chief transformation officer expressed concern over developing their AI too fast, as with everything changing so quickly, there was a danger of ‘falling into the trap of doing something too reactive’.
Typically, leaders were managing the risks by adopting a cautious approach and making sure they had the groundwork in place for AI . Most had taken small steps and built a safe foundation, so staff and pupils were ready for AI . They prioritised the safety of staff and pupils and made sure AI was used responsibly and ethically. They used internal trials and staff working groups to help them understand which tools might meet a provider’s needs best. One MAT digital lead described it as ‘getting the fundamentals and the foundation pieces right and then that makes the next level journey much easier’. Another school headteacher highlighted how a process of ‘pre-mortem’ helped with developing their initial strategy for AI :
We did this thing called a pre-mortem. So, we [sat] around a table and acted as if we had already rolled out the project and it’s failed, and then we work backwards and talk about all the reasons why it failed. There were people in SLT [senior leadership team] that said, oh, well, the children were using it to cheat, or the children put inappropriate content into it, or the children were just copying out what it gave them and put it into their homework or into their books. That suddenly allowed us to strategise [and say] OK, let’s go back to the beginning, get back in the time machine, and fix it all. And that really worked [for us].
Something these early adopters had in common was leaders who showed an appetite for risk and who were willing to innovate, once the fundamentals were in place. A school headteacher said:
We have to be able to take a calculated risk. If we want to be innovators in this space, and we want to give our children the best experience, someone’s got to do it first. And whoever does it first is going to make some mistakes. So, we’ve accepted that, but [we place] pupils’ safety right at the centre. Everything else is up for grabs around that. That’s the way we’ve approached it.
This created a culture of openness and trust that gave staff ownership of AI use and encouraged innovation. Allowing staff to ‘experiment’ and ‘explore’ AI were core principles for these leaders. Additionally, leaders said that they encouraged staff to share and talk about their successes and challenges in using AI with other members of staff, which often helped to support further buy-in – as one school headteacher explained:
The culture that we’ve developed and grown is about it being purposeful and responsible and professional. And therefore, actually, we felt if we lock this down and say we’re only allowing you to do things that we think are beneficial, we’re not going to get this full scope.
An assistant head from an independent school also told us:
The idea at the moment is that we’re giving staff a real free rein to kind of explore what works.
Leadership structures
Leaders recognised early on that AI encompasses several different areas of knowledge. The size of the organisation tended to determine what this looked like in practice for each provider. However, the leaders we spoke to agreed that AI was not an IT- or curriculum-based solution. Adopting AI would have implications across curriculum, IT, safeguarding, data management and teaching and learning. This needed to be reflected in the leadership of AI and in who was involved in decision-making and development of a wider strategy.
The mechanisms and structures that supported the adoption of AI and encouraged staff buy-in varied by provider type. In large colleges and MATs , strategic leadership of AI drew on expertise from across the organisation. (See Appendix A for the varied roles of those leading AI in different providers.) MAT leaders described a clear structure which had leadership at different levels and provided day-to-day leadership of AI use by staff and strategic thinking about AI . MATs typically had a digital leader in each academy to support staff, often the AI champion.
In larger providers and MATs , AI leadership brought together their data management teams, IT systems managers and curriculum leads. This kind of structure recognised that, unlike other forms of technology, AI required skills and knowledge across more than 1 department. This kind of structure was harder to develop in standalone primary and secondary schools. In these smaller schools, senior leaders often established staff working groups to pool knowledge.
A strategy for AI
The leaders we spoke to emphasised that senior leaders needed to have a clear vision for AI and know how they wanted staff to use it as part of their own practice. As one school headteacher suggested:
It requires all the leadership team to be on the same page with what you’re trying to achieve. Do you want this to reduce teacher workload primarily? Do you want this to enhance teaching and learning primarily, or do you want it to be both prongs of that kind of AI journey? The emphasis here is on intentionality – knowing the “why” behind the adoption is just as critical as the “how”.
However, while leaders tended to have a clear vision for AI in the short term, very few had a longer-term strategy beyond the initial testing and piloting stage. When we asked about their strategy for AI , most talked about their reasons for adopting AI and developing the guidance, policies and mechanisms that ensured safe, ethical and responsible use of AI by staff and pupils. Very few leaders had established what they hoped to achieve with AI longer term, or what success with AI looked like beyond the initial piloting stage. For example, they had rarely considered what they wanted the impact on pupils’ learning, on teaching or on staff workload to be.
This short-term way of thinking was often related to the newness of AI and the pace of change. Despite being ‘early adopters,’ most of the leaders we spoke to were still learning about the technology itself and how it could align with existing practices, as this college deputy principal explains:
Because this is so new and we are learning day by day, it’s really difficult to see where that end is and what we want.
Others, such as this college principal expressed the same sentiment more bluntly:
I think anybody who’s telling you they’ve got a strategy is lying to you because the truth of the matter is AI is moving so quickly that any plan wouldn’t survive first contact with the enemy. So, I think a strategy is overbaking it. Our approach is to be pragmatic: what works for the problems we’ve got and what might be interesting to play with for problems that might arise.
This following comment by the same college principal paints a vivid picture of how schools and colleges are focused on keeping up with the pace of change and learning about AI before deciding if and how to use it across their school, FE college or MAT :
It’s the Wild West and all we are at the minute is the sheriff. What comes in and what goes out of the town is what we’re managing to deal with at the minute. Who’s a useful citizen, who’s not a useful citizen, is what we’re making the determination of. Once that Wild West has become more of a frontier town, you can start to make informed choices.
In contrast to this short-term way of thinking, we also spoke to 5 providers who were developing pupil chatbots. These included 2 MATs with a strongly centralised curriculum and pedagogy. They saw chatbots as a way to maintain quality and consistency across the trust for improving attainment. However, they were still in the early stages of deciding how and what to evaluate as evidence of success.
Case study 2 – AI leadership in a multi-academy trust
This MAT , which includes both primary and secondary schools, became a Google academy in 2015. The Covid-19 pandemic triggered trust leaders’ decision to become fully digital. They gave pupils and staff access to Google classroom and provided all pupils with a Chromebook or an iPad. The trust also appointed a member of the senior leadership team in each academy as a digital lead and appointed a Google trainer in each academy. There was also at least 1 digital champion in each academy. Digital leads supported the implementation of the trust’s digital strategy, and the digital champions were the ‘voice pieces to champion the software and the technology’. Google trainers worked alongside the digital leads to support technology adoption and teacher training. This digital leadership meant there was a structure in place to support the adoption of AI . After the introduction of ChatGPT, leaders realised they had to move quickly to have a position on AI and began to look at their existing systems and digital strategy. The academies’ director and chief information officer were the driving force behind AI adoption. They started talking about AI at termly meetings of academy principals and senior leaders. They aimed to dispel myths, explain what AI was, highlight its advantages and disadvantages, and demonstrate how it should and should not be used. At this stage in their AI journey, trust leaders blocked staff access to AI tools to give themselves time to use and understand the technology before rolling it out across the MAT . They also created an AI strategy working group to test several AI products before deciding which to adopt across the MAT . The tool they chose ‘was nicely packaged for teachers and had in-built training and resources’. It was shared with principals and senior leaders before being launched across the trust for all staff to access. Staff were encouraged to explore and play around with the tool and learn how to use it by themselves. It was promoted by a ‘tip of the week’ for the tool and weekly staff bulletins. It was also referenced on any occasion where staff came together. Trust leaders also created a digital toolkit with information about other tools staff could use which are ethical, and GDPR - and copyright-compliant. The trust also tracked the use of individual AI tools and surveyed students and teachers about their use of AI twice a year. This helped with assessing where the trust was on their AI journey and the impact it was having on education and workload. Leaders were conscious that students were probably already using AI tools outside of school, and that their knowledge was potentially further ahead than that of the teachers. To help mitigate this, leaders decided that all staff in the academies should be taught how to use AI and also learn about compliance and good governance for the tools. Teachers were also trained on how to understand and detect students’ use of AI .
Governance of AI
The DfE has published guidance about safe and responsible use of AI .[footnote 73] However, schools and FE colleges in England can set their own rules for AI use, as long as they follow legal requirements around data protection, child safety, and intellectual property. All the leaders we interviewed had prioritised safe, ethical and responsible use of AI . Most had comprehensive policies and procedures in place to address the risks to staff, pupils and learners.
Leaders had researched the benefits and risks of AI before proceeding with any development of AI . They were aware of the risks to pupils and staff around bias, data protection, intellectual property and safeguarding. Some leaders told us they had set up their own AI strategy groups to test new AI tools before distributing them more widely to staff. They told us these groups helped to determine whether a specific AI tool was the best available product to use, the risks of the tool and how to overcome these risks. A few leaders also mentioned that they felt these groups drove product use – particularly in approving what platforms teachers and learners can use – as well as maximising the gains of any products they were using.
However, there was no clear consensus about what to include in a policy, or whether to have a separate AI policy. The policies these providers were using tended to perform several functions. They specified guidance and responsibilities for AI use by staff and students, described safe ethical use of AI and provided information for parents. Some leaders had decided to incorporate AI use into existing policies. These tended to be the providers’ acceptable use policy, staff codes of conduct, teaching and learning strategies, and the safeguarding policy.
AI policies
Several providers were considering whether a separate AI policy might be needed in the future as their AI journey matured. One head of school curriculum said they would eventually need a separate policy to make sure that everything was covered:
So, AI is embedded, but I think that as we progress with our journey, I think we probably do need our own school AI policy. I think that’s probably what the majority of organisations moving forward will have to have because we need to be aware of, you know, hacking and in terms of GDPR . All of those things we need to encompass and consider.
However, most leaders said they found developing policies for AI quite a challenging area, largely because of the pace of change in the sector. This meant leaders were being constantly vigilant and forward-looking. Regularly reviewing their AI policies was, therefore, an important principle of governance. Several leaders said they were reviewing and updating AI -related policies at least termly, if not more often. For example, one primary school leader said they regularly reviewed their AI policy to check that it was still strong enough and that it addressed key issues adequately.
With policies in place, ultimately, it was the responsibility of leaders and staff to make sure they and their students used AI safely, ethically and responsibly and understood the risks. As one independent school deputy head explained:
Your responsibility is: know how it works, including biases and the ethical issues around how training works and how it produces responses. Be honest when you’re using it. And be responsible for whatever you create.
Keeping staff and pupils safe and using AI tools ethically and effectively required openness, robust procedures and policies and effective training. It also needed providers to create a culture that made it clear that, when it comes to safeguarding, AI was everyone’s business. As one leader said, ‘The technology itself is not inherently unsafe, just the way it is used.’
Transparency
Among the challenges leaders described was the pace of change. They had to balance the speed at which new AI tools were launched and staff and students wanting to use them with safety and security.
Many new tools are emerging, and the technology is developing at great pace. We want to ensure we are moving at the right speed to benefit our staff and students while also ensuring their safety, the security of our systems and data, and being aware of the ethics of using these emerging technologies.
However, in a few cases, the governance around AI proved to be an inhibitor that affected the speed of uptake. For instance, one headteacher told us they were initially hesitant to make AI a formal part of their teaching and learning policy due to the fast-changing nature of the technology. Instead, their priority was to keep a close eye on safeguarding, security and online safety. Other leaders told us that they encouraged a culture of openness to mitigate the risks. If staff and pupils were talking openly about the tools they were using and how they were using them, leaders could make informed decisions about the risks and how to mitigate them.
Importantly, these leaders did not want to stop staff from innovating and experimenting, but they needed to be sure that the AI tools being used were safe and appropriate. Two providers had AI tools approval committees, and a list of approved tools staff could use. These committees included IT (network managers) and teaching and learning leaders who considered both data compliance and pedagogical principles to confirm the tools had educational value. In one college, the teaching and learning, IT and GDPR teams had a monthly ethics meeting to discuss requests to use new AI tools and decide on whether to approve them for staff to use. Others approved only tools they knew were safe.
Two of the leaders we spoke to suggested that the government needed to provide more guidance and support around safe AI tools and also shift the focus to learning. One independent school leader said:
What we want is safe, useful technology that doesn’t undermine the learning process. And I think there’s a huge amount of focus on the safe and useful bit and not enough focus on the not undermining learning.
Research has shown that dependence on AI tools might hinder the development of pupils’ critical thinking and problem-solving skills if they are not used effectively.[footnote 74]
Case study 2 (continued) – Updating policies for AI use When staff were given access to AI tools within the trust, leaders began to work on their strategy and consider governance around how best to use them. In the same way they had created a digital strategy, they also developed an AI strategy. This enabled the trust to be clearer in their communication with their academies. Following this, their academies were also told to update their honesty policies around producing exam work. This generated policies to help the academies know what they should and should not do around students’ use of AI . Guidance was also created on how to use AI within the MAT . There were 3 versions created, communicating the different types of tools, and spelling out what to do and what not to do.
Using AI inside and outside the classroom
Most of the leaders we interviewed were not prescriptive about the AI tools teachers could use and how they should use them. Some had piloted different tools with staff before deciding which to buy licences for. Others had a system to approve the AI tools staff wanted to use.
Adoption of AI was split equally between those who gave teacher workload as the main reason for its adoption and leaders who prioritised pupils. We have used the idea of teacher-facing and learner-facing AI tools to describe how providers were using AI and the types of tools they used.[footnote 75] Teacher-facing AI tools support teaching and are used by staff for lesson planning, creating resources and suggesting activities. They can also support with administration and give personalised feedback. Learner-facing tools are used by pupils themselves and include intelligent tutoring systems and AI chatbots.
We found that leaders were mainly using teacher-facing tools to reduce staff workload. This was most likely to be the case where schools had more recently decided to adopt AI .
Although leaders talked about reducing teacher workload, they often qualified this by adding that it was not about reducing teacher workload overall but increasing the time teachers could spend on the things that had a more direct impact on learning. For instance, AI allowed staff to focus on the ‘human bits’ of education that technology cannot easily replicate. A secondary school principal explained:
We just want to redistribute where that time is. So those admin staff at the front, I’d rather them not spend 2 hours spell-checking a policy or changing dates. I’d rather them be proactively chasing poor attenders, making phone calls, [doing] home visits, doing the human bits.
All the leaders we interviewed said that they used teacher-facing tools to reduce workload for both teaching and administrative staff. Commonly, teaching staff used teacher-facing tools for planning lessons, adapting resources and creating quizzes, and revision help. For example, the assistant headteacher of one school said the top categories their teachers used in their AI tool were ‘help me write’, ‘slideshow’, ‘model a text’, ‘adapt a text’, ‘lesson plan’ and ‘resource generation’.
Many described AI being used by administrative staff to reduce time spent on tasks such as writing letters to parents, summarising long documents or updating policies. One leader described using AI as ‘another person to bounce off’. They used AI to review or proofread letters, reports and other documents rather than asking another member of staff to do this.
Leaders were also clear that, although AI can help reduce workload and save them time, it still needs human oversight to quality assure its outputs. Some also told us that they are mindful that AI was not always the best option and that it ‘doesn’t quite replace the expert’. Teachers needed to use their professional judgement and pedagogical knowledge to decide where AI could enhance teaching and learning and where it might not.
Providers who had longer experience of using AI were more likely to be using it with pupils. Most often, we found that teachers modelled its use rather than allowing pupils to use it themselves. Leaders told us that, where teachers were using AI in the classroom to generate outputs, they could use this as an opportunity to develop pupils’ digital literacy. It was also a way for pupils to see first-hand some of the risks of using AI when users didn’t understand how it works, and to critically evaluate its outputs. In this example, a primary school headteacher described pupils’ response to AI generated images of doctors.
It produced 5 images of a doctor that were all white and all male. We just asked the children, ‘Tell us what you see.’ And, actually, some of the children didn’t have a clue. They didn’t clock on to it because of their own bias in their head – a doctor is a white male – but a couple of children said, ‘Well, there’s no women in here and there’s no one that looks like me.’
This demonstration of AI use in the classroom provided the catalyst for critical discussion about bias and misinformation.
FE colleges were more likely to permit learners to use AI themselves because they were older. Primary schools had not yet reached the stage where they allowed pupils to use AI independently. The only exceptions to this were schools that had developed their own chatbot and determined it was safe for pupils under 13 to use.
Personalising and adapting teaching
Most of the leaders mentioned AI ’s ability to adapt and personalise resources as one of its strongest benefits. Several leaders talked about using AI tools to adapt lesson resources to make them more accessible for different groups of students, particularly those who have a special educational need and/or speak English as an additional language. As this school headteacher described:
It’s how are we enabling them to all access the curriculum and get that real quality teaching experience. As well as keeping the teachers still smiling and able to have a bit of a weekend.
During the interviews, we heard several clear examples of how learning was being personalised for specific groups of students. For instance, a lead practitioner from one college described how AI was allowing Syrian students who spoke English as a second language to access the curriculum. Teachers used AI to translate and adapt lesson resources such as PowerPoint slides and assessments. They also generated a glossary of terms in Arabic to give students a ‘leg up’. The lead practitioner for eLearning highlighted how:
It just levels the playing field and allows them to progress through the college rather than because that lecturer doesn’t have that skill, which maybe the ESOL [English for speakers of other languages] lecturers do, and ChatGPT gives them that.
Leaders from another college mentioned that staff were using AI to help young carers catch up on lessons they had missed because of their caring responsibilities. In this example, generative AI had created a 10-minute podcast from teaching slides and materials used in a full lesson. This included AI -generated voices talking about the lesson content. The idea was that pupils could fit the podcast around other responsibilities or listen while travelling, such as when they were going to college on the bus.
In a further example, leaders from a secondary school told us they were training teaching assistants to use AI tools to help the pupils they supported. For this purpose, every teaching assistant had a laptop they could use to adapt resources and learning to an individual pupil’s level of understanding and/or need.
When the teaching assistants put in the pupils’ learning needs and level of understanding of a topic, the AI tool they were using was able to adapt worksheets, learning objectives and success criteria to make it easier for pupils to access learning at their level.
However, as the literature review indicates, there is a lack of research that identifies the most effective ways AI can be used to adapt and personalise learning.[footnote 76] We have found from our curriculum research reviews[footnote 77] that adaptations can be ultimately unhelpful where they provide ‘workarounds’ to immediate barriers, but fail to address these barriers so pupils can access the curriculum in full and in the long term. For instance, if all resources for a pupil are adapted to their current reading age, this could widen gaps between them and their peers. Likewise, evaluations need to determine if bitesize lessons in alternative formats ensure that intended concepts are still learned in full and avoid producing misconceptions. If we do not scrutinise adaptations in this way, then AI use for personalised learning could simply worsen issues around lowering expectations for some students. This raises the need for providers to evaluate the impact of AI on pupil outcomes and monitor how it is being used to support learning.
Pupil learning about AI
All the leaders we spoke to said that teaching students how to use AI safely was one of their top priorities. This was often because they were concerned that they could not control pupils’ and learners’ use of AI at home. One leader told us that pupils were shocked to learn about ‘deepfakes’ in particular.[footnote 78] Pupils had not realised that what they saw on social media could involve elements that were AI generated, even though they may look real.
Therefore, curriculum development was an important part of the AI package offered by these providers. Teaching pupils and learners how AI works and how it uses their data, and raising awareness of bias and misinformation, were seen as important parts of pupils’ and learners’ digital literacy and safeguarding. Some providers addressed safe, ethical use of AI through their interactions with pupils. Others had developed specific teaching units as part of their computing or personal, social and health education (PSHE) curriculum. These explained how different types of AI work and how they use data to generate the different types of outputs pupils had seen and used. Teaching about AI often covered topics such as deepfakes, safe use, hallucinations and the need to critically evaluate what AI generates.
Developing chatbots
A few leaders described how that were developing and using their own bespoke chatbots for individual pupils to use to support learning. These generated AI responses by drawing on the background curricula, pedagogical approaches and intellectual property that each school, college or MAT had. The chatbots were also being tailored to particular attainment challenges.
One of these leaders described AI chatbots as ‘second teachers’ in the classroom. The chatbot was providing real-time assistance and feedback on assignments as well as responding to queries. Pupils were told that the chatbot was not replacing teachers, but ‘replacing where the teacher isn’t’. Pupils could use the chatbot if they were stuck or wanted to try out different ideas and get feedback. The chatbot was designed so that it would not give pupils a direct answer, but it could help them understand why something was wrong, or what question they had to ask to get to the right answer.
In another MAT , leaders were beginning to think about how technology and AI could support their adopted pedagogical principles. They were starting to match the technology to those strategies, as this MAT Chief Transformation Officer explained.
So, if you’re going to do modelling, this is how you could do modelling in a technology-enabled way. If you’re going to do a “think, pair, share”, this is what you could do. We’re now starting to map AI -type tools on to that so that it’s deepening the opportunities.
Case study 3 – AI chatbots in a primary school This 2-form-entry primary school with a higher-than-average proportion of disadvantaged children started its generative AI journey in the summer term of 2024. The headteacher had a prior interest in technology and a few professional connections. Through one of these connections, the school was invited to trial and test an AI platform and chatbot that was designed and built in collaboration with teachers, and considered safe to use with children. Before participating in the trial, and with safe use and safeguarding at the forefront of any decisions made around AI , the headteacher ensured all staff received training on AI through an inset day focused on the topic. Similarly, before the chatbot was introduced to children, the leadership team established a code of conduct and code of ethics around AI use. Pupils were also taught about the risks and benefits of using AI , along with what it was, and how it worked as part of their curriculum. This was done by teachers demonstrating on screen in front of the whole class how ChatGPT could help with writing or maths, and children taking turns to ask the AI questions. Once leaders were confident pupils understood how AI worked, Year 6 pupils were introduced to the chatbot and given the opportunity to ‘talk’ to it and find ways of using it that made sense to them. They did this through structured time on their laptops, where the teacher would first model use before pupils would try to apply this independently. Pupils were encouraged to first ask the chatbot questions they would normally want to ask a teacher, to see what answers it gave. If the response did not feel right, they were to let the teacher know. Staff noticed that, for the chatbot to be effective, pupils needed to use specific and well-thought-out questions and prompts. Limitations in pupils’ spoken and written language made a big difference to what they got out of using it. As the headteacher explained: [It] was a learning process for us as teachers to understand that actually this technology is a little bit different to what we’re used to… In Google you can just type out a word and it will check out a lot of stuff and you can pick [what you want] from that. But with an AI bot, you’ve got to be really specific and purposeful about what you’re asking it to do. This meant that teachers could link AI to the development of pupils’ oracy and literacy skills. A few months after the pupils had begun using the chatbot, leaders believed that they had started to see a positive impact on their metacognitive skills.[footnote 79] The school told us the next step is to trial the chatbot in other year groups, once pupils are taught about the risks and benefits and feel ready to do so.
Assessment and feedback
Several providers told us they used AI for marking, assessment and feedback. In one school, teachers used AI to give feedback on essays, using specific criteria decided by the teacher or set by exam boards. This school also used an AI tool that produces PowerPoint outputs to create ‘low stakes’ testing and quizzes from slides, websites, YouTube videos or PDFs. The AI marked as pupils answered the questions.
However, leaders tended to be more cautious about using AI for assessment compared with other ways of using it. First, they had concerns about accuracy. Second, some felt pupils wanted to know their work had been marked by a human. And third, there was a worry that using AI for this purpose may result in teachers becoming less well aware of the students and their work – as this MAT academies director describes:
We’ve played around with putting English assessment objectives in [to the AI tool] and then an essay in, and [asking] ‘Can you mark it?’, and ‘Tell me what you think’. And it’s not bad, but I think that’s a bit where teachers are most reticent because [they] want to know that they’ve looked at the work.
Generally, leaders were clear that using AI should never result in taking the assessment role away from teachers. Most emphasised that AI should only be used as a supportive tool, with the teacher still the expert at either end of the process. Several also mentioned that it was important that teachers were transparent about when they had used AI for assessment purposes. This was so that parents and pupils were fully informed and could voice any opinions on its use.
Case study 4 – Using AI for feedback Learners at this FE college have been told they can use AI to mark their assignments after submitting them, to see what feedback it gives. However, leaders have told learners they need to be mindful that AI may not always produce an accurate response. The college is also aware that their learners may sometimes use AI to help write their assignments. Leaders felt greater transparency was needed around how learners had used AI . They suggested some areas may need to change feedback sheets so that the focus is on getting pupils to explain how they had used AI rather than confirming whether or not they had used it. There is also a tutorial program that runs across the college. It trains learners on appropriate use of AI and includes different ways they can use word prompts. This is considered an acceptable use of AI , as it helps to support the learning process. Leaders also described how learners can use AI to proofread or give feedback on how a piece of writing flowed.
What next for these providers
Several leaders we spoke to identified 2 aspects of their AI use that they had either not yet thought about or were just beginning to discuss. These were the pedagogical uses of AI , and how they evaluated the impact of AI .
As our study has already indicated, the first stage of AI adoption for these leaders focused on exploring the technology, before they decided how it might support specific learning outcomes and goals. They made sure teachers understood what AI was capable of and where it could potentially enhance learning and address specific challenges. They also prioritised safe, ethical and responsible use at this early exploratory stage.
Systematic thinking about integrating AI into curriculum and pedagogy was still at an early stage and was often a second stage of their journey. We found most leaders had not, as one MAT leader described, ‘thought systematically enough about how to support pedagogy through technology in this new way yet’. Leaders in 2 MATs described how their trusts were beginning to think about where AI could integrate with their own pedagogy. However, one barrier they highlighted was that available AI tools did not have a contextual understanding of their school or college and the needs of students. As this MAT chief transformation officer told us:
The OpenAI-type chatbots – ChatGPT, Gemini – do not know your curriculum. They do not know where your pupils are. They do not know what the misconceptions are typically for those topics.
This leader also explained how for MATs with a centralised curriculum and pedagogical principles, the benefit of open AI tools was still limited because:
It doesn’t have the background context, and particularly the sort of curriculum, content and pedagogical approaches of our trust, which is very well defined.
Evaluating the impact of AI
We also asked leaders how they evaluated the impact of AI and understood what successful use of AI looked like. Most relied on feedback from staff and students or tracked and monitored staff usage of AI tools, rather than collecting data that could be used to measure the impact of AI specifically on pupils. Leaders told us they used low-stakes quizzes and tests to assess the impact of AI on pupils’ ability to retain and recall knowledge. However, it is not always possible to evaluate the extent to which any impact was due to AI , rather than to any other factors such as pupils’ prior knowledge, or the teaching approach.
Leaders also used direct feedback from pupils to assess the impact of using AI . One school leader had begun to include questions about AI use in their termly pupil survey. This asked questions such as: Do you see the purpose of AI ? How are subjects using AI ? And do they see any impact from using AI ? Leaders also used staff surveys to understand the impact of AI on workload.
There is a lack of evidence about the impact of AI on educational outcomes or a clear understanding of what type of outcome to consider as evidence of successful AI adoption. Not knowing what to measure and/or what evidence to collect makes it hard to identify any direct impact of AI on outcomes. One school leader said that they had steered away from hard measurements of AI and its impact. They felt that this kind of accountability measure could potentially restrict staff from using and experimenting with AI . For the leaders we spoke to, success was linked to a coherent approach to introducing, using and embedding AI across the school, college or MAT . The impact of AI at the early stages of their AI journey was seen through the eyes of those using it. A positive impact was when they felt it was a useful tool and did what they wanted it to.
A few leaders who were using pupil-facing AI tools had conducted more formal evaluations of their impact on pupils. However, these collected qualitative data to understand the impact of AI on pupils’ metacognition, critical thinking and independent learning rather than measuring what pupils know, understand and can apply. As this primary school headteacher explained:
You can’t really measure how much it’s moved learning forward, but what you can do is measure some qualitative aspects of what it’s doing for the children in terms of their teaching and learning.
The lack of evaluation focusing on pupil outcomes could also be because many of these leaders were piloting and experimenting with AI as a tool to reduce teacher workload. For these early adopters, there is still some way to go if AI is to achieve its full potential and go beyond the hype and hyperbole. As one MAT chief executive officer noted:
I don’t want people to go away with the idea that we’ve got AI nailed and everybody’s using it as a tool to change, because in fact what I’ve learned over 30 years of using tech in education is there’s bandwagons… I don’t think AI is like that, but I think there’s a lot of hype around it and a lot of misunderstanding or myth and rumour about it.
Conclusion
Educators and policymakers have talked about the potential of all forms of digital EdTech to revolutionise education for at least 25 years, and UK governments and schools have invested heavily in software and internet connectivity.[footnote 80] This study has highlighted the journeys that 21 providers have been on as early adopters of the most recent form of EdTech innovation, namely AI . It provides information on the systems they have established and the barriers they have overcome to use AI in what they believe are safe and secure ways, while also being innovative and flexible in meeting the needs of staff and pupils alike.
Our study also indicates that these journeys are far from complete. The leaders we spoke to are aware that developing an overarching strategy for AI and providing effective means for evaluating the impact of AI are still works in progress. The findings show how leaders have built and developed their use of AI . However, they also highlight gaps in knowledge that may act as barriers to an effective, safe or responsible use of AI .
More research and evaluation of AI in education is required, specifically on what works effectively to achieve gains in knowledge and influence pupil outcomes. Many of the concerns around AI , particularly views about its impact on education, and potential threat to teachers’ professionalism and pupils’ knowledge, are not new. They have been raised in relation to EdTech more widely. Even 15 years ago, some believed EdTech had the potential to transform learning, and others felt there was a need for greater scrutiny of its ability to improve pupil outcomes.[footnote 81] However, some of the specific aspects of AI , such as its ability to predict and hallucinate, and the safeguarding issues it raises, create an urgent need to assess whether intended benefits outweigh any potential risks.
The findings from this research have also been helpful to inform Ofsted’s own position on AI during inspection. The use of AI is not a stand-alone part of our inspection and regulation practice, and inspectors do not directly evaluate the use of AI , nor any specific AI tools. However, inspectors can consider how AI is used across the provider and its impact on the outcomes and experiences of pupils and learners. They should expect that pupils or staff members may be using AI in connection with the education or care they receive or provide (for example to help pupils complete homework). There is no specific expectation that schools and FE colleges will use AI . However, the government is keen that they adopt and embrace AI as set out in its AI Opportunities Plan.
The findings from this research will also inform the inspector training we aim to develop this summer. It will help us make sure that inspectors can record the impact of AI , how this is monitored, and the checks and balances leaders have put in place to ensure AI is used ethically and safely.
We are aware that the experiences shared by these early adopter schools and colleges do not reflect how the wider sector is using AI . More research is needed to better understand how schools and FE colleges who are earlier on in their journey of AI adoption are using it, and the implications for our inspection and regulation practice. We also want to reassure ourselves that, when schools and FE colleges do use AI , it is in the best interests of children and learners. We want to enable AI innovation in the sector, and this small-scale study has been an important first step in that direction.
Appendix A: overview of participants
Provider name Provider type Age range Start of AI journey Participants (job title) College 1 FE college 16 to 19+ Nov 2023 Deputy Principal, Head of Digital Learning College 2 FE college 16 to 19 Feb 2023 Head of Teaching, Learning and Digital College 3 FE college 16 to 19 Jan 2023 Vice Principal, Deputy Chief Executive, Lead Practitioner: eLearning College 4 FE college 14 to 19 Nov 2022 Principal, Director of Digital Transformation Independent school 1 Independent school 13 to 18 Sep 2023 Head of Digital Teaching and Learning Independent school 2 Independent school 4 to 18 Nov 2022 Assistant Head – Staff Development, Head of IT Independent school 3 Independent prep school 4 to 11 Nov 2022 Deputy Head – Innovation and Partnerships MAT 1 Multi-academy trust N/A Feb 2023 Digital Lead, CEO MAT 2 Multi-academy trust N/A Mar 2024 Chief Transformation Officer MAT 3 Multi-academy trust N/A 2015 Chief Information Officer, Academies Director MAT 4 Multi-academy trust N/A Jun 2023 Project Manager, EdTech and AI Lead, Director: Curriculum and Assessment, Digital Lead, Director of Teaching and Learning, ICT Infrastructure Architect Pilot 1 Academy converter 11 to 18 Sep 2023 Headteacher, Assistant Headteacher Pilot 2 FE and HE college group 16+ Sep 2023 Deputy Head of Digital Innovation, Change and Transformation Manager School 1 University technical college 14 to 19 Academic year 2022/23 Head of Social Sciences, Principal, Assistant Principal/Designated Safeguarding Lead School 2 Pupil referral unit 11 to 18 Summer 2023 Head of school: remote and outreach, Head of School Curriculum School 3 Academy sponsor-led – multi-academy trust 11 to 18 Summer 2023 Principal School 4 Voluntary-aided school 4 to 11 Summer 2023 Digital Leader, Headteacher, Upper Key Stage 2 Phase Leader School 5 Community school 3 to 11 Nov 2023 Assistant headteacher, Year 5 teacher, Headteacher School 6 Academy sponsor-led – multi-academy trust 2 to 11 Summer 2023 Headteacher and Designated Safeguarding Lead, Year 3 teacher School 7 Community school 3 to 11 Summer 2024 Headteacher School 8 Academy converter – multi-academy trust 2 to 11 2023 MAT CEO and Executive Headteacher, English Hub and Professional Development Lead
Appendix B: detailed research methods
This was a small scale in-depth qualitative research project commissioned by the DfE . The aim was to understand how and why early adopter schools and FE colleges had embedded AI . There was no intention to assess the impact of AI or evaluate the quality of AI tools they were using. The aim was to understand the journeys these schools and FE colleges have been on and the practice they have developed, and to share this with other schools and FE colleges who may have an interest in adopting AI .
Our high-level research question was:
How are schools and FE colleges that are early adopters of AI using it to support teaching and learning as well as to manage administrative systems and processes?
Data collection
We collected evidence from:
a rapid evidence review of peer-reviewed academic literature – we prioritised recent systematic reviews and meta-analyses that included research on generative AI
a review of DfE publications relating to AI , including policy statements, surveys, research and guidelines
publications relating to , including policy statements, surveys, research and guidelines discussions with international inspectorates about their approaches to inspecting AI ; where possible, we also reviewed their publications relating to AI
; where possible, we also reviewed their publications relating to 21 online interviews, including 2 pilot interviews, with leaders or those with specific responsibility for developing and implementing AI from a purposive sample of schools, FE colleges and trusts who have already adopted AI
The data we collected from the review of publications and discussions with international inspectorates informed the questions we asked during the interviews.
The interviews were carried out by 2 of His Majesty’s Inspectors and 1 Ofsted inspector between January and February 2025. They were semi-structured and conducted online. We asked providers for a maximum of 3 people to join the interview. We carried out 2 pilot interviews to check the validity of our interview questions. The data collected from the pilots is included in our final analysis, as there was little change between the questions asked on the pilots and in the remaining interviews. Each interview was 90 minutes long and broadly covered 3 main topics:
strategic leadership and oversight
governance and safe and ethical use of AI
how AI is used by teachers and pupils/learners
We carried out the research in line with Ofsted’s research ethics policy and it was approved by our research ethics committee.[footnote 82] All participants gave us their full consent to be involved in the research. We used AI to produce a structure and early first draft of the literature review based on the project research questions and the literature identified by researchers working on the project. The AI draft was rewritten and edited by humans. External experts reviewed the final report and provided feedback on the validity of our findings and the literature review.
Sample selection
The aim of the online interviews was to illustrate what some leaders had put in place to support AI adoption and use. This was so we could identify common strategies that appeared to be effective, and which could be used in similar contexts. We therefore selected a purposive sample of settings to invite to interview.
We have defined early adopters as MATs , schools or FE colleges where leaders have been supporting and embedding the use of generative AI by staff for at least 12 months. In these settings, AI is used by staff to enhance teaching and learning and streamline administrative processes and procedures. For most of these schools and colleges, their AI journey began a few months after the launch of ChatGPT when leaders saw early on the potential for generative AI to reduce teacher workload and/or support pupil learning.
We carried out a range of validation checks to make sure these settings were early adopters of AI . We sourced participants from DfE recommendations, our own regional intelligence, research team contacts, recommendations from leaders we interviewed, and the AI in education website. We further validated participants by looking at what they had published about AI on their website and asking contextual questions in the email we sent inviting leaders to take part in the project. The final selection was also determined by whether leaders were available and/or willing to be part of the research.
As our focus was on schools and FE colleges that had already adopted AI , we were not concerned with identifying a nationally representative sample. However, we did want a varied mix of provider types within the purposive sample selected. This was to help identify any similarities or differences among provider types in the way they were adopting generative AI and to highlight any innovative practice to share with the sector. We included independent schools in the sample because we felt that their access to more resources might mean they are further ahead in their AI journey than government-funded schools. The final sample included 5 primary schools, 3 secondary schools, 1 pupil referral unit, 5 MATs /college groups, 4 FE colleges and 3 independent schools.
Data analysis
We received participants’ consent to use Microsoft Teams to record the video and audio from the interview. Microsoft Teams was also used to transcribe the audio. We analysed the data using a thematic approach and coded data using MaxQDA. The project lead developed an initial coding framework using the themes identified in the literature as well as additional themes developed during phase 1 of the research. New codes were then added based on the interview data. The data was coded by 2 researchers, and a sample of the coded entries was checked by the project lead using the coding framework. Any proposed changes to the framework were discussed and agreed by researchers before being added to it.
Limitations
Our study has limitations in that we spoke with a purposive sample of mostly senior leaders about AI use. This gives a top-down view of the intended implementation of AI and how well they think their school or college has managed this. The decision to interview leaders and not include other staff members and pupils was determined by the nature of the research as well as time and available resource. This was a small-scale, fast-paced exploratory research project. Gathering a range of views from pupils, learners, teachers and other staff members would provide additional perspectives on what is and is not working. It would also help us understand how to implement AI more effectively and assess its impact.
| 2025-06-27T00:00:00 |
https://www.gov.uk/government/publications/ai-in-schools-and-further-education-findings-from-early-adopters/the-biggest-risk-is-doing-nothing-insights-from-early-adopters-of-artificial-intelligence-in-schools-and-further-education-colleges
|
[
{
"date": "2025/06/27",
"position": 11,
"query": "AI education"
}
] |
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|
AI Solutions
|
AI Solutions
|
https://www.cdw.com
|
[] |
CDW accelerates time to value for AI projects with full-stack solutions that allow you to move fast, drive ROI and gain a competitive edge.
|
CDW can help with data governance, data quality and data security. We make AI sustainable, streamline the deployment of new use cases and enable your data to drive meaningful results.
Is Your Data Aligned with Your Project Goal?
CDW identifies and deploys the right SaaS applications to power your AI strategy, help you take advantage of the functionality within your existing AI platform or develop custom applications tailored to your organization.
Which App Will Facilitate It?
What Infrastructure Are You Going to Use?
CDW helps you deploy the right infrastructure for running AI workloads, whether in public or private clouds, on-premises or in hybrid environments.
| 2025-06-27T00:00:00 |
https://www.cdw.com/content/cdw/en/solutions/artificial-intelligence-ai.html
|
[
{
"date": "2025/06/27",
"position": 37,
"query": "AI employers"
},
{
"date": "2025/06/27",
"position": 83,
"query": "workplace AI adoption"
}
] |
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|
10+ Top AI Development Companies [2025]
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10+ Top AI Development Companies [2025]
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https://brainhub.eu
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Save hours with the research-based, non-sponsored ranking of top AI development companies. It gathers 11 top vendors that provide best AI development ...
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Top AI development companies - shortlist
These top AI development companies are sorted by their ability to develop software with agility and achieve immediate impact on projects, as this is the primary factor to consider when selecting a business.
A top AI software development company stands out for its innovation in AI technology and a proven record of delivering impactful solutions, backed by a team of experts in AI and related fields. This should be combined with ability to iterate swiftly and thorough testing processes.
Top 10 AI development companies - details
Analyze the details about each AI development company from this ranking – services, skills, talents on board, and the juice from reviews.
Brainhub delivers custom software solutions for scale-ups, enterprises, and companies looking to gain a technological advantage in the market. With a focus on software delivery excellence, the company combines technical expertise with efficient processes to meet diverse client needs.
The team emphasizes fast iterations and thorough testing to ensure high-quality outcomes. Their approach is designed to maintain control over scope, budget, timelines, and risks, enabling predictable and effective project delivery.
Brainhub has extensive experience in web application development, creating software that aligns with modern user expectations while helping businesses achieve measurable ROI. Their ability to deliver end-to-end solutions supports clients in scaling operations and driving growth.
The company works with well-known organizations, including "Big 4" firms, National Geographic, and Beam. Their core delivery principles - continuous delivery, business-focused decisions, and structured project governance - help clients achieve results that align with business objectives.
With a Clutch rating of 4.9/5, Brainhub is recognized for its reliable delivery processes and technical capabilities. They partner with clients to develop software that supports long-term business success.
STANDS OUT FOR: tech expertise, effective communication and project management, solution-oriented approach and flexibility
REWARDS: FT 1000 Europe’s Fastest-Growing Companies, Rising Star Awards from Deloitte: 50 Technology Fast Central Europe, Top .NET Developers 2023, Top 100 Sustained Growth Companies, Top 1000 Global Companies by Clutch, Top Software Developers 2023 by Clutch, Top Firm Custom Software Development 50pros
SERVICES: custom software development services, AI development services, web application development, mobile development, desktop app development, cloud consulting, staff augmentation, digital transformation, legacy modernization, UX/UI design
TALENTS: software engineers, QA engineers, solution architects, business analysts, UX/UI designers, project managers
TECHNOLOGIES: JavaScript, TypeScript, .NET, Node.js, React.js, React Native, NestJS, Electron.js, Python
SKILLS: GraphQL, Kubernetes, AWS, Azure, GCP, DevOps, workshops, hybrid mobile app development, iOS app development, Android app development, PWAs, CI/CD, automated tests, BDD, TDD, information architecture, visual design, backend engineering, API development, serverless architecture, testing, UX design, user interface design, UX audit, UX workshops, architecture development, MVP, prototyping, product scaling, AI Development, Generative AI
INTERNATIONAL CLIENTS: National Geographic, PwC, Cubotoo, Go Kong, Rulrr, National Geographic, Paradox Interactive, Beam, Jackbox Games
CLUTCH REVIEWS: 4.9 stars on Clutch from 47 reviews
APPRECIATED FOR: tech expertise, top code quality, project management and delivery, transparency, business-oriented approach, timeline and budget control, flexibility, communication skills, innovative approach
CULTURE & VALUES: Brainhub values initiative and accountability, encouraging team members to actively participate in the entire business process. The company emphasizes effective communication as a cornerstone for addressing challenges and achieving results. They prioritize informed decision-making, collaboration, and continuous improvement, fostering a culture of knowledge sharing and growth.
METHODOLOGY: Agile and Prince2Agile methodologies
COMPANY SIZE: 100+
LOCATION: Poland
INDUSTRY EXPERTISE: FinTech, MedTech, e-commerce, digital banking, accounting, tax consulting
<span class="colorbox1" fs-test-element="box1"><p>Contact Brainhub and consult your project</p></span>
<h3 id="stx">STX Next</h3>
STX Next is a global IT consulting firm delivering AI-powered software solutions. With over 19 years of experience, the company offers a comprehensive range of services, including AI and data solutions, cloud consulting, product design, and technology consulting.
The team at STX Next emphasizes transforming vast datasets into actionable insights through AI, enabling businesses to analyze large datasets, detect patterns, and make informed decisions. Their cloud strategies aim to optimize operations by reducing IT costs and increasing efficiency, keeping clients ahead in the competitive landscape.
STX Next has collaborated with renowned organizations such as Canon, Decathlon, Unity, and Mastercard, delivering scalable and reliable software solutions. Their core delivery principles include continuous delivery, business-focused decision-making, and structured project governance, ensuring that projects align with clients' strategic objectives.
With a Clutch rating of 4.7/5 from 93 reviews, STX Next is recognized for its effective project management, timely delivery, and strong communication skills. Clients appreciate their commitment to quality and ability to handle complex challenges efficiently.
STANDS OUT FOR: AI expertise, cloud solutions, product design, technology consulting
SERVICES: AI and data solutions, cloud consulting, product design, technology consulting
TALENTS: AI specialists, cloud engineers, product designers, technology consultants
TECHNOLOGIES: Python, Node.js, .NET, React, Angular, Vue.js
SKILLS: Machine learning, data engineering, cloud strategy, product design, technology consulting
INTERNATIONAL CLIENTS: Canon, Decathlon, Unity, Mastercard
CLUTCH REVIEWS: 4.7 stars on Clutch from 93 reviews
APPRECIATED FOR: effective project management, timely delivery, strong communication skills, commitment to quality
CULTURE & VALUES: STX Next fosters a culture of continuous improvement and knowledge sharing. The company values effective communication and informed decision-making, encouraging team members to participate in the entire business process actively. This collaborative environment prioritizes client needs, leading to successful project outcomes.
METHODOLOGY: Agile methodologies
COMPANY SIZE: 500+ professionals
LOCATION: Poland, Mexico
INDUSTRY EXPERTISE: AdTech, EdTech, FinTech, eCommerce, Transport & Logistics
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<h3 id="10clouds">10Clouds</h3>
Founded in 2009, 10Clouds is a software development firm specializing in FinTech and Blockchain solutions. They bring deep expertise across the entire product development lifecycle, along with AI services and staff augmentation. Over the years, they’ve racked up some impressive accolades, including being named one of Europe’s Fastest Growing Companies by the Financial Times, making Deloitte’s list of the 50 Fastest Growing Companies in Central and Eastern Europe, and earning a spot among Clutch’s top 100 companies worldwide.
SERVICES: web development, mobile development, web design, product design, UX design, DevOps services, MLOps services, blockchain
TECH STACK: React.js, Angular, React Native, Node.js, Vue.js, Swift, Flutter, Kotlin, Python, Elixir
TALENTS: developers, designers, QA specialists, product delivery managers
SKILLS: blockchain, machine learning, DevOps, MLOps, design, mobile apps, web apps
PORTFOLIO: Asmodee, StepStone, Baidu, TrustStamp, Forbes, Skedulo, Swile, Coinquista
REVIEWS: 4.9 stars on Clutch from 57 reviews
WHAT CLIENTS APPRECIATE: transparency, cost-consciousness, engagement, dedication, effective workflow and project management, code quality, flexibility
CULTURE & VALUES: feedback, humanism, progress, celebrating achievements, care, appreciation
METHODOLOGY: Agile methodology
COMPANY SIZE: 200+
LOCATION: Poland, Warsaw (HQ), Poznan, Wroclaw
INDUSTRIES THEY'RE ESPECIALLY EXPERIENCES WITH: FinTech, banking, EduTech, HealthTech, IT, business services
<h3 id="10pearls">10Pearls</h3>
10Pearls is a software development firm that covers everything from product development and technology acceleration to product design. They specialize in working with enterprise and midmarket clients, guiding them through every stage - from strategy and software development to digital acceleration. Their work has earned recognition from Clutch, Gartner, and Forrester, and in 2022, the Financial Times named them one of the fastest-growing businesses in the country.
SERVICES: software development, web application development, enterprise mobility, digital transformation, UX and UI design, QA services Cloud & DevOps solutions
TECH STACK: JavaScript, Node.js, React Native, .NET, C#, Java, R, Objective-C, PHP, Angular, React, Meteor, Xamarin, MongoDB, MySQL, Python, Golang, Swift,
CLIENTS: AmWell, Docker, Hobsons, HughesNet, Johnson & Johnson, PayPal, CocaCola, Intuit, Stripe, AARP, Telmate, Homeland Security, WeatherBug, Homeland Security, Zubie
TEAM: software developers, QA engineers, DevOps engineers, data scientists, UX/UI designers, graphic designers, project managers, business analysts, delivery managers
REVIEWS: 4.9 stars on Clutch from 32 reviews
SKILLS: Artificial Intelligence, IoT, chatbots, AR/VR, blockchain, metaverse, voice & language processing, data & analytics, continuous security, QA, DevOps, CloudOps, AWS, Azure, GCP, containerization, market research, Docker, cybersecurity, data science, UX design, UI design, prototyping, wireframing, customer experience
WHAT CUSTOMERS VALUE: adaptability, being attentive to the demands of the client, committment, adhering to a schedule, transparency, providing all-around assistance
CULTURE & VALUES: being human-centered, employee development, learning, growth, altruistic intentions, charity
METHODOLOGY: Agile methodology
COMPANY SIZE: 1500+
LOCATION: USA (Washington, New York), UK (London), Costa Rica (San Jose), Colombia (Medellin), UAE (Dubai), Peru (Lima), Pakistan (Islamabad, Karachi, Lahore),
FOUNDED IN: 2004
PRICE RANGE: $25 - $49 / hr
INDUSTRIES THEY’RE EXPERIENCED WITH: fintech, energy & natural resources, medtech, education, telecommunications
<h3 id="miquido">Miquido</h3>
Founded in 2011, Miquido has made a name for itself in software development, especially in mobile app development. Their rapid growth earned them a spot on Deloitte Technology Fast 50 as the fastest-growing mobile development company in Central and Eastern Europe. They’re also a Google Certified Agency and have been featured in top publications like Time and Forbes. On top of that, they’ve been recognized as one of the leading mobile app development companies in the UK.
SERVICES: product design, web development, mobile development, Artificial Intelligence, product strategy, digital transformation, legacy modernization, Proof of Concept, innovation research, UX/UI design, UX workshops, UX audit, Cloud deployment
SKILLS: native mobile app development, cross-platform mobile app development, Artificial Intelligence, Machine Learning, Data Science, Computer Vision, AWS, Google Cloud Platform, cloud app development, cloud deployment, UX design, UI design, Business Intelligence, product strategy, workshop, NLP, market research, prototyping, PoC
TECH STACK: JavaScript, Angular, React, Node.js, Golang, Java, Flutter, Swift, Kotlin, TypeScript, Kubernetes
TALENTS: designers, developers, project managers
PORTFOLIO: Skyscanner, Abbey Road Studios, BNP Paribas, Santander Bank, Herbalife, Play, TUI, Empik, Onkyo Music, HelloFresh, Pando, Klassik Radio, HID, Nestle, Aviva, AXA, SBAB, Nextbank
REVIEWS: 4.8 stars on Clutch from 36 reviews
WHAT CLIENTS APPRECIATE: reliability, flexibility, consistency, quick turnaround time, ability to onboard rapidly, versatile range of skills, transparency, communication skills, commitment
CULTURE & VALUES: transparency, honesty, business value, putting people first, personal growth, competence development, satisfaction.
METHODOLOGY: Agile methodology
COMPANY SIZE: 200+
LOCATION: Poland, Cracow
INDUSTRIES THEY'RE ESPECIALLY EXPERIENCES WITH: FinTech (banking, insurance), e-commerce, MedTech, entertainment.
<h3 id="geniusee">Geniusee</h3>
Geniusee, a software development company founded in 2017 in Ukraine, specializes in catering to startups, small and mid-sized organizations, with a particular emphasis on EdTech and FinTech product development. They take pride in their proficiency across 64 different technologies and highlight a portfolio of more than 100 accomplished projects. Geniusee has gained recognition as a prominent provider of Android app development services by GoodFirms and has been featured among the top developers from Ukraine according to Clutch.
SERVICES: mobile app development, web application development, UX/UI design, website support, DevOps support
TECH STACK: JavaScript, React, Vue, Node.js, React Native, Swift, Kotlin, Python, .NET, Java, PHP,
TALENTS: software developers, frontend developers, backend developers, DevOps engineers, QA engineers, project managers, business analysts, UX designers, UI designers
SKILLS: mobile apps, native mobile app development, hybrid mobile app development, DevOps, AWS, business analysis, QA testing, QC testing, blockchain, serverless architecture, Data Science, Artificial Intelligence, Machine Learning, DevOps engineering, discovery phase, product maintenance, legacy reengineering, POC, MVP development
REVIEWS: 5.0 stars on Clutch from 36 reviews
CLIENTS: Zytara, Chegg Money, FactMata, Scout and Drum Technologies Inc., Dell, DataRobot, QuitGenius, Vrazo, RealmFive, CraveRetail, Swoon Editions, Kumulus Technologies
WHAT CUSTOMERS VALUE: understanding the expectations, collaborating smoothly, comprehending the project requirements, communication skills, transparency, involvement
METHODOLOGY: Agile methodology
COMPANY SIZE: 150+
LOCATION: Ukraine (Kyiv, HQ)
FOUNDED IN: 2017
PRICE RANGE: $25 - $49 / hr
INDUSTRIES THEY'RE EXPERIENCED WITH: financial services, EdTech, retail, automotive, real estate, transportation, tourism
CULTURE & VALUES: openness, transparency, collaboration, commitment, responsibility, offering support, delivering on schedule.
<h3 id="neoteric">Neoteric</h3>
Neoteric is a software development agency established in 2005, with a strong emphasis on Artificial Intelligence development. Their key principles for successful remote software development revolve around effective communication, collaboration, and transparency. Design Rush has recognized them as one of the top technology experts to hire in 2021, while Clutch has acknowledged them as one of the leading Artificial Intelligence companies and top web developers in the same year.
SERVICES: web development, product design, AI development
TECH STACK: JavaScript, Angular, React, Node.js, Nest.js, AWS, CD/CI, TypeScript
TEAM: software engineers, UX/UI designers, QA engineers, scrum masters, data scientists, product owners, PO proxies
SKILLS: Artificial Intelligence, Machine Learning, workshops, PWA, design strategy, UX/UI design, product design workshop, Rappid, Design Sprint, product strategy, wireframing, prototyping, user research, API development, digital transformation, PoC, predictive analytics, NLP, scoring models
PORTFOLIO: Siemens, Nestle, The World Bank, Liveramp, Appoint.ly, Jeppesen, RapidSOS, Crowdstrike, Approchid, Nanoramic, Client.io, Egain, AlchemAI
REVIEWS: 4.9 stars on Clutch from 38 reviews
WHAT CLIENTS APPRECIATE: professionalism, communication skills, proactive attitude, transparency, accountability, engagement, taking ownership, being highly responsive, being highly receptive to feedback, flexibility towards client’s needs, punctuality, responsibility, strong management skills, being keen on adapting to timeline changes
CULTURE & VALUES: One of the company’s core values is ownership and taking responsibility for one’s goals. They treat one another as family members, value the freedom of employees to choose the best solutions, and responsibility which naturally arises on that. The team treasures personal and professional growth and notices the importance of supporting one another.
METHODOLOGY: Agile methodology
COMPANY SIZE: 120
LOCATION: Poland, Gdańsk
INDUSTRIES THEY’RE ESPECIALLY EXPERIENCES WITH: business, IT, supply chain, logistics, transport, telecommunications, fitness, e-commerce, healthcare, real estate, financial services, education
<h3 id="spro">S-Pro </h3>
S-Pro specializes in offering dedicated teams with exceptional subject matter expertise, focusing primarily on fintech. Their services include consulting and software engineering, catering to startups, banks, and various financial institutions. The company frequently collaborates with accelerators such as BCCS Cluster, Sente.Link, and Founder Institute to facilitate the growth of their partners. In 2021, S-Pro received recognition from Clutch as one of the top Ukrainian software development companies and one of the Global Top 1000 Firms. Additionally, The Manifest featured S-Pro as one of the highly reviewed blockchain companies in Ukraine.
SERVICES: software development, web application development, mobile development, design
KEY CLIENTS: MeterQubes, Crypto Wallet, Refundmatic
TECHNOLOGIES: JavaScript, React, Angular, Node.js, Python, Java, Golang, PHP, Laravel, React Native, Flutter, Swift, Kotlin
TALENTS ON BOARD: software developers, tech leaders, QA engineers, UX/UI designers, DevOps engineers, project managers, business analysts
REVIEWS: 4.9 stars on Clutch from 32 reviews
SKILLS: blockchain, API development, Big Data, cloud, DevOps, branding, Artificial Intelligence, Machine Learning, UX design, UI design, PoC, infrastructure engineering, architecture, MVP, product migration strategy, quality assurance, marketplaces, trading bots, Firebase
WHAT CUSTOMERS VALUE: exemplary delivery skills, cordial yet professional communication, responsiveness, being a reliable partner, being proactive, excellent project management, engagement, and flexibility.
METHODOLOGY: Agile methodology
COMPANY SIZE: 250+
LOCATION: Ukraine (Kyiv)
FOUNDED IN: 2014
PRICE RANGE: $50 - $99 / hr
INDUSTRIES THEY’RE ESPECIALLY EXPERIENCED WITH: fintech, healthcare, logistics and transport, retail, real estate
CULTURE & VALUES: The group lays a strong emphasis on ongoing development and a thirst for knowledge. They take pride in their approach being product-focused. They place a great importance on creativity and high-quality work.
<h3 id="dataart">DataArt</h3>
DataArt is an established software engineering and artificial intelligence development company with a rich history spanning more than two decades. They specialize in fintech product development and consulting services.
DataArt excels in digital transformation and modernizing legacy systems. They possess extensive expertise in cybersecurity and using machine learning models. Due to their expansive team and global presence, DataArt maintains offices and teams across multiple continents and time zones. This broad distribution contributes to their wide range of skills and capabilities.
RANGE OF SERVICES: digital transformation and system modernization, legacy re-engineering, data management and analytics, insurance software development services, cyber security services
TOP FINTECH CLIENTS: Monex Europe, Nasdaq, Bematech
THEY WORKED WITH: enterprises, medium-sized companies
TECHNOLOGIES: .NET, Angular.js, React.js, Node.js, Ruby on Rails, Oracle, Spring MVC
TALENTS ON BOARD: software engineers, solution architects, Cloud engineers, Data engineers, DevOps engineers, QA engineers, business analysts, product owners, project managers, scrum masters, delivery managers, UX/UI designers, product designers
REVIEWS: 5.0 stars on Clutch from 12 reviews
SKILLS: Big Data, business intelligence, AR/VR, business analysis, usability, cloud security audit, secure code review, penetration testing, security assurance and consulting, cloud computing, cloud-native development, cloud migration, customer experience, user experience, Artificial Intelligence, Machine Learning
WHAT CLIENTS APPRECIATE: grasping business needs quickly, professionalism, ability to deliver, strong work ethic, strong communication skills, understanding how business works
METHODOLOGY: Agile methodology
COMPANY SIZE: 5500+
LOCATION: USA (New York, Dallas, Orlando, Pittsfield), UK (London), Poland (Cracow, Lublin, Wroclaw), Germany (Munich), Switzerland (Zug), Argentina (Buenos Aires)
FOUNDED IN: 1997
PRICE RANGE: $50 - $99 / hr
CULTURE & VALUES: They call themselves engineers with open hearts, driven by the people-first principle. They integrate human values into their business and work relations: curiosity, empathy, trust, honesty, and intuition. The team strives to integrate those values with engineering excellence. They treasure flexibility and variety, cherish empowerment and education over bureaucracy, and highly value trust.
<h3 id="eteam">eTeam</h3>
eTeam is a Ukrainian firm that specializes in the development of web and mobile applications. Founded in 2016, their range of services includes managed teams and staff augmentation. They primarily cater to startups and small businesses as their client base. eTeam achieved global recognition by being listed as one of the Top 1000 Companies by Clutch.
SERVICES: web app development, mobile app development, UX/UI design
SKILLS: developers, designers, QA engineers, project managers, DevOps engineers, React, React Native, Node.js, Golang, TypeScript, Ruby, Kotlin, Swift, MongoDB, Angular.js, Express, AWS, Kubernetes, Docker, Jenkins, Google Cloud, Azure, data science, data engineering, data analysis, Machine Learning, Cybersecurity, DevOps, project management, MVP
PORTFOLIO: VanillaDirect Pay, InComm, Bluesnap, Genie, Taksware, Elegant Open Banking, Armatic, Roost, Hirebook
REVIEWS: 4.9 stars on Clutch from 25 reviews
WHAT CLIENTS APPRECIATE: high standards of work and high quality, transparency, outstanding project management, flexibility, top-notch coding skills, diversity of the team, engagement, responsive and skilled approach, adjusting when issues arise, independence, understanding the business needs
CULTURE & VALUES: They care about growth and career development, as well as establishing a great work environment for their employees. They care about bringing more value to the table, and sharing their experience beyond just making apps.
METHODOLOGY: Agile methodology
COMPANY SIZE: 120+
LOCATION: Ukraine (Kyiv), Mexico (Guadalajara), USA (Aliso Viejo, CA)
INDUSTRIES THEY'RE ESPECIALLY EXPERIENCES WITH: fintech, telecommunications, banking, IT services, retail, edtech, fitness, health, recruitment
<h3 id="geekyants"GeekyAnts</h3>
GeekyAnts is a design and development company founded in 2006 that provides dedicated teams and extensive services for product development. They specialize in web and mobile development. One of their notable achievements is the creation of the UI library NativeBase specifically for React Native. They hold official recognition as service providers by Google and have authored the official documentation for Flutter. As part of their efforts to support employment, they have developed a crash course aimed at programmers.
SERVICES: UX/UI design, API development, web app development, mobile app development, consulting
SKILLS: React.js, Angular, Flutter, Svelte, Golang, Node.js, React Native, Laravel, Python, Vue.js, TypeScript, Firebase, AWS, GraphQL, Next.js, Express, microservices, cross-platform apps, UX research, branding, UX design, UI design, DevOps, project management, business analysis, quality assurance, MVP
PORTFOLIO: Siveco Romania, ChildMind Institute, Tellius, PayPoint, QuinType, EndLink, Mobile Premier League, Lamno, Cloud9, Khatabook
REVIEWS: 4.8 stars on Clutch from 45 reviews
WHAT CLIENTS APPRECIATE: understanding the business objectives, high-quality work, seamless project management, commitment, proactivity, transparency, ownership, collaborative attitude, addressing feedback professionally and in a timely manner.
CULTURE & VALUES: One of the top company values is constant learning and sharing of experiences. Transparency and communicating things on time are the basis of their teamwork. They cherish constant feedback sessions and one-on-one meetings to maintain and open-door policy and face problems ahead of time. They also value the quality of their work and strive to perfection.
METHODOLOGY: Agile methodology
COMPANY SIZE: 300+
LOCATION: India (Bangalore), US (California), UK (London)
INDUSTRIES THEY’RE ESPECIALLY EXPERIENCES WITH: healthcare, finance, education, banking, gaming, manufacturing, real estate, e-commerce.
<h3 id="diceus">Diceus</h3>
Diceus is a software development company that focuses heavily on creating software solutions for fintech, banking, and insurance industries. They prioritize the use of cutting-edge technologies such as artificial intelligence, blockchain, big data, and cloud solutions to optimize the functionality and performance of their financial software products.
RANGE OF SERVICES: lending management software, blockchain solutions, regtech, payments software solutions, blockchain fintech app development, loan management software, money transfer app development, fintech application development, mobile banking apps, IT consulting, system integration, insurance software development, online banking, core banking systems, mobile banking
TOP FINTECH CLIENTS: Risk Point, Bank al Etihad, Teambase, BriteCore
THEY WORKED WITH: small and medium-sized companies, corporations
TECHNOLOGIES: React.js, Node.js, Vue.js, Angular.js, Golang, Flutter, Java, .NET, Python, PHP, Ruby, Objective.C
TALENTS ON BOARD: frontend developers, backend developers, designers, architects, QA and test engineers, business analysts, project managers
REVIEWS: 4.8 stars on Clutch from 40 reviews
SKILLS: DevOps, Artificial Intelligence, Machine Learning, blockchain, big data, cloud solutions, data science, cross-platform apps, payment gateway development, payment integration services, contactless payments and POS, real-time payments, Digital Ledger Technology, smart contracts, banking loan software solutions, fintech money lending software, loan module in core banking, business process automation, loan decisioning software, money transfer functionalities, CRM, mobile banking apps, payment applications, IoT, ERP, Robotic Process Automation, system integration, software architecture
WHAT CLIENTS APPRECIATE: responsiveness, professionalism, and organizational skills, result-oriented approach, effective communication, expertise, timely reporting, commitment and dedication
METHODOLOGY: Agile methodology
COMPANY SIZE: 100+
LOCATION: Ukraine (Kyiv), Denmark (Hellerup), Lithuania (Alytus), USA (New York City)
FOUNDED IN: 2011
PRICE RANGE: $50 - $99 / hr
CULTURE & VALUES: The company’s mission is to deliver high quality. They want to bring as many values as possible with the right technology and people. They combine expertise with a quality-driven delivery model.
How was this ranking created?
The companies listed in this ranking meet specific criteria to ensure their reputation and reliability:
They have received a significant number of positive client reviews, with a minimum of 30 reviews on platforms such as Clutch, and many of them have accumulated over 50 reviews in total.
These companies maintain an overall score of 4.8 or higher on Clutch, indicating consistently excellent client satisfaction.
No entirely negative reviews are found about these companies on Clutch, further attesting to their positive track record.
The companies featured in this ranking have been operating in the market for at least 5 years and possess extensive experience in software development, demonstrated through their robust portfolios.
They employ skilled teams capable of delivering scalable custom software solutions.
These companies exhibit high levels of engagement, proactive behavior, a sense of ownership, and a problem-solving focus based on their past projects.
They promote an Agile working culture, enabling flexibility and adaptability in their processes.
Offering cross-functional teams composed of diverse specialists, these companies are dedicated to improving their clients' business outcomes.
It's important to note that there are no sponsored sections in this ranking. These companies have been genuinely top-rated and have undergone thorough research and evaluation by real individuals, rather than relying solely on algorithms.
The information used for this ranking was derived from a comprehensive analysis of the companies' websites, rankings on various portals, reviews, testimonials, and the content they publish on blogs and social media.
FAQ about AI solution development services
What is an AI software company?
An AI software company is a company that specializes in developing and providing software solutions that leverage artificial intelligence (AI) technologies. These companies focus on designing, building, and deploying software applications that utilize machine learning, deep learning, natural language processing, computer vision, and other AI techniques. They develop AI algorithms, models, and systems to enable automated decision-making, predictive analytics, pattern recognition, and other intelligent capabilities in software applications. AI software companies cater to various industries and sectors, offering solutions for tasks such as automation, data analysis, recommendation systems, speech recognition, image processing, and more.
How to choose the best AI development company?
When choosing the best AI development company for your needs, consider the following factors:
Expertise and experience
Look for a company that has demonstrated expertise and experience in AI development. Evaluate their track record, portfolio of AI projects, and the technologies they specialize in.
Domain knowledge
Consider whether the company has experience in your industry, ai technology or domain. AI solutions can vary greatly across sectors, so finding a company that understands the specific challenges and requirements of your industry can be beneficial.
Skill set
Assess the company's skill set in relevant AI technologies such as machine learning, deep learning, natural language processing, computer vision, or robotics. Determine if they have the necessary expertise to develop the specific AI solutions you require.
Collaborative approach
Look for a company that adopts a collaborative approach to working with clients. They should actively involve you in the development process, understand your business goals, and offer solutions tailored to your needs.
Reputation and references
Research the company's reputation by checking client reviews, testimonials, and case studies. Reach out to their past clients for references to gain insights into their working style, professionalism, and project outcomes.
Data security and compliance
Ensure that the company has strong data security measures and is compliant with relevant regulations and privacy standards. This is especially important when dealing with sensitive data.
Scalability and support
Consider the company's ability to scale their AI solutions as your business grows. Additionally, assess the level of ongoing support they provide after the implementation of the AI solution.
Budget and cost
Evaluate the company's pricing structure and determine if it aligns with your budget. However, be cautious of companies that offer significantly lower prices but compromise on quality.
Communication and transparency
Effective communication and transparency are crucial for successful collaboration. Ensure that the company has clear communication channels, regular progress updates, and provides you with visibility into the development process.
Innovation and future-readiness
Assess the company's commitment to innovation and staying updated with the latest AI trends and technologies. Look for a company that demonstrates a forward-thinking mindset and is prepared for future business challenges and advancements in AI.
By considering these factors and conducting thorough research, you can identify the best AI development company that aligns with your specific requirements and can deliver high-quality AI solutions.
How is AI used in software development?
AI is transforming software development in several key ways. In short, AI is used in software development for enhancing efficiency, accuracy, and innovation. AI tools can automatically generate code snippets or boilerplate code. What's more, AI algorithms helps to detect bugs and vulnerabilities. AI can also analyze user interaction data to tailor software behavior and interfaces to individual users, improving user satisfaction and engagement. AI techniques are also used to enhance software security by identifying and mitigating potential threats and anomalies in real-time, making software systems more resilient to attacks.
| 2025-06-27T00:00:00 |
https://brainhub.eu/library/top-ai-development-companies
|
[
{
"date": "2025/06/27",
"position": 70,
"query": "AI employers"
}
] |
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|
Alphabet's AI Investments in 38 Companies in 2025
|
Alphabet's AI Investments in 38 Companies in 2025
|
https://research.aimultiple.com
|
[] |
Excluding Google's search efforts, Alphabet made investments into 38 AI companies such as Deep Mind, Nest, Waymo & startups in healthcare, finance, ...
|
After acquiring DeepMind, and spending more than $13 billion to build data centers Google became one of the leading players in the AI field Driven by its AI investments and search engine investments the tech giant reported a strong profit growth of 25+% in the second quarter of 2024, beating analysts’ expectations.
Alphabet
Alphabet is a conglomerate including Google which mainly consists of search, maps, YouTube, Chrome, Cloud, AdWords, AdSense, and the Android mobile phone platform.
Beyond Google, Alphabet consists mostly of moonshots and investments: Google X, Calico, Nest, Ventures, Fiber, and Capital.
To understand the current state of Alphabet’s AI strategy, one needs to look at:
Google, which we examined separately here since AI is baked into so many of Google’s products. Alphabet’s fully owned ventures outside Google, like Waymo or DeepMind Alphabet’s investments through Google Capital or Google Ventures
Focusing on 2&3, we structured these companies along the industries they serve. Most companies serve industries like Healthcare that are experiencing an AI revolution. Another large group of companies is active as AI enablers. Like Deepmind or API.ai they aim to build the building blocks of AI including systems, libraries, and APIs.
Google’s ventures are specifically focused on AI enablers, transport & logistics and healthcare.
Instead of diving into all of Alphabet’s bets in the field of AI, we will first focus on Deepmind, Waymo, and Nest, which are Alphabet’s leading investments leveraging AI.
DeepMind
DeepMind at the heart of Google operations
In April 2023, DeepMind merged with Google AI’s Google Brain division to become Google DeepMind, as part of the company’s ongoing attempts to accelerate AI research in response to OpenAI’s ChatGPT.
Google collaborates with DeepMind to solve some of its engineering challenges, like energy efficiency. By applying DeepMind’s ML to Google data centers, energy use for cooling was reduced by up to 40%. Their collaboration model is explained here.
DeepMind in chemistry
Google DeepMind’s AI researchers developed and used the neural network AI program AlphaFold to predict the intricate folding patterns of proteins, solving a five-decade scientific enigma. This computational technique for designing new proteins could fuel new boundaries of protein engineering by enabling scientists to create new proteins
AlphaFold is considered a “complete revolution” by the Nobel Committee, and it is being used to fold 200 million proteins globally.
DeepMind in digital health
DeepMind Health works with hospitals on mobile tools and AI research to help get patients from test to treatment as quickly and accurately as possible.
The company’s priorities are shaped by patients and clinicians, unlike top-down IT projects that can be costly and ineffective.
Nurses and doctors in that system and elsewhere simply don’t have the tools to instantly analyze each test result, determine the right treatment, and make sure that every single patient who needs complex or urgent care is escalated to the right specialist immediately.
MuZero, AlphaZero, and AlphaDev by DeepMind
DeepMind’s AI systems MuZero, AlphaZero, and AlphaDev approached the circuit as a neural network for accelerating chip design and improving performance.
DeepMind collaborated with YouTube to compress and transmit video, leveraging MuZero to improve the YouTube user experience by decreasing the bitrate by 4% while maintaining visual quality.
AlphaDev, a version of AlphaZero, made a novel breakthrough in computer science by optimizing the algorithm sorting process.
Sorting algorithms assist digital devices in processing and displaying information, including the ranking of online search results and social postings.
AlphaDev identified a method that improves performance for sorting sequences by 70% compared to the algorithms in the C++ library. This implies that results from user searches may be sorted significantly more quickly. When applied at scale, this saves enormous amounts of time and energy.
Artificial general intelligence with DeepMind
One of the crucial aspects of general intelligence is applying knowledge from one task to another.
DeepMind’s models solve advanced reasoning problems in mathematics: AlphaProof, a new reinforcement-learning-based system for formal math reasoning, and AlphaGeometry 2, an updated version of our geometry-solving system. These systems answered four of six problems from this year’s International Mathematical Olympiad (IMO), placing them on par with a silver medalist.
Waymo
Waymo is Google’s self-driving car project. It’s a large-scale AI model known as the Waymo Foundation Model, which underpins the vehicle’s capacity to sense its surroundings and make driving judgments.
Waymo continues to rev up autonomous driving with AI. In October 2024, Waymo co-CEO Tekedra Mawakana stated that the self-driving car business is providing 100,000 robo-taxi rides each week, doubling the 50,000 ride number in June.
CEO Dmitri Dolgov posted a video demonstrating how the software is continuously learning. The brief video shows how the Waymo Driver adapts its path as it travels through tunnels, storms, and narrow streets.
If you are interested in how ML-powered Google’s cars work, here are more geeky details from here.
Nest
Google Nest is a brand of smart AI-powered home appliances that includes smart speakers, smart displays. AI is baked into Nest. When you’re on your way home, Google will work with Nest to preheat or cool your home to your preferred temperature. Control your Nest with your voice from your tablet or phone app.
In 2024, Google announced 2 large smart home AI upgrades:
Natural language processing (NLP): Google claims that the revamped Nest now functions similarly to Amazon Alexa and Apple’s Siri, using natural language processing technology to respond to user inquiries.
Google claims that the revamped Nest now functions similarly to Amazon Alexa and Apple’s Siri, using natural language processing technology to respond to user inquiries. Gemini integration: Google is now integrating Gemini into the Google Home app for smart home Nest cameras, employing image recognition to provide descriptions of what the camera catches.
Nest’s built-in intelligent home appliances can help Google get into more houses, and allow users to get more involved in everyday life, some of which are carried out digitally each day.
Analysts are predicting that the Internet of Things will be a trillion-dollar market, which was one justification for Nest’s $ 3.2 billion price tag.
Bottom line
Google continues to innovate in AI, and we’ll keep track of their progress. To stay on the bleeding edge of AI, you can check out AI applications in marketing, sales, customer service, IT, data, or analytics.
You can also find our list of AI tools and services:
| 2025-06-27T00:00:00 |
https://research.aimultiple.com/alphabet-ai/
|
[
{
"date": "2025/06/27",
"position": 93,
"query": "AI employers"
}
] |
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|
As job losses loom, Anthropic launches program to track AI's ...
|
As job losses loom, Anthropic launches program to track AI’s economic fallout
|
https://techcrunch.com
|
[
"Rebecca Bellan",
"Senior Reporter",
"Marina Temkin",
"Anna Heim",
"Sarah Perez",
"Zack Whittaker",
"Maxwell Zeff",
"Lorenzo Franceschi-Bicchierai",
"--C-Author-Card-Image-Size Align-Items Center Display Flex Gap Var",
"Media"
] |
As warnings mount about AI's potential to displace millions of jobs, Anthropic on Friday launched a its Economic Futures Program, ...
|
Silicon Valley has opined on the promise of generative AI to forge new career paths and economic opportunities — like the newly coveted solo unicorn startup. Banks and analysts have touted AI’s potential to boost GDP. But those gains are unlikely to be distributed equally in the face of what many expect to be widespread AI-related job loss.
Amid this backdrop, Anthropic on Friday launched its Economic Futures Program, a new initiative to support research on AI’s impacts on the labor market and global economy and to develop policy proposals to prepare for the shift.
“Everybody’s asking questions about what are the economic impacts [of AI], both positive and negative,” Sarah Heck, head of policy programs and partnerships at Anthropic, told TechCrunch. “It’s really important to root these conversations in evidence and not have predetermined outcomes or views on what’s going to [happen].”
At least one prominent name has shared his views on the potential economic impact of AI: Anthropic’s CEO Dario Amodei. In May, Amodei predicted that AI could wipe out half of all entry-level white-collar jobs and spike unemployment to as high as 20% in the next one to five years.
When asked if one of the key goals of Anthropic’s Economic Futures Program was to research ways to mitigate AI-related job loss, Heck was cautious, noting that the disruptive shifts AI will bring could be “both good and bad.”
“I think the key goal is to figure out what is actually happening,” she said. “If there is job loss, then we should convene a collective group of thinkers to talk about mitigation. If there will be huge GDP expansion, great. We should also convene policy makers to figure out what to do with that. I don’t think any of this will be a monolith.”
The program builds on Anthropic’s existing Economic Index, launched in February, which open sources aggregated, anonymized data to analyze the effects of AI on labor markets and the economy over time — data that many of its competitors lock behind corporate walls.
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The program will focus on three main areas: providing grants to researchers investigating AI’s effect on labor, productivity, and value creation; creating forums to develop and evaluate policy proposals to prepare for AI’s economic impacts; and building datasets to track AI’s economic usage and impact.
Anthropic is kicking off the program with some action items.
The company has opened applications for its rapid grants of up to $50,000 for “empirical research on AI’s economic impacts,” as well as evidence-based policy proposals for Anthropic-hosted symposia events in Washington, D.C., and Europe in the fall. Anthropic is also seeking partnerships with independent research institutions and will provide partners with Claude API credits and other resources to support research.
For the grants, Heck noted that Anthropic is looking for individuals, academics, or teams that can come up with high-quality data in a short period of time.
“We want to be able to complete it within six months,” she said. “It doesn’t necessarily have to be peer-reviewed.”
For the symposia, Anthropic wants policy ideas from a wide variety of backgrounds and intellectual perspectives, said Heck. She noted that policy proposals would go “beyond labor.”
“We want to understand more about the transitions,” she said. “How do workflows happen in new ways? How are new jobs being created that nobody ever contemplated before? … How are certain skills remaining valuable while others are not?”
Heck said Anthropic also hopes to study the effects of AI on fiscal policy. For example, what happens if there’s a major shift in the way enterprises see value creation?
“We really want to open the aperture here on things that can be studied,” Heck said. “Labor is certainly one of them, but it’s a much broader swath.”
Anthropic rival OpenAI released its own Economic Blueprint in January, which focuses more on helping the public adopt AI tools, building robust AI infrastructure and establishing “AI economic zones” that streamline regulations to promote investment. While OpenAI’s Stargate project to build data centers across the U.S. in partnership with Oracle and SoftBank would create thousands of construction jobs, OpenAI doesn’t directly address AI-related job loss in its economic blueprint.
OpenAI’s blueprint does, however, outline frameworks where government could play a role in supply chain training pipelines, investing in AI literacy, supporting regional training programs, and scaling public university access to compute to foster local AI-literate workforces.
Anthropic’s economic impact program is part of a slow but growing shift among some tech companies to position themselves as part of the solution to the disruption they’re helping to create — whether out of reputational concern, genuine altruism, or a mix of both. For instance, on Thursday, ride-hail company Lyft launched a forum to gather input from human drivers as it starts integrating robotaxis into its platform.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://techcrunch.com/2025/06/27/as-job-losses-loom-anthropic-launches-program-to-track-ais-economic-fallout/
|
[
{
"date": "2025/06/27",
"position": 77,
"query": "AI job losses"
}
] |
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] |
2025 Summer School for Journalists and Media Practitioners
|
2025 Summer School for Journalists and Media Practitioners – Centre for Media Pluralism and Media Freedom
|
https://cmpf.eui.eu
|
[] |
Big tech companies have expanded their reach and diversified their offerings, including generative and general AI applications. This has enabled an entirely new ...
|
The Future of the Information Sphere and Journalism Intelligence Over the past decades, big technology companies have positioned themselves at the centre of our information systems, while challenging the very existence of traditional media and journalism and also increasingly aligning with political power. While these threats have also sparked creative resistance and some of the most remarkable journalism in recent years, the profession has simultaneously experienced a rapid decline, both in terms of a sustainable business model and its reach, particularly among young audiences who primarily access news through online platforms. Big tech companies have expanded their reach and diversified their offerings, including generative and general AI applications. This has enabled an entirely new ecosystem of communicators and opinion-influencers who are not journalists in the traditional sense but may play a similar or more persuasive role. In some cases, these voices disrupt the information sphere rather than enriching it. The 13th edition of the CMPF Summer School for Journalists and Media Practitioners (23-27 June 2025) will explore what these changes, along with the fact that private corporations with very limited liability control the infrastructure of the contemporary information sphere, mean for the future of journalism and a democratic information landscape. With renowned scholars and experts, we will examine opportunities for innovation and strategies to level the playing field between technology companies and journalism, ensuring the economic sustainability of the profession, particularly for investigative journalism. We will also discuss major regulatory interventions (at the EU and global levels) aimed at protecting and strengthening the integrity of the information space, enhancing the accountability and transparency of major online platforms, and safeguarding media freedom, with due consideration to geopolitical developments. A unique aspect of this year’s summer school will be a Practice Day, featuring a creative expression lab where journalists and artists can collaborate to imagine new, technology-assisted formats that may help revive the connection between journalism and its audience. The CMPF Summer School offers journalists and media practitioners an opportunity to learn, exchange ideas, and discuss key issues shaping journalism and informed citizenship. The programme will feature presentations by distinguished experts, followed by interactive sessions designed to enhance participants’ understanding, engagement, and networking.
Big Tech, AI, and journalism: practices within and beyond journalism
Mental well-being and safety of journalists
AI and media business: copyright, innovation, transformation, monetisation
EU Regulatory framework and global standards (Focus on the European Media Freedom Act, but also the Artificial Intelligence Act, Digital Services Act, Digital Markets Act, and Code of Practice on Disinformation)
Geopolitics of freedom of expression and freedom of the media
Key international developments in Internet governance and their potential impact on the future of journalism and the media sector
Creative resistance in journalism and by journalists, including strategies to protect journalists and media actors
Shared knowledge of trends affecting journalism, informed citizenship, and democracy;
Up-to-date overview of the technology and practice trends, legislative changes, and legal principles governing content online;
Strategies for the development of new business models in journalism;
Interactive session proceedings;
Facilitating networking among participants and between participants and speakers. A Certificate will be awarded to participants who successfully complete the training course.
The Summer School is open to early and mid-career journalists and other stakeholders in the news industry. Our aim is to gather participants from a variety of countries and backgrounds. The CMPF will select participants based on the information provided in the application form, assess the quality of applications, and take into account diversity criteria. The working language of the Summer School is English.
Fees Applicants who are not selected for a scholarship will automatically be considered for a self-funded place at the school. Scholarships are not available to participants who can be funded by their own institutions. Cost for self-funded participants: €200 (covering meals and refreshments at the venue). Scholarships We offer up to 31 scholarships within the following categories and conditions: CMPF Summer School Programme scholarships – 20 scholarships Available to applicants from countries participating in the Creative Europe programme, namely: The EU27;
Acceding countries, candidate countries and potential candidates participating in Creative Europe: Albania, Bosnia and Herzegovina, Georgia, Kosovo, Montenegro, North Macedonia, Serbia, Ukraine;
European Neighbourhood Policy countries participating in Creative Europe: Armenia, Tunisia;
EFTA countries which are part of the European Economic Area: Iceland, Norway, Lichtenstein. The CMPF scholarship covers travel expenses up to an established ceiling, accommodation for 5 nights, tuition fees, all course materials, access to the EUI library, Wi-Fi access at the EUI, social activities, lunches, and coffee breaks on lecture days. New! Scholarships for nationals of Turkey and Ukraine – 2 Available Funded by the Robert Schuman Centre, these scholarships are primarily intended for applicants from Turkey and Ukraine. Each scholarship provides a €1,000 contribution towards travel costs and accommodation for five nights. In addition, the Programme will cover tuition fees, all course materials, access to the EUI library, Wi-Fi on campus, social activities, lunches and coffee breaks on lecture days, as well as one cocktail event and one dinner. To be eligible for the scholarship, applicants must be nationals of Turkey or Ukraine.
In the event that there are no eligible applicants from Turkey or Ukraine, the scholarships may be awarded to applicants of other nationalities. Global scholarships – up to 5 scholarships Available to applicants from Africa, Asia-Pacific and Latin America, provided by the European Union in the framework of the Global Initiative on the Future of the Internet. The scholarship is intended for travel expenses (economy class fare), visas, accommodation for 6 nights, tuition fees, all course materials, access to the EUI library, Wi-Fi access at the EUI, social activities, lunches and coffee breaks on lecture days. For more information about global scholarships, applicants can contact Dr Patryk Pawlak at [email protected] EUI Widening Europe Programme scholarships – up to 4 scholarships Available to applicants from the countries part of the EUI Widening Europe Programme. The scholarship will contribute 1000 euros to cover travel expenses and accommodation for 5 nights. In addition, tuition fees, all course materials, access to the EUI library, Wi-Fi access at the EUI, social activities, lunches, and coffee breaks on lecture days, one cocktail, one dinner, will be offered by the Programme. To qualify for this scholarship, applicants must be nationals of one of the countries targeted by the Widening Europe programme: Inside the European Union : Bulgaria, Croatia, Cyprus, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, and Slovenia.
: Bulgaria, Croatia, Cyprus, Czechia, Estonia, Greece, Hungary, Latvia, Lithuania, Malta, Poland, Portugal, Romania, Slovakia, and Slovenia. Outside the European Union: Albania, Bosnia & Herzegovina, Georgia, Kosovo(1), Montenegro, North Macedonia, Serbia, Moldova, and Ukraine. (1) This designation is without prejudice to positions on status, and is in line with UNSCR 1244(1999) and the ICJ Opinion on the Kosovo declaration of independence. Applicants must also affiliated with an academic institution based in a country targeted by the Widening Europe programme (students currently enrolled in a master/doctoral program, postdoctoral researchers, lecturers, or professors) The selection will take into account the candidate’s professional profile, language skills and additional competencies, such as having an international profile, work experience, certificates and other achievements. Please note that the CMPF can provide an invitation letter to the selected participants but cannot assist with the Visa application process or expenses for travel documents. The grant amount is directly linked to course attendance. A daily signature is required, and any absence will result in a reduction of the grant—specifically, €200 per day. The deadline for submitting applications is 4 May 2025.
| 2025-06-27T00:00:00 |
https://cmpf.eui.eu/course/2025-summer-school-for-journalists-and-media-practitioners/
|
[
{
"date": "2025/06/27",
"position": 75,
"query": "AI journalism"
}
] |
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|
Microsoft Layoffs: Over 1000 Jobs to Be Cut Next Week, ...
|
Microsoft Layoffs: Over 1,000 Jobs to Be Cut Next Week, Including Xbox Staff
|
https://www.finalroundai.com
|
[] |
This would mark the company's third major round of workforce reductions in 2025, as it continues to shift resources toward artificial intelligence development ...
|
Microsoft is preparing to lay off thousands of employees in July, according to a report by Bloomberg. This would mark the company’s third major round of workforce reductions in 2025, as it continues to shift resources toward artificial intelligence development and streamline operations.
In May, the company laid off around 6,000 employees, primarily from product and engineering teams as part of a broader internal restructuring. The upcoming layoffs are expected to impact roles in sales and support functions, reflecting Microsoft’s evolving priorities and its effort to align teams with long term AI driven goals.
The layoffs come as Microsoft plans to spend $80 billion this fiscal year on A.I. infrastructure, primarily data centers, forcing the technology giant to cut costs elsewhere to maintain profit margins. The company's chief executive, Satya Nadella, recently told employees that artificial intelligence now generates 30 percent of Microsoft's internal code.
Fourth Major Round in 18 Months
The upcoming July layoffs mark Microsoft’s fourth major workforce reduction since early 2023.
In January 2023 Microsoft cut 10,000 jobs , or about 4.5% of its global workforce, citing post-pandemic recalibration.
Microsoft cut , or about 4.5% of its global workforce, citing post-pandemic recalibration. In January 2024 the company laid off around 1,900 gaming employees , following its acquisition of Activision Blizzard.
the company laid off around , following its acquisition of Activision Blizzard. In May 2025 Microsoft eliminated 6,000 roles, describing the move as an organizational shift to align with strategic goals. Official WARN filings showed 1,985 job cuts in Washington state alone, affecting teams across LinkedIn, GitHub, Azure, and Xbox.
Gaming divisions have been particularly hard hit, with Microsoft planning additional "major" Xbox layoffs as early as this week, according to The Verge.
Next in line are sales teams and middle managers, with Microsoft Customer and Partner Solutions among the hardest-hit divisions. The company is increasingly outsourcing sales for small and mid-sized business accounts to third-party firms.
Recent filings also revealed that software engineers comprised 22% of the 300 roles eliminated in Washington state in June 2025, reflecting a broader shift in workforce composition.
The Layoff Trend of Silicon Valley
Microsoft’s restructuring is part of a broader wave of mass layoffs sweeping through Silicon Valley. Since the start of 2025, tech companies have cut over 83,800 jobs, an average of 493 every single day.
"Microsoft needs to reduce headcount by at least 10,000 annually to offset the margin pressure from its AI investments," said Gil Luria, an analyst at D.A. Davidson, in an interview with Reuters.
The restructuring follows what insiders describe as a "builder ratio" strategy borrowed from Amazon, which prioritizes engineers over managers and seeks to flatten organizational hierarchies.
"It's like a horrible game of musical chairs," said V.L., a Microsoft employee laid off in May who spoke to KUOW on condition of anonymity. "There are fewer and fewer chairs for people in these industries."
Internal estimates suggest the July cuts could save Microsoft between $1.5 billion and $1.65 billion annually. Affected employees will receive 12 weeks of base pay plus two additional weeks for each year of service.
Andy Jassy, Amazon's chief executive, told employees this week that artificial intelligence agents would reduce the company's corporate workforce in coming years, according to Maginative.
As of now, Microsoft has not issued an official statement confirming the planned July layoffs.
| 2025-06-27T00:00:00 |
https://www.finalroundai.com/blog/microsoft-plans-july-layoffs-2025
|
[
{
"date": "2025/06/27",
"position": 73,
"query": "AI layoffs"
}
] |
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|
The Impact of Artificial Intelligence on Job Markets - Kanwhizz's
|
The Impact of Artificial Intelligence on Job Markets
|
https://kanwhizz.in
|
[] |
In each of these sectors, tools like ChatGPT are playing a supporting role—enhancing productivity without entirely displacing human workers. Geographic ...
|
The Impact of Artificial Intelligence on Job Markets
6/28/2025 12:04:00 PM KanWhizz Team
The influence of artificial intelligence is being felt across nearly every sector, revolutionizing not only how businesses operate but also how people work. From automating mundane tasks to powering sophisticated data analysis, AI is transforming job markets around the globe. This technological wave, driven by innovations like ChatGPT, is creating both promising opportunities and significant challenges for employers and employees alike.
In this post, we’ll examine how artificial intelligence is reshaping the labor landscape—displacing some jobs, creating others, and prompting a shift in the skills needed for success in the future workforce.
AI as a Disruptor and a Creator
Artificial intelligence is a double-edged sword: while it has the power to make industries more efficient, it also threatens to replace a wide range of jobs. The evolution of AI tools—from automated checkouts to intelligent virtual assistants—has made routine tasks easier to manage but has also reduced the need for human involvement in certain areas.
Roles at Risk
According to projections from global research institutions, millions of jobs could be automated in the coming years. Tasks that involve repetition or predictable outcomes—such as those in manufacturing, data entry, and customer support—are most susceptible. With tools like ChatGPT, even knowledge-based roles like writing, tech support, and data processing are beginning to evolve, requiring fewer manual hours.
The workforce has been disrupted by technology before. Historical shifts, such as the industrial revolution and the rise of the internet, also caused widespread transformation in employment. What sets artificial intelligence apart is how quickly and broadly it can make an impact.
Emerging Opportunities
Despite fears of job loss, artificial intelligence is also fueling job growth in new and emerging areas. There’s increasing demand for AI specialists, machine learning engineers, and data analysts. Even beyond technical roles, new positions like AI ethicists, trainers, and content moderators have started to appear—particularly in support of generative AI systems like ChatGPT.
In many cases, AI doesn’t eliminate jobs entirely; it changes them. Professionals now spend less time on manual or repetitive work and more on tasks that require problem-solving, creativity, and human interaction. The job market is adapting rapidly, with new roles replacing old ones at an unprecedented pace.
The Skills Shift in Modern Job Markets
The rise of artificial intelligence has created a growing need for both hard and soft skills. As businesses adopt AI-driven tools, employees must learn how to use them effectively and ethically.
Technical and Human Skills in Balance
While technical know-how in programming, analytics, and AI tools is increasingly important, it’s not the only requirement. Human-centered skills like critical thinking, communication, empathy, and adaptability are essential in areas where AI can’t replace human judgment.
Employees who understand how to use AI tools like ChatGPT can gain a major advantage. For instance, marketers use it for content ideas, while coders rely on it for debugging. Knowing how to guide AI outputs, verify results, and apply them effectively is now a key part of workplace efficiency.
Rethinking Education and Training
Education systems must adjust to meet the needs of the AI-driven economy. This means shifting away from memorization and toward practical, interdisciplinary learning. Schools and training programs should teach students how to think critically, solve complex problems, and use AI tools responsibly.
Private and public sectors are beginning to invest heavily in upskilling. Online platforms now offer courses in artificial intelligence, data science, and prompt engineering. These programs aim to close the skill gap and prepare workers for roles that didn’t exist just a few years ago.
Sector-Specific and Global Impacts
The effects of artificial intelligence vary depending on the industry and region. Some sectors will see widespread transformation, while others will experience a more gradual shift.
Industry Changes
High Automation : Industries like logistics, retail, and manufacturing are adopting AI to streamline operations and reduce human labor.
Moderate Impact : Fields such as education, finance, and healthcare are integrating AI to support—not replace—professionals.
Minimal Disruption : Jobs requiring empathy, creativity, and strategic thinking—such as those in leadership or the arts—remain largely unaffected but may still benefit from AI assistance.
In each of these sectors, tools like ChatGPT are playing a supporting role—enhancing productivity without entirely displacing human workers.
Geographic Disparities
Advanced economies with strong technological infrastructure are adopting AI at a faster pace. This gives them an edge in developing high-paying AI-related jobs. Meanwhile, developing nations face challenges like limited access to digital tools and education. Still, by leveraging mobile technologies and cloud platforms, these regions can find opportunities to leapfrog traditional development paths and integrate AI-driven solutions into their economies.
Ethical Questions and Responsibilities
As artificial intelligence becomes more common in the workplace, ethical concerns are rising. Decisions made by AI—such as resume screening or automated hiring—raise questions about bias, fairness, and accountability. It's essential to ensure that AI systems do not reinforce existing inequalities in the job market.
Moreover, generative AI models like ChatGPT have sparked debate over content ownership, misinformation, and privacy. Companies must develop guidelines for responsible AI usage while governments establish frameworks to regulate and monitor these technologies.
The Road Ahead: How to Prepare
Rather than fear the rise of artificial intelligence, individuals, companies, and governments must work together to adapt. The goal should not be to resist change, but to prepare for it thoughtfully.
Action Steps:
For Professionals : Continuously upgrade your skills. Embrace tools like ChatGPT to improve productivity, but develop human-centric abilities AI can’t replicate.
For Employers : Implement AI responsibly. Provide training programs to help your teams transition into new roles.
For Policymakers : Support job transition programs and introduce policies that protect workers while encouraging innovation.
Conclusion
Artificial intelligence is reshaping global job markets faster than ever before. While some positions will disappear, many more will evolve—or be created entirely. The emergence of tools like ChatGPT illustrates just how quickly roles and workflows can shift.
Those who adapt, learn, and embrace the possibilities of working alongside AI will find themselves better prepared for a rapidly changing future. By fostering education, ensuring ethical deployment, and investing in people, we can harness AI’s potential to build a smarter, more inclusive workforce.
| 2025-06-27T00:00:00 |
https://kanwhizz.in/blog/the-impact-of-artificial-intelligence-on-job-markets
|
[
{
"date": "2025/06/27",
"position": 40,
"query": "ChatGPT employment impact"
}
] |
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Artificial Intelligence in the Newsroom
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Artificial Intelligence in the Newsroom: Emerging Challenges in Southeast Europe
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https://seecheck.org
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Artificial Intelligence in the Newsroom: Emerging Challenges in Southeast Europe · With Little Regulation, Journalists Take the Lead on AI · From Spam to Scam: ...
|
By: Maida Salkanović
In October last year, millions of viewers of TV Pink—a nationally licensed broadcaster in Serbia—had a chance to watch a four-minute video in which acclaimed film director Emir Kusturica appeared to speak about his ties to intelligence agencies, admitted to “mocking his own people,” and hinted at influencing a high school shooter who killed ten people in May 2023, all while praising TV Pink’s owner. The video, however, was generated by artificial intelligence (AI).
It was a retaliatory move by Željko Mitrović, the station’s owner, following an article in which Kusturica criticized Mitrović’s influence on Serbian society. It was not the first time TV Pink weaponized fakes against dissenters. The station, closely aligned with the ruling regime, had previously produced deepfakes of prominent opposition figures Marinika Tepić and Dragan Đilas. The videos were either not labeled as AI-generated or were labeled inadequately, prompting the Association of Journalists of Serbia (UNS) to publicly urge TV Pink to stop the use of manipulative content.
Across the border in Croatia, just two months later, presidential candidate Ivana Kekin played a fake audio recording at a press conference in which Prime Minister Andrej Plenković appeared to endorse her candidacy. Unlike TV Pink, Kekin used the stunt as a public service announcement, to highlight just how easily disinformation can be manufactured and circulated. Still, the incident raises an important question: how much harder will journalists’ jobs become in a world where truth can be synthetically distorted at scale?
Artificial intelligence has become the phrase of the moment. In journalism, it inspires a range of reactions, from panic over job security to opportunistic experimentation aimed at boosting clicks and profits. The field remains largely unregulated, and amid the chaos, journalistic communities across the region are scrambling to understand these technologies, and to shape the ethical and professional standards that must govern their use.
With Little Regulation, Journalists Take the Lead on AI
Across Southeast Europe, regulatory approaches to artificial intelligence remain uneven, but momentum is building. As an EU member state, Croatia is obliged to align with the EU’s regulatory framework and, in May 2025, the Ministry of Justice, Public Administration and Digital Transition formed a working group to begin drafting the country’s national AI law to implement the EU AI Act.
In Serbia, a national Strategy for the Development of Artificial Intelligence was adopted in early 2025, and the country’s Code of Journalists, updated in late 2024, now includes references to the ethical use of AI in media.
Bosnia and Herzegovina and Montenegro currently lack comprehensive, AI-specific legislation, but both are increasingly engaging with regional and international initiatives related to AI use in media and governance. In November 2024, Montenegro signed the Council of Europe Framework Convention on Artificial Intelligence, signaling its commitment to ensuring that AI development upholds human rights, democracy, and the rule of law.
Media professionals and civil society actors across the region are increasingly engaging with the challenges and possibilities of AI in journalism, working to establish ethical guidelines and shared standards. In May 2025, representatives of press councils from Southeast Europe and Turkey gathered in Ohrid, North Macedonia, where they signed the Regional Declaration of Press Councils from Southeast Europe and Turkey on the Ethical and Transparent Use of Artificial Intelligence in the Media.
One of the first research studies on the topic was published by BIRN Serbia, with support from the OSCE, also in May 2025. The findings reveal that while journalists in Serbia are increasingly experimenting with AI tools in their reporting, they do so without adequate training, clear guidelines, or a cohesive editorial strategy.
Tanja Maksić, the author of the study, told SEE Check that her research showed journalists are looking for some form of guidance when it comes to using AI, whether through regulation, editorial frameworks, or support from self-regulatory bodies.
“The big problem here in Serbia,” she said, “is that we have a fairly authoritarian government, closed off to dialogue and any kind of consultation. It’s genuinely difficult to reach any constructive agreement with them on anything.”
Maksić sees it as a positive sign that, even within a captured media system, journalists are exploring new tools, seeking out resources, and expressing concern about the ethical implications of AI. However, she notes that copyright, which is one of the main concerns about AI globally, was rarely mentioned by those surveyed.
Many journalists are also turning their attention to how AI is affecting their peers. Slađan Tomić, a journalist working across Bosnian-Herzegovinian and Serbian media, interviewed journalists in Bosnia to better understand how they are using AI in their daily work.
“I’m afraid that AI tools like ChatGPT could, in some newsrooms, replace journalists or part of the newsroom staff, and also contribute to journalists becoming lazy and relying on the machine instead of using their own knowledge, talent, and skills. That leads to intellectual stagnation, the death of creativity, and, more importantly, the erosion of journalistic excellence. Machines cannot replace human beings, and that needs to be made clear,” said Tomić.
From Spam to Scam: Slop and Synthetic Content Flood the Web
AI, says Maksić, also generates large amounts of so-called “slop”, a term journalist John Oliver recently described as “the newest iteration of spam.” This kind of content, Maksić explains, “has no informational value, no authorial signature, clogs up platforms, obstructs the flow of information, and at times, is genuinely difficult to distinguish from real reporting.”
In most cases, AI slop is profit-driven and sometimes used in outright scams. Deepfakes of journalists are sometimes exploited to promote these schemes, relying on the trust those individuals command. In Croatia, for instance, the image of RTL editor and TV presenter Mojmira Pastorčić, alongside that of President Zoran Milanović, was used in a fabricated promotion for a supposed “investment platform,” a scam designed to extract money from citizens.
Rašid Krupalija, Editor-in-Chief of the Bosnian fact-checking platform Raskrinkavanje, told SEE Check that scammers behind these types of schemes typically choose TV journalists whose faces are easily recognizable to the average social media user. According to Krupalija, this tactic helps lend credibility to the product being promoted.
“Unfortunately, the AI tools used to create these deepfake videos are getting more sophisticated. While it was relatively easy to spot a fake in the beginning, it’s becoming increasingly difficult now, as the videos look and sound quite authentic,” he said.
Famous Bosnian journalist Senad Hadžifejzović has been a frequent target of these scams. In an effort to inform the public that the advertisements were fraudulent, Hadžifejzović aired public service announcements on his channel Face TV, urging viewers not to purchase what he called “poison and scams.” “If you want to kill yourself, then buy it,” he said, referring to the warnings broadcast on the station, though the impact of those warnings remains unknown.
He told SEE Check that they reported the matter to the Prosecutor’s Office but never received a response. “It’s all up to the investigative bodies and prosecutors: we don’t know that they’ve done anything,” he said.
Some social media platforms now require AI-generated content to be labeled, but the approach remains deeply flawed. Meta, for example, mandates labeling only for “photorealistic video or realistic-sounding audio” created using AI, excluding images from this requirement. Krupalija warns that AI-generated content will inevitably become more difficult to detect, a development that works in favor of those using it for scams.
“It is the job of journalists to investigate and report on these types of fraud,” Krupalija told SEE Check, “but reporting alone won’t be enough without efforts to promote media and information literacy among readers. That is, building their resilience and skills to recognize scams and other harmful online content.”
Such misuse not only defrauds the public but can erode trust in the very journalists whose identities are being co-opted. Over time, this could contribute to a broader crisis of confidence in the media profession itself, especially in regions where trust in journalism is already precarious. A notable example is the Slovak case, where a deepfake audio clip mimicking journalist Monika Tódová was used to fabricate a conversation about election fraud. The aim was not only to discredit the political candidate, but also the journalist, showing how AI tools can be weaponized to undermine both individual credibility and public trust in journalism as an institution.
The phenomenon underscores a key vulnerability that extends beyond scams: the increasing difficulty in detecting AI-generated content poses a serious threat not just to consumer protection, but to the integrity of political reporting and journalism more broadly. As synthetic content becomes more convincing, the risk of its abuse in politically motivated smear campaigns, disinformation efforts, or fabricated interviews grows. In this landscape, identifying AI-generated content is not only a matter of countering fraud: it is critical to preserving editorial standards and democratic accountability.
AI as an Assistant: Supporting Journalists, Not Replacing Them
AI can undoubtedly be a valuable tool for journalists, enhancing both productivity and efficiency. This potential was recognized by the Journalists’ Association of Serbia (UNS), which partnered with the Center for Youth Activism Development (CROA) from Sarajevo, the Albanian Center for Quality Journalism (ACQJ) from Tirana, and the Media Center from Čaglavica to implement the project “How to Use Artificial Intelligence in Journalism.” The initiative resulted in a regional research study on the use of AI in media, as well as the creation of a regional network through which young people exchange experiences, tools, and training related to AI applications.
Aleksandra Ničić, one of the trainers in the project, said she was surprised by the level of knowledge demonstrated by young and aspiring journalists, which was higher than expected across the board. She also noted subtle regional differences in focus and concerns.
“My impression is that participants from Belgrade were primarily interested in the practical use of AI tools, those from Sarajevo were more focused on the privacy implications, participants from Čaglavica engaged most with the issues of AI hallucinations and deepfakes, while participants from Tirana were the most active in discussing the broader consequences of AI in journalism,” she said.
Ničić believes that there will be no serious negative consequences for the profession, as long as artificial intelligence is treated as an assistant that supports journalistic work, rather than a colleague doing all the work and creating problems through hallucinations.
“In other words, if we use AI tools to handle repetitive or mechanical tasks that consume our time, or to generate ideas that we then critically assess, I believe there won’t be negative consequences for our work,” she said.
Journalist Slađan Tomić agrees that AI can be a valuable tool when used as an assistant, but not as a content generator. He noted that there are already examples of media outlets using AI to produce content with no human oversight. In some cases, he added, journalists fail to even remove the AI’s built-in prompts, such as ChatGPT’s phrase: ‘If you want a shorter version, I can write one,’ which ends up appearing in the published article.
The use of AI appears to be more advanced in media outlets with lower editorial standards. In Serbia, Maksić observed that tabloids are among those furthest along in integrating AI tools into their content. These outlets frequently rely on text-to-speech and text summarization tools, often with poor results.
“It’s a mechanical voice, it gets the cases wrong, the diction is off, you can clearly tell,” she said. “But they’re using it consistently, and at some point, they’ll probably bring it to an acceptable level.”
According to Maksić, the combination of lower editorial standards and greater financial resources allows these outlets to experiment more freely with AI technologies than their peers in more rigorous journalistic environments.
“When you consider that these outlets already have a tendency to produce propaganda, disinformation, and fake news, and that they’ve been heavily engaged in smear campaigns against independent and critical voices in society,it becomes genuinely worrying,” said Maksić. “The issue now is that AI will enable them to further multiply low-quality content. They’ve already shown they’re willing to do it without AI, and now they’re gaining a powerful new tool to spread and accelerate these harmful practices.”
New Tech, Old Questions
AI continues to dominate conversations in Southeast Europe. New regulations are being drafted, media trainings are underway, and professional circles are actively debating how to approach this fast-evolving technology.
But this isn’t entirely new terrain, Maksić points.
“We started with printed newspapers, then came social media, and now AI. Over time, the profession has shown a certain resilience, and an ability to integrate new technologies into its workflows,” she said.
There are many new challenges ahead, but many journalists believe they can be overcome, so long as AI is approached with caution, responsibility, and a strong ethical foundation.
“A machine can be a tool and a support to the human, but nothing can replace human thought,” Tomić concludes. “Through irrational, unethical, and excessive use of AI in journalism, we risk permanently devaluing the profession.”
| 2025-06-27T00:00:00 |
2025/06/27
|
https://seecheck.org/index.php/2025/06/27/artificial-intelligence-in-the-newsroom-emerging-challenges-in-southeast-europe/
|
[
{
"date": "2025/06/27",
"position": 42,
"query": "artificial intelligence journalism"
}
] |
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Webinar Report: Disinformation and AI – Towards Media ...
|
Webinar Report: Disinformation and AI – Towards Media Edukathon
|
https://www.ifj.org
|
[] |
Activities involved group testing of AI tools used in journalism, including text, audio, visual, and audiovisual content and a pannel discussion exploring the ...
|
The final session of the TADAM webinar series was held on 26 June and focused on "Disinformation and AI: Towards Media Edukathon". Activities involved group testing of AI tools used in journalism, including text, audio, visual, and audiovisual content and a pannel discussion to explore challenges facing journalists.
The event was designed to promote critical thinking and practical skills through expert-led insights and exchanges of good practices.
Activities involved group testing of AI tools used in journalism, including text, audio, visual, and audiovisual content and a panel discussion exploring the challenges facing journalists.
The panel discussion featured a diverse group of experts:
● Jon Schleuss, President of NewsGuild, USA and IFJ member
● Mihajlo Lahtov, Media and Information Literacy Specialist, North Macedonia
● Katerina Topalova, Journalist at MRTV, North Macedonia
● Maarit Jaakkola, Co-Director of Nordicom, Sweden
Panelists addressed AI’s dual impact on journalism - enhancing efficiency and innovation while also raising concerns around disinformation, algorithmic bias, and declining public trust. The conversation underscored the need for ethical standards, transparency, and continuous education to navigate these challenges responsibly.
As a next step, Jon Schleuss suggested to focus on AI journalism and ethics based on the IFJ code of ethics
The IFJ has published recommendations on the use of AI insisting that AI must serve journalism, not replace it, and calling for human oversight, fair compensation, and union-led negotiations with tech firms.
More on Tadam here.
WATCH the webinar
| 2025-06-27T00:00:00 |
https://www.ifj.org/media-centre/news/detail/category/tadam/article/webinar-report-disinformation-and-ai-towards-media-edukathon
|
[
{
"date": "2025/06/27",
"position": 55,
"query": "artificial intelligence journalism"
}
] |
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|
At Hugging Face, a former journalist helps make AI more ...
|
At Hugging Face, a former journalist helps make AI more accessible
|
https://blog.mozilla.org
|
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That's why I moved from journalism to AI. I could feel something shifting, not just in media, but everywhere, and I wanted to help make this foundational ...
|
Here at Mozilla, we are the first to admit the internet isn’t perfect, but we know the internet is pretty darn magical. The internet opens up doors and opportunities, allows for human connection, and lets everyone find where they belong — their corners of the internet. We all have an internet story worth sharing. In My Corner Of The Internet, we talk with people about the online spaces they can’t get enough of, the sites and forums that shaped them, and how they would design their own corner of the web.
We caught up with Florent Daudens, who led digital innovation in Canadian newsrooms before becoming press lead at Hugging Face, the open-source AI community. He talks about shaping his feeds to feel more like home, his move from journalism to AI, and why the best way to understand new tech is to start making things.
What is your favorite corner of the internet?
That rare, quiet part of the internet that actually makes you smarter without making you feel behind. For me, it’s a mix.
LinkedIn surprised me. I used to think of it as stiff and self-promotional, but it’s become where I exchange ideas with people wrestling with the same big questions: What’s AI doing to journalism? What’s worth building?
[X] is still very relevant for everything related to AI news. It’s where I get pulled into weird, fascinating rabbit holes. Someone posts a half-broken agent demo or a wild paper, and suddenly I have 12 tabs open. It’s chaotic in the best way.
And Hugging Face of course, to keep pace with AI releases!
I think what changed everything was narrowing my feeds. Once I stopped trying to follow everything and leaned into what really matters to me – AI, openness, news and creative industries – it all started to feel like home.
What is an internet deep dive that you can’t wait to jump back into?
My YouTube recommendations read like a personality test I didn’t mean to take:
obsessive AI build logs. I’m a sucker for “How I made this with that” videos to learn new skills related to AI.
Mandarin tutorials (six years in and still chasing tones…)
vintage French science shows that I now rewatch with my kid — equal parts nostalgia and wonder.
What is the one tab you always regret closing?
That post. You know the one — right under the other one. You meant to open it in a new tab, but you didn’t. And then the feed refreshed and it’s gone forever. A digital ghost.
What can you not stop talking about on the internet right now?
AI-generated videos that are totally unhinged and strangely beautiful. Like:
What was the first online community you engaged with?
CaraMail, back in France in the late ’90s. It was messy, anonymous, and kind of magical. That early feeling of connecting with people across borders, in French, about anything, was completely new. It opened up so many possibilities and shaped how I saw connection and community, and actually played a role in me moving to Montréal at 18.
If you could create your own corner of the internet, what would it look like?
Actually, I’m lucky; I am building it.
That’s why I moved from journalism to AI. I could feel something shifting, not just in media, but everywhere, and I wanted to help make this foundational technology open, accessible, and collaborative. As a former data journalist, I saw how open-source wasn’t just about sharing code. It was a force multiplier for learning, creativity, and community. With AI, that effect is even stronger.
So yeah, without a doubt: Hugging Face.
What articles and/or videos are you waiting to read/watch right now?
The LangGraph course on DeepLearning.ai on long-term agentic memory (it’s niche, I know)
And a new series on MCP, which my colleague Ben kicked off, because I genuinely think this protocol could unlock a whole new layer of what’s possible on the open web.
What’s the biggest opportunity you see right now at the intersection of AI, open-source and public-interest media?
Small experiments, bold new tools, but most of all, building.
With AI-assisted coding, I think the barrier to entry is lower than ever. You can go from idea to prototype really quickly, even without knowing how to code, but just by starting with your words and ideas. And that’s a game-changer.
Take AI agents: the only way to really understand their potential and their limits is to try building one yourself. That forces you into the mindset that matters most: empathy. Start with what people actually need, then design around that.
Open-source supercharges all of this. It lets you remix, test, and share. It makes scaling faster. And maybe most importantly, it’s the best way to stay independent from tech companies. You’re not just using tools; you’re shaping them.
Florent Daudens is the press lead at Hugging Face, the open-source AI community. A longtime advocate for the intersection of AI and journalism, he led the digital transformation of major Canadian media such as Le Devoir and Radio-Canada. He has overseen the development of AI-powered tools, helped shape ethical guidelines for AI, and trains newsrooms on its use. He also lectures on AI and journalism at Université de Montréal and ESJ Lille.
| 2025-06-27T00:00:00 |
https://blog.mozilla.org/en/internet-culture/interviews/hugging-face-florent-daudens/
|
[
{
"date": "2025/06/27",
"position": 67,
"query": "artificial intelligence journalism"
}
] |
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|
TWU Tech Newsletter: Fighting Back Against an AI Giveaway
|
TWU Tech Newsletter: Fighting Back Against an AI Giveaway – Transport Workers Union
|
https://www.twu.org
|
[] |
In letters to both the House and the Senate, TWU joined labor unions across the country to argue that a 10-year ban on state-level AI regulations will “leave ...
|
This is the monthly edition of the Transport Workers Union’s Transportation Technology Newsletter. We aim to inform and educate our members, the labor movement, the public and policymakers about developments in transportation technology – and what the TWU is doing to ensure that new technology doesn’t undermine safety or harm the livelihoods of hard-working blue-collar workers. For suggestions and questions, please email [email protected] or [email protected].
ITEM OF THE MONTH
NO PREEMPTION: The TWU is taking aim at a Big Tech giveaway in the pending House and Senate Republican budget reconciliation bill that would impose a 10-year ban on states from protecting workers from the harms of artificial intelligence.
In letters to both the House and the Senate, TWU joined labor unions across the country to argue that a 10-year ban on state-level AI regulations will “leave everyone vulnerable to wide-ranging abuses stemming from irresponsible AI.” If enacted, this measure would end any state-level attempts to keep dangerous AI-enabled autonomous vehicles off their roads.
“This is yet another attempt by Big Tech to unleash unproven and dangerous autonomous vehicles on our roads without any safety oversight or accountability,” said TWU International President John Samuelsen. “During a time when the federal government isn’t doing its job to properly regulate AI deployments, it is reckless to shut down state attempts to protect residents from the improper or dangerous use of AI in transportation and across the economy and public agencies.”
Republicans and Democrats – both state legislators and attorneys general across the political spectrum – are urging Congress to strike this ban on state AI regulations. “State legislatures—working in a bipartisan fashion—have taken intentional and thoughtful action to build guardrails while making efforts not to stymie technological innovation. A federally imposed moratorium would not only paralyze this innovation but would also leave Americans exposed to emerging risks associated with the new technology,” state legislators wrote in a letter to Congress.
WHAT ELSE IS COOKING
CALIFORNIA CALAMITY: When major protests erupted in Los Angeles earlier this month, Waymos couldn’t get out of the way. Viral images of Waymos engulfed in flames spread across the world and were a reminder that driverless vehicles can become “sitting ducks” as NBC News reported. The unattended Waymos left in the middle of areas that were under a city-imposed curfew are the latest example of driverless technology not being ready for complicated driving environments. Robotaxis have previously driven through active crime scenes and blocked first responders.
“Driverless cars became sitting ducks in a dangerous environment – another example of why having human operators is essential,” said TWU International Administrative Vice President Curtis Tate.
Waymo service in San Francisco was also suspended during recent protests. But while transit options within the affected areas were up and running within hours of the protests subsiding, with TWU members providing bus and rail service throughout the city, Waymo users posted to social media screenshots of Waymos avoiding huge swaths of the city days after the protests ended.
“Ultimately, Big Tech sees driverless cars as replacing rideshare drivers first before moving on to public transit systems,” Tate said. “But what we’ve seen in Los Angeles and San Francisco should be a wake up call for elected officials and transit agencies – the cars are easy targets for violence in areas where they should be removed and the tech companies themselves don’t trust their tech enough to restart rides while transit workers are able to get cities up and running again within hours of civil unrest subsiding.”
Beyond the protests, driverless cars are also causing everyday problems. In Santa Monica, residents became fed up with Waymos hogging parking spots while charging and incessant beeping from cars as they constantly moved around parking lots. Residents took matters into their own hands, blocking a parking lot with cones and cars to keep Waymos away. In response, Waymo called the police, according to KTLA. The Los Angeles times has more.
The issues haven’t stopped Waymo from continuing to expand. The company announced later in June that rides are now available over a larger swath of San Francisco, Los Angeles, and Silicon Valley.
BIG TRUCK WANTS MORE: The trucking lobby is continuing to pressure federal regulators to eliminate restrictions on driverless trucks that are intended to promote safety, Freight Waves reports. In May, the Autonomous Vehicle Industry Association wrote to DOT urging FMCSA to update hours-of-service and inspection requirements that currently require action by a human – and are additionally continuing to fight a ruling on roadside service devices that requires a human to place safety triangles behind a disabled truck on the side of the road. The TWU successfully fought to keep the current requirements in place and the FMCSA ruled against the autonomous trucking industry on the matter in January.
IN THE NEWS: TWU International President John Samuelsen was quoted in a recent New York Times story on the expansion of autonomous trucking, noting that a fast proliferation is “potentially dangerous from a safety perspective” but the industry is still moving quickly “like a freaking Corvette, doing zero to 60.”
DRIVERLESS CARS IN MICHIGAN: Ann Arbor-based May Mobility announced that its autonomous Toyota Sienna minivans will be available for booking on Uber by the end of 2025, starting in Texas. Currently, May Mobility operates on a smaller scale in markets like its home in Ann Arbor, Michigan, where TWU members at Local 171 currently provide transit service. The company also has its sites on autonomous buses, Axios reports, seeking to deploy autonomous 30 person minibuses by 2026.
“We will stay vigilant to ensure that TWU Bus Operators and Mechanics in cities like Ann Arbor aren’t pushed out in favor of unproven tech,” Tate said. “We’ve already won contract language giving us veto power over autonomous buses in Ohio, and we intend to push for similar contract language around the country.”
TESLA TURMOIL: Tesla robotaxis are now on the road in Austin, and within a day of launching a series of viral videos showed the autonomous cars driving erratically – and spurring the federal government to investigate. In one instance, a Tesla robotaxi breaked hard in the middle of traffic and in a vehicle drove down the wrong side of the road in another video. The launch comes as local officials in Texas worry about the proliferation of robotaxis in a state where local municipalities are prohibited by the state from imposing rules on their usage.
“It’s been very frustrating on our end from a safety standpoint,” Austin Police Lieutenant William White told the Dallas Morning News. “If these machines are learning, they’re not learning at a quick enough pace for sure.”
AIR LOS ANGELES: When athletes from around the world converge on Los Angeles for the 2028 Summer Olympics and Paralympics, Archer Aviation will serve as the official airtaxi provider of LA28, in addition to showcasing its service in conjunction with the 2026 FIFA World Cup and 2027 Super Bowl, both in Los Angeles.
LA28 announced that through its partnership with Archer, they will “look to integrate Archer’s Midnight eVTOL aircraft across the LA28 Games in a variety of ways, such as transporting VIPs, fans, and stakeholders, while electrifying vertiport take-off-and-landing hubs for key venues and providing support for emergency services and security.” Archer plans to serve the Olympics and Paralympics from several so-called “vertiports” located at several adjacent locations.
So-called eVTOL, or electric vertical take-off and landing aircraft, remains an unproven technology that takes off and lands like a helicopter and flies like a plane. The Archer version is an all-electric 4-seater that has yet to receive all certifications from the Federal Aviation Administration. Some eVTOL developers contemplate running airtaxi service without a pilot onboard with airtaxi development plans targeting cities across the country including New York City, Miami and San Francisco.
WHAT WE’RE READING:
United Nations Warns Driverless Cars Could Be Terrorist Targets. The Times UK.
Rail Track Inspections by AI-Enabled Tech? Trains.
Google Gemini AI Chatbot in Volvo Cars. The Verge.
| 2025-06-27T00:00:00 |
https://www.twu.org/twu-tech-newsletter-fighting-back-against-an-ai-giveaway/
|
[
{
"date": "2025/06/27",
"position": 24,
"query": "artificial intelligence labor union"
}
] |
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Will AI Replace or Transform Your Job? - CV Maker
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Will AI Replace or Transform Your Job?
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https://www.cvmaker.uk
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According to the World Economic Forum's 2025 Future of Jobs Report, by 2030, nearly 40% of current skills will be transformed or outdated. To stay relevant in ...
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As the workplace continues to evolve, many are wondering whether AI will transform their jobs. The United Kingdom (UK), now the third-largest artificial intelligence (AI) market, has become a vibrant hub for global talent and leading companies like Google DeepMind, ARM, and Wayve.
With AI already revolutionising industries like finance, healthcare, manufacturing, and the creative sectors, it’s time to explore how AI transforms jobs and what strategies can help individuals stay ahead in this rapidly changing landscape. While AI’s rise presents both challenges and opportunities, it’s clear that adapting to these changes will be essential for thriving in the future workplace.
In this article, we cover:
How is AI transforming the workplace?
How will AI reshape your job?
Tips for incorporating AI into your work.
Practical examples of AI in the workplace.
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How is AI transforming the workplace?
According to the 2025 Future of Jobs Report by the World Economic Forum, the job market is undergoing significant changes. AI is already leaving its mark across various industries, taking over tasks that humans once handled. In fact, here are a few areas where AI is already making an impact and transforming jobs or industries:
Administrative roles
AI chatbots and automation tools are increasingly taking on responsibilities traditionally managed by humans in administrative roles. Tasks like data entry, scheduling, and basic customer service are now being efficiently handled by these technologies, transforming the way organisations manage their workflows.
For example, virtual assistants like Google Assistant can manage calendars, schedule appointments, and send reminders without any human intervention. Automated email tools, such as AI-powered inbox assistants, can respond to routine emails, prioritise messages, and even sort them into relevant folders. Similarly, invoice processing has become more streamlined with tools like SAP Concur, which can scan, process, and approve invoices, reducing the time required for manual handling.
Retail jobs
AI is revolutionising the retail industry by taking over many tasks traditionally performed by human staff. Automated self-checkout systems, like those found in grocery chains and big-box retailers, allow customers to scan and pay for items independently, reducing the need for cashiers. AI-powered inventory management systems, such as those used by Amazon, track stock levels in real-time, predict demand, and even place restocking orders automatically, ensuring shelves are consistently stocked. Additionally, in-store robotic assistants, like Lowe's "LoweBot," help customers locate items while providing basic product information. These robots also assist with inventory checks, making retail operations more efficient.
Beyond the store, AI is reshaping the way customers engage with brands through personalised recommendations. Online retailers like Amazon and fashion platforms like Zalando use AI algorithms to analyse user preferences and browsing history, delivering tailored product suggestions that increase customer satisfaction and boost sales.
Manufacturing and assembly
AI-powered robots are transforming the industrial and manufacturing sectors by taking over repetitive and time-consuming tasks that were traditionally carried out by human workers. From assembly line operations to quality control and packaging, these robots are making production processes faster, more accurate, and cost-effective.
For example, automotive manufacturers like Tesla and Toyota utilise robotic arms to assemble cars with precision and consistency that humans would struggle to achieve over long periods. Similarly, in electronics manufacturing, robots handle the delicate task of soldering tiny components onto circuit boards, ensuring high levels of accuracy and reliability.
Transport and delivery
Autonomous vehicles and drones are revolutionising the logistics, delivery, and transportation industries by integrating AI systems that handle tasks like routing, driving, and even delivery operations without human input. Self-driving trucks are already being used for long-haul freight transport. Companies like Waymo and Tesla are testing autonomous semi-trucks that can travel long distances, reducing the reliance on human drivers and cutting transportation costs. These trucks use AI to detect obstacles, optimise routes, and adjust to road conditions in real-time, ensuring more efficient and safer deliveries.
Marketing
AI is transforming marketing by automating processes, making campaigns more precise, and saving time and resources. One impactful innovation is the use of AI tools for customer segmentation. These tools analyse data on customer demographics, purchase history, and online behaviour to group audiences into distinct segments. For example, an e-commerce platform might use AI to identify a group of customers who frequently purchase fitness products and another segment interested in home decor, allowing businesses to create tailored marketing strategies for each group.
Personalised email campaigns powered by AI take segmentation to the next level. Platforms like Mailchimp and HubSpot employ machine learning algorithms to deliver customised email content based on individual customer preferences and purchase history. For example, AI can predict which product recommendations will most likely engage a particular customer, increasing the chances of conversion. Similarly, AI enhances programmatic ad buying by automating the process of bidding on digital ad spaces in real-time. Platforms like Google Ads use AI to evaluate millions of data points, ensuring ads reach the right audience at the right moment, optimising both cost and effectiveness.
Data analysis
AI is rapidly transforming data-heavy roles by automating tasks like data collection, processing, and reporting, allowing professionals to focus on higher-value activities. For example, in the financial sector, AI tools such as Bloomberg Terminal and Kensho analyse market trends, process transaction data, and generate detailed reports in minutes, tasks that previously required hours of manual effort. Similarly, in healthcare, AI platforms like IBM Watson Health streamline the analysis of patient records and medical imaging, uncovering patterns that help diagnose diseases or predict treatment outcomes with remarkable speed and accuracy. Marketing teams also benefit from AI-driven tools like Google Analytics and Tableau, which collect web traffic data, visualise metrics, and identify customer behaviour trends–all with minimal human intervention.
Currently job hunting? Need a hand with improving your CV or cover letter? Explore these helpful resources to elevate your career:
Tips for writing a good CV.
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How will AI reshape your job?
While AI may replace specific tasks in your job, it’s also enhancing others by supporting your unique capabilities. Instead of replacing roles, AI can assist with tasks like data analysis, allowing you to focus on decision-making, creativity, and strategic thinking. As we explore various industries in the UK, it becomes clear that AI is not just about automation but collaboration, where human expertise and AI work together to drive better results.
Consumer markets
In retail and e-commerce, AI helps personalise shopping experiences, predict consumer behaviour, and manage inventory. However, human professionals are still needed to interpret data, develop creative marketing strategies, and build customer relationships. While AI can recommend products, human-driven customer service remains essential for addressing complex queries and building trust.
Financial services
AI in finance can streamline tasks like fraud detection, risk assessment, and investment predictions by quickly processing vast amounts of data. Yet, financial advisors, analysts, and other professionals are still essential for interpreting complex situations, providing personalised advice, and managing client relationships. For example, while AI might automate portfolio management, human financial advisors are necessary for tailoring investment strategies for high-net-worth individuals.
Medical and healthcare services
In healthcare, AI has the potential to revolutionise diagnostics, treatment plans, and patient care by analysing medical data and images more efficiently than ever before. However, doctors, nurses, and healthcare providers are indispensable for interpreting the results, making nuanced decisions, and offering compassionate care. AI might assist in identifying cancerous cells in imaging scans, but a doctor’s experience is essential in creating a final diagnosis and discussing treatment options with the patient.
Industrial products
AI optimises supply chains, enhances manufacturing processes, and monitors equipment for predictive maintenance. However, human engineers and technicians are still needed for complex troubleshooting, design innovation, and system optimisation. While AI can predict potential failures by analysing factory machine data, humans are still required to repair and modify machines requiring hands-on expertise.
Technology, media, and telecommunications
AI is already used for content generation, customer support, and network management in the tech and media industries. While these processes will continue improving with AI, human professionals will be needed for strategic decision-making, creative work, and ethical standards. AI can generate written articles or news reports, but human journalists are still required to ensure accuracy, add context, and maintain editorial standards.
In the future, the workforce will collaborate with AI, using its capabilities to enhance performance while relying on human insight and expertise for decision-making, creativity, and emotional intelligence. Rather than replacing jobs, AI is reshaping them, allowing you to focus on higher-value tasks that leverage your unique skills. For more guidance on adapting to the job market, refer to our AI CV Writing Guide.
Are you looking for jobs in any of these fields? Explore these CV examples to help get you started:
Tips for incorporating AI into your work
If you're considering a career change or looking to integrate AI into your current job, there are several steps you can take to stay ahead of the curve and embrace the future of work:
1. Invest in learning
Upskill by taking online courses, certifications, or degree programs in AI and machine learning. Key areas to explore include data science, programming languages (like Python or R), and AI ethics. According to the WEF, technological skills, especially AI and big data, will become increasingly vital in the next five years.
2. Understand the basics
Even if you don’t plan to become an AI expert, understanding how AI works and its potential applications in your field is essential. Many industries seek employees to bridge the gap between technology and business. Identify areas within your role or organisation where you can integrate AI into existing processes and tools to help employees focus on more strategic tasks.
Discover how to embrace functional change with AI by identifying areas where you can integrate the technology into your current processes and tools.
3. Seek out AI-focused roles
Explore career opportunities requiring AI expertise and look for ways to incorporate AI into your current job. AI applications are expanding across industries, and businesses are eager to stay competitive. Consider platforms like AI Jobs for AI-specific roles and networking opportunities.
4. Network and collaborate
Attend AI and tech-related forums, conferences, and communities to stay informed about the latest trends. Networking with professionals in the AI field can provide valuable insights and open doors to new job opportunities. Explore AI-related events on platforms like Eventbrite to connect with others in the field.
Now that we've explored jobs at risk of being replaced and those being transformed, how can you embrace AI rather than fear it? According to the World Economic Forum’s 2025 Future of Jobs Report, by 2030, nearly 40% of current skills will be transformed or outdated. To stay relevant in your role, you need to adapt to these technological shifts and explore how AI can improve your work.
Practical examples of AI in the workplace
Automate routine tasks using AI tools, like project management software or CRM systems. These tools can handle scheduling, client communication, and data analysis, giving you more time for strategic work.
Leverage AI to process large datasets quickly and make data-driven decisions. Whether in marketing, finance, or operations, AI insights can improve decision-making and business outcomes.
Use AI to enhance creativity and innovation in roles like design, writing, or marketing. Tools can speed up tasks like image editing, proofreading, or generating ideas, leaving you free to focus on aspects requiring human intuition and creativity.
Develop soft skills like empathy, negotiation, and leadership. These uniquely human qualities will remain in high demand as AI continues to evolve and automate technical tasks.
The World Economic Forum’s Centre for the New Economy and Society collaborates with businesses, academia, and governments to help people globally prepare for the future economy, through initiatives like the Jobs Initiative and Reskilling Revolution platforms.
Key takeaways
AI’s impact on industries: AI is not just a trend; it’s the dawn of a new era that will reshape every industry. While some sectors will race ahead, others may take a bit longer to adapt, but one thing is certain: change is coming, and it’s happening fast. The world of work is transforming right before our eyes, and the ability to adapt and stay flexible will be your secret weapon to not just survive but thrive in this AI-driven future.
Job evolution: Yes, some jobs will be replaced by automation. But here’s the silver lining as many roles will evolve, and new, exciting opportunities will emerge. It’s time to embrace the change with open arms, to upskill, and to take ownership of your career’s evolution. The jobs of tomorrow are waiting for you, and with the right mindset, you can be at the forefront of this revolution.
Lifelong learning: To stay ahead in this fast-changing world, adopt a mindset of continuous learning. Stay curious, be proactive in learning new skills, and keep seeking opportunities for growth. The key to futureproofing your career lies in a commitment to never stop growing because the future belongs to the curious, the adaptable, and the willing.
Government and business roles: The UK government is already investing in tech skills, particularly AI and data science, recognising that the future workforce needs these essential tools to succeed. Meanwhile, businesses are stepping up, offering upskilling programmes designed to keep their teams competitive and equipped for the AI-driven world ahead.
Staying adaptable: In the AI-driven future, adaptability is everything. Embrace the culture of continuous learning, stay flexible, and welcome technological advancements. By doing so, you’ll not only maintain career relevance, you’ll set yourself up for success in a world that is constantly evolving.
Next steps?
Whether you're seeking a job in AI or want to highlight your AI skills on a CV, having a good CV and cover letter is essential. Our intuitive AI CV Maker and Cover Letter Builder, along with over 170 CV examples, allows you to easily tailor your documents to your needs. Alternatively, you can take advantage of our CV Writing Services, where one of our experts can help you craft the perfect application.
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FAQs
What are the top fastest-growing jobs in the UK?
The fastest-growing jobs for 2025 in the UK reflect advances in technology, healthcare, and sustainability. Roles such as Artificial Intelligence/Machine Learning (AI/ML) Engineers are in high demand due to AI's transformative impact across industries, with an 86% increase in job postings.
Other in-demand jobs include Cybersecurity Specialists, Software Developers, Project Managers, Healthcare Professionals, and Environmental Officers. Additionally, roles related to Data Governance and School Teachers are seeing significant growth due to increasing educational and recruitment needs.
Will IT jobs be replaced by AI?
AI is set to reshape, not replace, IT jobs. While routine tasks like basic coding and system monitoring may be automated, new roles will emerge, including positions for AI/ML engineers, data scientists, cybersecurity specialists, and cloud architects. Human oversight remains crucial, as AI requires creativity, ethical decision-making, and supervision. To stay competitive, IT professionals must upskill in areas like AI, data analysis, cybersecurity, and cloud computing. Ultimately, the future of IT is about transformation, offering new opportunities for those who adapt.
What is the best AI-proof job?
While it's impossible to predict precisely how AI will evolve in the next decade, experts agree that AI will continue to advance rapidly. The risk of under-preparing for these changes far outweighs any potential benefits of over-preparing. Even if AI’s progress slows, leveraging its current capabilities, investing in relevant technologies, and building a skilled workforce will still provide significant advantages. The key to staying "AI-proof" is focusing on roles that require human skills AI cannot replicate, such as creativity, emotional intelligence, complex problem-solving, and ethical decision-making, while embracing AI to enhance productivity and innovation.
| 2025-06-27T00:00:00 |
https://www.cvmaker.uk/blog/career/what-jobs-will-ai-replace
|
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|
Robotics & Automation: An Ultimate Guide
|
ROBOTICS & AUTOMATION: AN ULTIMATE GUIDE
|
https://www.uti.edu
|
[] |
Robotics and Automation Training & Job Outlook. Training. Technical ... Labor Statistics' May 2023 Occupational Employment and Wages. Entry-level ...
|
Robotics and automation technicians are responsible for maintenance, repairs, diagnostics, troubleshooting and innovation in automation technology. It is an important industry that improves productivity in various fields.
If you're interested in cutting-edge manufacturing processes, you should learn everything you can about them. Robotics and automation technology is used in industries such as electronics, healthcare, manufacturing, food and beverage, agriculture and military.
Universal Technical Institute (UTI) offers a Robotics & Automation Technician training programfor aspiring technicians to learn using industry-standard equipment and develop the skills they need in the field. Graduates of UTI's program can use their knowledge and training to pursue entry-level positions in the industry! 1
Keep reading to learn more. In this blog post, we'll look at the robotics and automation trade and what it's like to become a technician in the industry.
Key Points:
- Robotics and automation technology is crucial in enhancing productivity across various industries, including electronics, healthcare, manufacturing, food and beverage, agriculture and military applications. This technology focuses on performing tasks with minimal human input, improving efficiency and reducing errors.
- Universal Technical Institute (UTI) offers a Robotics & Automation Technician program designed to equip students with the necessary skills to pursue entry-level positions in the field after graduation. The program includes hands-on training on industry-grade equipment and in-depth robotics and automation courses taught by experienced professionals.
- Robotics and automation technicians are responsible for maintenance, repairs, diagnostics, troubleshooting and innovation within the manufacturing industry and other sectors. Technical training is essential for developing the skills required to create, maintain and operate robotic solutions safely and securely.
- UTI's program covers key aspects of robotics and automation, including electrical circuitry and programming industrial robotic systems. Students benefit from both classroom and online instruction, as well as hands-on application in labs. UTI also provides career services to help graduates prepare for job opportunities.
What Is Robotics and Automation?
Robotics is an interdisciplinary branch of computer science and engineering that focuses on the integration and operation of robots. Robots are machines that can automatically perform a complex series of actions. Automation is used in manufacturing and warehousing, including robotic manufacturing, to perform production processes with minimal human input.
Robotics vs. automation
Automation uses technology, programming and other processes to get machines to perform tasks with minimal need for human input. Robotics is one of the ways automation is used. While automation focuses on how to get machines to create certain outcomes, the goal of robotics is to design robots to perform specific tasks without the need for human mediation.
Benefits of Robotics Automation
Robots in automation are intended to precisely perform tasks that are considered dull, dirty and dangerous. That’s because the algorithms it employs ensure consistency and reduce the possibility of error. Robots also have no biological limits and can operate for long periods of time, making them more efficient at their intended functions. Because robotic machines can be built to be highly durable and heavy-duty, they can handle more difficult and time-consuming tasks. They eliminate the potential hazards of tasks previously assigned to humans, such as lifting heavy loads, transporting noxious chemicals and many others.
Uses for Robotics and Automation
Industrial automation and robotics have many potential applications. Several industries rely on robotics and automation to keep their operations up to modern performance standards, including:
Electronics: Robotic arms are used to test, manufacture and fabricate printed circuit boards (PCBs), as well as for inspection and assembly of components.
Robotic arms are used to test, manufacture and fabricate printed circuit boards (PCBs), as well as for inspection and assembly of components. Healthcare: Industrial robots are used in sanitation, surgery and even for packaging medical supplies.
Industrial robots are used in sanitation, surgery and even for packaging medical supplies. Manufacturing: Robotic arms, gantry robotics and welding robots are used to create various technologies and their components.
Robotic arms, gantry robotics and welding robots are used to create various technologies and their components. Food and beverage: Robotic arms are used to help prepare and package food cleanly and safely.
Robotic arms are used to help prepare and package food cleanly and safely. Agriculture: Automation is used to assist with precise and repetitive tasks. Smart sensors collect data about current conditions and adjust processes accordingly.
What Are Robotics & Automation Technicians?
Robotics and automation technicians may also be referred to as robotics technicians or automation technicians. They typically work in the manufacturing industry or the manufacturing sector of other industries like those listed above. Some of their primary duties and responsibilities include automation technology maintenance, repairs, diagnostics, troubleshooting and innovation.
Robotics and Automation Training & Job Outlook
Training
Technical training can be beneficial for those looking to develop essential technical skills for the robotics and automation industries. With technological advancements, robotic solutions are increasingly finding their way into everyday processes at home and work. Aspiring technicians can learn how to create, maintain and operate these solutions safely and securely by enrolling in robotics and automation courses.
Universal Technical Institute offers robotics and automation technician training at campuses nationwide.1We provide comprehensive technical training in key aspects of robotics and automation through this program, with the main focus being on understanding automation processes. Our Robotics & Automation Technician program's courses cover the fundamentals of electrical circuitry, programming industrial robotic systems and more.
Along with classroom and online instruction, UTI’s robotics and automation students gain valuable hands-on training in our labs.
UTI also has a Career Services team that can help grads learn about and prepare for job opportunities. Alumni can use these resources for many years after graduation, even decades!
Careers in robotics and automation
According to the Bureau of Labor Statistics (BLS), the median annual salary for robotics and automation technicians in the United States was $65,080 in May 2023.59 This means half earned more and half earned less. Keep in mind that salary depends on several factors, including experience, employer, demand and cost of living in the area.
Most of our grads start out working as technicians or in other entry-level roles. As with any industry, over time, they may be able to advance in their career with experience and hard work. Some entry-level and advanced roles could include:77
Entry level
Robotics and automation technician
Control systems technician
Maintenance technician
Advanced
Instrumentation and controls technician
Service technician III
Senior maintenance technician
Process control technician
Product tech specialist
Test engineering technician
If this is a work environment that interests you, it could be a good career path to pursue.
Working conditions
Robotics and automation technicians often work alone or in small groups. If the machines they service are in use 24/7, they may be required to be on call around the clock, and they may work night and weekend shifts.
Because they’re frequently employed in manufacturing, robotics technicians and automation technicians often work in noisy environments. Some may be required to travel long distances to service technology on-site. This sensitive technology also often necessitates privacy and protection, resulting in most of their work being done indoors.
UTI Robotics and Automation Training
Training for a career in robotics and automation could be a great choice if you’re interested in learning more about these manufacturing processes.
At UTI, you can learn how to maintain and repair robotics systems from experienced automation professionals and develop key skills that could help you pursue a robotics and automation career after graduation.1
Request more informationto learn how UTI's robotics program can help you pursue your career goals if you want to work in this industry! Apply today to see where robotics and automation training can lead you.
| 2025-06-27T00:00:00 |
https://www.uti.edu/blog/robotics-and-automation/what-is-robotics-and-automation
|
[
{
"date": "2025/06/27",
"position": 76,
"query": "job automation statistics"
}
] |
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Disconnect Between AI Adoption And Policy Awareness In ...
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Disconnect Between AI Adoption And Policy Awareness In Workplace: KnowBe4
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https://securitymea.com
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[
"On",
"In",
"Tagged"
] |
Varying AI Adoption Rates: While the average percentage of employees using AI in the workplace is 60.2% globally, adoption rates varied by region. France ...
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KnowBe4 has shared new survey findings highlighting a severe AI governance gap. A new KnowBe4 survey of employees across Germany, South Africa, the Netherlands, France, the UK, and the US reveals that while a large majority of employees already engage with Artificial Intelligence (AI) tools at work, a strikingly low percentage are aware of their company’s official policies governing its use.
The findings reveal that, on average, 60.2% of employees are using AI tools in the workplace. In contrast, only 18.5% are aware of their company’s policy on AI usage. This significant gap suggests that the vast majority of AI activity within organizations is taking place without guidance or oversight. One in 10 employees (10%) have admitted to putting client data into an AI tool to complete a work task.
“An AI governance gap is like a ticking time bomb for organizations,” said Roger Grimes, data-driven defense evangelist at KnowBe4. “When the majority of your workforce is using AI but fewer than 20% understand the rules of engagement, you have a massive problem. AI tools are powerful, but without clear policies and training, employees may unknowingly feed sensitive information, like client data, into systems that were not designed to handle it securely. We often think of cyber risk as external, but in the age of AI, internal misuse, however unintentional, could lead to serious data breaches, compliance violations, and reputational damage.”
Other Takeaways Across Regions
Varying AI Adoption Rates: While the average percentage of employees using AI in the workplace is 60.2% globally, adoption rates varied by region. France shows the lowest adoption rate, with only 54.2% of employees saying they use AI tools at work, indicating a slower adoption rate. Conversely, South Africa records the highest at 70.1%, suggesting a more widespread use of AI.
While the average percentage of employees using AI in the workplace is 60.2% globally, adoption rates varied by region. France shows the lowest adoption rate, with only 54.2% of employees saying they use AI tools at work, indicating a slower adoption rate. Conversely, South Africa records the highest at 70.1%, suggesting a more widespread use of AI. Persistent Policy Awareness Gaps: An average of 14.4% of employees reported being unaware of their company’s AI policy. This lack of awareness is particularly notable in the Netherlands (16.1%) and the UK (15.8%), indicating a need for enhanced communication and training strategies.
An average of 14.4% of employees reported being unaware of their company’s AI policy. This lack of awareness is particularly notable in the Netherlands (16.1%) and the UK (15.8%), indicating a need for enhanced communication and training strategies. Sanctioned AI Use is Lagging: Only an average of 17% of employees use AI at work with their IT/security team’s knowledge. This figure, though highest in South Africa (23.6%), remains low overall, indicating a need for organizations to proactively provide and promote approved AI solutions.
The research emphasizes the critical need for organizations to bridge this awareness-usage gap. This requires not just establishing policies, but actively communicating them, providing comprehensive training on ethical and secure AI use, and offering approved, user-friendly AI tools to mitigate the significant risks posed by uncontrolled AI adoption.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://securitymea.com/2025/06/27/disconnect-between-ai-adoption-and-policy-awareness-in-workplace-knowbe4/
|
[
{
"date": "2025/06/27",
"position": 25,
"query": "workplace AI adoption"
}
] |
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] |
AI Readiness: How AI is redefining the workplace
|
AI Readiness: How AI is redefining the workplace
|
https://www.robertwalters.ie
|
[] |
Discover how to assess your organisation's AI readiness and prepare your teams, data, and systems for successful AI adoption and integration.
|
Recently, the rapid growth and adoption of AI has impacted how organisations operate daily, but also how they think about the future of their business. Organisations have begun to consider their level of AI readiness and ways to prepare their business for the future with an AI-ready workforce. This includes evaluating current capabilities, identifying critical skill gaps, investing in continuous learning, and aligning AI strategies with long-term business objectives. But what is AI readiness?
What is AI readiness?
AI readiness refers to an organisation’s ability to implement AI into their operations and this ranges from foundational readiness such as incorporating elementary tools to AI transformation – the high-level use of AI tools that have a significant impact on how a certain function operates. AI readiness encompasses all the steps an organisation should take prior to implementing AI tools or systems.
While the barrier to entry for using generative AI tools such as ChatGPT or Microsoft Co-Pilot is fairly low, effectively using AI to augment and automate processes in the workplace is significantly more difficult. This goes beyond AI knowledge and into ethical and responsible use that complies with data governance policies.
Adopting AI tools requires a strategic approach and a strong understanding of the implications of AI. To gain insight into where organisations currently are on this journey, we conducted a poll to see how our audience views AI, especially in relation to job creation and job losses.
Attitudes to AI
While it’s key for businesses and employees to adapt to this technology, the sentiment around AI in the workplace is varied. In March 2025, our Advisory team launched several LinkedIn polls to gain some insight into our current audience and their view on AI in the workplace. Here are a few key stats:
• 54% of respondents expect AI to create less new jobs than it replaces
• 72% of respondents believe AI will make things better
• 58% of respondents believe AI has not provided any measurable benefit for their organisation’s hiring process yet
• 56% of respondents believe AI will replace low-skill jobs by 2030
While these stats present only our LinkedIn audience and those who engaged with the relevant polls, it’s interesting to note that while many respondents feel AI will provide general improvements, a large majority also haven’t yet seen any benefit of AI particularly in their hiring process. While many believe AI will drive improvement, most haven’t seen measurable outcomes just yet.
There’s no doubt that AI still plays a significant role in the growth and potential success of organisations, but what impact has it already made?
The impact of AI on the workplace
The tech space is already witnessing an exceptional AI impact, with Meta CEO, Mark Zuckerberg, stating that AI will replace mid-level engineers by 2025. In an article shared by Forbes, it stated that Zuckerberg believes “AI can take over coding tasks, allowing human engineers to focus on higher-level problem-solving and creativity.”
Outside of tech, AI has proven a positive addition to different processes in HR management, logistics, and manufacturing capability enhancements. Additionally, the drug discovery space has noticed accelerated research timeframes and reduced costs with the applications of AI. This quick adoption of AI was likely made possible by the high level of AI readiness that may already exist within these organisations and sectors.
The world has witnessed immense technological advancement over the last few decades and at a pace that many could not have imagined. While innovation and ingenuity should be at the forefront of any future-focused business, what are the effects of unregulated AI application?
The risks of AI
A roundtable between TechCentral and NTT DATA revealed that generative AI is "reshaping IT infrastructure, software development, cybersecurity, and business operations". However, the roundtable also placed significance on AI governance to find a balance between innovation and data protection.
In 2023, UNESCO highlighted key examples of ethical dilemmas with AI use. This ranged from AI systems delivering biased results, to AI in the court of law, and AI creating art. In terms of creating Art, UNESCO shared that AI is reshaping “what it means to be an author” and that the creation of original art plays a key role in inclusive societies.
For HR, it’s no different. To implement impactful but ethical use of AI, organisations need to understand what can be automated and what can’t. AI should be used in a regulated way, whether that’s in hiring, onboarding, or even gathering employee engagement insights. For example, automating administrative tasks in HR is one option, but what happens when companies begin automating their employee reviews? How do they ensure unbiased results that consider the employees’ tenure, their role, and their current circumstances? This balancing act is a crucial part of regulating AI in many areas, and particularly in a division of the organisation that engages your most valuable asset – your people. While HR teams should consider how they use AI, there's no doubt that job seekers are already tapping into these tools.
AI for job seekers
The possibilities of AI cannot be overstated, particularly for job seekers who often see it as a useful tool to tailor their resumes or cover letters. Nearly 65% of job applicants use AI at some point in the application process, according to a trend report from Career Group Companies.
During an application, employers are often checking if you’re the right fit, either from a skills or culture perspective. The information you provide should be reflective of your true capabilities and AI should help you enhance your application, not embellish.
“While AI can be helpful for quick applications, hiring managers value authenticity and a true reflection of a candidate’s experience.” Christ Eldridge, CEO of Robert Walters UK&I
For organisations, however, Forbes reported that employees selected based on AI recommendations tend to perform better as AI removes human bias and prevents hiring managers from making decision based on first impressions. Still, AI isn’t ready to make unsupervised decisions and should be used as a tool for experienced professionals who have their organisation’s objectives top of mind.
How to prepare for the future of AI at work
According to McKinsey & Company’s, The State of AI, survey from March 2025, the redesign of workflows will have the most significant impact on an organisation’s ability to see EBIT impact from gen AI use. In the same survey, they noted that only 27% of respondents whose organisations use AI review content generated by AI.
So, if redesigning organisational workflows with AI is the key to seeing a potential monetary impact but so few organisations are reviewing their AI content, what does this mean for the future of work?
It’s crucial to address the current and potential risks of AI, as well as security concerns. McKinsey & Company’s survey also highlighted that while respondents from larger organisations are more likely to say that their employer is managing AI risk, they are not more likely to address these risks.
From our own LinkedIn polls, we also know that 40% of respondents shared AI has not been explored at all while 52% noted using AI in some areas. Only 8% of respondents stated that AI has been fully integrated into their organisation – indicating that these organisations likely had a high level of AI readiness in order to act as quickly.
To prepare for the future of the workplace that most certainly includes the use of AI, it’s crucial to balance your gen AI use with cybersecurity practices and operational processes designed to mitigate risk on your journey to becoming AI ready.
Bridging the AI gap: How our AI Readiness Masterclass helps organisations prepare
With 40 years in recruitment, our teams have recognised the growing need for AI use in recruitment and hiring. The AI Readiness Masterclass was born out of a need to address some of the key areas of concern for HR and talent acquisition professionals, and how AI can support these roles. As organisations assess their AI readiness, understanding how to strategically apply AI in recruitment becomes increasingly critical. Packaged into five one-hour sessions, the AI Readiness Masterclass delivers key benefits including the commercial value AI can deliver for your business, such as lower hiring costs and increased productivity within HR and recruitment.
Contact our Future of Work specialists to find out more about our AI Readiness Masterclass.
| 2025-06-27T00:00:00 |
https://www.robertwalters.ie/insights/hiring-advice/blog/ai-readiness-in-the-workplace.html
|
[
{
"date": "2025/06/27",
"position": 61,
"query": "workplace AI adoption"
}
] |
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|
Report: Most workforces not ready to leverage AI successfully
|
Report: Most workforces not ready to leverage AI successfully
|
https://www.siliconrepublic.com
|
[
"Colin Ryan",
"Colin Ryan Is A Senior Reporter",
"Sub-Editor With Silicon Republic",
"Having Joined The Company In January Coming A Background In Creative Media",
"Technology",
"Colin Has Previously Worked As A Researcher",
"Camera Operator. He Enjoys Watching Films",
"Listening To Music",
"Befriending Every Dog He Meets."
] |
Skills and trust. According to the report, there are three critical barriers that are inhibiting AI adoption: organisational change management, lack of employee ...
|
From C-suite disconnects to lack of trust, Kyndryl’s People Readiness Report highlights where organisations are falling short of successful AI adoption.
Last month, tech services provider Kyndryl released a global study that indicated a significant gap between AI investment and workforce preparedness in enterprise.
The People Readiness Report revealed that while 95pc of businesses have adopted AI in their operations, 71pc of business leaders believe their workforces are not ready to use the technology to its full potential.
The report, which surveyed more than 1,000 senior business and technology executives across 25 industries and eight geographies, found that only a small group of businesses – dubbed ‘AI Pacesetters’ – have aligned their workforce, technology and growth goals to benefit from AI adoption.
“The rest are often treating AI as a technical implementation rather than a transformation that demands cultural and operational change,” Nick Drouet, CTO of Kyndryl UK and Ireland explains to SiliconRepublic.com.
Drouet further explains that of the businesses leveraging AI, 66pc of them focus heavily on internal process optimisation while neglecting the ways in which AI can influence growth opportunities or workforce roles.
“Pacesetters, by contrast, embed AI into their enterprise and culture, and they empower their people through trust-building, change management and proactive upskilling. Not focusing on this holistic integration is where most businesses fall short.”
Skills and trust
According to the report, there are three critical barriers that are inhibiting AI adoption: organisational change management, lack of employee trust in AI and skills gaps.
“Many organisations underestimate the level of organisational change required,” says Drouet. “They also struggle to build trust in AI among employees – especially when fears of job displacement go unaddressed.”
Only four in 10 leaders surveyed say their organisation has fully implemented an overall AI adoption strategy to take them from their current state to a future state, while even fewer have implemented foundational strategies such as an AI governance framework or a change management strategy.
In fact, 53pc of responding leaders believe their workforce is ready to navigate changes related to AI over the next five years.
In terms of trust, half of leaders say there is a widespread fear of job displacement among their workforces, which is affecting employee engagement with AI. 45pc of CEOs state that their employees actively resist the tech.
While, 51pc of leaders say there is a lack of skilled talent to manage AI tech in their business.
“Solving the skills gap requires investment and intention,” says Drouet. “Businesses must understand not only what skills are missing but where demand is heading.”
C-suite contrasts
Interestingly, the report suggests that a disconnect exists between CEOs and CIOs/CTOs regarding how they view workplace readiness and how they can improve readiness.
According to the report, CEOs are more likely to report that their organisation is not currently using AI or is still in the early stages of implementation.
“CEOs tend to perceive more employee resistance to AI – 45pc say their teams are hostile or resistant – while 73pc of CTOs and CIOs say the opposite,” says Drouet. “This disconnect can result in misaligned strategies and missed opportunities.”
The disconnect is further present in regard to strategies for solving the skills gap.
80pc of CIOs and CTOs are focused on upskilling existing employees in AI, while 43pc of CEOs are prioritising hiring new employees to get those skills.
What can be done?
With the critical barriers to AI adoption identified, we asked Drouet how organisation can combat these challenges.
When it comes to trust, Drouet says transparency is key.
“Trust is foundational. People need to feel that AI is there to enhance, not replace, their work,” he says. “Organisations must be transparent about how AI will be used, explain its benefits and actively involve employees in its deployment.”
Drouet says that where most leaders fall in terms of AI adoption is underestimating the need for a full organisation transformation.
“Technology adoption is the easy part – it’s the cultural, governance and workforce alignment that make or break AI strategies,” he says.
“Kyndryl’s research shows Pacesetters are nearly three times more likely to have implemented a change management strategy and two times more likely to say their workforce is ready to navigate change. Too many organisations skip these foundational steps and focus only on AI tools.
“But tools don’t create value; people using them effectively do.”
Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://www.siliconrepublic.com/careers/kyndryl-ai-report-workforce-skills
|
[
{
"date": "2025/06/27",
"position": 77,
"query": "workplace AI adoption"
}
] |
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] |
HR leaders are under pressure to adopt AI – but at what ...
|
HR leaders are under pressure to adopt AI – but at what cost?
|
https://www.humanresourcesonline.net
|
[] |
Privacy: Given that AI solutions in HR will be used on employee data, it's essential for them to embed privacy directly into the solution. Tech adoption: ...
|
As AI reshapes human capital management, James Saxton, VP Global Product Ambassador at Dayforce, shares insights on how organisations can adopt AI thoughtfully – ensuring ethical use, regulatory alignment, and meaningful impact for both business and workforce.
This article is brought to you by Dayforce.
AI has evolved from an intriguing HR add-on to a transformative force. While its potential is clear, so are the risks. As such, HR leaders face a paradox: they must drive innovation and efficiency through AI, while also ensuring ethical use, fairness, and compliance in a tightening regulatory climate.
In such a charged environment, where boards are demanding shareholder value, employees expect agility, and regulators want accountability, responsible AI adoption isn’t optional. CHROs must move beyond experimentation and hype to build practical, people-first AI frameworks that can deliver value and earn trust.
6 challenges HR faces in adopting AI
While enthusiasm for AI is rising, so are the implementation headaches. Here are six of the most common roadblocks HR teams face when adopting AI:
Compliance: Regional frameworks are constantly evolving, and AI solutions must be agile enough to adapt to these changes, helping organisations manage compliance today and tomorrow.
Bias: Employers, legislators, and employees alike are asking: how fairly and accurately can AI make judgments, especially when the goalposts for addressing bias are constantly shifting?
Ethics: Beyond compliance, organisations must ensure their use of AI does not cause harm to individuals or society, or compromise on fairness, transparency, and respect.
Transparency and quality: Understanding how AI arrives at its conclusions is concerning, especially when AI drives decisions affecting people. Additionally, AI models can deteriorate over time without robust monitoring.
Privacy: Given that AI solutions in HR will be used on employee data, it’s essential for them to embed privacy directly into the solution.
Tech adoption: Increasing AI adoption means taking concrete steps to building literacy, comfort, and creative thinking required for people to use the technology effectively.
To navigate these challenges successfully, HR needs a clear vision and a well-defined strategy for adopting AI thoughtfully and ethically.
As James Saxton, VP Global Product Ambassador at Dayforce, highlights, employees are increasingly seeking less friction in their work lives — whether it’s having greater flexibility in their careers, or seamless, consumer-grade digital experiences like mobile self-service. This, while organisations face mounting challenges, such as economic uncertainty and complex regulations.
How can we find balance between these business needs and employee expectations? Well, that’s where HR can truly shine. By harnessing AI strategically, HR leaders can simplify processes, personalise employee experiences, and build adaptive systems that prioritise both organisational resilience and workforce wellbeing.
Given his experience in engaging with key customers, partners, and industry influencers on shaping actionable strategies, we probe Dayforce’s Saxton a little further on the five non-negotiable principles in driving responsible, effective, and sustainable implementation of AI in human capital management (HCM). Let's dive into the insights.
Non-negotiable 1: Prepare your people
Saxton shares, that to drive AI adoption, organisations must take concrete steps to build literacy, comfort, and creative thinking required for people to use the technology effectively.
"This is where employee readiness and upskilling come in. Before investing in any AI, it’s essential to involve your team, as they are the primary users of the technology,” he adds, advising organisations to start by creating tailored learning programmes for everyone who will interact with AI.
"This training should cover AI ethics and correct usage, tailored to individuals' roles and the way they are expected to utilise the system. It would help to offer learning options that fit employees’ learning styles and monitor their progress to help ensure understanding."
However, he notes, such targeted training can be hard to implement, so using a learning management system (LMS) embedded in the HCM solution can help empower employees and manage timelines and completion criteria, allowing teams to track learners’ progress and provide feedback.
But it’s not just about theory. “Once training is set, building an AI “sandbox” can help users test what they’ve learned in a controlled environment and use the software as they would in the real world. This will help them build practical skills, get comfortable with the tools, and help them learn to think creatively as they use the tool so they’re ready once the real-world solution is deployed,” Saxton says.
These play zones can turn hesitant learners into confident AI champions.
Non-negotiable 2: Get real about compliance
Compliance in AI isn’t static — it’s a moving target that requires ongoing attention. From strict frameworks like the EU’s GDPR to new local regulations, organisations need AI solutions that not only meet today’s requirements but are designed to evolve with global regulations.
Saxton queries: “Is the AI solution agile enough to manage compliance today and tomorrow? This is critical, especially since legislation moves fast, but AI moves faster, and something that is legal today may become illegal in the future.”
True compliance means choosing AI tools that include processes for monitoring regulatory updates, adjusting policies as needed, and providing clear, auditable evidence of compliance across different regions. Relying on static certifications or promises isn’t enough.
Non-negotiable 3: Use AI only where and when you require it
When it comes to AI in HCM, a one-size-fits-all approach is a huge risk. Here are some ways you can stay ahead of the curve:
Challenge the toggle trap: Avoid platforms with only on/off AI controls. Choose the proper tools that allow you to tailor AI by role, function, or region.
Respect individual agency: Not everyone needs AI all the time. Enable opt-ins, opt-outs, or contextual functionality to build trust and boost adoption.
Not everyone needs AI all the time. Enable opt-ins, opt-outs, or contextual functionality to build trust and boost adoption. Governance without gridlock: Regulations change quickly. Use solutions with agile governance to adapt AI use without disrupting operations.
Some important questions Saxton urges leaders to ponder are:
How specific can you be about where and when AI is used? Can employees opt in or out of having access to it? Can you turn the tool on and off depending on which regulatory jurisdiction it's being used in?
In short, when it comes to AI in HCM, flexibility isn’t just a feature — it’s a must-have strategy to unlock human potential while maintaining robust control.
Non-negotiable 4: Shatter the black box… where possible
AI must be explainable. With a lack of transparency as one of the top challenges to AI deployment — particularly for third-party solutions—organisations should demand clarity on how models make decisions, both at a general level and in specific scenarios.
“It’s important to ask: how does AI arrive at its conclusions? How does the model influence decisions, both specific and general? How does the system get consent? And how can we ensure quality over time, especially once the AI model is influenced by real-world data?”
The best outcomes happen when AI and human insight work hand-in-hand. Here’s how that could happen:
Keep your people involved, especially for sensitive or strategic decisions. AI should inform and assist human insight – not replace them. Ask the critical questions: How does the model reach its conclusions? Are users clearly informed when AI is in play? Is consent sought and recorded where customers are involved? Prioritise quality over bias by partnering with providers who actively monitor and update AI models to ensure decisions stay accurate, fair, and defensible over time.
Non-negotiable 5: Protect everyone
Security, privacy, and transparency must be integral from the get-go. As Saxton says, “Your AI solution must uphold the highest standards of privacy as it will be dealing with the most sensitive part of your organisation, your people.
"It’s essential for AI solutions to follow ‘Privacy by Design’, a methodology that helps responsible AI solution providers create technology that can help to anticipate and prevent invasive events before they happen."
In addition to following the ‘Privacy by Design’ principle in your AI stack, AI solutions should also be engineered to prevent breaches before they happen – from data collection to processing and storage.
HR also has a crucial role in fostering open dialogue about AI’s ethical use, its impact on people, and its limitations. Leaders should ask and verify the tough questions:
Where exactly are AI models hosted?
Is employee or customer data shared with third parties?
Can individuals opt out of having their data used for training?
Does the AI rely on public or proprietary data sets?
Before integrating AI tools into the workplace, organisations should not settle for vague assurances, instead, scrutinise how privacy and security safeguards are implemented. Protecting everyone means leaving nothing to chance.
The future: Toward a balanced, people-first AI strategy
Skills-based hiring and predictive talent models are becoming the new norm, while human-AI co-piloting is shifting from novelty to necessity. But success won’t hinge on technology alone, it depends on how well organisations centre employee experience in their AI journey. That means empowering employees to make smarter decisions, automate the mundane, and focus on more strategic, creative, and value-driven work.
The new employee experience includes learning how to partner with AI, not compete with it.
"Much about the conversation around AI has centred around whether this technology can replace humans or if humans can delegate tasks to machines, but a lot of technology still needs the human touch. AI’s main role is still to understand and elevate work, not replace it," Saxton affirms.
To truly benefit from AI, organisations must help employees learn the steps of this "new tango" — the delicate balance between human and machine collaboration.
Conclusion
AI can unlock new levels of productivity, efficiency, and agility – when used the right way. Saxton drives home the importance of a true AI partner, that can allow HR professionals to work on the business and for employees in a strategically connected and fulfilling role.
"No more Job Descriptions, no more training material content, corporate policy documents, reporting metrics, and so on – all this being done automatically for you, so you can focus on what truly matters and do the work that you’re meant to do."
That’s where Dayforce comes in, with its offering of comprehensive, AI-powered people platform designed to help organisations unlock their full potential and work with confidence.
Go on, find out more information on how Dayforce can transform your workforce management, please visit here.
Do the work you’re meant to do with Dayforce. Learn more at dayforce.com/asia.
| 2025-06-27T00:00:00 |
https://www.humanresourcesonline.net/hr-leaders-are-under-pressure-to-adopt-ai-but-at-what-cost
|
[
{
"date": "2025/06/27",
"position": 90,
"query": "workplace AI adoption"
}
] |
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|
Why AI Regulation Could Crush Florida's Economy
|
The $38 Billion Mistake: Why AI Regulation Could Crush Florida’s Economy
|
https://jamesmadison.org
|
[
"Edward Longe"
] |
Lower Wages: When workers become less productive due to regulatory constraints, employers can afford to pay them less. The 1.4% annual wage decline would ...
|
Table of Contents
The Regulatory Patchwork Problem: When 50 States Create 50 Different Rules On May 17, 2024, Governor Jared Polis signed Colorado’s Senate Bill 24-205 into law, making it the nation’s first comprehensive AI regulation. While proponents of AI oversight celebrated this milestone—alongside similar legislative efforts in California (ultimately vetoed), Virginia (also vetoed), Connecticut, and New York—critics argue that this emerging patchwork of state AI laws effectively creates a de facto national regulatory framework that raises compliance costs for businesses, confuses consumers, and raises potential Commerce Clause violations, as the constitutional provision designed to prevent states from interfering with interstate commerce may be implicated when state regulations effectively govern national AI practices. In response to concerns about the growing number of states passing their own comprehensive AI legislation and the emerging patchwork of regulatory frameworks, language was included in the House version of the One Big, Beautiful Bill that would have imposed a 10-year moratorium on state-level AI regulation. Specifically, the House language would have prevented states from regulating artificial intelligence models, “artificial intelligence systems,” or “automated decision systems.” States will, however, be free to continue to regulate the harms associated with AI use, such as discrimination, as well as free to continue to regulate the emergence of data centers or the ability of states to regulate social media platforms – a concern of many critics.
Florida’s $38 Billion Wake-Up Call Just like other states that have sought to pass AI regulations, Florida has considered new rules to regulate artificial intelligence (AI). While states like California, Texas, and New York have sought to pass comprehensive measures, Florida has sought a more piecemeal approach. For example, lawmakers considered a digital provenance bill that would have required Generative AI tools to make available their provenance data. This study examines the potential costs to the state’s economy of those and similar rules. The results are concerning: stricter AI rules, either in state, out of state, or a combination of both could significantly harm Florida’s economic growth, reduce jobs, and lower wages for workers across the state. Our model suggests that if Floridians need to comply with stringent AI regulations, the state could lose $38 billion in economic activity annually for the next decade. That’s roughly equivalent to losing all economic activity from a major metropolitan area. The regulations would also eliminate about 54,000 jobs annually and reduce average wages by 1.4% each year. It’s also important to recognize that, as one of 50 states in an economic union, if any state passes measures that restrict the use of AI, it will inevitably impact the ability of Florida companies to operate.
Crunching the Numbers Behind the Nightmare Scenario We used an economic model that simulates how the entire U.S. economy works, then focused on what would happen specifically in Florida, taking into account its economic position. The model predicts how changes in one part of the economy ripple through everything else. We made two key assumptions about how AI regulations would hurt productivity. First, we assumed that businesses would become 1% less efficient because regulatory red tape would prevent them from using AI tools effectively. Second, we assumed that individual workers would also become less productive, with the biggest impact on high-earning professionals who rely heavily on AI tools. Why would high earners be hurt more? Think about it this way: a lawyer using AI to research cases or a financial analyst using AI to spot market trends gets much more benefit from these tools than someone working in a restaurant or driving a truck. So when regulations limit AI use, the white-collar professionals lose more productivity. We think these assumptions are actually conservative – the real damage could be worse than what our model predicts because AI systems are and will be widespread.
Breaking Down the Billion-Dollar Breakdown Lost Economic Output: The $38 billion annual loss represents nearly 2% of Florida’s economic output. To put this in perspective, that’s like losing the entire economic contribution of Naples-Marco Island and Lakeland-Winter Haven. This money represents goods not made, services not provided, and innovations not developed. Job Losses: Losing 54,000 jobs every year might not sound significant in a state with 11.2 million workers, but these losses would concentrate in Florida’s most valuable industries. We’re talking about high-paying jobs in technology, finance, healthcare, and professional services – the jobs that drive economic growth and attract talented people to the state. Lower Wages: When workers become less productive due to regulatory constraints, employers can afford to pay them less. The 1.4% annual wage decline would compound over time, meaning workers would fall further and further behind what they could have earned without the regulations.
The Red Tape Trap: How Good Intentions Create Bad Economics Compliance Costs: New regulations don’t enforce themselves. Companies that use AI need to hire compliance officers, install monitoring systems, conduct audits, and potentially redesign their AI systems. All of this costs money that could otherwise go toward productive activities like hiring workers, developing products, or expanding operations. Innovation Slowdown: When regulations make it harder to experiment with AI, companies become more cautious about developing new AI applications. Over time, this means Florida businesses would fall behind competitors in other states who can freely innovate with AI technology. Uncertainty Problems: Businesses hate uncertainty. If AI regulations are unclear or keep changing, companies might simply avoid AI investments altogether rather than risk violating the rules. This ultra-conservative approach prevents businesses from gaining the productivity benefits that AI can provide. Competitive Disadvantage: If Florida restricts AI while other states don’t, Florida businesses would be competing with one hand tied behind their backs. A Florida marketing firm limited in its AI use would struggle to compete with a Texas firm that can use AI freely to serve clients better and faster.
The Hit List: Industries in the Regulatory Crosshairs Technology Companies: Technology firms that develop or heavily use AI would face the biggest challenges. This includes software companies, data analytics firms, and AI startups. Financial Services: Banks, insurance companies, and investment firms increasingly rely on AI for fraud detection, risk assessment, and customer service. Regulations could make these companies less competitive and effective. Healthcare: Hospitals and medical practices use AI for diagnosis, treatment planning, and administrative tasks. Regulatory constraints could slow medical innovation and reduce the quality of patient care. Professional Services: Law firms, consulting companies, and accounting practices that use AI to research, analyze data, and serve clients more efficiently would lose significant competitive advantages. Small Businesses: Small and medium-sized enterprises across Florida would be disproportionately impacted by AI regulations. Unlike large corporations with dedicated compliance teams and legal resources, small businesses often lack the infrastructure to navigate complex regulatory frameworks. They rely heavily on accessible AI tools for essential operations like automated customer service, inventory management, marketing optimization, and basic data analysis. Regulatory compliance costs could force many small businesses to abandon AI solutions entirely, putting them at a severe disadvantage against larger competitors who can absorb these expenses. Manufacturing: Factories use AI for quality control, predictive maintenance, and supply chain management. Regulations could force manufacturers to use less efficient methods, increasing costs and reducing reliability.
When Bad Economics Gets Worse: The Domino Effect The economic damage wouldn’t stop at the direct effects. When major employers become less productive and start laying off workers, those unemployed people spend less money at restaurants, stores, and service businesses. This creates a ripple effect that spreads the economic pain throughout the community. Additionally, if Florida becomes known as an anti-AI state, it could have trouble attracting new businesses and talented workers. Young professionals and innovative companies may choose to relocate to states that embrace AI technology rather than restrict it.
Your Wallet, Your Job, Your Future: The Personal Cost These economic projections aren’t just abstract numbers – they would affect real families and communities across Florida, reflected in: Fewer Job Opportunities: With 54,000 fewer jobs created each year, it would become harder for Floridians to find work, especially in high-paying professional fields, undercutting the state’s robust economy and job market. Slower Wage Growth: Even people who keep their jobs would see their paychecks grow more slowly than they would have without the regulations. Higher Costs: If Florida businesses become less efficient due to AI restrictions, they might need to charge higher prices for their products and services to maintain profitability.
The Tip of the Iceberg: Why Reality Could Be Even Worse Our study assumes relatively modest productivity losses from AI regulation. In reality, the damage could be much worse for several reasons: AI is Becoming More Important: AI technology is advancing rapidly and becoming essential for competitiveness in more industries. Today’s restrictions could create significantly larger competitive gaps in the future. Network Effects: Many AI applications become more valuable when widely adopted. If regulations prevent widespread AI adoption in Florida, the state could miss out on these network benefits entirely. Talent and Investment Flight: High-skilled workers and companies who rely on AI tools might move to other states with fewer restrictions, creating a brain drain that compounds the economic damage.
The Choice: Innovation Leader or Regulatory Loser This economic analysis reveals that stringent AI regulations would inflict severe damage on Florida’s economy while delivering questionable benefits. The quantitative evidence demonstrates that such regulatory overreach would eliminate billions in economic output, destroy tens of thousands of high paying jobs, and undermine Florida’s competitive position in the technology sector.
The Economic Reality
The data presents a stark choice for policymakers. Florida stands at a critical juncture where regulatory decisions will determine whether the state captures the immense economic opportunities of the AI revolution or falls behind competitor states that embrace technological innovation. States implementing restrictive AI frameworks risk creating regulatory deserts that drive investment, talent, and innovation to more business-friendly jurisdictions.
The productivity gains from AI adoption across industries—from healthcare and finance to manufacturing and logistics—represent a once-in-a-generation opportunity for economic expansion. Regulatory barriers that impede these advances don’t merely slow progress; they actively diminish Florida’s long term economic prospects and quality of life for residents.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://jamesmadison.org/the-38-billion-mistake-why-ai-regulation-could-crush-floridas-economy/
|
[
{
"date": "2025/06/26",
"position": 74,
"query": "technology regulation worker rights"
}
] |
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DHS Support of the Enforcement of Labor and Employment Laws
|
Enforcement of Labor and Employment Laws
|
https://www.dhs.gov
|
[] |
DHS supports federal, state, and local labor and employment agencies who enforce wage protections, workplace safety, labor rights, and other ...
|
1. What is deferred action?
Deferred action is a form of prosecutorial discretion to defer removal action (deportation) against a noncitizen for a certain period of time. Although deferred action does not confer lawful status or excuse any past or future periods of unlawful presence, a noncitizen granted deferred action is considered lawfully present in the United States for certain limited purposes, while the deferred action is in effect. If granted deferred action, a noncitizen may be eligible for employment authorization. DHS can terminate deferred action at any time, at its discretion.
2. How can a labor agency find out more about this process?
Labor agency representatives can contact [email protected] to learn more about this process and request technical assistance.
3. How does a labor agency support a noncitizen’s request for deferred action?
Labor and employment agencies can seek DHS support in an ongoing investigation or enforcement action by providing workers with a Statement of Interest that must be included as part of the noncitizen’s request for deferred action. The Statement of Interest must:
Be addressed to the Department of Homeland Security;
Detail the nature of the labor or employment agency’s investigation or enforcement action and the need for DHS support;
Describe the agency’s enforcement interests that provide the basis for their request;
Describe the worksite and the workers who may be helpful with the agency investigation or enforcement action; and
Provide an agency point of contact who can address follow up questions from DHS.
Labor agencies which are submitting a Statement of Interest for the first time or which have questions about the process are encouraged to seek technical assistance prior to submission. To request such assistance, please contact [email protected].
4. How does the centralized deferred action process work?
A noncitizen requesting deferred action based on a labor agency investigation or need submits a written request for deferred action by submitting:
Form G-325A, Biographic Information (for Deferred Action);
A Statement of Interest from a labor or employment agency addressed to DHS supporting the request;
Evidence to establish that the worker falls within the category of workers identified in the Statement of Interest, such as W-2s, pay stubs, timecards, and/or other documentary evidence such as a signed affidavit to demonstrate that the worker was employed during the period specified in the labor or employment agency statement;
Evidence of any additional factors supporting a favorable exercise of discretion;
Proof of the noncitizen’s identity and nationality;
If applicable, any document used to lawfully enter the U.S. or other evidence relating to the noncitizen’s immigration history or status;
For individuals requesting Employment Authorization through Part 3 of Form G-325A, submit the appropriate Employment Authorization fee. If a noncitizen is unable to pay the Form I-765 filing fee, they may request the fee be waived by filing Form I-912, Request for a Fee Waiver. Additional information is available on the fee waiver page; and
Form G-28, Notice of Entry of Appearance as Attorney or Accredited Representative, if applicable.
The noncitizen will submit this request to:
USCIS
Attn: Deferred Action
10 Application Way
Montclair, CA 91763-1350
Attorneys who are submitting deferred action requests for multiple noncitizens should submit each individual application in its own envelope.
USCIS will refer to ICE any deferred action requests that fall within ICE’s authority, including requests submitted by noncitizens who are in removal proceedings or have a final order of removal.
As with any request for deferred action, DHS will review requests by noncitizens based on a labor agency investigation or need on a case-by-case basis. DHS will review all positive and negative factors present in the requestor’s case when considering whether to exercise discretion to grant deferred action.
If deferred action is granted through this process, the initial period of deferred action and employment authorization will generally be for a period up to four years.
5. What agency will be reviewing requests for deferred action from noncitizen workers?
Requests for deferred action made by noncitizen workers who are participating in or otherwise necessary to a labor agency investigation should be submitted to USCIS through the central intake point specifically established to support labor agency investigative efforts for deferred action requests. USCIS will review the request, as well as the noncitizen’s immigration history, to determine which office within DHS has authority over the request. Upon reviewing the submission for completeness, USCIS will only forward to ICE requests for deferred action that are submitted by noncitizens who are in removal proceedings or have a final order of removal. USCIS and ICE, as appropriate, will consider and make a case-by-case determination of the deferred action request, and USCIS will consider all related Forms I-765, if submitted.
6. Will noncitizens who apply for deferred action based on a labor agency investigation or need through this process be referred to ICE for enforcement action?
Through the centralized intake process, USCIS will forward to ICE only those requests for deferred action from noncitizens who are in removal proceedings already or who have a final order of removal, so that ICE can consider the request for deferred action. All other requests will be processed by USCIS. Consistent with all USCIS adjudications, USCIS generally will not refer cases to ICE post-decision absent national security public safety, and border security concerns.
7. If deferred action is granted through this process, when does it end?
If deferred action is approved, it may be granted generally for an initial period of up to four years, subject to termination at any time. The recipient may also be eligible to submit a subsequent request for deferred action (see question 15 below), which will be adjudicated on a case-by-case basis when a labor agency provides a basis for such a request as it relates to the labor agency’s ongoing investigative or enforcement interests. Subsequent requests if granted, based on a labor agency investigation or need, will generally be for an additional period up to two years.
8. Can a noncitizen worker who would otherwise fit within the scope of a labor-related request for prosecutorial discretion described in this FAQ but who does not want to submit a Form I-765, Application for Employment Authorization, request deferred action?
Yes. A noncitizen worker who falls within the scope of a labor agency investigation but who does not want to submit a Form I-765, Application for Employment Authorization, may request deferred action by submitting Form G-325A without selecting Employment Authorization in Part 3.
9. Can a noncitizen currently present in the United States on a nonimmigrant visa (such as H-2) who falls within the scope of a labor agency investigation request labor investigation-based deferred action through the centralized process?
Yes. A noncitizen worker who is present in the United States on a nonimmigrant visa (such as H-2) and who falls within the scope of a labor agency investigation can request labor investigation-based deferred action through the centralized process. The worker should indicate in their written request for deferred action their current immigration status and the expiration date of that status, while also providing a copy of their Form I-94, Arrival/Departure Record, reflecting that status. This will facilitate timely processing of the deferred action request and minimize the risk of gaps in employment authorization or accrual of unlawful presence.
10. May a noncitizen granted deferred action be eligible for employment authorization?
Individuals granted deferred action may be eligible for employment authorization under existing regulations at 8 C.F.R. 274a.12(c)(14), which require them to demonstrate an economic necessity for employment. Noncitizens submitting a deferred action request may request employment authorization concurrently utilizing Part 3 of Form G-325A and submitting the appropriate Employment Authorization fee. If DHS grants deferred action, we will then adjudicate the Employment Authorization Request. Note that if DHS does not grant deferred action, we will not refund or return the Form I-765 fee. If the requestor does not want to request employment authorization with their deferred action request, they can send their Form G-325A without selecting Part 3, Employment Authorization.
11. Will a noncitizen receive a Social Security Number?
A noncitizen may request a Social Security number (SSN) through Part 4 of Form G-325A. If USCIS approves the noncitizen’s request for work authorization and the noncitizen completed the section on the application to request a Social Security card, then USCIS will send the Social Security Administration (SSA) the data needed to issue the noncitizen a Social Security card.
The noncitizen will receive two documents – their Employment Authorization Document (EAD), also known as the “USCIS I-766 card” or “work permit”, and in another envelope their Social Security card. The SSA will mail the Social Security card to the address the noncitizen listed on their I-765 application. The noncitizen should receive their Social Security card no later than 7 business days after they receive their EAD from USCIS.
If a noncitizen does not receive their Social Security card within 7 business days after receiving their EAD, they should call or visit an SSA office. For more information, please review this flyer.
12. Can a noncitizen initially granted a two year period of deferred action and related employment authorization request an increase of the initial grant period from two to four years?
Yes. To request that the initial period of deferred action and related employment authorization be increased from two to four years, a noncitizen should submit the following to the address listed in Question 4 above:
Form G-325A, Biographic Information (for Deferred Action). Please write “Labor DA Extension” on the top portion of the form.
Proof of the noncitizen’s identity and nationality;
The Statement of Interest from a labor or employment agency that supported the initial deferred action request;
A copy of the approval notice from the initial deferred action request; and
For individuals requesting Employment Authorization through Part 3 of Form G-325A, submit the appropriate Employment Authorization fee. If a noncitizen is unable to pay the Form I-765 filing fee, they may request the fee be waived by filing Form I-912, Request for a Fee Waiver. Additional information is available on the fee waiver page.
If granted, the period of deferred action, and related employment authorization, will generally be up to a total of four years from the date of the initial grant of deferred action.
13. A noncitizen was initially granted deferred action for a period of two years. If the noncitizen submits a request to increase their initial deferred action time period from two years to four years, are they required to replace their employment authorization card to reflect a total of four years of validity?
Yes. Extending the initial authorized period of deferred action from two years to four years does not automatically extend the validity period of employment authorization. To remain employment authorized for the duration of their deferred action, a noncitizen must request Employment Authorization using Part 3 of Form G-325A and pay the appropriate Employment Authorization fee.
14. How does a noncitizen submit a request for a replacement Employment Authorization Document (EAD)?
A. To request a replacement EAD due to the EAD being lost, stolen or damaged, please see the “Deferred Action” section on the Direct Filing Addresses for Form I-765, Application for Employment Authorization page to submit Form I-765 and applicable fees.
B. To request a replacement EAD due to an error on the card, please see the “Replacement for Card Error” section on the Direct Filing Addresses for Form I-765, Application for Employment Authorization page. There is separate guidance for:
If a noncitizen needs to replace a card USCIS issued because it contains incorrect information that is not due to a USCIS error.
If a noncitizen needs to replace a card USCIS issued because it contains incorrect information that is due to a USCIS error.
15. Can a noncitizen request subsequent periods of deferred action based on a labor agency investigation or need?
Yes. A noncitizen granted deferred action based on a labor agency investigation or need may request an additional two-year period of deferred action when there continues to be a labor agency need. Subsequent requests for deferred action based on a labor agency investigation or need must be accompanied by an updated labor agency Statement of Interest explaining the continued labor agency need. The Statement of Interest must also describe the scope of workers who are in need of protection based on the procedural posture of the labor agency’s investigation, prosecution, or enforcement action.
16. How can a labor agency support a subsequent request for deferred action?
A labor agency that has a continued need for workers to help in their investigation or prosecution of a case, or any related enforcement action, may support a noncitizen’s subsequent request for deferred action by providing the noncitizen with an updated Statement of Interest. The labor agency should make an assessment of the continuing need for worker participation and should reassess the scope of workers in need of protection based on the phase of the agency’s enforcement effort at that time.
Labor agencies must provide workers with this updated Statement of Interest, which must be included as part of the noncitizen’s subsequent request for deferred action.
17. How does a noncitizen request a subsequent period of deferred action based on a labor agency investigation or need?
A noncitizen requesting a subsequent period of deferred action based on a labor agency investigation or need must submit:
Form G-325A, Biographic Information (for Deferred Action);
A Statement of Interest from a labor or employment agency addressed to DHS supporting the request;
Evidence to establish that the worker falls within the scope specified in the Statement of Interest, such as W-2s, pay stubs, timecards, and/or other documentary evidence including a signed affidavit to demonstrate that the worker was employed during the period identified in the labor or employment agency statement
Evidence of any additional factors supporting a favorable exercise of discretion, which may include evidence of the worker’s past participation or willingness to actively participate in the labor agency investigation;
Proof of the noncitizen’s identity and nationality;
If applicable, any document used to lawfully enter the United States and/or other evidence relating to the noncitizen’s immigration history or status; and
For individuals requesting Employment Authorization through Part 3 of Form G-325A, submit the appropriate Employment Authorization fee. If a noncitizen is unable to pay the Form I-765 filing fee, they may request the fee be waived by filing Form I-912, Request for a Fee Waiver. Additional information is available on the fee waiver page.
The noncitizen will submit this request to:
USCIS
Attn: Deferred Action
10 Application Way
Montclair, CA 91763-1350
Attorneys who are submitting deferred action requests for multiple noncitizens should submit each individual application in its own envelope.
USCIS will refer to ICE any requests for deferred action that fall within ICE’s authority, including requests submitted by noncitizens who are in removal proceedings or have a final order of removal.
As with any request for deferred action, DHS will review requests for a subsequent period of deferred action based on a labor agency investigation or need on a case-by-case basis. DHS will review all positive and negative factors present in the requestor’s case when considering whether to exercise discretion to grant a subsequent period of deferred action. If granted, the subsequent period of deferred action will generally be for a period of up to 2 years, subject to termination at any time at the discretion of DHS.
18. When can a noncitizen submit their subsequent request for deferred action?
Noncitizens should submit any subsequent request for deferred action to DHS at least 120 days but no more than 180 days prior to the expiration of the current period of deferred action. Before submitting a subsequent request for deferred action, noncitizens should obtain an updated Statement of Interest from the labor agency and include the updated Statement of Interest with the subsequent request for deferred action to DHS.
Please note that if a subsequent request for deferred action is received by DHS with less than 120 days remaining in the current period of deferred action, there could be a gap between periods of deferred action and related employment authorization.
19. If a noncitizen’s request for a subsequent period of deferred action is granted, when will the new period of deferred action begin?
If a noncitizen submits a request for a subsequent period of deferred action to DHS at least 120 days before and no more than 180 days prior to the expiration of their current period of deferred action and the request is granted, the noncitizen will generally receive deferred action and employment authorization for a period of up to 2 years starting from the expiration date of the current period of deferred action.
If a noncitizen submits a request for a subsequent period of deferred action less than 120 days prior to the expiration of their current period of deferred action and the request is granted, there may be a gap between periods of deferred action and related employment authorization.
If a noncitizen submits a request for a subsequent period of deferred action after the expiration date of their current period of deferred action and the subsequent request is granted, the noncitizen will have a lapse in the duration of their deferred action period and related employment authorization. In such instances, the noncitizen will generally receive deferred action and employment authorization for a period of up to 2 years from the grant date of the new request.
20. How do I withdraw my pending request for labor investigation-based deferred action and employment authorization?
To withdraw a pending request for labor investigation-based deferred action and employment authorization, a noncitizen must submit a written withdrawal request with their name, signature, A#, and the receipt number listed on their Form I-797C, Notice of Action. The written withdrawal request should indicate that the noncitizen is are withdrawing their request for deferred action and employment authorization.
Withdrawal requests should be submitted to:
USCIS
Attn: Deferred Action – Labor Investigations – Withdrawal
10 Application Way
Montclair, CA 91763-1350
Please note that if a noncitizen withdraws their pending request, they will not receive a refund of any fees paid for their application for employment authorization. All withdrawals are final and may not be retracted. However, withdrawing a pending request will not bar the noncitizen from requesting deferred action in the future.
21. If a noncitizen was granted a two-year subsequent period of deferred action, can they request that the period be increased to four years?
No. If a noncitizen requested and were granted a subsequent period of deferred action, they have already obtained an initial two-year period and a subsequent two-year period for a total of four years. If there is a labor agency need beyond the four-year period of deferred action that the noncitizen has already obtained, the labor agency may issue a new Statement of Interest and the noncitizen may request another subsequent period of deferred action on that basis.
22. What other immigration benefits can a noncitizen worker pursue if they are not granted deferred action?
A noncitizen worker who is the victim of a crime or subject to exploitation by an unscrupulous employer and cooperating in a labor agency investigation may be eligible for other forms of immigration relief beyond deferred action, including eligibility for a T or U nonimmigrant visa or parole in place.
23. Can DHS terminate a noncitizen’s deferred action before it expires?
Yes. Deferred action is an exercise of prosecutorial discretion, and we may terminate a grant of deferred action at any time in DHS’s discretion. Prior to terminating a grant of deferred action, DHS will generally provide a Notice of Intent to Terminate (NOIT) and, if a NOIT is issued, will generally allow the requestor 60 days to respond and an opportunity to provide updated information about the individual request and surrounding circumstances before terminating a deferred action grant. If DHS terminates the grant of deferred action, DHS intends to provide the individual with notice of the termination. DHS retains prosecutorial discretion, and in the exercise of that discretion may take into account the circumstances that led to the initial grant of deferred action and any subsequent developments, notwithstanding the termination of deferred action under this process.
| 2025-06-27T00:00:00 |
https://www.dhs.gov/archive/enforcement-labor-and-employment-laws
|
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|
Hundreds of workers mobilize to 'Stop Gen AI' and help each other ...
|
Hundreds of workers mobilize to 'Stop Gen AI' and help each other survive AI automation
|
https://www.bloodinthemachine.com
|
[
"Brian Merchant"
] |
But we have a lot to get to, much of it concerning the ongoing fights against tech companies' exploitation of creative workers, in this week's ...
|
Greetings critics, coders, friends and luddites —
How are we holding up out there? Any one else feeling a few fleeting glimmers of hope breaking through the dark polluted skies? Increasingly rare stuff these days, gotta take it whilst we can. But we have a lot to get to, much of it concerning the ongoing fights against tech companies’ exploitation of creative workers, in this week’s Critical AI Report.
First up, we’ll meet new mutual aid group that’s formed to help creatives and knowledge workers and anyone else impacted by generative AI—tilted, aptly, Stop Gen AI—and its founder, cybersecurity expert Kim Crawley. We’ll look at the flurry of legal action around generative AI and what it means for writers, creatives, and workers. In two major legal cases, the judges ruled in favor of the tech companies against writers who’d brought lawsuits against them; but in a twist, those “losses” might actually be good news for humans. Plus a new report on algorithmic inequality in gig work, Tesla’s disastrous robotaxi launch, and more.
A quick note that a chunk of this report is for paying subscribers only, so if you can, chip in and help support this work—a massive thanks to everyone who already does! Because of you, I can:
-Complete in-depth reports on the impact of AI on workers
-Report on how Silicon Valley is successfully pushing lawmakers to ban AI regulation
-Pay rent
-Send my kids to summer camp
-Report on the ground on the destruction of Waymos in Los Angeles
-Focus reportage and analysis on workers, creators, artists, students, rather than big tech’s bottom line
-Read more news and studies and testimonials about AI than is probably healthy for any human to do
If any of that sounds worthwhile please consider becoming a paid subscriber, so I can do more of it, and better. Thanks, and onwards!
Part 1: Stop Gen AI
This week, I published the first installment of my ‘AI Killed My Job’ series, focusing on tech workers. Since starting the project, I’ve heard dozens of harrowing, dark, and absurd stories from engineers, product managers, marketers, coders, and beyond, each of whom have seen their livelihoods transformed, degraded, or replaced outright by AI.
One person who wrote in with an account was Kim Crawley. Crawley is a cybersecurity expert, author and college instructor who’s been hit hard by generative AI. Beyond that, she, like many knowledge workers, finds the whole commercial enterprise—and the Silicon Valley companies behind it—to be an ethical blight. She reached out to tell me that she and some peers were starting a project to do something about it. They were organizing a mutual aid group called Stop Gen AI.
Screenshot of Stop Gen AI’s website .
It is, as far as they could tell, the first mutual aid group intended both to help workers survive as generative AI companies erode their livelihoods and to organize a resistance among those who want to push back.
“My initial idea was to start a mutual aid fund to financially support creative and cognitive workers made poor because our overlords think we can be replaced,” Crawley tells me on Signal. “You know and I know that it doesn't matter how shitty the autocomplete torment nexus is, our overlords don't care.”
They booked a domain, StopGenAI.com, began holding meetings, wrote a manifesto, and organized a joint PayPal account. In addition to soliciting funds from the public, they’d offer incentives—anyone who donates $10 a year gets an [email protected] email address of their choosing. And the group, which is rife with creatives, plans on offering a bundle of art, games, and other works on Itch.io, the platform known for hosting indie games. Donations will be distributed to both members and nonmembers, with people in need—those who are housing insecure, for instance—receiving priority.
Stop Gen AI soon ballooned to 500 members. Artists, academics, folks with mutual aid leadership experience, and an organizer with eSims for Gaza all joined up. Donations to the group are open now, and it’s planning to launch the bundle soon.
For Crawley, all this is personal. Before Stop Gen AI, she says, she was feeling depressed.
“I made a pretty good income from researching and writing about cybersecurity, especially in 2021, 2022, 2023,” she told me. “I certainly wasn't a millionaire, but I was making enough money that I didn't worry about my finances. Starting in 2024, my income took a real hit.”
Freelance writing gigs dried up, and her working teaching cybersecurity at a university became jeopardized. “I teach a Master's level enterprise cybersecurity course,” she says. “But for how much longer, who knows? Because I am very outspoken publicly about how much I deeply hate Gen AI. Meanwhile the rest of the faculty and my bosses have drunk the Gen AI kool-aid.”
Crawley worries about the environmental impacts of AI as well as the labor ones. She felt that civil society was not doing enough to resist the rise of generative AI.
“I approached a bunch of unions and guilds,” Crawley says. “They didn't seem to be doing much to counter the Gen AI menace. That's when I realized I had to take matters into my own hands and not wait for someone else to take the initiative.” Stop Gen AI has already yielded dividends for Crawley, though they’re eager to grow and desperate to bring the group to public attention—there is power in organizing, and in solidarity, she finds.
“Starting the group had greatly improved by mental health,” she says. “Because we're doing something. We're not just watching Gen AI make all of us poor and destroy the planet.”
It’s not enough just to raise awareness anymore, she says. “I want us to actually organize and fight.”
Crawley asks that any interested parties visit the group’s website, StopGenAI.com, where there are instructions both for joining, and for donating. Info about the StopGenAI email address is here. I’m also including the Q+A with Crawley at the end of the post, as well, for those interested in reading our whole conversation.
The BITM News Brief:
Some good tech news for a change — the modular, easily repairable Fairphone, which also attempts to ethically source its supply chain, has announced a long-awaited upgrade.
If you’re in LA, go see Annie Dorsen’s Prometheus Firebringer, an acclaimed play that, as New York Mag put it, offers “looping interrogations into how the omnipresence of AI is already rewiring our brains.” It’s playing at the Redcat, tickets are here.
New research finds that, as the University of Pennsylvania’s PR team puts it, AI is “Perceived More Negatively than Climate Science or Science in General.” The paper is here.
“Tesla robotaxi incidents caught on camera in Austin draw regulators’ attention,” a great CNBC story by friend of BITM Lora Kolodny, with a headline that undersells the absolute menace Tesla robotaxis are to the people of Austin.
Power Switch Action and Gig Workers Rising have a new report out examining algorithmic manipulation, inequality, and working conditions for Uber drivers based on the responses of 2,500 workers. The report finds that the app’s algorithm works to prevent them from reaching bonus goals, penalizes workers for holding out for better fares, and more. Per the report: “vast majority of surveyed drivers report getting squeezed and manipulated by Uber’s pay algorithm, and commonly report serious financial hardship and psychological distress as a result of their unpredictable pay on the app.” It’s worth spending some time with the whole thing.
Scale AI, the company that just got a $14 billion acquihire investment from Meta, has a massive spam and security problem, as Sam Blum reports in Inc.
Friend of BITM Charley Johnson asked if I’d share his new course with Blood readers, and I am happy to oblige. He sent me this one liner about it: “Tech hype hides power. Reclaim it in Systems Change for Tech & Society Leaders—a live course with systems change expert Charley Johnson.”
Unfortunate headline of the week, presented without comment:
Part 2: AI lawsuits galore
Two high-profile class action lawsuits levied by creators against AI companies were decided in court this week, and while both were initially reported as wins for the tech industry, the reality is much, much more complex—and even, in certain ways, promising for writers and creatives taking on the AI giants.
| 2025-06-27T00:00:00 |
https://www.bloodinthemachine.com/p/hundreds-of-workers-mobilize-to-stop
|
[
{
"date": "2025/06/27",
"position": 98,
"query": "technology regulation creative workers"
},
{
"date": "2025/06/27",
"position": 59,
"query": "technology regulation creative workers"
}
] |
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|
Artificial Intelligence Risk for Employers - The Tin Man Prompts ...
|
The Tin Man Prompts Class Action Lawsuits (Lions and Tigers and Bears, Oh My!)
|
https://www.fennemorelaw.com
|
[
"Ashley Russell",
"About The Author"
] |
Even without the Biden-era or other AI civil-rights regulations, employee advocates are likely to claim that AI software can violate existing ...
|
Artificial Intelligence Risk for Employers – The Tin Man Prompts Class Action Lawsuits (Lions and Tigers and Bears, Oh My!)
Is the Tin Man more dangerous than the Lion? After all, he didn’t have a heart.
AI doesn’t have a heart either. Be warned: If AI HR software is improperly built, tested, or deployed, employee advocates assert that there is grave harm to employees, which can translate to substantial damages and attorney’s fees. In fact, in May 2025, a federal judge in California granted plaintiffs’ collective certification motion under the ADEA and FLSA against Workday, Inc., not as an employer, but as an “agent” of employers who denied work opportunities based on allegedly ageist and discriminatory AI software. If software developers are at risk, so too are employers.
To reduce risk, employers should consider the potential claims and regulatory requirements as they relate to using AI across the employment lifecycle. And before deploying AI HR software, employers should (1) read the licensing agreements, and (2) triple-check their employment practices liability insurance (EPLI) and other potentially applicable insurance policies for coverage of AI-based claims.[1]
Does my business use AI in its employment decisions?
With the efficiency of a computer-solved math problem, AI offers to streamline human resources functions. Indeed, three years ago, former EEOC Chairwoman Charlotte A. Burrows estimated that over 80% of employers use AI for work and hiring processes. That estimate would be higher today. Cradle to grave in the employment cycle: These automated decision-making systems (ADS) are used in developing job descriptions, recruiting, hiring, onboarding, evaluation, promotion, discipline, and separation of employees. According to recent estimates, there are more than 550 unregulated “bossware products” available to help employers manage workplaces with buzzword categories like “Labor Market Optimization,” “Workplace Performance/Productivity Monitoring,” “Workplace Benefits, Health and Well-being,” “Workplace Safety,” and “Reskilling/Retraining.”
In recruiting alone, the AI HR categories include:
AI-Driven Assessments (evaluating/sorting/ranking/eliminating candidates, or “candidate skills match”)
Applicant Recovery/Recycling Candidates (evaluating existing databases of candidates to “rediscover” people who might be a good fit)
Job Description Optimization (word and phrasing recommendations to help in descriptions)
Ad Automation (tester job ads on various platforms, helped by AI)
Job Market Forecasting (provide insight on talent pools based on job types, experience, and location)
Applicant Relationship Management (relationship software that can deliver a higher level of personalization to re-engage applicants)
Chatbots (AI chatbots/virtual assistants that interact with applications to help them find and apply for jobs, and may reject candidates along pre-defined parameters)
Resume Filtering/Scanners (screening tools to pare down resumes and applicants to make broad-stroke decisions, e.g., keyword sorting, ranking, and elimination of candidates with minimal human oversight)
Social Media Discovery (tools that scrape social and other online platforms to find candidates that may be a good fit, but who aren’t actively engaged in a job search, i.e., direct job ads to certain groups)
After finding applicants, employers may deploy AI to see if applicants are a good “job fit” using testing software that provides a score for personality, aptitudes, cognitive skills, or perceived “cultural fit” based on games or tests. AI may also look at criminal history adjudication, which in California is unlawful before a job offer is made. After this, via video interviews, employers may use interviewing technology that evaluates candidates based on facial expressions or speech patterns. Once hired, employers may then use AI tools that monitor efficiencies and reduce costs associated with human workers, e.g., monitoring software that rates employees on, for example, keystrokes.
In sum, the four areas of workforce AI products are: (1) labor, job market, and workplace “optimization”; (2) workplace performance, productivity monitoring; (3) workplace safety; and (4) workplace benefits.
So what’s the risk in deploying an HR Tin Man?
AI can speed up mundane tasks, but can be unreliable, have “hallucinations,” biases, and incomplete responses. Still, “AI vendors are making claims about accuracy without research to back up the claims. ‘The tools are frequently developed by software engineers who are unfamiliar with how to psychometrically, legally, and ethically validate an assessment tool,’” said Richard Landers, PhD, an I/O psychologist at the University of Minnesota who works with consulting firms that are using AI tech for hiring. Stringer, Heather. “AI in Hiring: More Research Required.” Monitor on Psychology, vol. 54, no. 1, Jan. 2023, p. 60. American Psychological Association. Indeed, unions and employee advocates claim the proliferation of AI in the workforce may erode labor standards, weaken workers’ voices and power, and increase the potential for discrimination and “other harms.” Employee advocates use talking points like “depersonalization,” “dehumanization,” “datafication of employment,” “algorithmic pay models,” “commodification of workers,” “algorithmic discrimination,” and “algorithmic fairness.”
For businesses, these talking points can mean lawsuits. And AI’s “bigger scale” means bigger risk in the form of class and collective action claims.
Federal Government – New Laws
As venture-backed AI moves fast, legislators and federal agencies are playing catch-up – or, in the case of regulators, their efforts have been affected by changing politics. In early 2022, the EEOC issued a reminder to employers that use of AI for assessing job applicants and employees may violate the Americans with Disabilities Act (ADA) (since, by way of example only, the use of manual tests such as keystroke counts may disadvantage disabled employees in the hiring process, and/or the use of facial expression technology during job interviews may disadvantage neurodiverse employees). Later, in 2023, the EEOC issued a technical assistance manual indicating long-standing legal principles under Title VII would apply when employers use AI in employment-related actions. In late 2023, the White House issued an Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence, EO 14110, 88 Fed. Reg. 75191-75226 (Nov. 1, 2023). And until 2025, the White House Office of Science and Technology Policy issued a Blueprint for an AI Bill of Rights.
But on January 20, 2025, President Trump rescinded EO 14110. (EO 14179: Removing Barriers to American Leadership in Artificial Intelligence). The new EO includes this statement: “To maintain [U.S.] leadership, we must develop AI systems that are free from ideological bias or engineered social agendas.”
While it may be a celebrated time for the tech sector, commentators predict AI backlash may come, similar to the concentration of wealth in the early 20th century, where the “public demands oversight to protect people from consumer harms, anti-competitive practices, and predatory behavior.” West, Darrell M. “The Coming AI Backlash Will Shape Future Regulation.” Brookings, 27 May 2025. Until then, “Congress is considering legislation that would preempt state regulations on AI and stop enforcement for the next 10 years.” Id. The current draft of the budget reconciliation bill proposes a 10-year ban on states enacting their own AI laws, which will limit state-initiated efforts to collar potential AI employment-law missteps. Turner Lee, Nicol, and Stewart, Josie. “States Are Legislating AI, but a Moratorium Could Stall Their Progress.” Brookings, 14 May 2025. (“[N]o State or political subdivision thereof may enforce any law or regulation regulating artificial intelligence models, artificial intelligence systems, or automated decision systems during the 10-year period beginning on the date of the enactment of this Act.”)
Even without the Biden-era or other AI civil-rights regulations, employee advocates are likely to claim that AI software can violate existing federal law, including:
Age Discrimination in Employment Act of 1967 (ADEA) (protects applicants and employees 40 years and older from discrimination on the basis of age in hiring, promotion, discharge, compensation, or other terms, conditions, or privileges of employment)
Title VII of the Civil Rights Act of 1964 (“Title VII”) (prohibits employment discrimination based on race, color, religion, sex (including gender, pregnancy, sexual orientation, and gender identity), or national origin)
Section 1981 of the Civil Rights Act of 1866 (prohibits discrimination based on race, color, and ethnicity)
The Equal Pay Act (prohibits sex-based wage discrimination)
The Immigration Reform and Control Act (prohibits discrimination based on citizenship and national origin)
Title I of the Americans with Disabilities Act (ADA) (prohibits employment discrimination against qualified individuals based on disability and those regarded as having a disability)
The Pregnant Workers Fairness Act (prohibits discrimination against job applicants or employees because of their need for a pregnancy-related accommodation)
The Uniformed Services Employment and Reemployment Rights Act (prohibits discrimination against past and current members of the uniformed services, as well as applicants to the uniformed services)
The Genetic Information Nondiscrimination Act (prohibits discrimination in employment and health insurance based on genetic information)
What’s an employer to do?
Given the speed of AI, you can’t assume the latest HR time-saving products are well-vetted and risk-free. Be aware of the risks and be informed.
Vet your AI vendors – ensure the AI vendor understands – and has built in safeguards to ensure compliance with – both federal and your state’s anti-discrimination laws, and ethical guidelines.
Read the software agreement’s indemnity, warranty, insurance, liability cap, and risk carveout provisions. While you’re at it, review your EPLI policies to see if there are carve-outs when HR decisions are aided by commercial ADM products.
Educate the HR and IT teams regarding AI discrimination.
Provide advance notice to candidates and employees who will be impacted by AI tools in accordance with applicable laws.
Track available demographic data of applicants and employees to identify any patterns that may suggest bias or have a potential disparate impact.
In sum, vet AI vendors and read both your software agreements and applicable policies. Train your HR team on AI risks. Provide notices to applicants and employees that you’re using AI. And testdecisions with human oversight, including review for bias and disparate impact.
[1] Even if you find an insurance policy that covers an AI HR transgression, insurers may limit their losses by exclusions or limiting coverage amounts.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://www.fennemorelaw.com/artificial-intelligence-risk-for-employers-the-tin-man-prompts-class-action-lawsuits-lions-and-tigers-and-bears-oh-my/
|
[
{
"date": "2025/06/27",
"position": 35,
"query": "artificial intelligence worker rights"
},
{
"date": "2025/06/27",
"position": 11,
"query": "artificial intelligence worker rights"
},
{
"date": "2025/06/27",
"position": 11,
"query": "artificial intelligence worker rights"
},
{
"date": "2025/06/27",
"position": 36,
"query": "artificial intelligence worker rights"
},
{
"date": "2025/06/27",
"position": 11,
"query": "artificial intelligence worker rights"
},
{
"date": "2025/06/27",
"position": 34,
"query": "artificial intelligence worker rights"
}
] |
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] |
Artificial Intelligence in Creative Industries: Advances Prior ...
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Artificial Intelligence in Creative Industries: Advances Prior to 2025
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https://arxiv.org
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Their research revealed that AI assistance significantly enhances problem resolution and customer satisfaction. Furthermore, they observed that AI ...
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| 2025-06-27T00:00:00 |
https://arxiv.org/html/2501.02725v4
|
[
{
"date": "2025/06/27",
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"query": "artificial intelligence creative workers"
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|
The Work Revolution: Skills-First, AI-Driven Future
|
The Work Revolution: Skills-First, AI-Driven Future
|
https://www.charterglobal.com
|
[
"Charter Global"
] |
Tasks that are rules-based, repetitive, or data-heavy are being delegated to machines. This frees up employees to focus on uniquely human strengths: creativity, ...
|
The workplace, as we’ve known it, is undergoing a transformation more profound than the shift to computers or the rise of the internet. Today jobs are being redefined, not eliminated.
Skills that were once considered niche are now mission-critical.
And for the first time in modern history, five generations are working side-by-side—each with different expectations, values, and ways of working. Three forces are driving this massive shift: The rise of generative AI and automation,
A global pivot to skills-based hiring, and
Evolving employee expectations around flexibility, purpose, and growth. These aren’t distant trends—they’re unfolding now. According to LinkedIn’s 2024 Work Change Report, the pace of workplace change has never been faster, and organizations that adapt will define the future of work. AI-Powered-Future AI-Powered-Future---02 AI-Powered-Future---03 AI-Powered-Future---04 AI-Powered-Future---05 AI-Powered-Future---06 AI-Powered-Future---07
In this blog, we’ll explore the key trends reshaping jobs and talent today, what they mean for employers and workers alike, and the actionable steps businesses can take to stay ahead. Whether you’re a business leader, HR professional, or simply curious about what’s next, this is your guide to navigating the work revolution.
AI Isn’t Taking Jobs—It’s Changing Them
Forget the old headline: “Robots are coming for your job.” That narrative is outdated.
Today, the more accurate story is this: AI is changing the way we work—at every level. From automating repetitive tasks to augmenting complex decision-making, artificial intelligence is rapidly becoming a digital co-pilot across industries. But instead of replacing workers wholesale, it’s reshaping roles, workflows, and the very skills needed to succeed.
According to LinkedIn’s 2024 Work Change Report, the share of job posts mentioning AI has nearly doubled in just two years.
Professionals in marketing, software development, finance, healthcare, and HR are increasingly expected to understand and interact with AI tools—whether it’s using generative AI to draft reports, analyzing data with predictive algorithms, or streamlining customer support with chatbots.
Rather than making people obsolete, AI is redefining value. Tasks that are rules-based, repetitive, or data-heavy are being delegated to machines. This frees up employees to focus on uniquely human strengths: creativity, empathy, strategic thinking, and problem-solving.
Take the role of a customer service agent. With the help of AI-driven knowledge bases and virtual assistants, agents can resolve queries faster and focus more on nuanced conversations that require emotional intelligence. In finance, analysts now rely on AI to scan vast datasets, enabling them to offer more timely and strategic advice.
But here’s the catch—this transformation demands new skills.
Workers need digital fluency, adaptability, and a mindset ready for continuous learning. Organizations, in turn, must provide training, tools, and a culture that empowers employees to work with AI, not against it.
In the AI-powered workplace, success won’t be defined by how much you know, but by how quickly you can learn, adapt, and collaborate—with both humans and machines.
Skills Over Degrees: The Rise of a Skills-First Economy
A decade ago, a college degree was often the golden ticket to a job. Today? Skills are the new currency of the workplace.
Across industries and roles, employers are shifting focus from formal credentials to demonstrated abilities. LinkedIn’s 2024 Work Change Report says, the number of job postings that don’t require a four-year degree has grown significantly—especially in tech, operations, and customer service roles. Instead of asking where you studied, hiring managers are now asking what you can do.
This shift to skills-first hiring is more than a talent strategy—it’s a business imperative.
The rapid pace of technological change means many of the skills required today didn’t exist five years ago. Think prompt engineering, AI model evaluation, cloud automation, or even TikTok marketing. Companies can’t afford to rely solely on traditional qualifications to fill modern roles.
Instead, they’re looking for capabilities like:
Digital literacy
Data analysis and visualization
Creative problem-solving
Communication and collaboration
Adaptability and learning agility
This trend is also fueling the rise of alternative credentialing—micro-certifications, bootcamps, and on-the-job learning that prove a candidate’s readiness. Platforms like LinkedIn Learning, Coursera, and internal corporate academies are helping professionals gain real-world, job-relevant skills faster than ever before.
From an equity standpoint, this shift is game-changing. It opens doors for talent from non-traditional backgrounds, reduces barriers for career changers, and broadens the pool of skilled candidates across regions and socioeconomic groups.
But to make a skills-first model work, organizations need to:
Map skills to roles clearly,
Create fair and consistent ways to assess them, and
Invest in continual upskilling and reskilling.
In the economy of tomorrow, the winners won’t be those with the longest resumes—but those with the most relevant and dynamic skillsets.
Since 2022 , the rate at which LinkedIn members add new skills to their profiles has increased by 140%. – LinkedIn
Internal Mobility: The New Growth Path for Talent
What if the best person for your next open role is already on your payroll?
That’s the idea behind internal mobility—the practice of moving existing employees into new roles, functions, or projects within an organization. And in today’s times, it’s emerging as one of the most powerful tools for talent retention and growth.
According to LinkedIn’s Work Change Report, employees who make internal moves are 75% more likely to stay at their company after two years.
That’s a compelling stat in a world where employee turnover is both costly and disruptive.
But internal mobility isn’t just about promotions. It includes:
Lateral moves to explore different domains,
Stretch assignments to build new competencies,
Cross-functional projects that foster innovation, and
Temporary rotations to address skill gaps.
Why is this gaining momentum now?
Because the skills landscape is evolving too quickly to rely only on external hiring. Instead of constantly searching for “unicorn” candidates, companies are realizing they can build the capabilities they need by investing in their existing talent. Plus, internal candidates already understand company culture, processes, and values—making transitions smoother and faster.
For employees, internal mobility signals opportunity. It says, “You don’t have to leave to grow.” This matters deeply in today’s workplace, where growth, purpose, and flexibility are top priorities—especially for younger generations.
So to leverage internal mobility at scale, companies are:
Building skills inventories of their workforce,
Using AI-powered talent marketplaces to match people to roles,
Encouraging managers to champion internal moves, and
Embedding learning and development into everyday workflows.
Ultimately, fostering internal mobility isn’t just good for morale—it’s a strategic advantage. It creates a more agile, engaged, and future-ready workforce that’s primed to evolve as business needs change.
LinkedIn Learning saw a 169% surge in non-technical professionals taking AI courses last year. – LinkedIn
Generational & Demographic Shifts Redefining Work Culture
For the first time in history, five generations are sharing the same workplace. From baby boomers to Gen Z, each group brings its own values, communication styles, and expectations. And these differences are reshaping what work looks like—how it’s structured, why it matters, and where it happens.
At the same time, global demographic shifts—including aging populations in some regions and a surge of young workers in others—are creating uneven talent dynamics. Some countries face acute talent shortages, while others are struggling to create enough opportunities for a growing workforce.
Amid this complexity, one message is clear: the modern workforce expects more than a paycheck.
Here’s what’s rising to the top:
Flexibility is now a baseline, not a perk.
– Remote and hybrid work options are often dealbreakers, not bonuses.
– Remote and hybrid work options are often dealbreakers, not bonuses. Purpose-driven work matters.
– Employees want to feel that their role contributes to something bigger than just quarterly results.
– Employees want to feel that their role contributes to something bigger than just quarterly results. Continuous learning is expected.
– Gen Z, in particular, views career development as a non-negotiable part of any job.
– Gen Z, in particular, views career development as a non-negotiable part of any job. Mental health and well-being are workplace issues.
– Benefits that support wellness, time off, and psychological safety are becoming essential.
And these expectations aren’t limited to younger generations. Workers across all age groups are re-evaluating what they want from work—especially after the pandemic reset the relationship between life and labor.
To respond, organizations are rethinking:
How they design roles and teams
How leaders communicate and support their people
What kinds of benefits, recognition, and career paths they offer
How they build inclusive, multigenerational cultures that foster belonging
The companies that thrive in this environment will be those that listen to their workforce, adapt quickly, and create a culture where every generation sees a future.
How Organizations Can Adapt: 5 Strategic Shifts to Make Now
To stay competitive, businesses must do more than acknowledge the transformation. They must act.
Here are five strategic shifts organizations can make today to future-proof their workforce and thrive in a skills-first, AI-powered world.
1. Shift to Skills-Based Hiring
Move beyond traditional resumes and degree requirements. Instead, focus on assessing real-world capabilities.
What to do: Redesign job descriptions to highlight required skills instead of credentials. Use skills assessments, portfolios, and practical simulations during hiring. Tap into non-traditional talent pools—career changers, bootcamp grads, and freelance specialists.
Why it matters:
Skills-based hiring not only broadens your candidate pool but also reduces bias and creates more equitable access to opportunity.
2. Build a Learning Culture
Continuous upskilling is no longer optional—it’s essential. With tech evolving rapidly, the half-life of skills is shrinking fast.
What to do: Encourage ongoing learning with micro-courses, certifications, and peer learning. Integrate learning platforms into everyday workflows. Recognize and reward skill development as part of performance reviews.
Why it matters:
A culture of learning builds resilience. It ensures your workforce can pivot as roles evolve and helps retain top talent seeking growth.
3. Enable Internal Career Mobility
Give your people reasons to grow within, not leave.
What to do: Develop clear career pathing across departments. Use AI-based tools to match internal talent to new roles or projects. Train managers to support mobility, not hoard talent.
Why it matters:
Internal mobility improves retention, fills roles faster, and ensures institutional knowledge stays within your organization.
4. Rethink Leadership and Culture
In a world where workers expect flexibility, autonomy, and purpose, traditional leadership models need an upgrade.
What to do: Train leaders to manage hybrid teams effectively and empathetically. Promote psychological safety, open feedback loops, and inclusive practices. Encourage leadership at every level—not just from the top.
Why it matters:
Culture is a key differentiator. It affects everything from innovation to retention to employer brand.
5. Embrace AI with Purpose
AI isn’t just a tool—it’s a transformation catalyst. But successful adoption requires thoughtful implementation and a human-first approach.
What to do: Identify tasks that can be enhanced or automated with AI (e.g., data entry, scheduling, report generation). Involve employees in shaping AI use cases and offer training to boost digital confidence. Ensure transparency and ethical use of AI tools.
Why it matters:
AI boosts productivity, but only when paired with the right human skills and mindset. Organizations that prepare their people to work with AI will lead.
Adapting to these shifts requires intention, investment, and ongoing effort. But the payoff is clear: a more agile, inclusive, and future-ready workforce that fuels long-term growth.
By 2030, 70% of the skills used in most jobs will change, with AI emerging as a catalyst. – LinkedIn
Conclusion: Future-Proofing Your Workforce Starts Now
The workplace is evolving at an exceptional pace. AI is transforming tasks, not eliminating jobs. Skills—not degrees—are becoming the new hiring standard. Employees are seeking flexibility, purpose, and growth more than ever before. And internal talent mobility is proving to be just as critical as external recruitment.
These aren’t isolated trends—they’re signals of a fundamental shift in how work gets done and what people value at work.
To thrive in this new world, organizations must act with urgency and intention. That means rethinking how talent is hired, developed, and retained. It means investing in learning, enabling flexibility, and embracing technology with a human-first mindset. The companies that succeed will be those that recognize change not as a disruption—but as a doorway to reinvention.
Ready to evolve with the future of work?
At Charter Global, we help organizations build future-ready workforces through strategic staffing, agile delivery models, and digital transformation expertise. Whether you’re adopting AI, building internal capabilities, or looking to redesign your talent strategy for the skills-first era, our People Solutions can help you move forward—smarter and faster.
Let’s co-create a workforce that’s built to adapt, grow, and is ready for what’s next.
Contact us today to start your workforce transformation journey.
Book a Consultation.
Or email us at [email protected] or call +1 770-326-9933.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://www.charterglobal.com/the-work-revolution-adapting-to-a-skills-first-ai-powered-future/
|
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"date": "2025/06/27",
"position": 8,
"query": "digital transformation creative workers"
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] |
Global Market Outlook
|
Global Market Outlook
|
https://russellinvestments.com
|
[] |
While equity and credit markets have recovered as we approach the midpoint of ... a key role in the depth and duration of a potential economic slowdown. While ...
|
In the latest faceoff between fear and fundamentals, the health and resilience of the global economy seems to be prevailing for now.
Fear—which ripped through markets in April after widespread tariffs and policy changes were announced—has largely been sidelined as signs of economic health continue to emerge. This is not unusual. Behavioral finance studies indicate long-term investors tend to let emotions cloud their judgment when volatility spikes before returning to a focus on fundamentals. This typical progression suggests our outlook for 2025 has been shaken, not stirred.
While policy uncertainty and geopolitical risks could result in more volatility this year, global stocks are benefiting from this more sanguine view and have roared back to all-time highs as of mid-June. Meanwhile, we spy risks of more lasting change in currency markets—the U.S. dollar for one—with important implications for portfolio strategy over the medium-term.
Coming into 2025, our outlook highlighted the fragile balance between healthy underlying U.S. growth and uncertainty from how the new administration would prioritize the four pillars of its economic agenda—tariffs, immigration, taxes and deregulation. With rich stock valuations and heightened policy risk, our portfolios entered the year defensively positioned, albeit moderately, with an emphasis on diversification across asset classes and global regions.
| 2025-06-27T00:00:00 |
https://russellinvestments.com/content/ri/ca/en/insights/global-market-outlook.html
|
[
{
"date": "2025/06/27",
"position": 84,
"query": "economic recession equity in the workplace"
}
] |
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|
White-Collar Hiring, Job Postings, Wage Growth ...
|
White-Collar Hiring, Job Postings, Wage Growth Have Slowed
|
https://www.businessinsider.com
|
[
"Brian Delk",
"Juliana Kaplan"
] |
Those postings fell 12.7% compared to blue-collar's 11.6% between Q1 2024 and Q1 2025. Office workers are seeing fewer open roles for titles like information ...
|
White-collar workers are struggling to get a pay raise, and new job postings are scarce.
White-collar workers are struggling to get a pay raise, and new job postings are scarce. Jacob Wackerhausen/Getty Images
White-collar workers are struggling to get a pay raise, and new job postings are scarce. Jacob Wackerhausen/Getty Images
This story is available exclusively to Business Insider subscribers. Become an Insider and start reading now.
Jobs are scarce and wages are stagnant for white-collar workers compared to the boom of a few years ago.
White-collar job postings nationwide are shrinking faster than their blue-collar equivalents, Revelio Labs, a workforce intelligence company, found. Those postings fell 12.7% compared to blue-collar's 11.6% between Q1 2024 and Q1 2025.
Office workers are seeing fewer open roles for titles like information specialists, sales representatives, and business analysts. These business and IT roles are seeing the largest decline as the labor market grapples with slowing job growth.
Several white-collar roles were on Revelio's list of occupations that had the biggest declines in openings between Q2 2023 and Q2 2025.
Additionally, some other white-collar roles with plummeting job postings included software developers, IT project managers, and market researchers.
Wages grew until mid-2024 for office workers before plateauing for the past year for office workers, Revelio found.
Blue-collar wage growth continued to steadily increase. These workers, however, still make less than white-collar workers on average, Revelio found.
But, it's not all doom and gloom for office workers, as some jobs are growing in comparison to the past two years. Account managers and recruiters are seeing the highest job posting growth in the past two years.
Tech jobs have been especially tough for people just entering the workforce. Gen Zers hoping to break into the industry are watching the biggest tech companies lay off workers and hear constant chatter about AI replacing many of their hard-earned coding skills.
"The adoption of Generative AI also appears to be a factor. This is a bit nuanced," said Zanele Munyikwa, an economist at Revelio Labs and author of the research. "Our own research shows that on a task-by-task basis, sales roles are actually less exposed to AI than professions like finance or engineering. However, it's possible the impact on hiring is so pronounced because AI is targeting the high-volume, top-of-the-funnel activities performed by junior reps, allowing companies to hit their targets with smaller teams."
Related stories Business Insider tells the innovative stories you want to know Business Insider tells the innovative stories you want to know
AI doesn't just loom over tech, as many entry-level white-collar jobs could be in the crosshairs of AI. AI leaders say jobs will change, and many will become obsolete. Companies are slimming down their workforces, especially middle managers.
Top executives are the biggest winners in the data. The salary gap between mid-senior management positions and C-suite leadership is growing as execs saw a 26.8% salary raise since 2023's first quarter.
With the older workforce eyeing retirement, blue-collar workers have been in demand for years. The blue-collar labor force often retires earlier than their peers in less physically demanding roles. This creates an employment gap as retirees are outpacing the Gen Zers and millennials securing these jobs.
Some people are switching careers entirely, seeking more stability in the blue-collar labor market. More high-school graduates are shifting from obtaining a degree to entering a trade as they weigh the cost of college against its return on investment. Whether it be fears of AI, decreased job stability, or less bargaining power for workers in office roles, the blue-collar job boom is working for some.
Are you having trouble finding a white-collar job? Contact these reporters at [email protected] and [email protected].
| 2025-06-27T00:00:00 |
https://www.businessinsider.com/white-collar-workers-slow-job-growth-hiring-wages-2025-6
|
[
{
"date": "2025/06/27",
"position": 34,
"query": "economic recession blue collar workers"
},
{
"date": "2025/06/27",
"position": 33,
"query": "labor regulation white collar workers"
}
] |
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] |
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News from and about LISEP
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LISEP Ludwig Institute for Shared Economic Prosperity
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https://lisep.org
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Inaccurate economic data could result in policy that means the difference between prosperity and recession. ... It's not just a blue collar or low-wage job ...
|
The Most Expensive State to Live in Isn’t New York or California, According to Data. See the Top 10
Costs have fluctuated significantly in recent years — and in most cases, they've been going up and up. That becomes particularly alarming when you consider that soaring costs of living have already greatly reduced quality of life for the bottom 60% of U.S. households, according to a May 2025 analysis from the Ludwig Institute for Shared Economic Prosperity.
The Most Expensive State to Live in Isn’t New York or California, According to Data. See the Top 10
Costs have fluctuated significantly in recent years — and in most cases, they've been going up and up. That becomes particularly alarming when you consider that soaring costs of living have already greatly reduced quality of life for the bottom 60% of U.S. households, according to a May 2025 analysis from the Ludwig Institute for Shared Economic Prosperity.
The Most Expensive State to Live in Isn’t New York or California, According to Data. See the Top 10
Costs have fluctuated significantly in recent years — and in most cases, they've been going up and up. That becomes particularly alarming when you consider that soaring costs of living have already greatly reduced quality of life for the bottom 60% of U.S. households, according to a May 2025 analysis from the Ludwig Institute for Shared Economic Prosperity.
| 2025-06-27T00:00:00 |
https://lisep.org/news
|
[
{
"date": "2025/06/27",
"position": 46,
"query": "economic recession blue collar workers"
}
] |
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|
Mixed PCE Inflation Report and Slowing Growth Provides ...
|
Mixed PCE Inflation Report and Slowing Growth Provides Murky Outlook for the Fed
|
https://hubbardobrieneconomics.com
|
[] |
Core market-based PCE inflation was 2.4 percent in May, up from 2.3 percent in April. So, both market-based measures show similar rates of inflation in May as ...
|
Image generated by ChatGTP-4o
Today (June 27), the BEA released monthly data on the personal consumption expenditures (PCE) price index as part of its “Personal Income and Outlays” report. The Fed relies on annual changes in the PCE price index to evaluate whether it’s meeting its 2 percent annual inflation target. The following figure shows headline PCE inflation (the blue line) and core PCE inflation (the red line)—which excludes energy and food prices—for the period since January 2016, with inflation measured as the percentage change in the PCE from the same month in the previous year. In May, headline PCE inflation was 2.3 percent, up from 2.2 percent in April. Core PCE inflation in May was 2.7 percent, up from 2.6 percent in April. Headline PCE inflation was equal to the forecast of economists surveyed, while core PCE inflation was slightly higher than forecast.
The following figure shows PCE inflation and core PCE inflation calculated by compounding the current month’s rate over an entire year. (The figure above shows what is sometimes called 12-month inflation, while this figure shows 1-month inflation.) Measured this way, PCE inflation increased in from 1.4 percent in April to 1.6 percent in May. Core PCE inflation also increased from 1.6 percent in April to 2.2 percent in May. So, both 1-month PCE inflation estimates are close to the Fed’s 2 percent target. The usual caution applies that 1-month inflation figures are volatile (as can be seen in the figure), so we shouldn’t attempt to draw wider conclusions from one month’s data. In addition, these data likely don’t capture fully the higher prices likely to result from the tariff increases the Trump administration announced on April 2.
Fed Chair Jerome Powell has frequently noted that inflation in non-market services can skew PCE inflation. Non-market services are services whose prices the BEA imputes rather than measures directly. For instance, the BEA assumes that prices of financial services—such as brokerage fees—vary with the prices of financial assets. So that if stock prices fall, the prices of financial services included in the PCE price index also fall. Powell has argued that these imputed prices “don’t really tell us much about … tightness in the economy. They don’t really reflect that.” The following figure shows 12-month headline inflation (the blue line) and 12-month core inflation (the red line) for market-based PCE. (The BEA explains the market-based PCE measure here.)
Headline market-based PCE inflation was 2.1 percent in May, up from 1.9 percent in April. Core market-based PCE inflation was 2.4 percent in May, up from 2.3 percent in April. So, both market-based measures show similar rates of inflation in May as the total measures do. In the following figure, we look at 1-month inflation using these measures. The 1-month inflation rates are both lower than the 12-month rates. One-month headline market-based inflation was 1.5 percent in May, down from 2.3 percent in April. One-month core market-based inflation was 2.1 percent in May, down from 2.7 percent in April. As the figure shows, the 1-month inflation rates are more volatile than the 12-month rates, which is why the Fed relies on the 12-month rates when gauging how close it is coming to hitting its target inflation rate.
Earlier this week, the BEA released a revised estimate of real GDP growth during the first quarter of 2025—January through March. The BEA’s advance estimate, released on April 30, was that real GDP fell by 0.3 percent in the first quarter, measured at an annual rate. (We discussed the BEA’s advance estimate in this blog post.) The BEA’s revised estimate is that real GDP fell by 0.5 percent in the first quarter. The following figure shows the current estimated rates of real GDP growth in each quarter beginning in 2021.
As we noted in our post discussing the advance estimate, one way to strip out the effects of imports, inventory investment, and government purchases—which can all be volatile—is to look at real final sales to private domestic purchasers, which includes only spending by U.S. households and firms on domestic production. According to the advance estimate, real final sales to private domestic purchasers increased by 3.0 percent in the first quarter of 2025. According to the revised estimate, real final sales to private domestic purchasers increased by only 1.9 percent in the first quarter, down from 2.9 percent growth in the fourth quarter of 2024. These revised data indicate that economic growth likely slowed in the first quarter.
In summary, this week’s data provide some evidence that the inflation rate is getting close to the Fed’s 2 percent annual target and that economic growth may be slowing. Do these data make it more likely that the Fed’s policymaking Federal Open Market Committee (FOMC) will cut its target for the federal funds rate relatively soon?
Investors who buy and sell federal funds futures contracts still expect that the FOMC will leave its federal funds rate target unchanged at its next meetings on July 29–30 and September 16–17. Investors expect that the committee will cut its target at its October 28–29 meeting. (We discuss the futures market for federal funds in this blog post.) There remains a possibility, though, that future macroeconomic data releases, such as the June employment data to be released on July 3, may lead the FOMC to cut its target rate sooner.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://hubbardobrieneconomics.com/2025/06/27/mixed-pce-inflation-report-and-slowing-growth-provides-murky-outlook-for-the-fed/
|
[
{
"date": "2025/06/27",
"position": 57,
"query": "market concentration and monopolies inflation rate"
}
] |
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] |
U.S. corporate income tax rates 2023, by state
|
Corporate income tax rates U.S. by state 2023
|
https://www.statista.com
|
[
"Abigail Tierney",
"Jun"
] |
For 2023, the US state with the highest tax rate on the highest corporate income bracket is New Jersey, with a top corporate income tax rate of **** percent.
|
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Tax Foundation. (January 24, 2023). Top marginal corporate income tax rates in the United States in 2023, by state [Graph]. In Statista . Retrieved August 07, 2025, from https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
Tax Foundation. "Top marginal corporate income tax rates in the United States in 2023, by state ." Chart. January 24, 2023. Statista. Accessed August 07, 2025. https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
Tax Foundation. (2023). Top marginal corporate income tax rates in the United States in 2023, by state . Statista . Statista Inc.. Accessed: August 07, 2025. https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
Tax Foundation. "Top Marginal Corporate Income Tax Rates in The United States in 2023, by State ." Statista , Statista Inc., 24 Jan 2023, https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
Tax Foundation, Top marginal corporate income tax rates in the United States in 2023, by state Statista, https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/ (last visited August 07, 2025)
Top marginal corporate income tax rates in the United States in 2023, by state [Graph], Tax Foundation, January 24, 2023. [Online]. Available: https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
| 2025-06-27T00:00:00 |
https://www.statista.com/statistics/762795/corporate-income-tax-rates-by-state-us/
|
[
{
"date": "2025/06/27",
"position": 89,
"query": "corporate taxes inflation rate"
}
] |
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|
Corporate Profits | U.S. Bureau of Economic Analysis (BEA)
|
U.S. Bureau of Economic Analysis (BEA)
|
https://www.bea.gov
|
[] |
Corporate profits represents the portion of the total income earned from current production that is accounted for by US corporations.
|
Corporate profits represents the portion of the total income earned from current production that is accounted for by U.S. corporations. The estimates of corporate profits are an integral part of the national income and product accounts (NIPAs), a set of accounts prepared by the Bureau of Economic Analysis (BEA) that provides a logical and consistent framework for presenting statistics on U.S. economic activity.
Corporate profits is one of the most closely watched U.S. economic indicators. Profitability provides a summary measure of corporate financial health and thus serves as an essential indicator of economic performance. Profits are a source of retained earnings, providing much of the funding for capital investments that raise productive capacity. The estimates of profits and of related measures may also be used to evaluate the effects on corporations of changes in policy or in economic conditions.
| 2025-06-27T00:00:00 |
https://www.bea.gov/data/income-saving/corporate-profits
|
[
{
"date": "2025/06/27",
"position": 93,
"query": "corporate taxes inflation rate"
}
] |
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The Top Benefits of Offering a 401(k) Plan for Your Business
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The Top Benefits of Offering a 401(k) Plan for Your Business
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https://www.vestwell.com
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By starting a 401(k) plan, you're investing in both your employees' well-being and your company's long-term success. With today's tax credits and simplified ...
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As a business owner, you're constantly balancing priorities—from managing daily operations to keeping your finances in check. With so much on your plate, offering a 401(k) plan may seem like just another task on the to-do list.
However, offering a retirement plan can be a powerful tool that delivers lasting benefits for both your company and your employees. Beyond helping employees save for the future, a 401(k) plan can help you attract and retain talent, meet your state’s retirement plan mandate, and take advantage of favorable tax credits.
The Business Case for Offering a 401(k) Plan
Whether you're a small business owner or managing a growing organization, implementing a retirement plan is an investment that can pay off in more ways than one. Below, we’ll explore four reasons why offering a 401(k) plan can be a game-changer for your business.
Attract and Retain Talent
In today’s competitive job market, a strong benefits package can be the deciding factor for candidates. With over 85% of employees expecting a retirement plan, offering a 401(k) is essential for attracting top talent. Signaling long-term investment in employee welfare and financial security, 401(k) plans can help companies stand out.
Once you've secured top-tier talent, a 401(k) can also aid in retention. In fact, research shows that workers with an active 401(k) plan are 32% less likely to leave in any given month. A retirement benefit can combat employee turnover expenses—which can cost a company up to two times the employee's salary. With options like eligibility criteria, employer contributions, and vesting, businesses can set up a plan that rewards employees who stay for longer.
Meet Your State’s Retirement Mandate
Over 20 U.S. states have launched public retirement savings programs to help their residents save for the future. Many of these programs have deadlines that require certain businesses to either register for the program or launch their own retirement plan, such as a 401(k). Businesses that don’t comply can face penalties. Launching a 401(k) plan helps you stay compliant with these rapidly developing state regulations.
Take Advantage of Tax Savings
Businesses may be eligible for thousands of dollars in tax credits. These credits—a dollar-for-dollar amount that reduces the tax owed by the business—are designed to help offset the costs of setting up a plan. Here are the credits currently available:
The Startup Credit: A tax credit for certain costs associated with starting and operating a new plan, up to $5,000 per year for the first three years.
A tax credit for certain costs associated with starting and operating a new plan, up to $5,000 per year for the first three years. The Auto-Enrollment Credit: Employers who add auto-enrollment to their plan may claim a tax credit of $500 per year for up to three years.
Employers who add auto-enrollment to their plan may claim a tax credit of $500 per year for up to three years. The Employer Contribution Tax Credit: Businesses may receive tax credits of up to $1,000 per year for contributions made to each eligible employee’s account, for up to five years.
Also, if you decide to provide an employer contribution to your employees’ plans, those expenses are typically tax deductible. And, of course, your own personal contributions to your 401(k) plan can lower your personal taxes for the year, too.
Improve Retirement Readiness
Over 75% of employees are worried about their financial situation. Providing a 401(k) plan is a proactive approach to enhancing your employees' retirement readiness, ultimately helping employees to be more focused and productive at work.
As employees increasingly look to employer-sponsored plans as a primary source of retirement savings, enabling options like automatic enrollment and employer contributions can boost savings rates and promote a culture that prioritizes future planning and employee well-being.
Set yourself up for retirement readiness too! Just like employees, business owners need a retirement plan that helps them save for their future.
The Bottom Line
Since their introduction in 1978, 401(k) plans have become a powerful tool not only for helping individuals save for retirement but also for bolstering the businesses that offer them. Beyond supporting employees’ financial futures, these plans provide valuable tax incentives and play a key role in attracting and retaining top talent.
By starting a 401(k) plan, you’re investing in both your employees' well-being and your company’s long-term success. With today’s tax credits and simplified setup options, there’s never been a better time to get started.
Take the next step today and unlock the benefits of a retirement plan designed to help your employees thrive—and your business grow.
| 2025-06-27T00:00:00 |
https://www.vestwell.com/blog/the-top-benefits-of-offering-a-401k-plan-for-your-business
|
[
{
"date": "2025/06/27",
"position": 50,
"query": "corporate taxes worker well-being"
}
] |
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|
California employment law update: Ghost job ban, 'No ...
|
California employment law update: Ghost job ban, 'No Robo Bosses' AI bill, and Know Your Rights Act
|
https://www.dailyjournal.com
|
[] |
... workplace decisions, and worker education on their legal rights. Each reflects ... Labor Unions' push for the regulation of worker-centric technology.
|
As the 2025 Legislature moves toward its August deadline, California lawmakers are considering three major employment bills targeting job advertising transparency, AI-driven workplace decisions, and worker education on their legal rights. Each reflects emerging challenges in employment law, from deceptive recruiting and algorithmic management to shifting federal labor enforcement. Here's a detailed look at what each bill proposes, its potential impact, and the timing ahead.
AB 1251: Banning "ghost" job postings
Assembly Bill 1251, introduced by Assemblymember Marc Berman (D-Menlo Park), aims to curb deceptive "ghost" job postings -- ads for positions that are not genuinely open. It requires private employers posting job ads to disclose whether there are actual vacancies. False listings would be considered unfair competition, enforceable by the Labor Commissioner, with civil penalties between $100 and $10,000 per violation.
This bill was introduced following publication of a 2024 survey conducted by ResumeBuilder.com, in which nearly 39% of hiring managers (of the 650 surveyed) acknowledged using ghost ads to gather resume pipelines, project company growth, or create internal pressure. Beyond misleading applicants, such postings waste applicants' time, collect unnecessary personal data, and distort job market data. The author of the bill notes that AB 1251 "would add much needed transparency," and notes further that "Californians deserve to know whether the job they are spending the time and energy to apply for is actually real."
Opponents of the bill, including the California Chamber of Commerce, argue that the bill invites overly punitive enforcement, overlaps with existing laws, and may penalize harmless inaccuracies.
Potential impacts of AB 1251:
• Employers would need to audit postings, carefully label roles as real vacancies, and ensure ongoing compliance.
• HR processes may need adjustment, incorporating oversight, training, and possible legal review.
• Employers will need to find other ways to build a pipeline to meet immediate hiring needs, so there will be more need to plan ahead and carefully.
• Applicants would benefit from greater transparency and reduced time wasted.
• A violation of the bill constitutes unfair competition, allowing the state labor commissioner's office to investigate and levy fines. Enforcement via the California Department of Labor Standards Enforcement and remedies under the state's Unfair Competition Law (which including injunctive relief and restitution may spark litigation and agency actions -- especially if "minor errors" allow private suits.
SB 7 ("No Robo Bosses" Act): Regulating AI in employment
Senate Bill 7, the "No Robo Bosses" Act, introduced by Senator Jerry McNerney (D-Stockton), directly addresses AI-powered automated decision systems (ADS) in employment. ADS are broadly defined, including machine learning, statistical analysis, and AI used to generate scores, classifications, or recommendations for hiring, promotion, discipline, or termination.
The Act would require employers and vendors to warn applicants and workers at least 30 days prior to the use of ADS, or if already in use, by Feb 1, 2026. Employers or vendors must also maintain and disclose an inventory of ADS tools used in employment decisions. Importantly, employers are prohibited from relying solely on ADS for critical decisions (including, but not limited to, those relating to termination of employment). Employers or vendors must also provide a written notice to a worker who has been affected by an employment-related decision made by an ADS and provide that worker with a form to appeal the decision within 30 days of the notification. The Act requires a human to perform the appellate review, and the reviewer must meet specific criteria as well as be objective in performing the review and considering the evidence.
SB 7 places California among the most proactive jurisdictions regulating algorithmic management, extending current privacy and bias protections into the AI sphere. The bill echoes broader policy momentum under the California Federation of Labor Unions' push for the regulation of worker-centric technology.
Potential impacts of SB 7:
• Employers would need to map and track their ADS systems, generate required notices, establish human oversight, and design data access and appeals frameworks.
• Risk management: Companies relying heavily on AI would have to balance efficiency gains against compliance burdens.
• Workers would benefit from due process, ensuring algorithmic decisions do not escape scrutiny. Workers will benefit from enhanced privacy protections.
• Enforcement: DLSE assumes responsibility for enforcement, and civil penalties are possible for noncompliance.
SB 294: Workplace Know Your Rights Act
Senate Bill 294, authored by Senator Eloise Gomez Reyes (D-San Bernardino), proposes a statutory "Workplace Know Your Rights Act" to improve employee awareness of workplace and civil rights. It mandates employers to issue standalone written notices to employees (new and current) annually -- covering topics like wages, paid leave, workers' comp, disability rights, immigration inspection protocols, data privacy, anti-retaliation, union rights, constitutional protections, and emergency rights.
Additional requirements include listing government enforcement agencies, notifying designated emergency contacts if employees are detained at work, and providing Labor Commissioner-prepared template notices/videos by mid-2026.
Advocates of SB 294 assert the bill empowers a more informed workforce, reducing legal misunderstandings. Critics, including the Chamber of Commerce, caution about notice fatigue and overlapping employer obligations.
Potential impacts of SB 294:
• Employers would be required to integrate new notices into onboarding and annual review processes; remote and multicultural workplaces may require multilingual adaptations.
• Training: Responsibility would increase to ensure employees understand their legal protections -- growing administrative burden.
• Workers would benefit from clearer, centralized information -- especially on immigration protections and law enforcement interaction.
• Enforcement: Civil penalties could reach up to $500 per employee per violation, with potential public prosecutor actions.
Timing and legislative next steps
All three bills noted above have cleared one or both houses and now face committee review ahead of final votes:
• AB 1251 (Ghost job ban) passed the Assembly (62-9) and was referred to multiple Senate committees as of June 11.
• SB 7 (AI regulation) is engrossed and currently in the Assembly, awaiting committee hearings.
• SB 294 (Know Your Rights Act) has been referred to Labor & Employment and Judiciary committees, having passed prior votes and awaiting appropriations review.
Upcoming deadlines:
• Appropriations deadline (early August): Bills must pass out of fiscal committees.
• Floor deadline (mid-August): Must be passed by both houses by the legislative session end (around Aug. 31).
• Governor's Action: Signed or vetoed by Oct. 12.
Employer action items
If these bills are enacted, employers should be ready to implement the changes required by the final versions of the laws. These changes may include the following actions:
1. Audit hiring practices: Clearly label actual job vacancies, update job advertisements, and track all job postings.
2. Inventory and map AI systems: Compile comprehensive lists of automated decision systems (ADS), identify areas where employee training is needed, and plan for employee notifications regarding these systems.
3. Develop notice systems: Establish template-based written notices to inform employees of their rights, outline detention procedures, and ensure these notices are distributed annually.
4. Train HR teams: Provide education on policies for AI oversight, handling data requests, and managing appeals processes.
5. Coordinate with legal counsel: Work with legal advisors to prepare policies, develop compliance materials, and address potential enforcement risks.
6. Plan for immigration enforcement: Create emergency contact protocols and systems for notifying employees in the event of detention.
California's 2024 legislative agenda demonstrates a decisive move towards greater workforce transparency and stronger worker safeguards. Assembly Bill 1251 intends to combat deceptive job advertising by prohibiting misleading or inaccurate employment postings. Senate Bill 7 mandates human review of any workplace decision made or assisted by artificial intelligence and grants employees a formal avenue to challenge decisions they believe are erroneous. Senate Bill 294 heightens employees' awareness of their fundamental workplace rights by requiring employers to distribute comprehensive notices outlining those protections.
If enacted, these laws will impose new administrative responsibilities on employers while advancing transparency, fairness, and worker empowerment in one of the nation's most influential labor law landscapes.
The next two months are critical -- all three bills must clear fiscal and floor votes before August's deadline, after which they await Gov. Gavin Newsom's decision by mid-October.
The views expressed in this article are exclusively those of the author and do not necessarily reflect those of Sidley Austin LLP and its partners. This article has been prepared for informational purposes only and does not constitute legal advice. This information is not intended to create, and receipt of it does not constitute a lawyer-client relationship. Readers should not act upon this without seeking advice from professional advisers.
| 2025-06-27T00:00:00 |
https://www.dailyjournal.com/mcle/1701-california-employment-law-update-ghost-job-ban-no-robo-bosses-ai-bill-and-know-your-rights-act
|
[
{
"date": "2025/06/27",
"position": 49,
"query": "technology regulation worker rights"
}
] |
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|
Preliminary Certification of Collective Action Against ...
|
A New Storm for Employers May Be Forming in the California Sunshine: Preliminary Certification of Collective Action Against Human Resource AI Software Provider
|
https://www.fennemorelaw.com
|
[
"Ashley Russell",
"About The Author"
] |
... legal concepts artificial intelligence law and online technology of legal regulations controlling artificial. Previous Post ... Workplace Employee Rights ...
|
A New Storm for Employers May Be Forming in the California Sunshine: Preliminary Certification of Collective Action Against Human Resource AI Software Provider
Mobley v Workday, Inc., N.D. Cal Case No. Case No. 23-cv-00770-RFL (May 2025)
On May 16, 2025, the Hon. Rita Lin of the Northern District of California granted a workers’ preliminary certification motion of a collective action against Workday, Inc., an AI human resource software provider. Plaintiffs’ theories of liability included claims under the Age Discrimination in Employment Act (ADEA) and Fair Labor Standards Act (FLSA).
In Mobley v Workday, Inc., N.D. Cal Case No. Case No. 23-cv-00770-RFL, plaintiffs challenge Workday’s software that has an alleged policy or practice of job screening that “disproportionately disqualifies individuals over the age of forty (40) from securing gainful employment.” The lawsuit alleges both collective and class action claims. Plaintiffs define the collective to include job applicants who were “denied employment recommendations” by AI. The plaintiffs allege that Workday uses “’artificial intelligence,’ ‘machine learning,’ assessments, tests, and pymetrics to decide who to recommend for jobs and who to reject.”[1] Still further, plaintiffs allege that Workday’s customers are employers who have delegated traditional hiring functions, including rejecting applicants, to the algorithmic decision-making tools. And, the lead plaintiffs assert that they and the opt-in class members are similarly situated because of Workday’s algorithmic decision-making tools denied them employment. The lawsuit further claims that Workday aided and abetted employers to engage in unlawful discrimination. According to the lawsuit, “Workday’s software is not simply implementing in a rote way the criteria that employers set forth, but is instead participating in the decision-making process by recommending some candidates to move forward and rejecting others” (Order Granting in Part and Denying in Part Motion to Dismiss).
Specifically, the lead plaintiff in Mobley claims he was denied employment for every one of the 100-plus applications that he submitted using Workday’s platform. On behalf of a collective group, Mr. Mobley alleges that Workday has a “unified” policy whereby Workday’s AI recommendation system to score, sort, rank, or screen applicants has a disparate impact on applicants over forty (40) – a violation of the ADEA and FLSA. In ruling in favor of the employees at the preliminary stage, Judge Lin stated: “If the collective is in the ‘hundreds of millions’ of people, as Workday speculates, that is because Workday has been plausibly accused of discriminating against a broad swath of applicants. Allegedly widespread discrimination is not a basis for denying notice.” Discovery is now moving forward, and the class certification motion on the plaintiffs’ other claims is calendared to be heard in 2026.
The Mobley-style legal theory based on AI has been used before. In late 2023, the EEOC settled its first AI hiring discrimination lawsuit against iTutorGroup, Inc. (Equal Employment Opportunity Commission v. iTutorGroup, Inc., E.D.N.Y. Case No. 1:22-cv-2565-PKC-PKY). Based on the ADEA, the lawsuit alleged that iTutorGroup engaged in age discrimination because the AI hiring program it used “automatically reject[ed] female applicants age 55 or older and male applicants age 60 or older.”
Class Action or Collective Action: Should I Care?
As to the procedural devices, they’re similar. Businesses should care because the risk is multiplied to potentially cover every applicant, or every employee, whose employment opportunities were negatively impacted by the use of AI. Collective actions under the ADEA and FLSA use the same certification procedural mechanism. At the early conditional certification stage, the plaintiff’s burden is modest. The first step involves an analysis of the plaintiff’s allegations with minimal or no discovery – where plaintiffs must simply show that the putative collective members are “similarly situated.” After the close of discovery, a second step involves the defendant filing a motion for decertification. But after initial certification, courts typically order the employer to (i) produce to plaintiff’s counsel a list of all putative members of the collective action with their contact information, and (ii) provide notice to be disseminated to all putative collective members. As further procedural background, under the ADEA/FLSA collective-action process, putative members must “opt in” to the action, and settlements have less stringent requirements for approval than class actions under FRCP Rule 23, which has an “opt out” process – but typically still require court supervision and approval.
What Should Employers Do?
If ultimately an AI software provider has liability for software-initiated employment decisions, so too may the employers who use the software. Thus, employers should: (a) thoroughly vet their AI human resource software vendors, and ask questions about compliance with anti-discrimination laws; (b) read the software licensing agreements closely, including an indemnity and duty to defend provisions; (c) review relevant insurance policies to evaluate whether they cover claims arising out of the use of artificial intelligence; and (d) educate HR and IT teams on the potential for AI discrimination, including the need to spot check for potential adverse impact and other discrimination claims.
[1]Pymetrics is a platform that uses neurosciences based games to help companies with hiring and talent management.
| 2025-06-27T00:00:00 |
2025/06/27
|
https://www.fennemorelaw.com/a-new-storm-for-employers-may-be-forming-in-the-california-sunshine-preliminary-certification-of-collective-action-against-human-resource-ai-software-provider/
|
[
{
"date": "2025/06/27",
"position": 87,
"query": "technology regulation worker rights"
}
] |
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] |
How AI is Redefining Creativity in Gaming
|
How AI is Redefining Creativity in Gaming
|
https://www.techuk.org
|
[
"Stephane Decroix"
] |
By fostering synergies, the UK can drive economic growth, create high-value jobs, and solidify its position as a world leader in technology and innovation.
|
How AI is Redefining Creativity in Gaming
The UK’s creative industries are renowned for their innovation, talent, and cultural impact. Within this vibrant ecosystem, gaming has emerged as one of the most dynamic – yet under-recognised – subsectors.
Sitting at the intersection of art, storytelling, and technology, gaming is a natural frontier for the use of emerging technologies. In the 1990s, game developers pioneered real-time 3D rendering techniques that ultimately led to the development of dedicated graphics processing units (GPUs). More recently, gaming applications have been at the forefront of advances in VR and AR.
AI promises to transform every aspect of the gaming industry. As the boundaries between the physical and digital worlds continue to blur, AI is redefining what interactive entertainment can be - from game development and the creative process, to how players interact with virtual worlds and express creativity.
To maintain their position among the world’s very best gaming ecosystems, it is vital that UK studios are at the forefront of this new era.
AI’s role in levelling up the gaming experience: from development to gameplay
AI is nothing new in gaming. From the release of Halo in 2001, which featured adaptive AI enemy characters, to the newest AI-powered assistants and game assets showcased at the Game Developers Conference in March, it has been part of many of the industry’s most notable breakthroughs. In recent years generative AI has accelerated the pace of innovation, enabling developers to automate complex and time-consuming tasks such as procedural content generation, where content is automatically generated using algorithms rather than manually. Developers are free to focus on more creative and strategic aspects of game design – the part of the job that most developers love the most and are best at.
The accessibility of AI tools is levelling the playing field, allowing indie developers to compete with larger studios, developing more dynamic gaming experiences more cheaply and efficiently. This democratisation of game development is fostering a more diverse landscape, where great ideas can flourish regardless of studio size or budget. The UK’s indie game development scene is well placed to lead the charge.
AI is also transforming how players interact. Natural language processing (NLP) allows for more realistic and responsive non-player characters (NPCs), enabling players to engage in meaningful interactions. This not only enhances immersion but also leads to new storytelling possibilities. AI is helping create more accessible gaming experiences for players with disabilities, offering features such as automatic subtitles, alternative control schemes, and adaptive difficulty levels.
Beyond gameplay, AI is being used to optimise game design, monetisation strategies, and player retention through predictive analytics. Real-time game balancing using AI ensures fairness, while AI-powered anti-cheat systems maintain competitive integrity in multiplayer games. These innovations are not just improving games, they’re reshaping the ecosystem.
How Tencent’s AI tools drive innovation
With a deep commitment to innovation, Tencent has developed AI-powered tools and platforms to support game development and player experiences.
One standout initiative is GiiNEX, Tencent’s advanced game AI engine. GiiNEX streamlines the creation of photorealistic 3D virtual cities and supports everything from 2D animations to dialogue generation. Its core components, an algorithm model, AI training platform, and online inference engine, cover the full lifecycle of game development from R&D to operations.
Another innovation is F.A.C.U.L. (Fluent AI Command Understanding), which enables voice commands to be seamlessly integrated into gameplay. This technology enhances tactical gameplay and accessibility, making games more intuitive – and inclusive. Arena Breakout’, a first-person shooter (FPS) game, is the first ever game that allows players to interact with AI companions through natural language using F.A.C.U.L. FPS usually limits human-AI communication to simple commands like `attack' and `follow' – but F.A.C.U.L. transforms this, allowing complex instructions and feedback alongside the ability to identify in-game objects, creating a far more immersive experience.
Tencent is also leveraging AI for dynamic content generation, player coaching, and esports at a global level. For example, some studios within the Tencent family are exploring how AI can improve tournament organisation and analysis.
The human touch: creativity and accountability
Despite the power of AI, we should be clear-eyed about the importance of human creativity. AI is a tool – not a replacement. Great game design lies in human ingenuity, intuition, and emotional connection. Here in the UK, we are seeing a high-profile debate around AI and creativity play out in headlines and social media. And like other creative industries, we see time and again that it’s that human connection that really makes a game unique and create a community. That will not change.
Gaming AI: a catalyst for economic growth and innovation
The impact and benefits of AI in gaming extends far beyond entertainment. Game technologies are inspiring innovations in adjacent sectors, from medtech to aerospace. For instance, simulation technologies used in gaming are now helping doctors perform remote surgeries and training pilots in ultra-realistic environments.
This cross-sector innovation is particularly exciting for the UK, which is a global leader not only in gaming but also medtech and other creative industries. By fostering synergies, the UK can drive economic growth, create high-value jobs, and solidify its position as a world leader in technology and innovation.
Looking ahead
From enhancing player experiences to enabling smaller studios to develop games at a lower cost, AI is set to redefine what games can be and who can create them.
Tencent is proud to support this journey, working alongside UK developers, studios, and innovators to build the future of gaming – one where AI and human creativity come together to create experiences that are more immersive, inclusive, and inspiring than ever.
techUK – Unleashing UK Tech and Innovation
The UK is home to emerging technologies that have the power to revolutionise entire industries. From quantum to semiconductors; from gaming to the New Space Economy, they all have the unique opportunity to help prepare for what comes next.
techUK members lead the development of these technologies. Together we are working with Government and other stakeholders to address tech innovation priorities and build an innovation ecosystem that will benefit people, society, economy and the planet - and unleash the UK as a global leader in tech and innovation.
For more information, or to get in touch, please visit our Innovation Hub and click ‘contact us’.
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| 2025-06-27T00:00:00 |
https://www.techuk.org/resource/how-ai-is-redefining-creativity-in-gaming.html
|
[
{
"date": "2025/06/27",
"position": 36,
"query": "technology regulation creative workers"
}
] |
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Taking stock of the Creative Industries Sector Plan
|
Taking stock of the Creative Industries Sector Plan
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https://pec.ac.uk
|
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Taking stock of the Creative Industries Sector Plan. 27 June 2025 22 July 2025; AI, Intellectual Property and Regulation, Business Models and Access to Finance, ...
|
by Bernard Hay, Head of Policy
The Creative Industries Sector Plan, published earlier this week, marks a significant milestone in government support for the country’s creative industries.
Part of the UK Government’s Industrial Strategy, the plan provides a ten-year framework with the significant ambition to double business investment in the sector from £17bn to £31bn by 2035, and “to ensure that the UK becomes the number one destination worldwide for investment in creativity and innovation”. Alongside targeted support for four ‘frontier’ creative sub-sectors – ‘Advertising and Marketing’, ‘Film and TV’, ‘Video Games’, and ‘Music, Performing and Visual Arts’ – it includes multiple sector-wide commitments.
The growing body of creative industries data and evidence can also be seen reflected in the Sector Plan, including research from the AHRC-funded Creative PEC and our Research Partners. In this blog, we summarise some of the key sector-wide announcements and situate them in relation to the evidence base.
R&D and Innovation
Over the last decade or more, the Creative PEC, British Academy and others have highlighted the need for policymakers to consider the distinctive ways R&D and innovation works in the creative industries. Human-capital intensive, reliant on a dynamic freelance workforce, and often utilising a full breadth of disciplines including Arts, Humanities and Social Sciences, the sector makes an outsized contribution to R&D in the economy.
It is therefore welcome that the plan includes multiple interventions to increase creative industries R&D and innovation. In addition to confirming that UKRI’s creative industries strategy will be published later this year, it includes £25 million from the Department of Culture, Media and Sport to support creative technology innovation through the AHRC’s CoSTAR initiative; £100 million for a second round of the Creative Industries Clusters programme (with the launch of the next competition expected early 2026); and measures to boost technology adoption.
HMRC have also committed to updating their guidance on which activities are eligible for R&D tax relief, thereby improving its effectiveness in stimulating private R&D investment in the creative industries. By the end of the year, new guidance will be published to clarify that for R&D projects in the creative sector, arts activities that directly contribute to resolving scientific and technological uncertainties will be eligible for tax relief purposes. Whilst this is not a revision of the definition of R&D used for tax relief purposes, it marks a significant shift in clarifying how the relief can support interdisciplinary innovation in the creative sector. Together with the other announcements in the plan, this suite of interventions could have a catalytic effect.
Access to Finance
Although creative firms have a higher growth appetite than the general population of firms, our recent research on access to finance with Creative UK highlighted that they also face significant obstacles in accessing the right kind of finance to facilitate this. For instance, our research has shown how Venture Capital funding for the sector is concentrated in London and the South-East, and that market failures exist when it comes to creative firms being able to access private investment. A recent blog by Professor Nick Wilson for the Creative PEC estimates that the equity finance gap for the sector alone may be as high as £1.4bn.
The new £4bn Industrial Strategy Group Capital initiative from the British Business Bank, announced earlier in the Spending Review, offers an important source of scale-up finance for small and medium sized creative businesses across the country. The forthcoming change in the National Wealth Fund’s mandate to support Industrial Strategy growth sectors including the creative industries, could with the right implementation play a significant role in augmenting the UK’s growing national infrastructure for creative technologies and R&D – whether it be supporting new production labs, creative studios, or immersive exhibition spaces.
Given the IP-rich nature of the sector, a further initiative to watch is the forthcoming review by the British Business Bank and Intellectual Property Office to explore alternative models of finance – including IP-backed finance – for the sector, with the results due later this year.
Trade and Exports
The last decade has witnessed a rapid acceleration in the growth of the UK’s creative exports, largely driven by creative services exports which in 2021 accounted for 14% of all UK service exports. In addition, the creative industries are central to our booming trade in digital services, which have been a focus of the Department and Trade’s new trade strategy also published this week. Last year, our State of the Nations report on UK creative industries trade estimated that the sector could account for 67% of the UK’s exports in digital services.
The Sector Plan strikes an important balance in prioritising established and emerging markets for the sector. Whilst Europe and North America remain our primary creative services exports destinations for instance (accounting for 45% and 39% of creative services exports in 2021 respectively), Asia remains an important destination for creative exports.
Additional export support, including through an increase in funding available via the Export Finance scheme, and further trade missions and funds available for firms to participate in, will also help to encourage exporting with creative microbusinesses and SMEs. Targeting support at regions where there are relatively low shares of exporting creative firms, and firms that have recently stopped exporting post-Covid and EU-exit, could play a key role in boosting regional creative economies.
Skills and Job Quality
Skills challenges are a persistent and entrenched issue facing the creative industries. Our recent report on Skills Mismatches in the Creative Industries found that 65% of hard-to-fill vacancies in the sector are due to skills shortages, compared to 41% in all sectors. Creative employers are also more likely to report that skills deficiencies are holding back innovation. In short, ensuring the skills system works better for the creative industries will be vital to driving sector growth and increasing the number of high-skilled, quality jobs.
Two notable interventions announced are the Government’s intention to appoint a freelance champion, who will represent the interests of the 28% of creative industries workers who are self-employed, and the parallel development of new industry-recognised training and a new Skills Passport, which will help workers make visible to employers their skills and competencies.
Underpinning effective skills policy is robust, relevant and timely labour market information on current and future skills needs. As a part of this, the Creative PEC and Work Advance are undertaking a comprehensive creative industries skills audit, funded by DCMS and the CIC, and supported by industry and Skills England.
Creative Places
Following the government’s announcement of priority city-regions for the UK’s creative industries, the Sector Plan announces a £150 million Creative Places fund which will be made available to six Mayoral Combined Authorities in England. Alongside UKRI’s Creative Industries Clusters Programme investment, these will enable places that are prioritising the creative industries in their local growth plans to develop tailored interventions that respond to local needs and strengths. This will be supported by a new Creative Places Forum, to ensure best practice is shared across the country.
There is now an established evidence base highlighting the existence and benefits of supporting creative clusters at a range of scales. These are places where creative businesses and workers co-local, collaborate and compete, and through this produce a range of economic benefits for themselves and the places of which they are a part. Given the scale of regional disparities in the UK’s creative industries, such interventions are crucial to shifting the dial on local growth in the creative industries across the whole country.
One approach the Government has also committed to supporting is strengthened collaboration between creative clusters over larger geographic areas – what are known as ‘creative corridors’. Such corridors can already be seen at various stages in development in places like the North of England with One Creative North, the Thames Estuary, and in the West of England-South Wales. Together, these place-based initiatives offer huge opportunities for locally-led policy innovation, underpinned by robust evaluation to share and scale ‘what works’ in boosting regional creative economies.
Data and Evaluation
As the Sector Plan itself notes, the UK has been world-leading in developing definitions, statistics and evidence on the creative industries. The commitment to improve access to official statistics on the creative industries, including by harnessing existing ONS data sources where regular sector-specific data is not currently published, will be foundational to ensuring the implementation of the Industrial Strategy remains responsive to new evidence. Equally important will be the review of Standard Industrial Classification codes to look at how creative sub-sectors like Video Games and Music are captured in the data.
The rapidly evolving nature of the creative industries, coupled with global transitions spanning the rise of Generative AI, the climate crisis, and changing international relations and trade-flows, make it hard to predict what the next ten years might have in store. They will also require new evidence and insight. The Sector Plan provides an important and welcome frame to deliver against over the next decade. The Creative PEC looks forward to playing its part, working with government and the creative industries eco-system, to ensure the sector continues to thrive.
| 2025-06-27T00:00:00 |
https://pec.ac.uk/blog_entries/taking-stock-of-the-creative-industries-sector-plan/
|
[
{
"date": "2025/06/27",
"position": 50,
"query": "technology regulation creative workers"
}
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|
New York Fashion Workers Act Reshapes Model Protections
|
New York Fashion Workers Act Reshapes Model Protections
|
https://natlawreview.com
|
[] |
Generally, the fashion industry does not have any significant industry-specific regulation, at least not when it comes to engaging models or other creative ...
|
Effective June 2025, New York began implementing significant changes for the fashion industry with respect to the practices of model engagement under the provisions of the new Fashion Workers Act (FWA).1 While the name of the new law suggests applicability to a broad range of creative talent and labor across the fashion supply chain, the scope of FWA focuses exclusively on models.
FWA is the first U.S. law to introduce transparency requirements for model contracts, impose fiduciary duties on model management companies, and establish new workplace protections for models working in New York State. This article discusses the history of fashion regulation and the main requirements under FWA, and makes recommendations for how fashion businesses should address compliance with FWA.
History of Fashion Regulation
Generally, the fashion industry does not have any significant industry-specific regulation, at least not when it comes to engaging models or other creative talent. Fashion industry participants may instead be governed by a patchwork of more general laws pertaining to labor and employment, data privacy, advertising, environmental, trade and import/export, antitrust and competition, intellectual property (IP including those protecting trademarks, copyrights, and patents), and right of publicity, as well as emerging laws pertaining to generative AI technology that may be used to replicate models’ likenesses (often referred to as “Deep Fakes”).
Unlike talent agencies, modeling agencies have historically classified themselves as management companies under New York General Business Law § 171(8) and models as independent contractors instead of employees. This classification exempted modeling agencies from following New York’s labor protection requirements, such as minimum wage, overtime, or safety requirements. Individual model contracts would often include difficult to understand compensation and fee structures, unclear payment deadlines, and multi-year term durations that would tie the model to the agency. At the same time, models – typically young people that may lack legal and business experience or support structures – working in New York City would have to deal with high rents and costs of living. While lacking clarity on when and how much they would be paid, models were frequently required to work long hours without adequate breaks or overtime pay. This imbalance of power is further compounded by a possible requirement to pose nude and related safety issues; being asked to wear garments or pose in settings that may present a risk of physical injury; and the proliferation of the use AI-generated portraits of models for which the model may not be compensated.
Concerns about potential exploitation of young people working as models were increasingly brought to the attention of legislators by activists and consumers related to transparency and environmental, social, and governance (ESG) accountability in the fashion industry. New York’s new FWA, enacted in response to the concerns raised by members of the public, is a comprehensive novel framework in the fashion industry with which participants need to become familiar.
The Fashion Workers Act
The legislative justification memo accompanying FWA emphasizes New York’s central role in the U.S. fashion industry: it is home to world-renowned creative talent, leading production companies, and top fashion and design schools. The memo states that New York’s fashion industry employs 180,000 people, accounting for 6 percent of the New York City’s workforce, and generates a total of $10.9 billion in wages. A prime example of its significant economic impact is New York Fashion Week, a semiannual series of events where designers showcase their collections to buyers, the press, and the general public. The enterprise brings in nearly $600 million in income each year.
The legislative justification memo further states that despite the massive success of New York’s fashion industry, the creative workforce behind the industry's success – specifically, models, influencers, and performing artists – are generally not afforded basic labor protections in New York. The new Act aims to address the persistent inequity in New York’s modeling sector, such as unfair contracts and payment practices, and create a system that imposes various requirements and prohibitions on model management companies and clients.
FWA defines the following terms:
“Client” means a retail store, a manufacturer, a clothing designer, an advertising agency, a photographer, a publishing company, or any other such person or entity that receives modeling services from a model, directly or through intermediaries.
“Model” means an individual, regardless of the individual’s status as an independent contractor or employee, who performs modeling services for a client and/or model management company or who provides showroom, parts, or fit modeling services.
“Model management company” means any person or entity, other than a person or entity licensed as an employment agency under article 11 of the general business law, that: Is in the business of managing models participating in entertainments, exhibitions or performances Procures or attempts to procure, for a fee, employment or engagements for persons seeking employment or engagements as models Renders vocational guidance or counseling services to models for a fee.
FWA applies to model management companies that engage in business in New York or enter into any arrangement with a client or model for the purpose of providing services in New York. In other words, this Act applies to any entity using a fee-based structure to hire models in New York, including model management companies and fashion businesses working with them, as well those who engage models directly for photoshoots and other ad campaigns in New York.
Key requirements and prohibitions on model management companies (or agencies):
Starting December 21, 2025, and no later than June 19, 2026, agencies representing models and creatives will be required to register with the New York Department of Labor (NYDOL). The registration must be renewed every two years. Agencies with five or fewer employees must pay a $500 registration fee, and agencies with more than five employees must pay a $700 registration fee.
Agencies shall: Be deemed to have fiduciary duties to act in good faith, with utmost honesty and in their models’ best interests. This fiduciary duty includes all aspects of the agency’s representation, such as negotiations, contracts, financial management, and the protection of the models’ legal and financial rights Conduct due diligence to ensure that models are not at risk of unreasonable danger Use best efforts to procure paid employment and other opportunities for their signed models Ensure any work requiring nudity or sexually explicit material is voluntarily consented to by the model, in accordance with section 52-C (3) of the Civil Rights Law (which sets forth requirements for consent to the creation, disclosure, dissemination, or publication of sexually explicit material) Prior to the start of a model’s engagement, provide the model with copies of a deal memo as well as the final agreement outlining terms of employment the agency negotiated for the model, which must include compensation terms Specify any items that will be initially paid for by the agency, but ultimately deducted from the model’s compensation, itemizing how each charge shall be computed, as well as provide supporting documentation validating all charges on a quarterly basis Disclose any financial relationship that may exist between the agency and the client Notify models no longer represented by the agency if the agency is collecting any royalties that may be due to the model Post a physical copy of the agency’s certificate of registration in the agency’s physical office and a digital copy on its website Include the agency’s registration number in any advertisement for the purpose of soliciting models as well as in any contract with a model or a client Obtain clear written consent, separate from the representation agreement, before agencies can use, license, or sell a model’s digital replica. 2
Agencies shall not: Require or collect any fee or deposit from a model for entering into an agency agreement Procure an accommodation for which the model will have to pay without providing a written disclosure of the rate charged in advance of the model’s stay Deduct or offset any fee or expense other than the agreed upon commission laid out in the contract or any other items advanced by the agency that were previously disclosed to and approved by the model Advance the cost of travel or visa-related costs without the model’s informed consent Require a model to sign an agency contract for a term greater than three years Require a model to sign an agency contract that renews without the model’s affirmative written consent Impose a commission fee greater than 20 percent of the model’s compensation Engage in discrimination or harassment against a model because of any protected status Retaliate against models filing complaints or declining to participate in castings or bookings based on reasonable, good faith concerns over ongoing FWA violations Create, alter, or manipulate a model’s digital replica using artificial intelligence without clear written consent from the model.
Key requirements and prohibitions for clients:
Clients shall:
Pay models for overtime hours for any engagement exceeding 8 hours in a 24-hour period, at an hourly rate at least 50 percent higher than the contracted hourly rate
Provide at least one 30-minute meal break for any engagement exceeding 8 hours in a 24-hour period
Only offer employment to a model that does not pose an unreasonable risk of danger to the model
Ensure any employment involving nudity or sexually explicit material complies with section 52-C (3) of the Civil Rights Law
Permit the model to be accompanied by their agent, manager, chaperone, or other representative to any engagement
Provide liability insurance to cover and safeguard the health and safety of models
Obtain clear, prior written consent for any creation or use of a model’s digital replica.
NYDOL will be the primary enforcer of the FWA. Violators may be subject to a civil penalty for up to $3,000 for a first violation and $5,000 for a second or subsequent violation. Models have the right to file an action in court or a complaint with the NYDOL within six years of the alleged violations. The New York State Attorney General also will have a right to file an action to enforce FWA if there is a reasonable cause to believe that a model management company, a model management group, or a client has repeatedly engaged in illegal or fraudulent business practices.3
Impact of the Act
This Act will be a big change for models and modeling agencies in New York, holding agencies accountable and providing models with basic labor rights. Outside New York, no other U.S. state has adopted a fashion industry–specific law regulating agencies or protecting models in a similarly comprehensive way. Because New York is so influential in the fashion industry, it is likely that it will be a trailblazer for other states, such as California, to introduce similar protections for fashion workers in their states.
Conclusion
Agencies and fashion businesses should start preparing now in order to comply with the new law. Some key points of the law and issues to consider may include:
Agencies must register with the NYDOL starting December 21, 2025 (and must be registered by June 19, 2026).
Clients should consider confirming for themselves that agencies are properly registered before entering contracts.
Both agencies and clients should assess their existing policies and practices, and maintain ongoing compliance checks.
Agencies should review their standard agreements with models for compliance with the FWA framework, as many terms of existing contracts may violate FWA.
Agencies should itemize all deductions and fees that are going to be charged to the model up front.
Agencies should provide models with (1) deal memos listing total compensation and (2) final agreements negotiated with clients at least 24 hours before the model begins the engagement.
Agencies should establish and/or assess existing antiharassment policies and safe reporting mechanisms.
Clients should extend these protections to their sets and events, including training staff on workplace safety.
This is not an exhaustive list, nor does failing to comply with each point render the agency or client noncompliant with FWA. Every situation is different and action should be taken after consultation with a lawyer familiar with the industry.
1 FWA is available at https://legislation.nysenate.gov/pdf/bills/2023/s2477a.
2 The term “digital replica” is defined in FWA as “a significant, computer-generated or artificial intelligence-enhanced representation of a model’s likeliness, including but not limited to, their face, body or voice, which substantially replicates or replaces the model’s appearance or performance, excluding routine photographic edits such as color correction, minor retouching, or other standard post-production modifications.” Notably, if the AI enforcement moratorium that is currently part of the pending federal budget reconciliation bill ultimately passes – and it is poised to pass – enforcement of state law requirements pertaining to generative AI may be suspended for ten (10) years.
3 New York State Fashion Workers Act FAQ, available at https://dol.ny.gov/new-york-state-fashion-workers-act-faqs.
| 2025-06-27T00:00:00 |
https://natlawreview.com/article/new-york-expands-legal-protections-models-fashion-industry
|
[
{
"date": "2025/06/27",
"position": 56,
"query": "technology regulation creative workers"
}
] |
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America's incredible stock market rebound is complete as ...
|
America’s incredible stock market rebound is complete as S&P 500, Nasdaq hit record highs
|
https://www.aol.com
|
[
"Aol Staff",
"David Goldman",
"John Towfighi",
"June",
"At Pm"
] |
... economic growth, jobs and the Fed's latest interest rate decision. Major companies like Apple, Amazon, Meta and Microsoft reported their latest financial ...
|
The S&P 500 and Nasdaq on Friday closed at all-time highs. - Richard Drew/AP
The US stock market on Friday hit an all-time high, its first since mid-February. It marked the culmination of a remarkable recovery on Wall Street since the S&P 500 was flirting with bear market territory in early April.
The S&P 500 gained 0.5%, to close at a record 6,173.07. It marked the first new record high since February 19.
Markets almost didn’t hit that record on Friday — they turned south in the late afternoon after President Donald Trump said the United States is ending trade talks with Canada because of a new digital services tax America’s northern neighbor imposed on companies. Trump said a new tariff on Canada would be announced within a week.
That soured sentiment on Wall Street — but not enough to keep stocks lower. Stocks resumed their march higher one hour before the closing bell and rallied to finish the week.
The Nasdaq Composite also hit a record high Friday, gaining 0.5%. It marked the first all-time high since December 16. The Nasdaq has become synonymous with Big Tech, and the AI boom has fueled a tech rally in recent months. The smaller Nasdaq 100, made up of top tech stocks, set a record earlier this week.
The Dow rose 432 points, or 1%. It had been up as much as 580 points earlier in the day. The blue-chip index, dragged down by UnitedHealth, which has tumbled 39% this year, plus Apple, Merck and Nike, still has about 1,200 points, or 2.7%, left to gain before it hits its all-time high.
The Dow, S&P 500 and Nasdaq each posted their biggest weekly gain in six weeks.
The S&P 500 has been on a wild round trip: The index shed $9.8 trillion in market value from its previous record on February 19 to its low point on April 8, before recovering all of those losses across the past two and a half months.
A wild ride
On April 8, when stocks were on the verge of plunging into a bear market, few Wall Street experts were predicting that the market would come close to a record high just 80 days later.
It’s been an incredible journey over the past several months. Trump alarmed traders with historic tariffs that economists said could reignite inflation and plunge the economy into a recession.
The Trump administration then doubled down on those tariffs, culminating in the president’s April 2 “Liberation Day” announcement. That sent tariffs for dozens of nations surging — some as high as 50%. US tariffs on China eventually eclipsed 145% for some products, effectively creating a blockade on America’s second-biggest trading partner.
On April 9, heeding warnings from the stock, currency and especially the Treasury markets, the administration paused its “reciprocal” tariffs for 90 days, sending markets surging. Last month, the Trump administration reached frameworks for trade deals with the United Kingdom and China, giving investors a sense that the most punishing trade policies were in the past, and more trade deals could be forthcoming.
“The sell-offs should never have happened,” said Art Hogan, chief market strategist at B. Riley Wealth Management. “There was no need for that. It was a completely manufactured crisis.”
Markets got another boost Friday after China signaled it would reopen its rare earth market to the United States. The news came just hours after White House officials said the two sides had reached a deal — a major breakthrough following weeks of negotiations.
Despite a 10% universal tariff that remains in place, in addition to 50% tariffs on steel and aluminum, and 25% tariffs on autos and auto parts, markets have largely looked past trade in recent weeks, focusing instead on other reasons for optimism or concern.
Treasury Secretary Scott Bessent on Friday said he believes trade negotiations with other countries could be “wrapped up” by Labor Day, providing a more relaxed framework for inking deals than the original July 9 deadline.
Bessent said in an interview on Fox Business that the United States has 18 “important trading partners” that it is seeking to make deals with. “If we can ink 10 or 12 of the important 18 … then I think we could have trade wrapped up by Labor Day,” he said, without specifying the countries.
A boom in AI, fueled by surging sales for Nvidia’s chips and a Republican-led effort to deregulate the industry, has been one of the primary catalysts that have fueled stocks and helped investors move beyond the trade war. Hopes for a rate cut from the Federal Reserve, backed by reasonably strong economic numbers and low inflation, have also helped stocks in recent months.
Markets had temporarily grown jittery after the House passed Trump’s sweeping tax cut and domestic policy agenda last month. But demand for Treasury bonds has remained surprisingly strong, giving investors confidence that foreign countries and investors will continue to support America’s debt, allowing the country to borrow unimpeded.
“Investors get the joke now,” said Hogan. “We’re going to hear something, whether it’s on Air Force One or on Truth Social, that we’re going to have to take with a massive grain of salt.”
Challenges ahead
Stocks face several challenges in the coming weeks and months.
If Congress reaches a stalemate in the domestic policy bill, which includes a provision to raise the debt ceiling, America could once again come close to defaulting on its debt because it is unable to borrow enough money to pay back its creditors. And if few (or no) more trade deals are forthcoming, tariffs could rise again as soon as July 9 as the 90-day reciprocal tariff pause expires.
The prospect of war breaking out in the Middle East remains a concern after a fragile truce between Israel and Iran was reached this week. And existing tariffs threaten to raise prices in the coming months, which could hurt economic growth.
Stocks face less existential threats, as well. Valuations are surging well above earnings expectations. The S&P 500’s price-to-earnings ratio has surged past 23, a relatively high number that means stocks have become quite expensive compared to their profit expectations.
Markets were celebrating at the close today. But it’s not clear how long the party will last.
CNN’s Matt Egan contributed to this report.
For more CNN news and newsletters create an account at CNN.com
| 2025-06-27T00:00:00 |
https://www.aol.com/america-incredible-stock-market-rebound-121431809.html
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|
Machine Learning and Workforce Planning with Legion AI
|
Machine Learning and Workforce Planning with Legion AI
|
https://legion.co
|
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Discover how Legion AI uses machine learning to boost forecast accuracy, reduce labor costs, and align staffing with real-time business demand.
|
Workforce planning has undergone a profound transformation. What was once driven by static spreadsheets and reactive staffing decisions has evolved into a strategic capability essential for navigating today’s complex and fast-paced business environment. Forward-thinking organizations recognize that traditional approaches can no longer keep up with market volatility, rising customer expectations, and the need for real-time operational agility.
Despite this shift, many companies still rely on outdated forecasting methods that fail to reflect the dynamic nature of demand. The consequences are all too familiar: under- or overstaffed shifts, missed revenue targets, overburdened teams—and at the core of it all, inaccurate labor forecasting.
Imagine walking into a bustling store on a Saturday afternoon, only to find your frontline workforce stretched thin. Not because talent wasn’t available, but because outdated forecasting missed the mark. These blind spots don’t just disrupt operations; they affect the bottom line, degrade customer satisfaction, and erode employee engagement.
In this blog post, we’ll explore how Legion AI is leveraging machine learning to enable businesses to predict labor demand with greater accuracy and how that capability directly contributes to improved operational efficiency, workforce alignment, and revenue growth.
Machine learning is a subset of AI that uses algorithms to enable computers to learn from and make predictions or decisions based on data.
The Evolution of Forecasting in Workforce Planning
Forecasting has long played a critical role in workforce planning, helping businesses align labor with expected demand. However, as operational complexity increases and the pace of change accelerates, forecasting based solely on historical trends often struggles to keep up. Today’s organizations require forecasting that reflects past patterns and adapts in real time to shifting demand drivers and external conditions.
Legion’s forecasting engine is built specifically for this modern labor environment. It doesn’t just predict based on past behaviors; it continuously learns and adjusts by integrating a wide range of data inputs, including weather, local events, sales promotions, and even custom events unique to the business. This allows Legion to anticipate demand shifts with greater accuracy and context.
The core of Legion’s forecasting engine is built on advanced machine learning, layered with a proprietary AI infrastructure that processes vast volumes of operational data. Legion generates highly tailored forecasts for each location, customer, and demand driver, such as foot traffic and transaction volume. On top of these primary drivers, Legion also accounts for store-specific variables such as events, promotions, and location-specific factors like weather and local events. The result is a smarter, more responsive approach to labor planning that evolves with the business and delivers actionable insights that drive both cost efficiency and service excellence.
Make Accurate Decisions with Legion Demand Forecasting
Legacy workforce solutions often force businesses to compromise, offering fragmented capabilities instead of a comprehensive approach. It’s time to move beyond outdated systems and embrace a solution that delivers smarter, faster, and more adaptive workforce planning. Here’s how:
Integrated External Events
Legion’s system processes various external data sources, such as weather forecasts, economic conditions, community events, and internal events like sales volume and staffing trends. Legion AI evaluates how these factors have historically influenced demand, identifies patterns, and applies this learning to future projections. As a result, forecasts reflect not only past behavior, but also the real-time impact of what’s happening now and what’s coming next.
Adaptive Training and Retraining
A key differentiator for Legion is the adaptive nature of its forecasting process. Models are retrained weekly using the latest available data, allowing the system to respond quickly to emerging trends or disruptions. If a protest, sports event, or sudden weather shift is expected to affect demand, Legion AI automatically factors it in. Additionally, the system provides smart forecast boundaries—upper and lower ranges—so operations teams can plan with full visibility into potential best- and worst-case labor needs.
Intelligent Automation
Legion’s forecasting doesn’t end with prediction, it powers intelligent automation across workforce planning. Forecasts feed directly into labor plans and schedules, eliminating manual guesswork. With demand predicted at 15-minute, 30-minute, or daily intervals, managers gain precise control over labor allocation. This level of granularity ensures that staffing is not only aligned to customer traffic but also optimized for cost efficiency, compliance, and employee satisfaction.
Measurable Impact
Forecasting accuracy isn’t just a technical advantage—it delivers real financial results. According to a study by Forrester, a 1% improvement in forecast accuracy can reduce labor costs by 0.5% and increase sales conversion by up to 0.25%. At scale, this level of precision translates to substantial gains in profitability and operational efficiency. The link between accurate forecasting and business performance is both clear and measurable.
Transparency
Most forecasting tools function as black boxes, offering little to no insight into what’s driving projections. Legion solves this through built-in transparency. Features like event attribution clearly show which external or internal factors influence demand. If a forecasted spike is tied to a nearby event or weather system, managers see that information directly, enabling them to act confidently and explain decisions when needed.
Forecast Intervals
Instead of presenting a single rigid forecast, Legion provides predictive ranges for each projection. By offering upper and lower bounds, the platform gives managers a statistically reliable window for labor planning. This is especially valuable in volatile conditions, where flexibility is more practical than pinpoint certainty. Leaders can better balance risk, cost, and service expectations with this approach.
What Makes Legion AI’s Forecasting So Unique?
Forecasting isn’t just about prediction—it’s about adaptability, scale, strategy, and people. Legion AI stands out by approaching labor forecasting with real-world complexity in mind, delivering accurate, dynamic insights that power better decisions across every level of the organization.
Value Human Inputs and Feedback
Legion’s AI is designed to navigate real-world unpredictability. It incorporates robust feedback mechanisms that allow users to adjust forecasts when human insight is needed— like unexpected store closures or a local disruption. These manual interventions are not ignored; they’re incorporated into Legion’s learning loop, helping the model learn from each real-world correction. This creates a collaborative relationship between AI and human expertise, blending automation with on-the-ground intelligence for smarter, more refined forecasting over time.
Built for Scale
Legion’s forecasting engine is designed to scale effortlessly. Whether a company has ten locations or ten thousand, the AI infrastructure supports high-volume, distributed forecasting without compromising performance. The system doesn’t just scale technically, it learns the unique operational rhythms of each location and adapts its models accordingly. Legion is ideal for enterprises operating across diverse geographies, business models, and customer patterns.
Short & Long Range Forecasting
Legion doesn’t just focus on short-term scheduling. Its forecasting engine supports both immediate planning and long-term strategic analysis. Businesses can model various ‘what-if’ scenarios to evaluate the labor impact of upcoming marketing campaigns, new store openings, or regional events. Legion’s ability to support scenario planning at scale gives organizations a powerful advantage in navigating uncertainty and planning for growth.
Continuous Accuracy Monitoring
Legion doesn’t leave accuracy to chance. The platform continuously measures and benchmarks forecasting performance using a weighted accuracy model tailored to the specific dynamics of each dataset. If performance degrades or new data patterns emerge, Legion automatically retrains its models to preserve precision and reliability. This approach ensures forecasts remain aligned with real-time business conditions, without manual intervention.
Designed & Built for People
Legion’s forecasting engine is built with both the business and the workforce in mind. Forecasts integrate directly into schedule automation and employee self-service tools, ensuring optimized planning that delivers real value to the frontline. Employees benefit from more predictable schedules, better alignment with their preferences, and fairer shift assignments, leading to higher engagement and lower attrition. Smarter forecasting isn’t just a business win—it’s a workforce win.
Legion’s Demand Forecasting stands apart as a true enabler of operational agility. It brings together the power of artificial intelligence, real-time data, and human expertise to create forecasts that are not only accurate but meaningful and that translate directly into better business outcomes.
Every Week With Legion AI 1.2M shifts automatically generated
1.7M optimizations performed
300K models trained
1.6B data points forecasted
Win-Win for Both the Business and Employees
Legion’s forecasting capabilities deliver strategic value to the business. By ensuring that labor deployment is consistently aligned with real-world demand, companies can avoid the high costs of overstaffing, reduce the chaos of reactive scheduling, and build more predictable operating models. The benefits for employees are equally clear: smarter staffing leads to more consistent schedules, fewer last-minute changes, and higher engagement. Customers see the impact with faster service, better coverage, and improved brand experience.
As organizations face continued uncertainty, tighter margins, and higher expectations from both employees and customers, demand forecasting can no longer be an afterthought. It must be a strategic function—automated, intelligent, and deeply embedded into how labor is planned, deployed, and managed.
Take Our Demand Forecasting Accuracy Challenge
Ready to compare our forecast accuracy to your existing solution and against predictability scores?
Let us prove what we can deliver for your company by taking our Demand Forecasting Accuracy Challenge.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://legion.co/blog/2025/06/26/machine-learning-workforce-planning/
|
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] |
Why Humanity, not AI, will shape the future of Brand Design
|
Why Humanity, not AI, will shape the future of Brand Design
|
https://medium.com
|
[
"Lilian Santini"
] |
AI can generate hundreds of variations in minutes, analyze styles, and offer up visual ideas we might not have stumbled upon alone. It can speed up our process, ...
|
Why Humanity, not AI, will shape the future of Brand Design Lilian Santini 2 min read · Feb 20, 2023 -- Listen Share
Brand identity design has always been about more than logos and color palettes. At its best, it’s an act of translation: taking the heart and vision of a business and shaping it into something people can instantly recognize, trust, and remember. In a world full of noise, this ability to create meaning is both an art and a responsibility.
Today, we have more resources than ever before. Artificial intelligence, especially tools like Midjourney, has opened up a world of possibility in brand design. AI can generate hundreds of variations in minutes, analyze styles, and offer up visual ideas we might not have stumbled upon alone. It can speed up our process, make brainstorming more dynamic, and help us customize work for clients with fresh eyes.
However, no AI will ever replace the intuition, discernment, or deep listening that happens between a designer and their client. The best brands aren’t born from an algorithm. They’re born from human experience: curiosity, empathy, and the courage to see what others might miss.
Over the past few years, I’ve woven AI image generation into my design practice at The Copper Portico, and I’ve seen real benefits:
• Faster ideation and concept development (AI lets us get “what if?” ideas on the page in seconds)
• Greater room for customization (AI is a flexible sketchbook, not the final word)
• More cost-effective creative exploration (especially valuable for smaller businesses)
• New creative sparks (AI can surprise us, but it’s up to us to know what fits and what doesn’t)
Still, the most important ingredient isn’t the technology — it’s us. AI expands our toolkit, but it’s human imagination, ethics, and emotional intelligence that give brands their depth and soul. The future of design belongs to those who are willing to experiment, to ask better questions, and to use these new tools in service of more meaningful, memorable brands.
As AI continues to evolve, my hope is that we don’t lose sight of what really sets us apart: our point of view, our lived experience, and our ability to connect with others in a way that can’t be automated. The best designs will always be those that reflect a real human story.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://medium.com/@liliansantini/the-benefits-of-ai-image-generation-for-brand-identity-design-93fd881a5f34
|
[
{
"date": "2023/02/01",
"position": 28,
"query": "artificial intelligence graphic design"
}
] |
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How AI is being used in the hiring process, and how job seekers can ...
|
How AI is being used in the hiring process, and how job seekers can get their foot in the door
|
https://www.wxyz.com
|
[
"Jolie Sherman"
] |
(WXYZ) — It's safe to say Artificial Intelligence is changing the way we live our lives, including how we apply for jobs.
|
(WXYZ) — It's safe to say Artificial Intelligence is changing the way we live our lives, including how we apply for jobs. That's because it's become a tool to build resumes and screen applications, affecting both job seekers and employers.
Watch Jolie's full story in the video player below
How AI is being used in the hiring process, and how job seekers can get their foot in the door
Chelsea Jordan will be the first to tell you that finding a well-paying job in metro Detroit has been difficult.
WXYZ
"The job search has been going on for about a year," she told me.
The mother from Inkster says she's applied for at least 100 jobs in the last 12 months and wasn't getting any calls. But that changed once she started using Artificial Intelligence to enhance her resume.
"I'm getting more interviews now than I was before I was using AI," Chelsea said.
And she's not alone: a recent study conducted by the recruiting agency, Kelly Services, shows that out of a thousand job seekers in the U.S., nearly 80 percent are using AI in the application process. Most of the job seekers are using it to build resumes and find openings, while others are using it to write cover letters and prepare for interviews.
But it's not just job seekers using AI, hiring managers are using it, too.
That same study found that out of roughly 1,000 managers in the U.S., 66 percent say their company uses AI to screen applications.
WXYZ
"They want to use AI to get things faster, look at resumes faster, interview faster, but I still want to make the decision," said Mark Saltrelli.
Mark, the vice president of engineering and recruiting at Kelly Services, says at the end of the day you still need to make sure you are who you say you are and be able to talk about your experience.
"To really differentiate yourself, you still need that people aspect," Mark said. "You still need to validate that you've done the job well, and others can refer you into it."
But Chelsea says sometimes the hardest part is getting past those automated screenings.
"It was how I was formatting it, and the terminology I was using," Chelsea said. It looks good in general, but for this specific job, if it's going to get through to a person, it has to say specific words. I guess it just opened my eyes up to, for a lack of a better term, the job search can be a game that you have to play, and I think that AI has helped me do that."
Where Your Voice Matters
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.wxyz.com/news/voices/how-ai-is-being-used-in-the-hiring-process-and-how-job-seekers-can-get-their-foot-in-the-door
|
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AI in Healthcare Statistics: Market Insights & Growth - Binariks
|
AI in Healthcare Statistics: 20+ Key Facts for 2025-2029
|
https://binariks.com
|
[
"Business Director",
"Healthcare Industry"
] |
The global AI in the healthcare market was valued at .69 billion in 2024 and is expected to grow at a CAGR of 38.5% from 2023 to 2033. The ...
|
Artificial Intelligence has a great demand in the healthcare industry. For now, 94% of healthcare providers, life science companies, and tech vendors use AI in some capacity. This number is crucial proof to demonstrate the benefits of AI in healthcare and how significant it is for the industry.
However, there are still some issues that are slowing down the adoption of AI in healthcare. One of them is the lack of experienced specialists, leading to development delays, mistakes, increased costs in healthcare. Healthcare providers turn to tech companies for expertise in adopting AI, and naturally, demand for such expertise makes competition among the latter highly intensive.
AI healthcare market overview
The global AI in the healthcare market was valued at .69 billion in 2024 and is expected to grow at a CAGR of 38.5% from 2023 to 2033. The healthcare AI market is experiencing a remarkable and significant surge in its growth and acceptance. Here are some facts about AI in healthcare:
Healthcare is considered the second-largest industry for AI in 2024.
North America dominates the market, accounting for over 54% of revenue as of 2024.
The use of AI-powered medical imaging technologies is a significant trend among healthtech companies, with 76% of all AI-enabled medical devices authorized for sale in the US being those used in radiology.
Medical businesses continue to grow and expand, so mergers and acquisitions are becoming very common. Larger players are acquiring smaller companies to enhance their AI capabilities or extend their market coverage.
The integration of AI with complementary technologies like robotics or blockchain promotes precision medicine and transforms healthcare delivery.
Generative AI-powered virtual assistants and chatbots are in high demand, with 47% of healthcare organizations already using or planning to implement them. These AI solutions can automate up to 30% of patient interactions, significantly reducing providers' administrative workload.
Take your software to new heights with AI and ML services Read more
AI healthcare market segmentation: by application
By application of AI in healthcare, the top segments dominating are Robot-Assisted Surgery, Clinical Trials, and Connected Machines. Cybersecurity, Dosage Error Reduction, and Diagnostics are developing slower.
AI-powered robots make surgical procedures more precise. Complicated surgeries in cardiovascular or neurology need absolute accuracy, and AI robotics can ensure it. AI algorithms are also changing clinical trials processes. The power of rapid data analysis at every stage of clinical trials makes them more accurate, cost-effective, and patient-centric.
Besides, AI has a crucial role in using connected machines in healthcare. AI integrates data from IoT devices and sensors into a single healthcare system. This gives an opportunity to gather, exchange, and analyze huge amounts of data.
To understand which therapeutic areas have most AI support, let's take a look at the FDA-approved list of AI-enabled applications. For now, FDA approved a total of 950 medical devices using AI, and radiology has 76% of them. This report shows us that radiological imaging has become very promising and usable in practice.
All the applications mentioned above highlight how AI is rapidly advancing across multiple facets of healthcare, from operating rooms to patient monitoring and diagnostics. Let's explore how AI is transforming each of these areas in more detail:
Robot-assisted surgery
AI is now a standard in cardiovascular, neurological, and orthopedic surgery. It minimizes human error and ensures extreme precision.
There is a rising trend of AI-enabled robotic systems entering outpatient surgery centers, reducing recovery time and operational costs. 53% of EU healthcare organizations stated they plan to use medical robotics by the end of 2025.
Clinical trials
AI is revolutionizing how trials are designed and managed. It optimizes recruitment, data analysis, and adaptive protocols. In particular, it is increasingly used in decentralized trials with AI for remote patient participation and real-time analytics.
Connected machines
AI integrates data from ICU monitors, wearable tech, and in-home sensors to support early intervention. Next-generation AI systems are now enabling predictive hospital asset management to reduce ICU overloads. 72% of EU healthcare organizations plan to use AI for patient monitoring.
Diagnostics (imaging)
Diagnostics is the most established area of AI use in healthcare. AI helps interpret X-rays, CT scans, and MRIs more quickly and accurately.
Regulatory agencies have recently fast-tracked approvals for multimodal AI imaging tools that combine various scan types.
45% of Americans view GenAI as helpful in interpreting medical tests, X-rays, and other diagnostic images.
Dosage error reduction
AI tools aim to reduce prescribing mistakes through automated dose checks and alerts. Integration with clinical decision support systems (CDSS) is improving prescription safety in pilot programs.
Cybersecurity
AI monitors network traffic and detects anomalies to protect sensitive patient data. According to statistics of AI in healthcare, 83% of US consumers view AI's potential for error as a barrier, and 86% state they are concerned about transparency.
As of May 2025, the US Food and Drug Administration (FDA) has authorized 950 AI/ML-enabled medical devices for marketing in the US. This information is available on the FDA's official AI/ML-Enabled Medical Devices list .
Radiology continues to be the leading field, with a significant number of these devices designed to assist in imaging and diagnostic processes.
Looking ahead:
Radiology will stay at the forefront, especially with real-time, multimodal imaging tools.
AI in diagnostics beyond imaging (e.g., pathology, genomics) is set to grow at 35% CAGR over 5 years.
over 5 years. Operational AI will become standard for scheduling, inventory, and billing.
AI healthcare market segmentation: by technology
Based on components, the global AI in the healthcare market can be divided into software, hardware, and services. The software segment has the largest share and is expected to have the fastest growth.
This segment includes Machine Learning platforms, Natural Language Processing (NLP) and text analysis tools, Deep Learning Platforms, Computer Vision, Speech and Audio Recognition, Integrated Development environments (IDEs) and AI frameworks. Global trend of digital transformation , and capabilities of AI software solutions made it the leading component in healthcare.
Finally, based on technology, the AI healthcare market can be divided into Machine Learning, Natural Language Processing (NLP), Computer Vision , and Context-Aware Computing. Machine Learning is a leader now, but NLP has the potential to be the market leader in the future. The AI in healthcare statistics shows the demand for analyzing human language data will soon be more than for images and contextual information.
Will adopting GenAI mark the next chapter for your business? Download our free whitepaper now to find out. Get the guide
4 drivers affecting rise of AI in healthcare
The growing amount of medical data: According to the DATCON index, the healthcare data explosion will exceed 10 trillion gigabytes in 2025. Now AI algorithms can help operate it and give important insights. Emerging global issues: COVID-19 has become a litmus test for identifying problems in healthcare. Medical AI can enable healthcare providers to do more with less. Moreover, it can revolutionize AI and the healthcare market and pay more attention to prediction than treatment. Population aging: With the increase in life expectancy, people need more medical help and care. AI-enabled technologies can help people to live healthier and longer lives. Lack of medical staff: With a shortage of medical workers, the duties and burden on existing employees increase. That leads to mistakes and negatively affects performance and patient care. AI can help automate routine tasks and give new opportunities for treatment.
How to become FHIR-compliant A detailed approach to FHIR implementation Download whitepaper
Regional analysis
North America has a dominant position in the healthcare artificial intelligence market. Healthcare companies in the USA have great support from the government. Moreover, the public and private sectors have built a coordinated collaboration and managed to adopt AI technologies earlier.
The second-largest region in the AI healthcare market is Europe. Germany, the United Kingdom, France, Spain, Ireland, Switzerland, and Belgium are key players in life sciences. They are focusing on R&D activity and biotechnology drug discovery.
Asia-Pacific region is a quickly growing market and is expected to register a CAGR of 8.5% by 2028. Because of the rising geriatric population, medical tourism, and the growing research activities, Asian countries accelerated the adoption of medical AI.
The healthcare artificial intelligence market in South America is focusing on developing Remote Patient Monitoring (RPM) and telehealth. The Middle East and Africa will grow more slowly than other regions. Their focus is AI-based telehealth services and increasing collaborations between healthcare facilities.
Tech giants in a competitive landscape
Big tech giants have been accelerating their pursuit of the AI healthcare market for the last several years. They make collaborations, develop AI-powered solutions, and invest in AI startups intensively.
Google is building its life science brand and actively uses AI for its purpose. DeepMind, the artificial intelligence company owned by Google, is the key player in this market. Google focuses on AI pharmaceutical R&D, radiology, and imaging. Also, it is interested in healthcare search and unstructured data analysis.
is building its life science brand and actively uses AI for its purpose. DeepMind, the artificial intelligence company owned by Google, is the key player in this market. Google focuses on AI pharmaceutical R&D, radiology, and imaging. Also, it is interested in healthcare search and unstructured data analysis. Microsoft is a leader in health IT services. Azure Cloud is becoming the leading environment for enterprises' provider-focused software. Besides, Microsoft Corporation is competing to collect and sell medical data.
is a leader in health IT services. Azure Cloud is becoming the leading environment for enterprises' provider-focused software. Besides, Microsoft Corporation is competing to collect and sell medical data. Amazon has its HIPAA -appropriated cloud service for healthcare data processing. It is also focused on AI in precision medicine, medical supply chain, insurance, and care delivery.
has its HIPAA -appropriated cloud service for healthcare data processing. It is also focused on AI in precision medicine, medical supply chain, insurance, and care delivery. Apple holds the largest share of wearable devices. iPhone and Apple Watch are mainly used for gathering patients' data for further processing with AI. Besides, Apple's Health App offers a patient-doctor environment with multiple functions via iPhone.
Conclusion
Statistics demonstrate the growing use of artificial intelligence in healthcare. We observe remarkable results, including growing trust in medical providers for AI and increasing interest from investors in developing AI-enabled healthcare solutions.
It appears that we can expect significant advancements soon. Still, with a lack of skilled specialists and inefficient cooperation between the public and private sectors, the journey remains complex and requires the right expertise.
With over 60 healthcare projects, Binariks is the partner of choice for providers, payers, and innovators ready to make AI work in real healthcare settings. Our AI Center of Excellence gives you access to cross-functional experts and a proven, business-first approach, ensuring safe, compliant, and impactful solutions.
See how our clients benefit: explore our case studies and discover how we help you overcome barriers, unlock new value, and lead in digital health innovation. Ready to move from uncertainty to competitive advantage? Contact Binariks to unlock what's possible with AI in healthcare.
| 2025-06-26T00:00:00 |
https://binariks.com/blog/artificial-intelligence-ai-healthcare-market/
|
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|
Unemployment among young college graduates outpaces overall ...
|
Unemployment among young college graduates outpaces overall US joblessness rate
|
https://www.kgw.com
|
[] |
The unemployment rate has stayed low mostly because layoffs are still relatively rare. The actual hiring rate — new hires as a percentage of all ...
|
For college graduates 22 to 27 years old, the unemployment rate was 5.8% in March — the highest, excluding the pandemic, since 2012.
WASHINGTON — While completing a master’s degree in data analysis, Palwasha Zahid moved from Dallas to a town near Silicon Valley. The location made it easy to visit the campuses of tech stalwarts such as Google, Apple, and Nvidia.
Zahid, 25, completed her studies in December, but so far she hasn't found a job in the industry that surrounds her.
“It stings a little bit,” she said. “I never imagined it would be this difficult just to get a foot in the door.”
Young people graduating from college this spring and summer are facing one of the toughest job markets in more than a decade. The unemployment rate for degree holders ages 22 to 27 has reached its highest level in a dozen years, excluding the coronavirus pandemic. Joblessness among that group is now higher than the overall unemployment rate, and the gap is larger than it has been in more than three decades.
The rise in unemployment has worried many economists as well as officials at the Federal Reserve because it could be an early sign of trouble for the economy. It suggests businesses are holding off on hiring new workers because of rampant uncertainty stemming from the Trump administration's tariff increases, which could slow growth.
“Young people are bearing the brunt of a lot of economic uncertainty,” Brad Hersbein, senior economist at the Upjohn Institute, a labor-focused think tank, said. “The people that you often are most hesitant in hiring when economic conditions are uncertain are entry-level positions.”
The growth of artificial intelligence may be playing an additional role by eating away at positions for beginners in white-collar professions such as information technology, finance, and law.
Higher unemployment for younger graduates has also renewed concerns about the value of a college degree. More workers than ever have a four-year degree, which makes it less of a distinguishing factor in job applications. Murat Tasci, an economist at JPMorgan, calculates that 45% of workers have a four-year degree, up from 26% in 1992.
While the difficulty of finding work has demoralized young people like Zahid, most economists argue that holding a college degree still offers clear lifetime benefits. Graduates earn higher pay and experience much less unemployment over their lifetimes.
The overall U.S. unemployment rate is a still-low 4.2%, and the government's monthly jobs reports show the economy is generating modest job gains. But the additional jobs are concentrated in health care, government, and restaurants and hotels. Job gains in professions with more college grads, such as information technology, legal services, and accounting have languished in the past 12 months.
The unemployment rate has stayed low mostly because layoffs are still relatively rare. The actual hiring rate — new hires as a percentage of all jobs — has fallen to 2014 levels, when the unemployment rate was much higher, at 6.2%. Economists call it a no-hire, no-fire economy.
For college graduates 22 to 27 years old, the unemployment rate was 5.8% in March — the highest, excluding the pandemic, since 2012, and far above the nationwide rate.
Lexie Lindo, 23, saw how reluctant companies were to hire while applying for more than 100 jobs last summer and fall after graduating from Clark Atlanta University with a business degree and 3.8 GPA. She had several summer internships in fields such as logistics and real estate while getting her degree, but no offer came.
“Nobody was taking interviews or responding back to any applications that I filled out,” Lindo, who is from Auburn, Georgia, said. "My resume is full, there’s no gaps or anything. Every summer I’m doing something. It’s just, ‘OK, so what else are you looking for?’”
She has returned to Clark for a master's program in supply chain studies and has an internship this summer at a Fortune 500 company in Austin, Texas. She's hopeful it will lead to a job next year.
Artificial intelligence could be a culprit, particularly in IT. Matthew Martin, senior U.S. economist at Oxford Economics, has calculated that employment for college graduates 28 and above in computer science and mathematical occupations has increased a slight 0.8% since 2022. For those ages 22 to 27, it has fallen 8%, according to Martin.
Company announcements have further fueled concerns. Tobi Lutke, CEO of online commerce software company Shopify, said in an April memo that before requesting new hires, “teams must demonstrate why they cannot get what they want done using AI.”
Last week, Amazon CEO Andy Jassy said AI would likely reduce the company's corporate workforce over the next few years.
“We will need fewer people doing some of the jobs that are being done today, and more people doing other types of jobs,” Jassy said in a message to employees. “We expect that this will reduce our total corporate workforce as we get efficiency gains from using AI extensively across the company.”
Zahid worries that AI is hurting her chances. She remembers seeing big billboard ads for AI at the San Francisco airport that asked, “Why hire a human when you could use AI?”
Still, many economists argue that blaming AI is premature. Most companies are in the early stages of adopting the technology.
Professional networking platform LinkedIn categorized occupations based on their exposure to AI and did not see big hiring differences between professions where AI was more prevalent and where it wasn't, said Kory Kantenga, the firm's head of economics for the Americas.
“We don’t see any broad-based evidence that AI is having a disproportionate impact in the labor market or even a disproportionate impact on younger workers versus older workers,” Kantenga said.
He added that the Federal Reserve's interest rate hikes have also slowed hiring in tech. Many IT firms expanded when the Fed pinned its short-term rate at nearly zero after the pandemic. In 2022, the Fed began cranking up rates to combat inflation, which made it harder to borrow and grow.
In fact, IT's hiring spree when rates were low — fueled by millions of Americans ramping up their online shopping and video conferencing — left many firms with too many workers, economists say.
Cory Stahle, an economist at the job-listings website Indeed, says postings for software development jobs, for example, have fallen 40% compared with four years ago. It's a sharp shift for students who began studying computer science when hiring was near its peak.
Zahid, who lives in Dublin, California, has experienced this whiplash firsthand. When she entered college in 2019, her father, who is a network engineer, encouraged her to study IT and said it would be easy for her to get a job in the field.
She initially studied psychology but decided she wanted something more hands-on and gravitated to data analysis. Her husband, 33, has a software development job, and friends of hers in IT received immediate job offers upon graduation a few years ago. Such rapid hiring seems to have disappeared now, she said.
She has her college diploma, but hasn't hung it up yet.
“I will put it up when I actually get a job, confirming that it was worth it all,” she said.
___
AP Writer Matt Sedensky in New York contributed to this report.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.kgw.com/article/news/nation-world/unemployment-young-college-graduates-overall-joblessness-rate/507-6281513a-ee36-490e-bcf7-a6993fe4e204
|
[
{
"date": "2025/06/26",
"position": 82,
"query": "AI unemployment rate"
},
{
"date": "2025/06/26",
"position": 93,
"query": "AI unemployment rate"
}
] |
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Engaging the workforce to develop AI governance
|
Engaging the workforce to develop AI governance
|
https://aicenterforgovernment.org
|
[
"Ai Center For Government",
"June"
] |
Agencies can deepen staff expertise by taking advantage of government-based AI courses, free AI Government Leadership Programs and custom ...
|
The Partnership for Public Service AI Center for Government™ champions pragmatic, people-centered approaches to building AI governance structures — and believes government agencies can design AI strategies to serve the public, satisfy employee need and enable smart, safe innovation.
1. Start with the mission
Agencies that treat AI as an operational, mission-delivery tool can focus their efforts to use AI to solve real problems and boost efficiency. When mission comes first, AI can effectively and appropriately support leadership, employees and the public.
2. Agency partnerships: Collaborate to innovate!
Government agencies with a broad range of internal offices and departmental missions can develop successful enterprise-wide AI governance practices by prioritizing engagement and participation. Through leadership convenings, workforce forums and communities of interest, for example, agencies can involve staff to ensure AI governance is fit-for-purpose across the organization.
3. AI literacy
AI training is key not only to deployment of AI, but also to AI strategy development. Agencies can deepen staff expertise by taking advantage of government-based AI courses, free AI Government Leadership Programs and custom internally-developed trainings. Employees can better contribute to strategy and implementation by understanding the tools.
4. Clear communication
During the AI governance (re)design process, agency leadership and AI strategy development teams can reduce friction and confusion by prioritizing clear, ongoing communication. Creating opportunity for employees to ask questions, surface concerns and share what doesn’t make sense will create a feedback loop that strengthens internal policy now and deployment later.
5. Manage change and make it stick
AI governance should be designed for impact and made to stick; it should be made to be part of the everyday workflow and implemented with change management principles in mind. Successful AI governance strategies are responsive and flexible enough to evolve with changing leadership, priorities or technology. Well-implemented plans keep the whole human and the whole organization in mind.
6. We can do this!
Through our touchpoints with governments across the country, we’ve seen that with the right balance of structure and flexibility, even large, complex agencies can build systems that are responsive, responsible and built to last.
It’s not about tech for tech’s sake. It’s about making government work better for everyone.
We’re here to help!
| 2025-06-26T00:00:00 |
2025/06/26
|
https://aicenterforgovernment.org/2025/06/26/engaging-the-workforce-to-develop-ai-governance/
|
[
{
"date": "2025/06/26",
"position": 93,
"query": "government AI workforce policy"
},
{
"date": "2025/06/26",
"position": 95,
"query": "government AI workforce policy"
},
{
"date": "2025/06/26",
"position": 79,
"query": "government AI workforce policy"
},
{
"date": "2025/06/26",
"position": 62,
"query": "government AI workforce policy"
},
{
"date": "2025/06/26",
"position": 77,
"query": "government AI workforce policy"
}
] |
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] |
AI & the retraining challenge - AI Policy Perspectives
|
AI & the retraining challenge
|
https://www.aipolicyperspectives.com
|
[
"Ai Policy Perspectives"
] |
However, in the US, where labor unionisation has been mostly declining, these latter technologies also widened income and, especially, wealth ...
|
In this essay, Julian Jacobs writes about the history of US public worker retraining programmes, their efficacy, and how they might fare as AI diffuses throughout the economy.
Source: Venus Krier
When asked to reflect on how AI may affect society, people frequently rate the loss of their job as their top concern. Such worries are not new. During the Industrial Revolution, the Luddites smashed textile machines and fought with mill owners, even if their demands were more nuanced than is often ascribed. At the turn of the 20th century, public administrators overseeing the US economy feared how new kinds of glassware and steel might impact workers.
Such fears are understandable. Jobs, particularly the skilled trades that are often most vulnerable to automation, can provide financial independence and feelings of status, purpose, and community. This is of course not true for all jobs, all the time. In his seminal book Working, Studs Terkel observed that many people feel their jobs are defined by a lack of meaning, as well as an intense disconnectedness, fatigue, and a droning anxiety about wasting their lives. But surveys of today’s employees, at least in the West, suggest that a majority are relatively satisfied with their job and that as many as 50%, or more, would want to continue working even if they did not need the money.
1. Will we need retraining to respond to AI?
When new technologies disrupted employment in the past, they typically led to an increase in aggregate employment, albeit not always immediately. This was true with the steam engine and spinning jenny during the Industrial Revolution, and with industrial robots and digitisation in the 20th Century. However, in the US, where labor unionisation has been mostly declining, these latter technologies also widened income and, especially, wealth inequalities. This was mainly due to two dynamics - the extent of which economists continue to dispute.
First, the technologies increased productivity and economic growth, but a growing share of this expanding pie went to capital owners, especially those with a significant ownership stake in fast-growing enterprises. Labour’s share, once inflation was accounted for, declined. In 2022, according to the economist Loukas Karabarbounis, the share of income going to labor in the US hit its lowest point since the Great Depression, at just under 60% of national income.
Second, the technologies complemented people with certain skills while displacing others - what economists refer to as ‘skills-based technological change’. Digitisation and industrial robots automated middle-wage occupations such as clerks, bookkeepers, and assembly line workers, while enabling high-paying roles for software engineers, roboticists and data analysts. These new roles also fostered demand for lower-wage roles, including to provide services to higher earners, for example in the retail, healthcare, food service or personal grooming sectors. These two trends made the labour market more polarised, as workers who lost their jobs or were unable to benefit from new technologies struggled to move into higher-wage work. If we expect AI’s impacts to be similar, this could provide a rationale for governments to fund large retraining programmes to help people retain their jobs and move into higher-wage roles.
Will AI’s effects be similar? We don’t know. Some efforts to address this question break jobs down into bundles of tasks and evaluate AI’s ability to perform them, now and in the future. These evaluations cover a wide range of jobs and tasks, but they don’t tell us whether organisations that hire people to do these tasks are investing in AI, or changing their hiring. Other studies do assess the impact of AI on real-world employment outcomes, for example on freelance employment, but only cover a small share of the labour market. No assessments yet give us breadth and depth.
In the near-term, the employees at greatest risk from AI are likely those that work in, or would like to work in, occupations where tasks are currently performed (almost) entirely on a computer. And where some degree of human error is already common and not catastrophic. This may include graduates aspiring to work in consultancies, legal firms or content agencies or the large number of people who work in remote customer service roles. If new kinds of AI-enabled robots become more capable and cheap, then other kinds of roles, for example in warehouses, could be at risk.
If AI starts to cause people in these roles to lose their jobs en masse, we can expect loud calls for new public retraining programmes. The idea that the government should help to retrain people in response to new technologies, trade shifts or other ‘shocks’ is ubiquitous in policy briefs, consulting reports, and academic research, including on AI. But, these reports typically don’t specify what an AI-induced retraining programme should look like, who should do it, and what lessons, if any, we should draw from past efforts.
In the remainder of this essay, I trace the history of US public retraining programmes and their impacts. In short, I find little evidence that they have been effective. In future essays, I hope to consider lessons from private retraining programmes and other countries.
2. A brief history of US public retraining programmes
In 1933, as the Great Depression reached its darkest moments, President Roosevelt signed the Wagner-Peyser Act, creating the United States Employment Services, a new national network of offices to help the ~25% of the labour force that was out of work. Their retraining offering was rudimentary but provided a foundation to build from. Since then, retraining has become an integral ‘Active Labour Market Policy’ in the US and beyond. If passive labour market policies, like unemployment insurance, aim to provide a safety net for the unemployed, active policies like retraining and job search assistance aim to provide a ladder back into stable work.
In 1962, John F Kennedy signed the Manpower Development and Training Act, the first federal retraining programme to operate at scale. Over the next decade, it retrained 1.9m people to navigate the ‘constantly changing economy’. For men, this typically meant retraining as machine shop workers, auto mechanics, and welders. For women, clerical and administrative roles. In 1973, Richard Nixon replaced the MDTA with the short-lived Comprehensive Employment and Training Act, which focussed on getting low-income individuals, the long-term unemployed, and students into subsidised, entry-level jobs in public sector agencies and nonprofits.
CETA began a process of decentralising US public retraining, putting decisions about how to run the programmes into the hands of cities and states, rather than the federal government. In 1982, the Reagan administration accelerated this further, when it passed the Job Training Partnership Act. In line with Reaganomics, the JPTA aimed to further empower local organisations to deliver retraining and boost private sector employment. To do so, it established Private Industry Councils, with representatives from local businesses, to help direct and supervise the programmes. It also tightly means-tested participants, with the vast majority coming from low-income or “hard to serve" backgrounds, which included people with disabilities, the homeless, offenders, welfare recipients, and out-of-school youth. The training focussed on cultivating basic skills, such as remedial reading and maths, ‘work habits’, such as punctuality and résumé writing, and short, entry-level courses for clerical, services or trades work.
As I expand on below, today the JPTA is typically viewed as a policy failure. In 1998, Bill Clinton replaced it with the Workforce Investment Act, which significantly widened the criteria for participation, making retraining available to anybody who wanted it, while giving low-income and disadvantaged people priority. If the JTPA was essentially a poverty reduction scheme, the WIA aspired to become a universal employment service, including for displaced middle-income workers, when budgets allowed. The WIA also sought to prepare individuals for a more fluid labour market. Unlike JPTA and CETA, which offered participants little choice, the WIA provided individuals with “individual training accounts” so that they could (at least in theory) choose the skills and sectors to invest their time in, once they had completed some general training.
In 2014, Barack Obama replaced the WIA with the more streamlined Workforce Investment and Opportunity Act, which today provides most US federally-funded retraining. The WIOA allows participants to directly participate in their preferred retraining services, without the need to first participate in more general training. It also allows regions to offer more locally-relevant retraining and has tried to increase accountability, by requiring more third-party evaluations.
Every year, approximately ~500,000 participants take part in the WIOA’s ‘Adult’ and ‘Dislocated Worker’ streams, of whom ~200,000 receive training vouchers, at a cost of ~$500m. As noted by David Deming and colleagues, this is a relatively low figure, when one considers that the US government spends 25bn a year on Pell Grants for undergraduate education. Most WIOA participants are low-income, but their profiles vary. For example, most of those on the ‘Adult’ stream are below or near the poverty line, with limited education or employment experience. In contrast, most of those on the “Dislocated Worker’ stream have lost stable employment, for example in manufacturing, and are more likely to be older with more substantial work experience. Although not mandatory, people who receive welfare and public assistance are encouraged to apply and make up approximately one third of WIOA adult participants, according to a 2022 evaluation.
The skills that WIOA retraining programmes impart and their method of instruction vary considerably across US states, owing to differences in the capacity of local retraining providers (who must bid for federal funding), employer needs, and political considerations, which can override more objective readings of labour demand. The result is a patchwork. In 2015, Burt Barnow and Jeffrey Smith distinguished between different types of retraining, from small group sessions focussed on basic skills to subsidised apprenticeships. In the early days of the Clinton era Workforce Investment Act, 47% of participants enrolled in formal, classroom retraining programmes, but this figure ranged widely across states, from 14-96%. According to data from 2023-24, less than 10% of WIOA training involved paid on-the-job training, and just 2% involved apprenticeships.
Source: Burt Barnow and Jeffrey Smith; Venus Krier
Summing together, over the last 80 years, we have seen a steady evolution in US public retraining, from more centralised, New Deal-style programmes to more decentralized efforts targeting local private sector employers. The goal has shifted from addressing widespread unemployment to reducing poverty and back towards a more universal employment service that integrates retraining with other policies, such as job search support.
We can expect further changes. The Trump Administration has proposed merging the WIOA’s programs into a single funding stream titled ‘Make America Skilled Again,’ which may result in a significant funding cut and replace much of today's federally funded training with apprenticeships. However, the proposal will likely be altered significantly as Congress deliberates on its details. So the future of US federal retraining, and how it may respond to AI, is still very much to be determined.
3. Does public retraining work? The evidence base
Consider the hypothetical example of ‘Tony’, who used to be employed at a mid-sized auto parts manufacturing plant in Ohio, until his employer steadily introduced a wave of industrial robots. As documented by Thomas Phillippon in The Great Reversal, many US localities are dominated by a handful of ‘good’ employers. The absence of alternative options reduces worker bargaining power and wages. It also makes layoffs more challenging as employees like Tony must compete against many others, most of whom also lack transferable skills to pursue other roles.
After a prolonged job search, Tony enters a Workforce Investment and Opportunity Act retraining program, run by his local American Job Centre. Owing to his low-income status, he is given priority. Upon arrival, the Centre screens Tony to see if he qualifies for the Dislocated Worker stream. Once confirmed, he is asked to participate in maths, problem-solving, and reading assessments, as well as an aptitude test. From there, a counsellor reviews labour market data and recognising Tony’s background in the auto trade, proposes a variety of skilled trades. She also proposes the opportunity to reskill into a new sector, like medical assistance. If Tony is under pressure to return to work quickly, he may move directly to job search assistance and on-the-job retraining. Alternatively, he may pursue longer classroom retraining at a community college or technical school. If all goes well, he will have regular meetings with his counsellor, participate in soft skills workshops and networking events, and will land a secure new job, with regular progress check-ins.
In reality, successful examples like Tony are rare. In 2016, the year of Donald Trump’s first election, David Autor, David Dorn & Gordon Hanson published ‘The China Shock’ - arguably the most impactful US economics paper of the past decade. The study demonstrated that, since the 1990s, import competition from China had devastated large parts of the American workforce, particularly regions focussed on manufacturing textiles, furniture, toys and other light goods. The shock reverberated across communities, igniting brain drain and depressing economic and social prospects for a generation. Meanwhile, other sectors, regions and employees benefited from cheaper imports.
The China Shock, and the wider technology-based automation that was occurring in these sectors, led to a glut of displaced workers and a stream of youth in search of alternative employment opportunities. This was the sort of challenge that the Clinton-era Workforce Investment Act and the Obama-era Workforce Investment and Opportunity Act were designed to address. However, Autor and colleagues showed that many displaced employees either failed to find employment or were forced to take up new roles in the service sector, for example as cashiers or security guards, that were often less-skilled, lower-paid and less rewarding.
These findings chime with the more formal evidence base on US public retraining programmes. To evaluate retraining programmes, researchers expend considerable effort to track key variables, including the proportion of participants who find work shortly after exiting, the proportion who remain employed for at least six months, and their average earnings. In general, researchers have failed to show any statistically significant benefit on these outcomes.
Teasing out why - and what this means for future retraining programmes, including for AI - is difficult. One key challenge is non-random selection. The population that takes part in retraining is not representative of the wider population of people who have been displaced. This means that we do not know the extent to which participants’ subsequent labour market outcomes are due to the impact of the retraining programme (or lack thereof), or other characteristics that are more common among participants, such as a willingness to take part in retraining in the first place.
To address this issue, researchers use quasi-experiments that aim to approximate randomised controlled trials by matching a group of people that did participate in public retraining programmes - say 10,000 middle-aged men from rural districts - with a similar group that didn’t. However, to do this well, researchers need to know what characteristics are most relevant to future labour market outcomes - prior education?, proximity to a nearby city? - so that they can control them. And many potentially important social or psychological characteristics are impossible to reliably capture in datasets. On top of this, researchers must try to account for the huge variance in the focus, format, and resources of different states’ programmes. As a result, some researchers conclude that it is impossible to make reliable causal claims about why public retraining is, or isn’t effective.
The evidence that does exist provides cause for skepticism. For example, a National Study to evaluate the Reagan-era Job Training Partnership Act, involved a genuine randomised controlled trial that ran from 1987 to 1992, with a representative sample of more than 20,000 participants. It found no statistically significant improvement in employment rates, employment duration, or earnings. In 2019, a 10-year evaluation of the Workforce Investment Act, and the Workforce Investment and Opportunity Act found that, while intensive one-on-one career counselling did improve employment and earnings outcomes, the programme’s retraining streams did not.
As of 2023, the most recent data available, 70% of participants in WIOA were employed in the 2nd and 4th quarters after finishing their retraining programmes. But these outcomes are not compared to a control group, so we don't know if, or to what extent, WIOA is truly improving them. Even when WIOA retraining is helping people find jobs, research by David Deming and colleagues suggests that ~40% of participants are being trained into ‘low-wage’ support roles, particularly in the healthcare sector. The most common roles, such as nursing assistants, come with an annual salary of less than $25,000. Demand for these roles is high, which explains the WIOA’s focus on them, but they often offer little scope for career growth.
There is even evidence that some retraining programmes may hurt participants. For example, a 2012 evaluation of the US Trade Adjustment Assistance programme, which provides retraining to workers displaced by outsourcing and trade, found that participants had lower employment rates in the two years after they were laid off, compared to similar workers who did not participate, potentially due to the opportunity cost of not being able to apply for more immediate work opportunities. Even four years after losing their job, TAA participants were underemployed and earned slightly less, compared to non- participants.
4. Why does retraining fail?
In the absence of clear causal evidence, researchers are left to speculate as to why US public retraining programmes have underwhelmed.
A first challenge relates to the participants and their ability and willingness to take part. Some potential participants may avoid retraining, or drop out, due to the costs involved, which range from transport to arranging childcare - single parents are over-represented among participants. These cost pressures are particularly strong for candidates who are still in work but at risk of losing their jobs. For those with little savings, even the offer of a payment to take part in retraining, may be insufficient.
In other instances, there may be a mismatch between the training and career paths on offer and what candidates are interested in, or capable of. For example, older workers, often close to retirement, have been overrepresented in some of the jobs displaced by digitisation and may be less enticed by retraining into a brand new sector. Retraining participants are also disproportionately likely to have been homeless, an offender, or to lack the basic skills that longer classroom training requires.
All participants may be bewildered by the choice on the offer. As David Deming and colleagues note, the WIOA funds ~7,000 Eligible Training Providers and ~75,000 programmes, in more than 700 occupational fields. Although it aspires to provide ‘informed consumer choice’, via its voucher system, the websites describing different programmes can quickly overwhelm candidates, while failing to provide the comparable programme information and performance data that people need.
A second challenge relates to training providers and their ability to offer a high-quality service that is well-curated to local employers’ needs. Experts note huge variance in the quality and format of local training providers, but with such a large number of providers, the evidence base does not allow us to reliably tease out the good from the bad. Training providers also struggle with the bureaucracy that public programmes entail and the challenge of ensuring that the skills they provide are useful in an ever more specialised economy, where many skills are not easily transferable. Providers also need to look beyond the current labour market and anticipate future skills demands - a task that has always been hard, if not impossible, and which AI is now exacerbating.
A final challenge is that there may simply not be enough skilled jobs for people to retrain into. The typical question for workers looking to retrain is not: “How do I find employment?”, but rather “How do I get a more secure, better paid job?” Past technologies did not increase the aggregate unemployment rate, but there is evidence that they did lead to short to medium-term reduction in the number of ‘skilled’ occupations for workers to retrain into. In the AI era, similar challenges could emerge if, for example, new university graduates were unable to find the kind of role, or career path, they expected.
Retraining & AI: Four ideas
What does this mean for concerns about AI? At a minimum, we should avoid assuming that public retraining programmes will be a useful response. The baseline hypothesis is probably that they won’t. However, there should be ways to make them more useful.
Here are four ideas:
1. Develop better labour market projections
There is vast uncertainty about how quickly AI capabilities will develop, diffuse through the economy, and affect workers. This uncertainty will not disappear any time soon. But AI labs and policymakers could make it easier for retraining providers to understand the jobs that may get displaced, prove more resilient, or emerge. At the moment the US Bureau of Labour Statistics provides high-level forecasts for demand for different occupations. AI labs could work with policymakers and researchers to develop much richer and more granular forecasts that draw on, among others, the latest AI capability evaluations; insights from how users are querying LLMs, online job postings, and government surveys of employers and graduates.
2. Experiment with new retraining approaches
Almost 80% of Workforce Investment and Opportunity Act retraining takes place fully in-person, while just 7% takes place fully online. This creates barriers for people in more remote regions and hinders innovation. It will be difficult to usefully change this, because delivering high-quality online education is hard. As the scholar Mary Burns noted in an evidence review for UNESCO, “few innovations have generated such excitement and idealism - and such disappointment and cynicism - as (digital) technology in education.”
But now is the time to experiment. Education providers are learning from their failures, such as the early Massive Open Online Courses that crudely transposed offline learning content. Some providers are shifting to hybrid formats, while others are developing targeted micro-credential courses. In the AI community, labs are training large language models, and the tutors based on them, to be more ‘pedagogically inspired’, while educators and students are using multimodal AI to personalise learning materials to the language, format, or substance they want. Against this backdrop, there should be opportunities to design AI-enabled retraining programmes that are more dynamic and better able to respond to labour market needs. Another goal of such retraining, at least for some participants, could be on how to use AI systems most effectively.
3. Collate better evidence about what works
At the moment, we have little evidence about what works, or doesn’t, with respect to public retraining programmes. This is particularly true for training provided to workers displaced by new technologies. Future evaluations should target this group, with a focus on those affected by AI, and understand how outcomes are affected by factors such as programme type, age, gender, geography, prior education, and existing skills. This will require better data collection by public sector institutions, with a focus on RCT-style evaluations, but also smaller-scale experiments that academics or companies could run, with standardised measures to harmonise the two.
One area to explore is the nascent positive evidence on training programmes that are co-designed with employers, with specific sectors in mind. Some RCTs in this area show positive effects on earnings and employment, but they are small, typically including a few thousand people, and we do not know if they will generalise across geographies and sectors.
4. Consider goals beyond employment
Finally, it may be time to reevaluate whether ‘work’ should remain the central way to measure a person’s economic contributions and the central goal of any retraining programme. As Tom Rachman wrote in a recent essay: educational policy tends to tinker with the ‘What’ of learning (curriculum) and fret about the ‘How’ (methods). But it’s the Why that demands its boldest recalibration since the Enlightenment.
In theory, education can serve many functions, from boosting an individual’s agency to promoting national unity and assimilation. But preparing students for a career has come to dominate both retraining and broader education. If scenarios where AI has more dramatic economic effects materialise, we need to think about what other knowledge, skills and values people will need to navigate this transition, and the other ways that they can contribute to society. For example, future training programmes could include best practices on how to use AI agents, or how to improve community life and ward against atomization. This more flexible understanding of ‘work’ and ‘training’ could give us a better chance of navigating the AI economic transition in a way that preserves worker livelihoods, opportunity, and dignity.
I would like to acknowledge and thank Burt Barnow for his guidance and contributions in developing this essay. Thank you to Venus Krier for the illustrations contained in this piece.
| 2025-06-26T00:00:00 |
https://www.aipolicyperspectives.com/p/ai-and-the-retraining-challenge
|
[
{
"date": "2025/06/26",
"position": 88,
"query": "universal basic income AI"
}
] |
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|
How AI is being used in the hiring process, and how job seekers can ...
|
How AI is being used in the hiring process, and how job seekers can get their foot in the door
|
https://www.yahoo.com
|
[] |
How AI is being used in the hiring process, and how job seekers can get their foot in the door. WXYZ-Detroit Videos. Thu, June 26, 2025 at 3 ...
|
24,959 people played the daily Crossword recently. Can you solve it faster than others?
24,959 people played the daily Crossword recently. Can you solve it faster than others?
| 2025-06-26T00:00:00 |
https://www.yahoo.com/news/ai-being-used-hiring-process-105347111.html
|
[
{
"date": "2025/06/26",
"position": 86,
"query": "AI hiring"
}
] |
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] |
|
AI Education Podcast
|
AI in Education Podcast
|
https://podcasts.apple.com
|
[] |
They talk about Artificial Intelligence in Education - what it is, how it works, and the different ways it is being used.
|
After starting with an existential crisis - "Are we basically doing the AI equivalent of a maths calculator podcast from the 1970s?" - in this news and research update, Dan and Ray unpack the latest developments in AI and education. Starting with China’s decision to shut down AI tools during national exams, they then revisit NSW’s EduChat chatbot, now in widespread use, with compelling data on time savings for teachers and learning benefits for students. The hosts dive into fresh research from the LEGO Foundation and Microsoft, both highlighting how young students engage with generative AI—and the equity and creativity issues that come with it. They also tackle the viral MIT study suggesting AI could cause "cognitive debt" and discuss why such claims should be taken with academic caution. Finally, Dan and Ray trace the recurring media fear that each new technology - from books to bicycles - has been accused of making us stupid. As always, they bring wit, warmth, and real insight into how AI is shaping education. Links and references for the studies, news and research discussed: News China shuts down AI tools during nationwide college exams [Bloomberg, The Verge] AI is in every NSW public school classroom. Is that a good thing? Anthropic's copyright case with Claude Research Lego research into children's use of ChatGPT [Project website] New Microsoft report on AI in Education announced at ISTE Your Brain on ChatGPT: Accumulation of Cognitive Debt when Using an AI Assistant for Essay Writing Task [Project website - Natalie Kosmina's LinkedIn announcement post - Time news story] And finally For your enjoyment, Donald Clark's "Sisyphean nature of moral panics against new technology" aka What's making us dumb this time? And if you want more enjoyment like Donald's article, then you'll love the Pessimists Archive on Twitter or their newsletter
| 2025-06-26T00:00:00 |
https://podcasts.apple.com/us/podcast/ai-education-podcast/id1481311877
|
[
{
"date": "2025/06/26",
"position": 27,
"query": "AI education"
}
] |
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|
AI is already in the classroom. It's time colleges caught up.
|
AI is already in the classroom. It’s time colleges caught up.
|
https://www.freethink.com
|
[] |
Rather than banning AI, schools should adapt by designing assignments that promote responsible use and keep the focus on learning.
|
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Over the past three years, large language models like ChatGPT have gone from curiosities to everyday tools used on college campuses around the world. While some professors embrace them, others ban them. Many institutions fall somewhere in between, setting vague policies or relying on detection tools that have been proven ineffective in many instances. AI may be the most significant innovation in education since the personal computer, and although this revolution has many different characteristics, it echoes past battles over calculators and computers — battles that were ultimately lost by those who tried to resist.
Technology doesn’t wait for policy, and as a current undergraduate student, I believe that the sooner schools catch up, the better we can use these tools to improve learning rather than undermine it. Still, an important question remains: Is it fair to compare AI with past innovations like calculators and the early internet, or is this a fundamentally different challenge?
If personal computers were a bicycle for the mind, today’s AI tools are more like self-driving race cars.
AI is not the first technology to disrupt higher education. In the 1970s, the pocket calculator triggered a wave of backlash among educational institutions. Teachers warned that it would weaken students’ arithmetic skills, and some schools tried to ban calculators altogether. But others saw the potential: If students no longer had to do long division by hand, they could focus on bigger-picture math problems. Eventually, calculators became standard classroom tools, allowing students to shift their focus from manual computation to understanding formulas and solving higher-level, conceptual problems. Studies show that calculators can improve conceptual understanding when used correctly.
This same cycle repeated in the 1990s with personal computers and the early internet. Critics feared that spell-check and copy-paste would erode writing skills, and that search engines like Google and communal encyclopedias like Wikipedia would replace real research. And yes, some students misused those tools. But once schools embraced the technology and taught students how to use it well, evaluate sources, and cite correctly, their academic work improved. Students were no longer limited to the outdated books in their campus libraries, but suddenly had access to a multitude of books, articles, and datasets in multiple languages, at any time.
The cycle of resistance and delayed acceptance is a recurring phenomenon in large institutions, especially those with long-standing traditions in education, such as Columbia University. These universities, responsible for the education of millions of Americans, cannot afford to change course without serious caution. Even when faculty are eager to adapt, such as by updating policies on AI use in student essays, their efforts are often delayed by the university’s complex bureaucracy and layered approval processes. These systems are designed to ensure thoughtful decision-making, but they can struggle to keep pace with rapid technological change. For example, a 2024 global survey conducted by the Digital Education Council found that 86% of students already use AI in their studies, underscoring the technology’s rapid and widespread adoption across disciplines.
However, it’s clear that the AI revolution is broader and more complex than past technological shifts. Instead of simply speeding up our work, AI can perform tasks that once required deep thinking and creativity, such as writing code or entire essays. Steve Jobs famously referred to the personal computer as a “bicycle for the mind” in 1981, believing it could enhance human intelligence, especially in education, the area where he envisioned the personal computer having the most impact.
But if personal computers were a bicycle for the mind, today’s AI tools are more like self-driving race cars: They don’t just help us think faster — they can take over the wheel entirely.
Rather than fostering an environment of uncertainty and mistrust, universities should redirect their energy toward adaptation.
The debate about integrating AI into the education system mirrors earlier debates, but it feels louder and more urgent. ChatGPT can help students draft essays, debug code, explain complex concepts, or practice new languages. Its capabilities dwarf those ushered in by calculators and the internet. For this reason, it can easily be misused, but banning it outright, as many universities have attempted, is a battle that was lost before it even started. Telling students not to use a tool that is nearly undetectable and freely available won’t stop its use; it will only push it underground and widen the gap between students who are proficient in using it and those who don’t yet know how to use it effectively.
Moreover, the AI detectors that many universities rely on as a first line of defense have proven deeply flawed; for example, some have flagged writing from international students because their sentence structure tends to be simpler. The current tension reveals a deeper problem. An experienced English professor probably doesn’t need software to spot AI-generated essays: The tone, structure, and sudden leap in fluency are often glaring to a trained reader. But without empirical proof, there is no ethical way to penalize the student. Intuition, no matter how informed, cannot serve as formal evidence. This leaves educators in an impossible position; they can either ignore the changes they notice or act on suspicion using imperfect AI detectors.
At the same time, my anecdotal experience suggests a strange double standard is emerging. In one of my classes, for example, the professor explicitly banned the use of AI but told us the assignment would be made harder because he assumed we’d use it anyway. On the other hand, some students who are unfamiliar with AI or choose not to use it are falling behind because the expectations for writing and coding have quietly shifted. Rather than fostering an environment of uncertainty and mistrust, universities should redirect their energy toward adaptation. That means adjusting assignments, rethinking evaluation, and integrating AI use transparently so the focus remains on learning, not on detection.
Professors can start by building trust and treating students as partners rather than suspects. Many serious students still want to develop strong writing, communication, and critical thinking skills, especially the ability to read and write at an academic level. Instructors can tap into that motivation by designing tasks that AI can assist with but not complete on its own. They can ask students to compare chatbot drafts with their own revisions, explain how they used AI in their writing process, reflect on the strengths and weaknesses of AI-generated responses, or even participate in short oral exams. These steps can make AI use more productive and, most importantly, keep the focus where it belongs: on human learning. While it’s true that AI may have a greater impact than tools of the past, it can still be incorporated into the learning process if it is approached with care, creativity, and a clear purpose.
Artificial intelligence is here to stay, and it will only grow more powerful with time. Detecting it will become harder, its capabilities will expand, and its presence will become even more embedded in student life. But this shouldn’t be seen as a threat or the end of education as we know it. We saw how the rise of the internet brought about similar fears, yet it ultimately made learning richer and more accessible. Resistance to change is part of human nature, and large institutions like universities often move slowly. But whether they choose to lift AI restrictions or not, one thing is clear: The current “in-between” approach is failing both students and faculty. It’s time for schools to stop pretending this technology can be “defeated” and instead begin building an education system that works with AI, not against it.
This article was reprinted with permission of Big Think, where it was originally published.
We’d love to hear from you! If you have a comment about this article or if you have a tip for a future Freethink story, please email us at [email protected].
| 2025-06-26T00:00:00 |
https://www.freethink.com/artificial-intelligence/ai-education
|
[
{
"date": "2025/06/26",
"position": 55,
"query": "AI education"
}
] |
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|
Pros and Cons of AI in Education
|
Pros and Cons of AI in Education: Opportunities & Challenges
|
https://ai-pro.org
|
[
"Ai-Pro Team",
"Articles"
] |
The Pros of AI in Education · 1. Personalized Learning Experiences · 2. Enhanced Efficiency for Educators · 3. Increased Accessibility and Inclusion · 4. Data- ...
|
Teacher pointing to Yes and No chart
Pros and Cons of AI in Education
Two students representing Yes and No
Introduction to AI in Education
Artificial Intelligence (AI) has rapidly evolved from a futuristic concept to a practical tool reshaping various industries, with education being one of the most profoundly affected. From adaptive learning platforms and intelligent tutoring systems to automated grading and personalized curricula, AI’s integration into educational settings has transformed traditional teaching and learning paradigms. However, while AI offers remarkable opportunities to enhance efficiency, accessibility, and personalization, it also introduces significant challenges related to ethics, equity, privacy, and the human aspect of education.
In this comprehensive guide, we’ll explore the multifaceted impact of AI on education, delving into its key advantages, drawbacks, and the nuanced considerations that educators, students, and policymakers must balance. By understanding both the pros and cons of AI in education, stakeholders can make informed decisions that maximize benefits while mitigating potential harms.
Understanding the Basics of AI in education
Education process chart
Definition and Scope
AI in education refers to the use of computer algorithms, machine learning, and data-driven technologies to enhance, automate, or supplement educational processes. This includes a wide array of applications such as:
Adaptive learning platforms that tailor content to individual student needs.
Automated grading systems for essays and assessments.
Intelligent tutoring systems (ITS) that provide personalized feedback.
Chatbots and virtual assistants for student support.
Predictive analytics to identify students at risk or optimize learning pathways.
Evolution and Adoption
The adoption of AI in education has accelerated, spurred by advances in technology, increased digital connectivity, and the need for scalable learning solutions. Major tech companies and educational institutions are investing in AI-driven tools, with applications spanning K-12, higher education, and lifelong learning. While some schools have fully integrated AI into their classrooms, others are in exploratory phases, considering the implications for pedagogy and student outcomes.
The Pros of AI in Education
Man holding a check sign with stars
AI’s promise in education lies in its ability to revolutionize learning experiences, making them more personalized, efficient, and accessible. Below are the key advantages that AI brings to educational environments.
1. Personalized Learning Experiences
One of AI’s most celebrated benefits is its capacity to deliver personalized learning at scale. By analyzing data on individual students’ performance, learning styles, and progress, AI-powered systems can:
Adapt content in real-time: Students receive materials tailored to their current understanding, pacing, and interests.
Offer targeted feedback: AI identifies strengths and weaknesses, helping students focus on areas needing improvement.
Facilitate mastery learning: Learners can move forward only when they’ve demonstrated understanding, reducing gaps in knowledge.
Example: Platforms like DreamBox and Knewton use AI algorithms to adjust math problems or reading passages in response to a student’s answers, promoting continuous engagement and improvement.
2. Enhanced Efficiency for Educators
AI can automate many time-consuming administrative and instructional tasks, allowing educators to focus more on teaching and mentoring. Key efficiency gains include:
Automated grading: AI systems can assess multiple-choice, fill-in-the-blank, and even short-answer questions with speed and consistency.
Scheduling and resource allocation: Tools that optimize class schedules, room assignments, and learning resource distribution.
Streamlined communication: Chatbots answer routine questions, freeing up teachers’ time.
Example: Tools like Gradescope and Turnitin use AI to expedite grading and plagiarism detection, enabling faster feedback for students.
3. Increased Accessibility and Inclusion
AI can help break down barriers to education for students with diverse needs, including those with disabilities or in underserved communities. Benefits include:
Assistive technologies: AI-powered speech recognition, text-to-speech, and translation tools support learners with visual, auditory, or language challenges.
Remote learning support: Intelligent tutoring and virtual classrooms bridge gaps in teacher availability, especially in rural or low-resource regions.
Customized content: AI can generate materials at various reading levels or in multiple languages for greater inclusivity.
Example: Microsoft’s Immersive Reader uses AI to improve reading comprehension for students with dyslexia or other learning differences.
4. Data-Driven Decision Making
AI provides actionable insights by analyzing vast amounts of data from student interactions, assessments, and engagement metrics. These insights are made possible by technologies like large language models (LLMs), which can interpret patterns, understand natural language, and generate predictions at scale. This empowers educators to:
Identify at-risk students: Early warning systems flag learners who may need additional support.
Optimize curricula: Data reveals which teaching methods or materials are most effective.
Measure educational outcomes: Continuous analysis supports evidence-based policy and practice.
Example: Predictive analytics platforms help universities improve retention rates by identifying students likely to drop out and intervening proactively.
5. 24/7 Learning and Support
Unlike traditional classrooms constrained by schedules, AI-driven tools offer round-the-clock access to learning resources and support services:
On-demand tutoring: Virtual tutors provide explanations and practice problems anytime.
Instant feedback: Students receive answers to questions immediately, facilitating independent learning.
Scalable support: AI chatbots can handle thousands of student queries simultaneously.
Example: Khan Academy’s AI-powered chatbots answer questions and guide learners through complex concepts, regardless of time zone or classroom hours.
6. Fostering Creativity and Innovation
AI can inspire new ways of learning, teaching, and problem-solving by:
Generating creative content: AI tools help students brainstorm, write, and visualize ideas.
Simulating real-world scenarios: Virtual labs and simulations let students experiment safely.
Encouraging critical thinking: Adaptive challenges and open-ended projects require students to think deeply and creatively.
Example: AI-powered platforms like Google’s Teachable Machine enable students to create and train their own AI models, fostering hands-on learning.
The Cons of AI in Education
Woman doing a “No” signal
Despite its many advantages, AI in education is not without significant drawbacks and risks. Understanding these challenges is crucial for responsible adoption and policy development.
1. Loss of Human Interaction and Social Skills
AI-powered systems, while efficient, cannot replicate the empathy, mentorship, and nuanced understanding that human educators provide. Potential consequences include:
Reduced personal connection: Students may feel isolated or unsupported without meaningful teacher-student interactions.
Diminished social learning: Classroom discussions, group projects, and peer feedback foster collaboration and communication skills that AI cannot fully substitute.
Impaired emotional development: Young learners especially benefit from role models and social cues that only humans can offer.
Example: Overreliance on AI for instruction in virtual environments can limit opportunities for students to develop teamwork and leadership skills.
2. Bias and Fairness Issues
AI systems are only as unbiased as the data and algorithms they are built upon. Risks include:
Algorithmic bias: If training data reflects societal prejudices, AI may perpetuate or even amplify inequities (e.g., in grading, admissions, or resource allocation).
Lack of transparency: Black-box AI models make it difficult to identify and correct errors or biases.
Unequal benefits: Students from privileged backgrounds may benefit more from AI tools due to better access and support.
Example: AI grading tools have faced criticism for unfairly scoring students from certain demographic groups due to biased training data.
3. Privacy and Security Concerns
AI-driven educational platforms collect massive amounts of student data, raising significant privacy and cybersecurity issues:
Sensitive data exposure: Breaches or mismanagement can lead to unauthorized access to personal and academic records.
Surveillance risks: Continuous monitoring of student behavior may infringe on privacy rights and create a climate of mistrust.
Unclear consent: Students and parents may not fully understand how their data is used or shared.
Example: The use of AI-powered proctoring software during remote exams has sparked controversy over surveillance and data protection.
4. Dependence on Technology and Equity Gaps
Teachers and students require digital literacy training to effectively use AI tools in the classroom. Without foundational skills to work with AI, the benefits of these technologies can’t be fully realized—particularly in schools with limited access to resources or support. Bridging this skills gap is essential to ensure equitable adoption and prevent further educational disparities. Investing in professional development and AI education is just as important as investing in the tools themselves.
Example: During the COVID-19 pandemic, students without high-speed internet or AI-enabled devices fell behind peers with better access.
5. Threats to Academic Integrity
AI makes it easier for students to access information and generate content, often with minimal effort. Tools like AI text generators can quickly produce essays, summaries, or homework answers, which blurs the line between assistance and academic dishonesty. This raises concerns about originality and integrity, especially as advanced AI-generated content becomes harder to detect with traditional plagiarism checkers. As students increasingly rely on these tools, there’s a growing need to redefine academic standards and encourage critical thinking over convenience.
Automated essay writing is one of the most controversial uses of AI in education. Advanced AI essay writers can produce well-structured essays or solve complex problems with little to no human input. While these tools can aid learning when used ethically, they also make it easier for students to submit work that isn’t their own, raising serious concerns about plagiarism. As the quality of AI-generated content improves, it becomes increasingly difficult for educators to verify authenticity using traditional methods.
Example: The rise of AI-powered homework helpers has sparked debate over what constitutes academic dishonesty in the digital age.
6. High Implementation and Maintenance Costs
While AI promises long-term efficiency gains, the upfront and ongoing costs can be prohibitive:
Investment in infrastructure: Schools need robust networks, devices, and software to support AI.
Continuous updates: AI systems require regular maintenance, training, and updates to remain effective and secure.
Hidden costs: Licensing fees, technical support, and teacher training can strain budgets.
Example: Small schools or districts with limited funds may struggle to adopt or sustain AI initiatives, leading to unequal access.
Real-World Examples of AI in Education
Kid smiling at a robot in a computer
To better illustrate the practical impact of AI in education, let’s examine a few real-world case studies:
Personalized Learning Platforms
DreamBox Learning: Uses AI to adaptively adjust math lessons for K-8 students, analyzing their responses in real time to offer tailored challenges and support.
Duolingo: Employs machine learning to customize language lessons, track progress, and recommend review topics, making language acquisition more efficient.
Intelligent Tutoring and Virtual Assistants
Khan Academy’s AI Tutor: Offers step-by-step hints and explanations, providing instant feedback and scaffolding for learners struggling with concepts.
IBM Watson Tutor: Assists teachers by analyzing classroom data, suggesting differentiated instruction strategies, and even answering student questions.
Administrative Automation
Gradescope: Automates grading for assignments and exams, allowing instructors to review and adjust AI-generated scores, saving time and reducing grading bias.
Chatbots in Higher Education: Universities like Georgia State have deployed AI chatbots to answer admissions, financial aid, and course registration questions, improving student support.
Accessibility and Special Education
Microsoft Immersive Reader : Supports students with dyslexia by reading text aloud, changing fonts, and breaking down complex sentences.
AI-Powered Speech Recognition: Tools like Otter.ai transcribe lectures in real time, aiding students with hearing impairments.
Ethical Considerations and Policy Implications
Hologram that shows security symbol
The integration of AI into educational systems raises critical ethical questions and policy challenges that must be addressed to ensure responsible use.
Data Privacy and Security
Consent and transparency: Schools must clearly communicate how student data is collected, used, and protected.
Compliance: Adherence to data protection regulations like FERPA (in the U.S.) and GDPR (in Europe) is essential.
Fairness and Bias Mitigation
Regular audits: AI systems should be evaluated for bias and fairness, with mechanisms in place to address disparities.
Inclusive design: Involving diverse stakeholders in AI development helps prevent the amplification of societal biases.
Teacher and Student Empowerment
Professional development: Ongoing training for educators is crucial to effectively integrate AI tools and interpret AI-driven insights.
Student agency: AI should augment, not replace, student decision-making and creativity.
Accountability and Oversight
Clear guidelines: Policymakers must establish standards for AI use, including accountability for errors or adverse impacts.
Stakeholder engagement: Continuous dialogue with parents, students, teachers, and technologists ensures that AI aligns with educational values.
The Future of AI in Education: Opportunities and Challenges Ahead
Futuristic graphic of learning and education symbols
As AI technology continues to advance, its influence on education will only deepen. The future holds immense promise, but also new complexities.
Emerging Trends
AI-powered formative assessment: Real-time analysis of student work to inform immediate instructional adjustments.
Virtual and augmented reality: AI-driven simulations for immersive, experiential learning.
Emotion AI: Systems that detect student emotions and engagement levels to better personalize instruction.
Navigating the Path Forward
Balancing efficiency with humanity: AI should enhance, not diminish, the central role of human relationships in education.
Ensuring equitable access: Policymakers and educators must prioritize closing the digital divide to prevent worsening educational inequalities.
Promoting lifelong learning: As AI transforms the workforce, education systems must adapt to cultivate skills like critical thinking, adaptability, and digital literacy.
Conclusion: Weighing the Pros and Cons of AI in Education
Artificial intelligence is undeniably reshaping the educational landscape, offering powerful tools to personalize learning, streamline administration, and increase access for students of all backgrounds and abilities. The potential for AI to revolutionize education is immense, from adaptive tutoring and 24/7 support to data-driven insights that improve teaching and learning outcomes.
Yet, these benefits come with significant challenges. The risks of bias, privacy violations, loss of human connection, and unequal access must be carefully managed. Educators, policymakers, and technology developers share a collective responsibility to ensure that AI is deployed ethically, transparently, and inclusively.
Ultimately, the key to harnessing AI’s transformative potential in education lies in thoughtful implementation—one that prioritizes student well-being, safeguards data privacy, promotes equity, and maintains the irreplaceable value of human educators. By balancing innovation with caution, we can shape an educational future where AI is a powerful ally in fostering knowledge, creativity, and lifelong learning for all.
Frequently Asked Questions (FAQs) About AI in Education
Is AI replacing teachers in the classroom?
No, AI is not replacing teachers but rather augmenting their roles. While AI can automate certain tasks and provide personalized learning experiences, human educators remain essential for mentorship, emotional support, and fostering critical thinking and creativity.
How can schools ensure AI is used ethically?
Schools should adopt clear policies regarding data privacy, bias mitigation, and transparency. Involving teachers, parents, and students in decision-making, regularly auditing AI systems, and providing professional development are important steps toward ethical AI use.
What are the main barriers to AI adoption in education?
Key barriers include high implementation costs, lack of digital infrastructure, digital literacy gaps among teachers and students, and concerns about data privacy and bias.
Can AI help students with special needs?
Yes, AI-powered assistive technologies can greatly benefit students with disabilities by providing tailored support, accessible content, and tools that accommodate diverse learning needs.
Will AI widen the digital divide?
If not implemented thoughtfully, AI could exacerbate inequalities by favoring students with better access to technology. Ensuring equitable access to devices, connectivity, and training is essential to prevent this outcome.
Forward-thinking educators are tapping into the power of AI-driven chatbots to support student inquiry and using AI art tools to spark imagination in the classroom—real-world examples of how intelligent technology is reshaping not just how we teach, but how students learn and create.
Further Reading and Resources
By understanding the pros and cons of AI in education, stakeholders can take informed steps to harness its benefits while safeguarding against its risks, ensuring that the future of learning is both innovative and inclusive.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://ai-pro.org/learn-ai/articles/pros-and-cons-of-ai-in-education/
|
[
{
"date": "2025/06/26",
"position": 71,
"query": "AI education"
}
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AI Tools for Education - Research Guides
|
AI Tools for Education
|
https://guides.libraries.uc.edu
|
[
"Katie Foran-Mulcahy"
] |
The following guide contains AI tools for educational use, including tools for teaching, presentations, accessibility, and general assistance for learners.
|
This guide was created by Mazid UI Hasan, eLearning Graduate Assistant in the CECH School of Education, University of Cincinnati. Additional content provided by Dr. Janet Zydney, CECH School of Education, and Katie Foran-Mulcahy, UC Libraries.
The following guide contains AI tools for educational use, including tools for teaching, presentations, accessibility, and general assistance for learners. See the left navigation menu for quick access to each AI tool category.
Artificial Intelligence (AI) has caused significant discussions in different fields, including education, since late 2022. Many educators have been grappling with the disruptive potential of generative AI, like ChatGPT, particularly regarding academic integrity. On the other hand, many others pointed out that technological advancements often disrupted existing practices historically, but when efficiently adopted, these technologies improved the current practices. Similarly, if we look beyond the disruptive nature of AI in education, AI can offer significant positive impacts on teaching and learning.
AI claims to revolutionize personalized learning by analyzing individual student needs, tailoring content and feedback, and creating customized learning pathways. AI tutors can offer conversational guidance and support, while tools like Diffit can adjust learning materials to individual reading levels. AI-powered text-to-speech, speech-to-text, and image description tools further enhance accessibility for diverse learners. AI tools can empower educators by providing lesson plans and quizzes, recommending resources, and assisting with grading and insights.
This guide highlights numerous benefits of using AI in education, describing several AI tools and their functionalities to improve educational practices. However, despite their great potential, there are concerns regarding the inaccuracies and biases in these tools’ responses. Sometimes, the tools may provide incorrect information and references and reproduce existing biases from the materials on which they were trained. Therefore, it is strongly recommended that with all AI, teachers check the output for accuracy and biases. Given this need to verify the accuracy of the output from Generative AI, teachers need to be careful about having students directly use AI tools without teacher intervention. Using AI tools for grading and feedback can introduce biases and unfairly grade students from certain demographics. There also can be issues regarding data privacy and security of the information provided to these tools.
AI tools highlighted in this guide are provided for informational purposes only. Highlighted tools have not necessarily been approved or endorsed by the University of Cincinnati or Digital Technology Solutions (DTS).
For information on AI tools and academic integrity concerns, consult the UC Student Code of Conduct, which lists "unauthorized use of artificial intelligence" as a form of academic misconduct under "Cheating" (p.9-10). Further information on generative AI and academic integrity can be found on the Preventing Student Plagiarism LibGuide from UC Libraries, linked below.
| 2025-06-26T00:00:00 |
https://guides.libraries.uc.edu/ai-education
|
[
{
"date": "2025/06/26",
"position": 74,
"query": "AI education"
}
] |
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|
Data Security for Employers in the Era of AI, Remote Work ...
|
Data Security for Employers in the Era of AI, Remote Work and Ransomware
|
https://www.littler.com
|
[] |
In an era of rapidly advancing technology, employers face an increasingly complex landscape of data security challenges. Two points remain constant: ...
|
In an era of rapidly advancing technology, employers face an increasingly complex landscape of data security challenges. Two points remain constant: employees are still the top cause of data breaches, and HR data remains among the highest at-risk data at most companies. But the risks have grown.
During this 60-minute webinar, Littler attorneys Zoe Argento, William Simmons and Andrew Gray will explore the intersection of artificial intelligence (AI) and data protection, delving into the dynamic “cat-and-mouse” game where sophisticated phishing attacks are met with equally advanced AI-driven detection systems. They will also discuss the challenges posed by increasing regulatory scrutiny, high levels of remote work, ransomware attacks and the collection of new types of data, such as AI profiling data. Focusing on practical strategies, panelists will discuss how employers can manage data protection throughout the employment lifecycle – from conducting thorough background checks and training and monitoring to securing sensitive information during offboarding. Additionally, Stephanie Swenson from ComplianceHR will walk you through PolicySmart™ and the Reference Center, solutions designed to simplify the complexity of employment law.
Time:
10:00 - 11:00 a.m. PT
11:00 a.m. - 12:00 p.m. MT
12:00 - 1:00 p.m. CT
1:00 - 2:00 p.m. ET
| 2025-06-26T00:00:00 |
https://www.littler.com/events/data-security-employers-era-ai-remote-work-and-ransomware
|
[
{
"date": "2025/06/26",
"position": 34,
"query": "AI employers"
}
] |
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|
Flawless | AI-Assisted Performance Editing
|
AI-Assisted Performance Editing
|
https://flawlessai.com
|
[] |
Discover Flawless, AI-assisted performance and dialogue editing for film, TV and commercials that puts actors first. Get started today.
|
AI Done Right We believe in using AI responsibly, ethically and transparently. Our technology exists to enhance human creativity—not replace it.
Cinematic Excellence Every frame exported by our technology deserves to meet the standards of the big screen.
Artists First Artists should chart the creative path, with technology accelerating the journey—not steering it.
| 2025-06-26T00:00:00 |
https://flawlessai.com/
|
[
{
"date": "2025/06/26",
"position": 40,
"query": "AI employers"
}
] |
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|
G42 | Inventing a Better Everyday
|
Inventing a Better Everyday
|
https://www.g42.ai
|
[
"Tentwenty",
"Webdesign",
"Webshops",
"E-Marketing"
] |
G42 is a technology group that invents visionary artificial intelligence for a better everyday. Born in Abu Dhabi and operating across the world, ...
|
We’re G42 — born in Abu Dhabi, building globally, and pushing AI to do more for everyone.
We see artificial intelligence as a force for good. A partner to humanity. A tool to make lives healthier, journeys safer, and the future more connected.
From decoding disease to exploring deep space, we’re not waiting for tomorrow. We’re creating it — with partners, with purpose, and with people in mind.
At G42, progress is personal. And every day is a chance to invent better.
| 2025-06-26T00:00:00 |
https://www.g42.ai/
|
[
{
"date": "2025/06/26",
"position": 82,
"query": "AI employers"
}
] |
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|
Can AI Tools Meet Journalistic Standards?
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Can AI Tools Meet Journalistic Standards?
|
https://www.cjr.org
|
[] |
Certainly, AI is being used effectively by some journalists to crunch numbers at lightning speed and make sense of vast databases. That's a big benefit, one ...
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Sign up for The Media Today, CJR’s daily newsletter.
Tech companies promise that AI tools can do more with less—so perhaps they can help news outlets survive declining subscription sales and evaporating advertising revenue. Certainly, AI is being used effectively by some journalists to crunch numbers at lightning speed and make sense of vast databases. That’s a big benefit, one that has contributed to prizewinning work in the public interest.
But more than two years after the public release of large language models (LLMs), the promise that the media industry might benefit from AI seems unlikely to bear out, or at least not fully.
Generative AI tools rely on media companies to feed them accurate and up-to-date information. At the same time, AI products are developing into something akin to a newsroom competitor, and a particularly problematic one at that: well-funded, high-volume, and at times unscrupulous.
We decided to survey cases of AI-produced text in the news industry with an eye on ethics. Can AI tools meet the standards of traditional reporting and publishing? Our research finds several recent instances in which AI tools failed to rise to the occasion.
One of the primary problems with AI-generated text is that none of the most common AI software models—including OpenAI’s ChatGPT, Google’s Gemini, Microsoft’s Copilot, and Meta’s Meta AI—are able to reliably and accurately cite and quote their sources. These tools commonly “hallucinate” authors and titles. Or they might quote real authors and books, with the content of the quotes invented. The software also fails to cite completely, at times copying text from published sources without attribution. This leaves news organizations open to accusations of plagiarism.
Last year, Forbes called out the AI tool Perplexity for ingesting its article on Google CEO Eric Schmidt and turning the story into an AI-generated article, podcast, and video without any attribution to the outlet. On YouTube, depressingly, the Perplexity video outranked Forbes’s original story. When confronted by Forbes’s John Paczkowski on X, Perplexity CEO Aravind Srinivas blamed the incident on “rough edges” in the tool.
In a Wired article titled “Perplexity Plagiarized Our Story About How Perplexity Is a Bullshit Machine,” also published last year, Tim Marchman and Dhruv Mehrotra described how they prompted Perplexity to summarize a recent story they’d published. Marchman and Mehrotra found that in its response, Perplexity exactly reproduced one of their sentences as if it had generated the words—a move that appeared to them to be plagiarism. The legal experts Marchman and Mehrotra spoke with were split on whether the lifted sentence would qualify as willful infringement of copyright claims, but there were other problems: Perplexity’s AI data crawlers had seemingly gone around the AI blockers Wired had put in place to prevent the use of its content.
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Whether this type of generative AI production is legally considered plagiarism and copyright infringement—and therefore, whether media outlets should be paid for the ingestion of their work by generative AI tools—will likely be determined by several upcoming lawsuits.
The New York Times, the Center for Investigative Reporting (which oversees Mother Jones and Reveal), Intercept Media, and eight media outlets owned by Alden Global Capital have filed lawsuits accusing OpenAI and Microsoft of violating copyright laws by ingesting their content. In the Times’ suit, filed in the Southern District of New York in December 2023, the outlet accuses OpenAI of trying “to free-ride on The Times’s massive investment in its journalism by using it to build substitutive products without permission or payment.”
The Times’ filing includes several pages of examples in which OpenAI’s ChatGPT copied text from its archives and reproduced this text verbatim for users.
One possible—and worrying—outcome of all this is that generative AI tools will put news outlets out of business, ironically diminishing the supply of content available for AI tools to train on.
Some media companies, including The Atlantic, Vox Media, FT Group, the Associated Press, and News Corp, have made deals with AI companies to license portions of their content. It’s worth noting that in May 2025, the Times signed an AI licensing deal with Amazon, which allows the tech company to use the outlet’s content across its platforms.
Reporters and editors eye these deals warily. A week before The Atlantic announced its deal with OpenAI, Jessica Lessin, CEO of tech-journalism outlet The Information, warned against what she saw as a Faustian bargain with AI companies.
The tech companies “attempt to take all of the audience (and trust) that great journalism attracts, without ever having to do the complicated and expensive work of the journalism itself. And it never, ever works as planned,” Lessin wrote.
Generative AI results are only as good as the materials on which the systems are trained. Without a reliable way to distinguish between high- and low-quality input, the output is often compromised. In May 2024, Google unveiled its AI Overview, a tool meant to supplant search results. But it quickly proved flawed. The AI—seemingly regurgitating a twelve-year-old Reddit thread—produced a pizza recipe that included one-eighth of a cup of Elmer’s glue. In response to another query asking how many rocks someone should eat daily, AI Overview said “at least one small rock per day,” apparently sourcing its information from an Onion article.
Mistakes like these make these multibillion-dollar tools seem incapable of even basic common sense.
The AI tools also contain biases that are not so easily visible. Emilio Ferrara, a professor at the University of Southern California and research team leader at the USC Information Sciences Institute, found biases in data used during the training of generative AI, in its learning processes, within the tool’s infrastructure, or during deployment. Some of these biases are implicitly expressed in the selection of training-data texts that contain existing cultural biases, in the types of data collected, and through confirmation bias—the way an AI may be trained to yield particular results. More explicitly, a model may also produce stereotypes.
Generative AI models “may inadvertently learn and perpetuate biases present in their training data, even if the data has been filtered and cleaned to the extent possible,” Ferrara found. Ultimately, these LLMs reflect the biases of the people who program their algorithms and the internet’s complex ecosystem of users and creators—as well as, sometimes, the limited availability of content on a particular topic or authored by a particular group or in a particular language.
The bias can be most profoundly illustrated with image-oriented generative AI tools, which have consistently generated troubling results for nonwhite and non-male subjects. Attempts to correct these biases have thus far been clunky, at best, as when Google’s Gemini was asked to produce an illustration of a 1943 German soldier and generated drawings of Asian and Black Nazis. Or when prompts to illustrate the Founding Fathers resulted in images of people of multiple ethnic backgrounds.
Sometimes, the bias feels almost satirical. The automated-news company Hoodline runs a group of hyperlocal websites based primarily on AI-generated local news feeds and uses AI to generate location-specific reporter personas. Putting the dystopian nightmare of the business model aside, the names the AI generated for its personas reflected stereotypes about the communities they were intended to represent. Boston “reporters” had stereotypically Irish names like “Will O’Brien” and “Sam Cavanaugh,” while San Francisco’s AI-generated staffers were given names reflecting the city’s diversity, among them Leticia Ruiz, Tony Ng, and Erik Tanaka, Nieman Labs reported.
And then there are the user-side biases: primarily, a lack of understanding of AI’s limitations.
The apparent convenience of putting large language models in the hands of consumers who will rely solely on that information is “pretty worrisome,” said Mark Lavallee, the Knight Center’s director of technology, product, and strategy. “If you ask a question a certain way, it’s going to answer it a certain way.”
Nearly everyone agrees that keeping a human “in the loop”—and close to any generative AI use, to monitor for misfires—is a key factor of ethical AI use. But it’s unclear what that will look like in practice. If a journalist uses an AI tool to analyze fifty pages of documents, for example, should the journalist then review all the documents to ensure the synthesis is accurate and unbiased? If the business side of a company sets up a deal for AI-sponsored content, who monitors the result?
Perhaps nobody knows the challenge better than Sports Illustrated. In late 2023, the tech site Futurism noticed that some of SI’s stories were written by people who didn’t exist. One fake byline, Drew Ortiz, had a bio claiming “he grew up in a farmhouse, surrounded by woods, fields, and a creek.” The headshot attached to his profile was an AI-generated image available for purchase on a site called Generated Photos.
When Futurism inquired about the apparently fake writers, the company promptly deleted all content associated with those bylines. In a statement to Futurism, Sports Illustrated revealed that the content had been produced by a company called AdVon, which describes itself as “a digital commerce platform, developing trusted, SEO-optimized, user-centric AI and content solutions.”
SI said AdVon had assured it that “all of the articles in question were written and edited by humans. According to AdVon, their writers, editors, and researchers create and curate content and follow a policy that involves using both counter-plagiarism and counter-AI software on all content.” The fake headshots and bios, AdVon claimed, were “to protect author privacy,” a move SI was quick to clarify they didn’t condone.
The robotic writing in some of the posts raised eyebrows. One of Ortiz’s shopping guides, for “Full-Size Volleyballs,” awkwardly explains: “Even people who don’t watch sports can easily understand the intensity and skill required to play volleyball whenever they watch clips.”
In an all-hands meeting the day after the Futurism article was published, SI executives informed their staff that they had terminated their relationship with AdVon. (Meanwhile, SI’s parent company, Arena Group, publicly disputed the claim that it had published AI-generated work.) The damage was already done. Sports Illustrated, one of the oldest and once most respected sports outlets, lost much of its credibility with staff and readers alike.
“Along with basic principles of honesty, trust, journalistic ethics, etc.—I take seriously the weight of a Sports Illustrated byline. It meant something to me long before I ever dreamed of working here. This report was horrifying to read,” wrote staff writer Emma Baccellieri on X, commenting on Futurism’s story.
Two months later, after Sports Illustrated’s publisher announced it was in “substantial debt,” Baccellieri and nearly all her coworkers were laid off. Most, including Baccellieri, were soon rehired by SI’s new publisher.
Sean McGregor, founding director of the Digital Safety Research Institute and a member of the Partnership on AI, likens companies’ and newsrooms’ use of AI to the experience of riding in a self-driving car. As people become comfortable with the technology, they become inured to its inherent risks.
“There’s a tendency in all places where automation is introduced, where, you know, it’s a great tool, empowering people, and then it gets to a point of adequate performance…where you no longer have the ability, because of the way that our brains work, to pay attention and to safeguard the system,” he said.
Most consumers aren’t yet comfortable with the marriage of AI and news production. In 2023, Benjamin Toff from the University of Minnesota and Felix M. Simon from Oxford University’s Internet Institute surveyed 1,483 people about their attitudes toward AI. Their survey found that more than 80 percent believed news organizations should “alert readers or viewers that AI was used.” Among people who believed consumers should be alerted, 78 percent said news organizations should “provide an explanatory note describing how AI was used.”
AI has the potential to help journalists do their jobs more efficiently. Used wisely, it can be a marvelous reporting tool. But, undeniably, it also has the potential to misinform, falsely cite, and fabricate information. The role of journalists is to expose deception and misinformation, but AI, for all its promise, has made it exponentially more difficult for journalists—and ordinary citizens—to do just that. We would advise newsrooms and journalists to proceed with caution, but it may be too late for that.
Clarification: An earlier version of this article did not include the rehiring, by a new publisher, of most of the Sports Illustrated staff.
| 2025-06-26T00:00:00 |
https://www.cjr.org/analysis/can-ai-tools-meet-journalistic-standards.php
|
[
{
"date": "2025/06/26",
"position": 6,
"query": "AI journalism"
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|
No Turning Back: AI's Growing Role in News
|
No Turning Back: AI’s Growing Role in News
|
https://www.aspendigital.org
|
[] |
AI's role in news presents many risks, but it also represents an opportunity to fulfill the public service mission of journalism.
|
This illustration was generated by AI.
When generative AI burst into public consciousness, many in the news industry experienced a familiar sense of dread. Social media had decimated newsroom business models over the last decade, and here is yet another set of technologies out to kill them. But that sentiment is misguided. Indeed, AI’s role in news presents many risks to publishers, but—like the advent of the world wide web decades prior—it also represents an opportunity to fulfill the public service mission of journalism.
“No Turning Back: AI’s Growing Role in News” is the second in a series of reports on AI and News from Aspen Digital. It summarizes key insights from our March 2025 gathering in London of top news executives from the UK and Europe. What struck us most is how far things have come since we had a similar meeting in New York last spring. The following are our takeaways, authored by Dr. Felix Simon:
News rooms are using AI, but carefully – Newsrooms are embracing AI but for incremental improvements rather than revolutionary transformation. Most have focused on automating routine tasks like transcription, translation, and headline creation. Many organizations ’have established internal training programs, created specialized AI roles, and identified internal “AI influencers” to encourage adoption and mitigate resistance.
– Newsrooms are embracing AI but for incremental improvements rather than revolutionary transformation. Most have focused on automating routine tasks like transcription, translation, and headline creation. Many organizations ’have established internal training programs, created specialized AI roles, and identified internal “AI influencers” to encourage adoption and mitigate resistance. A shift to “distinctive journalism” – To mitigate the threats from AI presents for news discovery, publishers are focusing on the kind of distinctive journalism that AI cannot easily replicate, including investigative and enterprise reporting, and nuanced analysis.
Is imitation the highest form of flattery? – A central industry dispute involves the use of publishers’ content to train AI systems. Many technology companies have scraped news content without compensation, often justifying this through broad interpretations of fair use doctrine. In response, some publishers advocate for stricter enforcement of existing copyright laws and oppose copyright exemptions that favor AI developers. Proposed solutions include collective licensing arrangements or managed marketplaces to connect AI developers and rights holders.
Do audiences care? – User perspective is a major gap in the industry conversation. Despite frequent references to trust and transparency, limited attention has been paid to how audiences, especially non-expert or marginalized users, experience AI-driven news.
Small but mighty – Smaller media outlets, Global South news organisations, and freelancers are often overlooked in discussions around AI and news.
The report contains more detail and testimonials from key industry players.
| 2025-06-26T00:00:00 |
https://www.aspendigital.org/report/ai-role-in-news/
|
[
{
"date": "2025/06/26",
"position": 8,
"query": "AI journalism"
},
{
"date": "2025/06/26",
"position": 4,
"query": "artificial intelligence journalism"
}
] |
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] |
|
How newsrooms around the world use AI
|
How newsrooms around the world use AI: a JournalismAI 2023 global survey — JournalismAI
|
https://www.journalismai.info
|
[
"Charlie Beckett"
] |
A global survey of journalism and artificial intelligence. So much has happened in the world of AI since then and we're excited to share with you some of ...
|
Over the last few months, newsrooms and media organisations from around the world have been completing the second iteration of our JournalismAI survey. We conducted the first one in 2019 and published our findings in a report; New powers, new responsibilities. A global survey of journalism and artificial intelligence. So much has happened in the world of AI since then and we’re excited to share with you some of our preliminary findings in this new and updated research.
This year, we made it a point to reach a more diverse group of participants in terms of size, we invited small and large newsrooms; experience, participants include emerging and legacy organisations; and region; contributions came in from Latin America, Africa, the Middle East and North Africa, Asia Pacific, Europe, and North America. We’re immensely grateful to each and every one of the respondents.
More than 60 newsrooms and media organisations have shared their insights with us so far, with more coming in as we publish this update. The survey includes 35 questions ranging from the technical, to the ethical, the region-specific, and of course, we dedicated a section to generative AI technologies! The survey has been supplemented with great conversations we’ve had with many that have enriched this research.
This article will provide a glimpse of our findings thus far. A more thorough analysis of all the input we receive will follow in the comprehensive report we are publishing this September.
We can tell you that most newsrooms we surveyed have already experimented with generative AI technologies like ChatGPT, but not necessarily to create content. The use cases we’ve learned about are quite diverse: code writing, summaries, enhancing headlines and SEO. One respondent said they were using ChatGPT as a ‘banter buddy’, “Imagine having a trusted companion in ChatGPT, ready to engage in lively banter and brainstorming sessions,” they said.
Most respondents agree that generative AI technologies present a new set of opportunities other AI technologies have not provided, but they are more divided as to whether they also bring a unique set of challenges.
When we asked, “why do you use AI technologies in your newsroom? What do you hope to achieve by using these technologies?” More than half the respondents said they hoped to automate mundane tasks and simplify workflows to free up journalists to engage in “more creative, relevant, and innovative work.”
Limited resources and technical expertise are still the most significant challenges to AI adoption in the newsroom, compared to what respondents said in 2019, but this does not mean hiring more technical people. Many respondents told us that achieving interoperability and synchronisation with other departments was a challenge. It’s also about bridging the knowledge gap between technologists and journalists, in terms of tech skills and journalistic skills as well. This requires a nuanced understanding by the newsroom leadership of the types of training needed for each department. Similarly, the ethical aspect continues to be a central concern. Setting ethical guidelines and debiasing techniques seemed to be the most difficult area for many newsrooms.
For non-English speaking journalists, the challenges are more pronounced. Respondents highlighted language limitations of AI tools in other languages like transcription tools and algorithmic bias is experienced at seemingly higher margins than in English. This has pushed some to develop their own tools in-house, which takes considerable time and resources. One respondent lamented: “Applications used in [news] gathering failed, sometimes while trying to collect Arabic language data, this is why we developed our own internal tool.” In addition, many environments struggle with internet infrastructure and penetration, which makes AI adoption in the newsroom a luxury. Despite those challenges, the enthusiasm – as well as the scepticism – for AI in the newsroom remains high!
The full report will be launched at a yet to be confirmed date in September 2023. Sign up to the JournalismAI newsletter to be the first to know when this report will be launched.
This article was written by Mira Yaseen, Lead Researcher of JournalismAI, and Professor Charlie Beckett, who is leading the project.
| 2025-06-26T00:00:00 |
https://www.journalismai.info/blog/the-great-audience-experiment-a-research-synthesis-and-agenda-y68bj-f7wkk
|
[
{
"date": "2025/06/26",
"position": 12,
"query": "AI journalism"
},
{
"date": "2025/06/26",
"position": 23,
"query": "artificial intelligence journalism"
}
] |
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] |
|
Reuters Institute for the Study of Journalism | Reuters Institute ...
|
Reuters Institute for the Study of Journalism
|
https://reutersinstitute.politics.ox.ac.uk
|
[
"Nic Newman",
"Dr Craig T. Robertson",
"Dr Amy Ross Arguedas",
"Dr Richard Fletcher",
"Prof. Rasmus Kleis Nielsen",
"Dr Imke Henkel",
"François Nel",
"Neil Thurman",
"Sina Thäsler-Kordonouri",
"Dr Ayala Panievsky"
] |
Explore the Digital News Report 2025. This year's report offers insights on news influencers, platform changes, AI in journalism, reader revenue, news avoidance ...
|
Search by topic AI and journalism Audience Engagement Business of news Climate journalism Conflict Reporting COVID-19 Data Journalism Foreign correspondence Investigative Journalism Israel-Gaza war Journalism and elections Journalism beats Journalism in exile Journalists at work Local News Media regulation Mental Health Misinformation News Avoidance News podcasts Newsroom Diversity Newsroom Leadership Platforms and social media Polarisation Press Freedom Public Service Media Solutions Journalism Trust In News War in Ukraine Workplace conditions
Search by country Afghanistan Albania Algeria Argentina Armenia Australia Austria Azerbaijan Bangladesh Belarus Belgium Bosnia and Herzegovina Botswana Brazil British Virgin Islands Bulgaria Burkina Faso Cambodia Cameroon Canada Chile China Colombia Costa Rica Croatia Cuba Cyprus Czech Republic Democratic Republic of Congo Denmark Dominican Republic Ecuador Egypt El Salvador Estonia Ethiopia Fiji Finland France Georgia Germany Ghana Greece Guatemala Haiti Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Japan Jordan Kashmir Kazakhstan Kenya Korea Kyrgyzstan Latvia Lebanon Lesotho Lithuania Luxembourg Malaysia Malta Mexico Mongolia Montenegro Morocco Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Nigeria Northern Macedonia Norway Pakistan Palestine Panama Paraguay Peru Philippines Poland Portugal Qatar Romania Russia Samoa Serbia Singapore Slovakia Slovenia South Africa South Korea Spain Sudan Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand Trinidad and Tobago Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay Venezuela Vietnam Yemen Zambia Zimbabwe
| 2025-06-26T00:00:00 |
https://reutersinstitute.politics.ox.ac.uk/
|
[
{
"date": "2025/06/26",
"position": 33,
"query": "AI journalism"
}
] |
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|
EJTA publishes Recommendations for Integrating AI in ...
|
EJTA publishes Recommendations for Integrating AI in Journalism Teaching
|
https://ejta.eu
|
[
"Eufactcheck Launches With Bootcamp In Utrecht"
] |
It provides practical guidance on incorporating AI into journalism curricula while safeguarding the core values of journalistic integrity, transparency, and ...
|
Antwerp / Utrecht, June 26, 2025 — The European Journalism Training Association (EJTA) has released its first comprehensive set of recommendations to help journalism schools across Europe navigate the challenges and opportunities of artificial intelligence (AI) in journalism education.
Titled “Recommendations for Integrating AI in Journalism Teaching”, the document is the result of two years of collaboration by the EJTA AI Taskforce, involving educators from over twenty journalism schools. It provides practical guidance on incorporating AI into journalism curricula while safeguarding the core values of journalistic integrity, transparency, and responsibility.
“With AI rapidly transforming both journalism and education, we saw an urgent need for journalism-specific guidelines,” said Frederik Marain, co-coordinator of the EJTA AI Taskforce. “This document aims to support journalism educators in preparing students for an AI-driven media landscape.”
The publication includes 10 key recommendations, covering both the responsible use of AI in journalistic products and how to effectively embed AI-related skills and knowledge into teaching. Topics include transparency in AI usage, rejecting unreliable AI-detection tools, understanding AI’s ethical challenges, and ensuring students develop a critical understanding of the technology underpinning AI.
“This is just the beginning,” added Shannon Bakker, co-coordinator of the Taskforce. “This version is the “1.0” version. As AI continues to evolve, we encourage feedback and collaboration to refine and expand these guidelines.”
The document is now publicly available on the EJTA website.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://ejta.eu/index.php/2025/06/26/ejta-publishes-recommendations-for-integrating-ai-in-journalism-teaching/
|
[
{
"date": "2025/06/26",
"position": 63,
"query": "AI journalism"
},
{
"date": "2025/06/26",
"position": 66,
"query": "artificial intelligence journalism"
}
] |
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AI job growth in Design and Make: 2025 report | Autodesk News
|
Autodesk launches 2025 AI Jobs Report: Demand for AI skills in Design and Make jobs surge
|
https://adsknews.autodesk.com
|
[] |
Mentions of AI in US job listings have surged by 56.1% in 2025 (through April), building on explosive growth in 2023 (+114.8%) and 2024 (+120.6%). What was once ...
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AI fluency is no longer optional across various Design and Make roles. Mentions of AI in US job listings have surged by 56.1% in 2025 (through April), building on explosive growth in 2023 (+114.8%) and 2024 (+120.6%). What was once a niche skillset is now a core qualification, reshaping hiring across nearly every function—from engineering to marketing to operations.
A new class of roles is emerging—and they’re not all technical. Positions like AI Engineer (+143.2%), Prompt Engineer (+135.8%), and AI Content Creator (+134.5%) are among the fastest growing this year. These roles reflect the rise of an AI-native workforce that blends technical fluency with creativity, communication, and applied insight.
Human skills aren’t being replaced—they’re being revalued. In fact, design has overtaken technical expertise as the most in-demand skill in AI-related job postings. Also in the top 10: communication, collaboration, and leadership. As AI systems become more capable, companies are placing greater value on the human qualities that guide, scale, and govern them—judgment, empathy, and imagination.
Autodesk today released its first AI Jobs Report, offering a detailed look at how artificial intelligence is reshaping the workforce across Design and Make industries—including architecture, engineering, construction, product design, manufacturing, media, and entertainment. The findings show that AI-driven job growth remains strong in 2025, with emerging roles, new skill demands, and regional shifts redefining what it means to be ready for the future of work.
Autodesk’s AI Jobs Report makes one thing clear: the future of work isn’t waiting. It’s here and moving quickly. The winners in this AI-driven economy will be the ones who can merge tech fluency with human creativity, ethics, and leadership.
Conducted in partnership with research firm GlobalData, the report analyzed nearly 3 million job listings across a two-year period and identified four major workforce trends:
AI fluency is on track to become a baseline expectation across the workforce
Mentions of AI in general job listings have skyrocketed: up 114.8% in 2023, up 120.6% in 2024, and up 56.1% year to date in 2025, signaling AI capabilities are no longer confined to technical or specialized roles.
Fluency in AI is quickly becoming a core requirement for career longevity across industries.
AI is creating a new class of jobs—with both technical and non-technical AI roles growing in parallel
From AI Coach to AI Strategist, entirely new roles are starting to gain traction across Design and Make, reflecting growing demand for those who can translate, communicate, scale, and govern AI—not just build it.
Roles in AI product, compliance, and enablement are also accelerating, further signaling the formation of a new, AI-native job class.
The data also shows that technical and non-technical AI roles are growing at similar rates. Listings for AI Engineer (+143.2%) and AI Solutions Architect (+109.3%) surged year over year—tracking closely with non-technical roles like AI Content Creator (+134.5%).
The top 10 fastest growing AI titles in Design and Make industries: AI Engineer: +143.2% AI Content Creator: +134.5% AI Solutions Architect: +109.3% Prompt Engineer: +95.5% AI Systems Designer: +92.6% AI Product Manager: +89.7% AI Coach: +57.7% AI Compliance Manager: +46.0% Machine Learning Engineer: +35.3% AI Strategist: +34.8%
Demand for human-centered skills in AI roles is increasing
In 2025, design skills have surpassed coding, cloud, and other technical competencies to become the most in-demand skill in AI-specific job listings—underscoring the growing importance of human-centered thinking in how AI is built and applied.
Communication, leadership, people, and collaboration skills also land in the top 10, signaling that interpersonal fluency and the ability to lead teams remain essential in AI-driven roles.
The top 10 most in-demand skills for AI roles in Design and Make industries: Design skills Technical skills Application Lifecycle Management skills Communication skills Coding skills People skills Leadership skills Analytical skills Collaboration skills Cloud service skills
Companies hiring for AI roles are looking for more than technical expertise—they’re seeking talent that can bring human ingenuity and strategic judgment to the work. As AI jobs grow, so does the demand for the uniquely human skills that make AI effective.
AI hiring by region
Autodesk’s report looked at the percent change in AI job listings year over year and found that Asia has surged ahead in AI hiring, with job listings growing 94.2% year over year—outpacing North America (+88.9%) and highlighting a widening global divide.
While Asia and North America are leading AI hiring growth, South America lags at 63.4%, raising questions about global AI readiness and talent investment strategies.
For the full 2025 AI Jobs Report and press assets, visit the AI Jobs Report media kit.
Methodology
Autodesk’s AI Jobs Report was conducted in partnership with third-party analytics firm GlobalData and examined nearly 3 million job postings over a two-year period across Design and Make industries—including architecture, engineering, construction, product design, manufacturing, media, and entertainment.
GlobalData used its proprietary Job Analytics platform to track daily job postings from companies globally. It also used its advanced platform to analyze job postings on company career pages and other trusted sources. All data is anonymized and does not include private or individual recruiter listings.
About the results
The data spans three rolling 12-month periods measured from May to April: the 2023 cycle includes postings from May 2022 through April 2023, the 2024 cycle includes postings from May 2023 through April 2024 (1.289 million job listings), and the 2025 cycle includes postings from May 2024 through April 2025 (1.488 million job listings). These totals include both AI-related and general job listings.
“AI-related” roles are defined as those where artificial intelligence is a major component of the position or its primary function—beyond surface-level mentions or general familiarity. This includes roles explicitly centered around AI use, implementation, oversight, and application.
| 2025-06-26T00:00:00 |
https://adsknews.autodesk.com/en/news/ai-jobs-report/
|
[
{
"date": "2025/06/26",
"position": 6,
"query": "AI labor market trends"
},
{
"date": "2025/06/26",
"position": 16,
"query": "future of work AI"
}
] |
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] |
|
How Generative AI is Disrupting the Relationship Between ...
|
How Generative AI is Disrupting the Relationship Between the Labor Market and Stocks
|
https://www.chmura.com
|
[
"Chris Chmura",
"James Stinchcomb"
] |
Unlike previous iterations of automation, generative AI enabled the automation of tasks designated for white-collar workers with high educational attainment and ...
|
How Generative AI is Disrupting the Relationship Between the Labor Market and Stocks
The historic link between stock market performance and job postings has broken down amid the rise of generative AI. While investor enthusiasm over AI technologies has sent stock prices soaring - particularly in tech - job postings have plunged, especially in high-wage white-collar sectors like software and finance. This divergence raises questions about whether we’re seeing a fundamental labor market shift, an AI investment bubble, or both.
| 2025-06-26T00:00:00 |
https://www.chmura.com/blog/stock-market-and-job-posting-divergence-the-impact-of-generative-ai-on-the-relationship-between-the-labor-market-and-stocks
|
[
{
"date": "2025/06/26",
"position": 10,
"query": "AI labor market trends"
},
{
"date": "2025/06/26",
"position": 7,
"query": "ChatGPT employment impact"
}
] |
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|
Tech Giants Slash Thousands Of Jobs In 2025 Amid ...
|
Tech Giants Like Microsoft, Google, IBM Slash Thousands Of Jobs In 2025 Amid Restructuring, AI Integration
|
https://www.ndtv.com
|
[] |
Tech Companies Layoffs: Major tech companies are cutting thousands of jobs in 2025, driven by economic factors and the increasing integration of AI, which is ...
|
Tech Companies Layoffs: The tech industry is experiencing a major wave of layoffs in 2025, with leading companies like Microsoft, Google, Amazon, and IBM cutting thousands of jobs. This broad restructuring is driven by factors including sluggish revenue growth, global economic instability, and the increasing adoption of artificial intelligence, which is transforming traditional job roles.
Data from Layoffs.fyi indicates over 61,000 tech professionals have been laid off across more than 130 companies this year. Microsoft alone cut 6,000 positions, streamlining management and emphasising engineering over administrative roles. Google let go of hundreds in its Android, Pixel, and Chrome teams, following a voluntary exit programme.
IBM has allegedly laid off 8,000 employees, largely in HR, as AI takes over HR functions. Amazon recently eliminated approximately 100 jobs from its Devices and Services division, and Microsoft plans cuts within its Xbox division.
Bumble layoffs:
Bumble, the online dating platform, is set to lay off around 240 employees, representing approximately 30% of its global workforce. The decision, approved by the board, is part of a strategic realignment to optimize operations. In a securities filing, Bumble stated that these workforce reductions are expected to generate $40 million in annual cost savings. These savings will primarily be reinvested into product and technology development initiatives. The company is based in Austin, Texas.
Microsoft layoffs:
According to Reuters, Microsoft is planning to cut thousands of jobs, particularly in sales, as the tech giant streamlines its workforce amid increased investments in artificial intelligence, citing people familiar with the matter. The latest layoffs follow Microsoft's previous round of job cuts in May, which affected about 6,000 employees.
The tech giant has ramped up its investments in AI, aiming to solidify its leadership as companies across industries accelerate the integration of AI into their products and services to maintain a competitive edge. The layoffs are expected to be announced early next month, following the end of the tech giant's fiscal year. Microsoft had 228,000 workers as of June last year.
| 2025-06-26T00:00:00 |
https://www.ndtv.com/feature/tech-giants-slash-thousands-of-jobs-in-2025-amid-restructuring-ai-integration-8767043
|
[
{
"date": "2025/06/26",
"position": 84,
"query": "AI layoffs"
}
] |
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|
How to Develop an Ethical AI Policy for your Newsroom
|
How to Develop an Ethical AI Policy for your Newsroom
|
https://texastipi.org
|
[
"Silvia Dalben Furtado"
] |
With the growing development of generative artificial intelligence systems, journalists all over the world are discussing ethical principles and values to adopt ...
|
With the growing development of generative artificial intelligence systems, journalists all over the world are discussing ethical principles and values to adopt these technologies in the newsroom. One important step in this process is developing guidelines to assist editors and reporters in deciding when and how these innovations can be safely incorporated into journalism practices.
Motivated by this discussion, TIPI recently participated in a panel titled “Developing an Ethical AI Policy for Your Newsroom” at the latest conference organized by the National Institute for Computer-Assisted Reporting (NICAR). The panel, moderated by Darla Cameron, Chief Product Officer at The Texas Tribune, featured four other participants, including Silvia DalBen Furtado, a student affiliate at TIPI and PhD candidate at the School of Journalism and Media at UT Austin.
Currently, Silvia is conducting a study with Tina Lassiter, a PhD student at the School of Information, in which they evaluate journalists’ perceptions about regulations and guidelines on the use of generative AI technologies in US newsrooms. During the panel, they presented the preliminary results of this study, which included a survey (N=310) and in-depth interviews (N=23), that highlighted these key findings:
1. Many journalists are already using or experimenting with generative AI technologies in their routines, and they agree there should be some regulation or guidance regarding its use in newsrooms.
2. Many of them don’t seem to understand the difference between AI and Generative AI. Thus, beyond discussing AI policies, it is essential to clearly explain the historical evolution of AI as a field and examine how AI technologies have shaped journalism practices over decades.
3. Our findings indicate that many AI guidelines have been developed through a top-down approach. However, the reporters and editors who participated in this study expressed a desire for more active involvement in the discussion, advocating for a bottom-up approach.
4. Moreover, journalists express interest in having standardized guidelines established by a consortium of news organizations, rather than develop individually by each newsroom. Many journalists fear that government regulations could lead to censorship of journalistic activities.
| 2025-06-26T00:00:00 |
https://texastipi.org/how-to-develop-an-ethical-ai-policy-for-your-newsroom/
|
[
{
"date": "2025/06/26",
"position": 40,
"query": "artificial intelligence journalism"
}
] |
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|
AI and Workers: Artificially Managed, Actually Exploited
|
AI and Workers: Artificially Managed, Actually Exploited
|
https://seecheck.org
|
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“AI-driven management is already intensifying pressure on 427 million workers worldwide,” warned the International Trade Union Confederation (ITUC), which ...
|
Original article (in Croatian) was published on 05/02/2025; Author: Anja Vladisavljević
In the age of artificial intelligence, the enormous burden of technological progress is borne by workers, who are increasingly insecure and subjectable to control.
“AI-driven management is already intensifying pressure on 427 million workers worldwide,” warned the International Trade Union Confederation (ITUC), which called for urgent measures to protect workers’ lives and rights in the age of digitalization and artificial intelligence (AI) on the occasion of International Workers’ Memorial Day, 28 April.
On that April day, they reported to have remembered the dead workers and promised to continue fighting for the living. “Technology should work for us, not against us,” they wrote.
One of the most brutal examples of technology turning against workers is the case of Filipino Jasper Dalman, a Foodpanda platform courier who died in a terrible car accident in 2023 while doing his job. His death, according to the ITUC and the Philippine trade union of which Dalman was a member, showed “the deadly consequences of algorithmic exploitation that set impossible productivity targets”.
The tragic case from the Philippines is not isolated. The application of new technologies, the ITUC points out, without proper consultation with workers and their trade unions, is already causing serious violations of workers’ rights. Algorithmic management and the use of AI expose workers to precarious conditions, undermine workplace safety, undermine hard-won labour rights and discriminate against workers.
While May Day beans, distributed at gatherings, in squares and parks, are still being digested, it may be a good time to ask ourselves what it means to be a worker in the era of digitization.
Algorithm the boss
As ITUC points out in its new report (“Artificial intelligence and digitalization: A matter of life and death for workers”), with the development of technology, a multitude of jobs and modes of operation appeared that did not exist before: app-controlled couriers and drivers, content moderators, big data analysts and machine learning engineers.
While in traditional industries and industries there is a boss, manager, supervisor, a line of command and a union that oversee working conditions, in these newer occupations these functions are replaced (or completely abolished) by applications and digital tools.
Platform work is the most obvious example of how technology can be (mis)used against workers. Under the guise of flexibility and simplicity, digital platforms – applications for transport, delivery and other services – use algorithmic management to control workers, while offering them the minimum of the protection they have in traditional employment.
Algorithmic management involves continuous monitoring of workers’ behaviour through devices (e.g. mobile phone) and applications and continuous performance assessment based on collected data (user ratings, task execution speed, number of rejected orders).
Due to constant supervision, workers are under pressure to maintain or increase their efficiency, although very often they do not even know how the algorithms work. At the same time, they do not have much room to complain or seek feedback when faced with unfair treatment, because these are automated ratings and decisions, so they are not communicated with by a living person.
The range of industries where algorithmic management is used is expanding. One of the most famous examples is the Amazon online store where workers are monitored with special scanners to measure their productivity and record errors. In call centres, workers are also supervised by the machine, and some of such software can even automatically analyse and evaluate what workers are saying, what phrases they are using, and whether the mood during the call was “negative” or “positive.”
In the US, nurses are increasingly working through platforms (which connect them to hospitals and healthcare facilities) and are struggling with similar problems as Uber workers. According to a study by the Roosevelt Institute, there are automated metrics in the world of gig nursing that assess their reliability based on the number of shifts they complete, how early they cancel shifts, and whether they stay late at work. Women workers are offered different shifts, often for different amounts of “wages”, and they are often assigned to institutions where they have not worked before.
Algorithm the recruiter
Similar to the above cases, where the technology of supervision of workers is justified using the argument of time and resource effectiveness, it increasingly happens that workers are “scanned” even before they end up in a workplace. Artificial intelligence is also being integrated into recruitment processes and is displacing recruiters who have otherwise reviewed resumes, conducted interviews and made judgments based on experience and impression.
AI-driven tools address the logistics of selecting and scheduling interviews, eliminating time-consuming administrative work. AI can also analyse vast amounts of workforce data to anticipate a particular company’s future hiring needs and identify “ideal candidates” based on the company’s profile.
It can review the candidate’s CV, motivation letter, work experience, but also profiles on social networks, which calls into question their privacy and data security. If job interviews are conducted via a video link, which is no longer rare, facial recognition and voice analysis algorithms can evaluate responses, body language, word choice, and candidate tone, evaluating them against a predefined metric.
As analysed by the American Civil Liberties Union (ACLU), many of these tools pose a huge risk of exacerbating existing workplace discrimination based on race, gender, disability, and many other characteristics, despite marketing claims that they are objective and less discriminatory.
AI tools are trained with a large amount of data and make predictions about future outcomes based on correlations and patterns in that data. The tools used by a particular company can be “fed” with data on the employer’s own workforce and previous recruitment processes, so they can automatically eliminate certain social groups.
For example, if the system is trained on data from a company that employed more men than women, the tool could favour male candidates. Such favouritism can also be achieved in a very banal way, as can be seen in the case described by the BBC.
In one company, an AI resume checker was trained on the resumes of past workers, giving candidates extra credits if they listed baseball or basketball as hobbies because they were associated with “more successful staff,” often men. Candidates who mentioned softball – usually women – were demoted. Another example of bias singled out by the BBC is the case of a candidate who passed the selection process after changing his date of birth on his CV to make himself appear younger (before that change, and with the same application, he was excluded).
In some cases, the BBC explains, the biased selection criteria are clear – such as age discrimination or sexism – but in some they can be very blurry. As in the aforementioned cases of algorithmic management of workers, the lack of transparency is problematic, especially when candidates are rejected without knowing for what reason.
Does the worker help the AI or the AI worker?
At the same time, the potential positive impacts that artificial intelligence can have in the world of labour should be taken into account. As we read in various online sources (1, 2), and this is also not missed by workers’ rights organisations, AI can automate repetitive tasks, increase efficiency and reduce operating costs – freeing workers from monotonous duties and allowing them to focus on more creative work.
But since we are enumerating pros and cons as if we were at school, we cannot omit the most obvious con. One of the primary concerns about AI is the potential loss of jobs. As AI systems become increasingly capable of performing tasks traditionally performed by humans, there is an understandable fear that some occupations will become obsolete.
According to a poll onducted among American workers in January this year, there is widespread recognition of the impact of artificial intelligence on job losses, with 43 percent personally knowing someone who has lost their job due to artificial intelligence, and 89 percent expressing concern about their own job loss.
In September 2023, the first survey on AI perception in Croatia was conducted. For 60 percent of respondents, AI represents a sense of uncertainty or concern, while 30 percent find it useful.
However, in parallel, a perspective emerges that questions the notion of artificial intelligence exclusively as a job eliminator. In some cases, workers retain their jobs, but, contrary to the promise, the implementation of artificial intelligence brings them an increase in workload.
As we wrote earlier, such burdens are felt by workers in the field of culture. Literary translators, for example, are already receiving queries for the so-called post-edit, i.e. editing translations generated by artificial intelligence. This gives them a lot more work because they have to fix errors caused by machine translation.
In journalism, things are similar. If a journalist searches for answers via ChatGPT, they definitely have to go through several more rounds of verification, because this tool, unless well trained, does not distinguish between reliable and unreliable information, and often does not reveal where it got its source from. In principle, it is still difficult to leave AI to perform tasks without human supervision.
After all, the point of some jobs is the growth and development of artificial intelligence. We do not mean IT professionals, but “data workers” who, in stressful and precarious conditions and for low fees, collect, process, recognize, mark, moderate or enter data for the purpose of training and functioning of artificial intelligence systems.
Among them are moderators of social media content who are constantly exposed to toxic and disturbing content, in order to once train artificial intelligence to become more skilled in recognizing this harmful content.
Technology for the workers’ struggle
Finally, let’s return to the ITUC’s message, that technology should work for the workers, not against them. In addition to traditional actions, strikes and protests, which warn of the erosion of workers’ rights through technology and advocate for better legislation, workers and trade unions are considering using AI and digital tools to protect and improve workers’ rights.
As automation in the workplace poses a significant challenge for recruiting workers into the union – as many workers work remotely, through apps, and in short-term jobs – unions must also embrace digital tools to improve their outreach and advocacy.
The United Nations University (UNU) sees technological adaptation as part of the solution. Virtual platforms can connect union representatives with geographically dispersed members, and social media campaigns and targeted digital advertising can raise awareness of union benefits and workers’ rights in AI-driven workplaces.
“The answer to the challenges posed by automation lies in adaptation and innovation. Trade unions must embrace the same technologies that are disrupting the workplace. AI-powered chatbots can facilitate member recruitment and engagement, even reaching workers in traditionally difficult-to-organize sectors like domestic work,” suggests the United Nations University.
Indeed, some have already taken steps. WAO (Workers’ Algorithm Observatory), an initiative from Princeton University that assists workers and their allies in the research and monitoring of algorithmic systems in platform work, has developed tools to explore how algorithmic management affects workers. Through tools such as FairFare and the Shipt calculator, WAO allows workers to anonymously share information about their experiences, thus detecting irregularities in algorithmic wage and working conditions determination.
There are similar ideas on the domestic trade union scene. As Tomislav Kiš from Novi sindikat explained for the European AI & Society Fund, the goal of his organization is to organize domestic and foreign platform workers to pressure the Government into changing existing laws or create new legal solutions.
“To achieve this, we need to strengthen the capacity of workers to organize and express their demands, whether through advocacy or material support. One of our ideas is to develop our own app to control and verify the data managed by the platforms. This would be a first step towards gaining insight into how platforms work and, consequently, limiting the power they develop based on the data collected. Having accurate data would strengthen the bargaining power of trade unions in regulating mutual relations,” Kiš said.
It’s not unusual or unnecessary for much of the public discussion about AI to focus on how it threatens jobs and deepens inequality, but it may be time to change the narrative.
If algorithms can track and control workers, they can also be repurposed to protect them. As we often hear when we talk about artificial intelligence, technology itself is not the problem, the problem is how we use it.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://seecheck.org/index.php/2025/06/26/ai-and-workers-artificially-managed-actually-exploited/
|
[
{
"date": "2025/06/26",
"position": 19,
"query": "artificial intelligence labor union"
}
] |
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] |
A Blueprint for an Artificial Intelligence Future
|
A Blueprint for an Artificial Intelligence Future
|
https://www.ccctu.org
|
[] |
Local 1600 and unions everywhere must not wait. This is a moment to lead. The future of academic labor, workplace democracy, and student success depends on our ...
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Artificial Intelligence (AI) is no longer a distant prospect. It’s already reshaping our work and the curricula of our colleges. In the near future, we could potentially see this technology impact hiring practices, instructional design, student services, and much, much more. That’s why Local 1600 has released a new report: “ Facing the AI Future: A Call to Action for Union Members .”
This report is both a warning and a roadmap. If we act strategically, AI can enhance our work and reaffirm the human mission of education. But if we fail to act, AI will be used to undermine our jobs, erode academic freedom, and automate away the relationships at the heart of teaching and learning.
Key Takeaways from the Report:
Protecting Our Members: We must update our contracts and push for legislation that prevents employers from using AI to replace human educators, professionals, and staff. Collective bargaining must include language on algorithmic oversight, transparency, and human review.
Transforming Our Work (On Our Terms): We must explore forward-looking models, like AI-assisted apprenticeship structures, where AI handles repetitive tasks and frees human workers for higher-value, more creative work. We must demand control over how new technologies are implemented.
Keeping AI Aligned with Human Goals: From hiring software to student success dashboards, algorithms are already shaping decisions (often without accountability). We must fight for audits, transparency, and clear appeals processes to prevent biased or harmful outcomes.
Rethinking Learning: AI will change what students need to learn and how they learn it. Our faculty are already seeing the major impact of AI and are working to consider how to move forward. The report calls for curricula that center critical thinking, creativity, and AI literacy for students and workers.
Seeking Legislative Protections: During the “innovation interim” where change will be rapid and impactful, we must seek protections at the state and federal level. This should include worker protections from displacement, funding for retraining, and requirements for oversight.
Why This Matters to Our Union:
Local 1600 and unions everywhere must not wait. This is a moment to lead. The future of academic labor, workplace democracy, and student success depends on our actions today. AI policy is labor policy. It’s also educational policy.
This report is the beginning of a broader conversation for Local 1600. We’ll be holding meetings and sessions in the coming months to discuss next steps, develop bargaining strategies, and craft model legislative language. We encourage all members to read the report and join the conversation.
Facing the AI Future_ A Call to Action for Union Leadership .pdf Download PDF • 649KB
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.ccctu.org/post/a-blueprint-for-an-artificial-intelligence-future
|
[
{
"date": "2025/06/26",
"position": 48,
"query": "artificial intelligence labor union"
}
] |
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] |
Ayres says Government AI plan includes role for unions
|
Ayres says Government AI plan includes role for unions
|
https://csirostaff.org.au
|
[
"Anthony Keenan"
] |
... Artificial Intelligence (AI) will protect local workers and industries alike. Meanwhile, Chief Executive Doug Hilton has spruiked a role for CSIRO as part ...
|
Science Minister Tim Ayres has pitched the potential productivity benefits of Artificial Intelligence, while insisting that unions have a role to play in representing the rights of workers during the introduction of the new technology.
That’s a challenge the movement is gearing up to accept, with senior union leaders stating that responsible regulation of Artificial Intelligence (AI) will protect local workers and industries alike.
Meanwhile, Chief Executive Doug Hilton has spruiked a role for CSIRO as part of the Federal Government’s larger challenge to boost economic productivity across the Australian economy.
Lean in to AI
In comments to an AI summit in Sydney earlier this month, Minister Ayres said Australia must “lean in, to secure a stake in global digital and AI development”.
“The Australian challenge is to lean in to adopt AI to lift productivity and living standards, deliver investment in infrastructure and capability and protect our security… other countries in the region are moving fast and so must we.”
The Minister said that the government would work with trade unions to “make sure that AI adoption makes jobs better”, adding that “confidence in AI adoption is key” and could be built by upskilling Australians.
Role for unions
“I will be looking in particular at how we can strengthen worker voice and agency as technology is diffused into every workplace in the Australian economy and I look forward to working with our trade union movement on all of this,” Minister Ayres said.
It’s an approach welcomed by the union movement. “We want to work with employers and government to realise the positive ambition,” ACTU secretary Sally McManus said.
“To achieve good adoption of AI, Australia needs responsible regulation which both protects Australian workers and Australian industries from malicious use and theft by overseas big tech,” Ms McManus said.
Fork in the road
Meanwhile, CSIRO Chief Executive Doug Hilton welcomed the policy focus on Australia’s productivity challenge, but says it presents the government with some stark choices in the funding of science.
In an interview with InnovationAUS, Dr Hilton said the challenges of falling government funding and business investment in research and development were not new, but they had become more urgent.
“It’s a fork in the road for Australia. We have an opportunity to consider the role of science in our national life in a way that is pretty stark,” Dr Hilton said.
CSIRO can help
“If I think about what CSIRO does for many different sectors of Australian industry, the two things that come to mind are productivity improvement and sustainability.”
“If we want Australia to be prosperous, if we want to maintain the standard of living for our kids and our grandkids, then we really need science and ideas to drive that productivity.”
“And CSIRO can certainly help. That’s not the only part of the (productivity) solution, but we can certainly help,” he said.
Related content
| 2025-06-26T00:00:00 |
2025/06/26
|
https://csirostaff.org.au/news/2025/06/26/ayres-says-government-ai-plan-includes-role-for-unions/
|
[
{
"date": "2025/06/26",
"position": 74,
"query": "artificial intelligence labor union"
}
] |
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European Union AI Act: Meaning, Regulations, risks, and ...
|
European Union AI Act: Meaning, Regulations, risks, and compliance
|
https://www.scrut.io
|
[
"Susmita Joseph",
"Authored By"
] |
The EU Artificial Intelligence Act (AI Act) is the European Union's first comprehensive regulatory framework for AI, aiming to ensure that AI systems are safe, ...
|
As artificial intelligence (AI) continues to rapidly evolve, regulators are stepping up to ensure its responsible use. The EU Artificial Intelligence Act (AI Act) is the European Union's first comprehensive regulatory framework for AI, aiming to ensure that AI systems are safe, ethical, and aligned with fundamental rights.
In this blog, we explore the AI Act's key provisions, including its focus on general-purpose AI models, risk categories, and prohibited practices, along with its broader implications for organizations navigating the complex regulatory landscape.
What is the EU Artificial Intelligence (AI) Act?
The EU Artificial Intelligence Act, formally known as Regulation (EU) 2024/1689, is the European Union's first comprehensive legal framework for artificial intelligence. It establishes harmonized rules for the development, placement on the market, and use of AI systems across the EU, aiming to ensure that AI is safe, respects fundamental rights, and upholds the Union's values.
Introduced by the European Commission in April 2021 and formally adopted by the European Parliament and the Council in 2024, the Act entered into force on August 1, 2024. Non-compliance can result in substantial administrative fines, including up to €35 million or 7% of a company's global annual turnover for the most serious violations, such as using prohibited AI systems.
Providers, deployers, importers, and distributors of AI systems are responsible for compliance. They are expected to implement internal controls, maintain documentation, and ensure conformity before placing systems on the market.
From the Union's side, national authorities carry out market surveillance and enforcement actions, while the AI Office coordinates oversight efforts across Member States, especially for cross-border and high-risk AI use cases.
What role does the General-purpose AI model play?
General-purpose AI (GPAI) refers to an AI model that demonstrates significant generality and can competently perform a wide range of tasks, regardless of how it is marketed or integrated into various systems.
The role of GPAI models is to offer a versatile, scalable foundation that can be applied across industries and integrated into various downstream AI systems. While they may not be inherently high-risk, they can become part of high-risk systems, and providers are expected to cooperate to ensure compliance with the AI Act.
Rules for GPAI models under Regulation (EU) 2024/1689 include:
1. Technical documentation: Providers must create detailed documentation covering the model's training, testing, and evaluation results.
2. Information for downstream providers: Providers must supply essential information to those integrating the GPAI model to ensure an understanding of its capabilities and limitations.
3. Copyright compliance: Providers must respect the EU Copyright Directive and ensure compliance.
4. Training data transparency: A summary of the content used for training the model must be publicly available.
5. Free and open licenses: GPAI models under open licenses must comply with the above three obligations unless they are deemed systemic.
6. Systemic risks: If the model meets the threshold of over 1025 floating point operations (FLOPs) used in training, it is considered to present systemic risks, requiring additional measures:
Who does the EU AI Act apply to?
The EU AI Act applies to a wide range of actors involved in the development, deployment, and distribution of artificial intelligence systems within the EU market, regardless of whether they are based in the EU or outside. The regulation takes a lifecycle approach, assigning specific responsibilities to each actor based on their role, to ensure that AI systems placed on the EU market are trustworthy and safe.
1. Providers
A provider is any natural or legal person, public authority, agency, or other body that develops an AI system or a general-purpose AI model and places it on the market or puts it into service under its own name or trademark.
Providers are at the core of the EU AI Act's compliance structure. They are responsible for ensuring that AI systems meet all relevant legal requirements before being placed on the EU market. This includes implementing risk management procedures, drawing up technical documentation, undergoing conformity assessments (for high-risk AI), and ensuring transparency.
The developments in AI especially the increasing complexity and autonomy of systems have made it necessary to hold providers accountable for design choices and training practices from the outset.
2. Deployers
A deployer is any entity or individual that uses an AI system in a professional capacity within the EU, excluding personal non-professional use.
Deployers are responsible for using AI systems in line with their intended purpose and for ensuring that any obligations related to transparency, human oversight, or accuracy are respected in their specific use context. As AI use grows across sectors like healthcare, HR, and finance, deployers play a crucial role in how AI impacts end-users and society. The EU AI Act reflects this by requiring deployers especially of high-risk systems to implement appropriate safeguards and monitor AI performance in real-world settings.
3. Importers
An importer is any natural or legal person based in the EU who places on the market an AI system developed by a provider established outside the EU.
Importers act as the bridge between non-EU AI developers and the European market. They are required to ensure that the foreign-developed AI systems comply with EU law before distribution. This includes verifying that conformity assessments have been completed, the necessary technical documentation exists, and instructions for use are available. With the EU AI Act setting a high bar for safety and rights protections, importers share the responsibility of ensuring that AI systems from outside the EU meet these expectations.
What are some prohibited AI practices?
The EU AI Act sets clear boundaries by explicitly banning certain uses of artificial intelligence that pose unacceptable risks to fundamental rights, safety, and democratic values. These prohibited practices are considered so harmful that they are not permitted under any circumstances within the EU.
AI systems that use subliminal techniques to distort a person's behavior in a way that causes or is likely to cause physical or psychological harm
AI systems that exploit the vulnerabilities of a specific group due to age, disability, or socioeconomic situation, with the intent to materially distort their behavior
Real-time remote biometric identification systems in publicly accessible spaces for law enforcement purposes (with narrow exceptions such as searching for specific victims or preventing an imminent threat)
AI systems used for social scoring by public authorities that lead to detrimental treatment of individuals or groups in a way that is unjustified or disproportionate
AI systems that evaluate or classify people based on behavior or characteristics, resulting in unjustified or disproportionate consequences
AI systems used by law enforcement to make predictions solely based on profiling, location, or past criminal behavior
Emotion recognition systems used in workplaces or educational institutions, except in specific circumstances justified by law
The untargeted scraping of facial images from the internet or CCTV footage to create or expand facial recognition databases
What are the AI Act Risk levels?
The EU Artificial Intelligence Act (Regulation (EU) 2024/1689) adopts a risk-based approach to regulating AI, grouping AI systems into four categories based on the level of risk they pose to fundamental rights, health, safety, and society. The compliance obligations for each category vary stricter rules apply to higher-risk systems, while minimal-risk systems are largely exempt. Understanding these categories is essential for both AI providers and deployers to align with the regulatory requirements.
1. High-risk AI systems
These are AI systems that can significantly impact people's lives, particularly in safety-critical sectors or fundamental rights contexts. High-risk systems are subject to strict requirements such as risk management, high-quality data governance, technical documentation, human oversight, and post-market monitoring.
Use cases include:
AI used in medical devices and diagnostic tools
AI for recruitment and employee evaluation
Credit scoring or loan approval systems
AI in critical infrastructure, like transport or energy
AI used in education for student assessments
Organizations developing or using high-risk AI must adopt a robust risk management framework, maintain high-quality training data, document the system's capabilities and limitations, and ensure human oversight is built into the system.
Pre-market conformity assessments and continuous post-market monitoring are essential. Cross-functional collaboration between compliance, engineering, and product teams is critical for fulfilling these requirements.
2. Unacceptable-risk AI systems
These systems are considered a clear threat to citizens' rights and freedoms and are therefore prohibited under the Act. The ban is absolute, with very limited exceptions for law enforcement under strict safeguards.
Use cases include:
Social scoring by governments or corporations
AI that exploits vulnerable individuals (e.g., children or disabled persons)
Biometric categorization based on sensitive characteristics (e.g., race, religion, political beliefs)
Emotion recognition in workplaces or educational institutions
Indiscriminate scraping of biometric data from CCTV for facial recognition
The only way to comply is to avoid these practices altogether. Organizations must conduct thorough due diligence when designing or procuring AI systems to ensure that none of the functionalities fall into this category.
If there's a borderline case, seek expert legal advice early in the development lifecycle to mitigate legal and reputational risks.
3. Limited-risk AI systems
These are AI systems that pose some risk but not enough to trigger full regulation. The main obligation here is transparency users must be informed that they are interacting with an AI system or viewing AI-generated content.
Use cases include:
Chatbots and virtual assistants
AI-generated images, videos, or audio (e.g., deepfakes)
Recommender systems on e-commerce or media platforms
Transparency obligations include disclosing when content is AI-generated and informing users that they are communicating with a machine. A clear user interface design and accurate labeling can help organizations fulfill these requirements.
Although the rules are lighter here, ethical use and documentation are still advisable for trust and accountability.
4. Minimal-risk AI systems
These systems are considered low-risk and are exempt from legal obligations under the Act. They are still encouraged to follow voluntary codes of conduct and ethical AI principles.
Use cases include:
AI spam filters in email clients
AI used for weather prediction
AI in video games for character behavior or world generation
Even in the absence of legal obligations, organizations are encouraged to voluntarily uphold transparency, fairness, and privacy best practices. Doing so can future-proof systems and strengthen user trust, especially as public expectations and future regulations evolve.
What are some related laws affecting AI?
While the EU Artificial Intelligence Act (Regulation (EU) 2024/1689) is the cornerstone of AI regulation in the region, it does not operate in isolation. Several other EU laws complement and intersect with it to ensure a comprehensive regulatory approach. These frameworks address areas such as liability, privacy, product safety, and consumer protection each playing a critical role in governing how AI systems are developed, deployed, and used.
1. AI Liability Directive (AILD)
The proposed AI Liability Directive aims to harmonize rules for non-contractual civil liability related to AI systems. It introduces a rebuttable presumption of causality to ease the burden of proof for victims seeking compensation for damages caused by AI. However, as of March 2025, the European Commission has withdrawn the proposal from consideration, leaving its future uncertain.
2. General Data Protection Regulation (GDPR)
The GDPR (Regulation (EU) 2016/679) governs the processing of personal data within the EU. It mandates that organizations obtain explicit consent for data collection, ensure data accuracy, and uphold individuals' rights to access, rectify, and erase their data. For AI systems processing personal data, GDPR compliance is essential to protect user privacy and maintain trust.
3. Product Liability Directive (PLD)
The revised Product Liability Directive (Directive (EU) 2024/2853) modernizes liability rules to encompass digital products, including AI systems. It expands the definition of product to cover software and AI, shifts the burden of proof to manufacturers in certain cases, and allows claims for psychological harm and data loss. This directive ensures that consumers can seek compensation for damages caused by defective AI products.
4. General Product Safety Regulation (GPSR) 2023/988/EU
The GPSR, effective from December 13, 2024, replaces the previous General Product Safety Directive. It aims to ensure that all consumer products, including those incorporating AI, are safe for use. The regulation introduces stricter safety requirements, mandates clear product information, and enhances market surveillance to protect consumers from hazardous products.
What's the current status of the EU AI Act?
In June 2024, the EU adopted the world's first rules on artificial intelligence. The Artificial Intelligence Act (AI Act) officially entered into force on 1 August 2024, with its provisions coming into effect gradually to give stakeholders time to adapt. While the regulation will be fully applicable 24 months after entry into force, several obligations kick in earlier particularly those relating to unacceptable risk and general-purpose AI (GPAI) models.
Here's a quick overview of the key compliance milestones:
2 February 2025 : The ban on AI systems posing unacceptable risk begins to apply. This includes systems that manipulate human behavior, exploit vulnerabilities, or implement social scoring.
: The ban on AI systems posing unacceptable risk begins to apply. This includes systems that manipulate human behavior, exploit vulnerabilities, or implement social scoring. 2 May 2025 (nine months after entry into force): Codes of practice are expected to be adopted for providers of GPAI, offering voluntary compliance guidance before harmonized standards are finalized.
(nine months after entry into force): Codes of practice are expected to be adopted for providers of GPAI, offering voluntary compliance guidance before harmonized standards are finalized. 2 August 2025 : Rules on general-purpose AI models will apply to new GPAI models placed on the market. Existing models (i.e., those available before this date) have until 2 August 2027 to meet these requirements.
: Rules on general-purpose AI models will apply to new GPAI models placed on the market. Existing models (i.e., those available before this date) have until 2 August 2027 to meet these requirements. 2 August 2026 : Rules for high-risk AI systems come into effect. These cover systems used in sensitive areas such as hiring, law enforcement, healthcare, and critical infrastructure.
: Rules for high-risk AI systems come into effect. These cover systems used in sensitive areas such as hiring, law enforcement, healthcare, and critical infrastructure. 2 August 2027: Provisions apply to AI systems considered products or safety components of products already regulated under specific EU product safety laws (e.g., machinery or medical devices).
In summary, while the AI Act is already in force, organizations have staggered deadlines depending on the AI system's category and function. High-risk AI providers, in particular, have a 36-month window to ensure full compliance, ending in August 2027. Until then, businesses, regulators, and civil society groups are preparing for one of the most significant digital policy shifts in recent history.
What are some of the best AI frameworks and standards?
Apart from the EU AI Act, several global frameworks help organizations build, secure, and govern responsible AI systems, such as:
1. ISO 42001
ISO 42001 is a certifiable standard that guides organizations in managing AI risks through structured policies, controls, and continuous improvement.
2. NIST AI RMF
NIST AI RMF is a voluntary framework to help organizations govern and reduce AI risks, focusing on fairness, transparency, and security.
3. OWASP AI Security and Privacy Guide
The OWASP AI Security and Privacy Guide provides actionable best practices to secure AI systems and protect privacy, from threat modeling to incident response.
4. Google's Secure AI Framework
Google's Secure AI Framework emphasizes safe AI development and deployment, with principles like security by design and continuous monitoring.
Futureproof your AI compliance with Scrut
As AI regulations evolve and new frameworks emerge, keeping up and staying compliant can feel like a moving target. Scrut helps your organization stay ahead of the curve by centralizing AI risk management, automating evidence collection, and aligning your controls with global standards.
Whether you're navigating the EU AI Act or preparing for future audits, Scrut ensures you're not just reacting to change you're ready for it. Schedule a demo today to learn more.
FAQs
When was the EU AI Act passed?
The EU AI Act was passed on March 13, 2024, by the European Parliament. It was subsequently approved by the EU Council on May 21, 2024. The Act was published in the EU Official Journal on July 12, 2024, and entered into force on August 1, 2024.
Is AI safe?
AI safety depends on how it's built and governed. Regulations like the EU AI Act set rules for high-risk and generative AI to ensure transparency, accountability, and user protection.
What is the purpose of the European Union?
The European Union aims to promote economic cooperation, peace, and human rights across its member states. The European Commission is responsible for proposing and passing legislation like the EU AI Act.
How are the big AI companies regulated under the EU AI Act?
Big AI companies like Meta, Google, Microsoft, and ChatGPT are regulated based on the risk level of their AI systems. They must comply with the EU AI Act's provisions for high-risk AI, ensuring transparency, accountability, and safety standards are met.
What penalties can companies face under the EU AI Act?
Companies can face penalties of up to $35 million or 7% of their annual revenue, whichever is higher, for non-compliance with the EU AI Act.
What is the AI Regulation Agreement done by some EU Countries?
Germany, France, and Italy agreed on AI regulation, focusing on mandatory self-regulation for foundation models and promoting transparency and accountability. The agreement targets all AI providers, including smaller companies, with potential future penalties for non-compliance, while emphasizing the regulation of AI applications rather than the technology itself.
| 2025-06-26T00:00:00 |
https://www.scrut.io/post/eu-artificial-intelligence-ai-act
|
[
{
"date": "2025/06/26",
"position": 83,
"query": "artificial intelligence labor union"
}
] |
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|
Artificial Intelligence
|
Artificial Intelligence
|
https://www.filene.org
|
[] |
As the digital landscape and emerging technology continues to evolve, credit unions must assess the potential benefits of using artificial intelligence…
|
Over the last 50 years we’ve experienced a time of rapid growth and change when it comes to technology and how it has impacted not only our everyday lives but also credit union business models. Recently OpenAI released ChatGPT which according to UBS research, "is the fastest-growing consumer application in history." Generative AI like ChatGPT is a true game changer for the financial services industry when it comes to driving efficiency through talent and process automation, enhanced member experience, while also posing potential security risks.
Depending on where your credit union is in the AI journey, Filene has you covered. We invite you to explore our current research insights and trends on the topic of artificial intelligence to help give you the information you need to implement the right AI strategy at your institution.
| 2025-06-26T00:00:00 |
https://www.filene.org/research/topics/artificial-intelligence
|
[
{
"date": "2025/06/26",
"position": 89,
"query": "artificial intelligence labor union"
}
] |
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] |
|
Expected impact of technology adoption on jobs worldwide ...
|
Expected impact of technology adoption on jobs worldwide 2023-2027
|
https://www.statista.com
|
[
"Ahmed Sherif",
"Jun"
] |
In the next five years, the overall work scenario expects the increasing implementation of new technologies.
|
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World Economic Forum. (April 30, 2023). Expected impact of technology adoption on jobs worldwide from 2023 to 2027 [Graph]. In Statista . Retrieved July 15, 2025, from https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
World Economic Forum. "Expected impact of technology adoption on jobs worldwide from 2023 to 2027." Chart. April 30, 2023. Statista. Accessed July 15, 2025. https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
World Economic Forum. (2023). Expected impact of technology adoption on jobs worldwide from 2023 to 2027 . Statista . Statista Inc.. Accessed: July 15, 2025. https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
World Economic Forum. "Expected Impact of Technology Adoption on Jobs Worldwide from 2023 to 2027." Statista , Statista Inc., 30 Apr 2023, https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
World Economic Forum, Expected impact of technology adoption on jobs worldwide from 2023 to 2027 Statista, https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/ (last visited July 15, 2025)
Expected impact of technology adoption on jobs worldwide from 2023 to 2027 [Graph], World Economic Forum, April 30, 2023. [Online]. Available: https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
| 2025-06-26T00:00:00 |
https://www.statista.com/statistics/1382997/impact-on-jobs-technology-adoption-forecast/
|
[
{
"date": "2025/06/26",
"position": 7,
"query": "job automation statistics"
}
] |
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] |
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Top 16 IT Automation Trends, Predictions & Stats in 2025
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Top 16 IT Automation Trends, Predictions & Stats in 2025
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https://research.aimultiple.com
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Gartner forecasts that 30% of enterprises will automate more than half of their network activities by 2026, highlighting a major shift toward automated network ...
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Great business and IT leaders invest in areas that will be valuable in the future and avoid hyped-up tech that do not deliver results.
Knowing these IT automation trends can help business and IT leaders to unlock the full benefits of IT automation. Certain trends touch every aspect of IT automation and should be considered by all practitioners:
Common IT automation trends
IT automation enables organisations to automate repetitive tasks in IT operations, integrate systems and manage infrastructure by deploying various tools. Figure 1 shows the upward trajectory of interest in IT automation since 2011, pointing out the gradual recognition of its potential its potential to drive operational efficiency.
1- Artificial Intelligence (AI) & machine learning (ML)
90%of enterprise apps and software are expected to use AI by 2025. 61% of ML applications are in the automation market.
AI and ML technologies empower IT automation tools to autonomously learn and adapt, forming intelligent automation solutions. These tools analyze data, make decisions, and automate complex tasks to optimize business processes and enhance system performance. In fact, 50% of enterprises are projected to adopt AI orchestration platforms, a sharp rise from under 10% in 2020.
The ways AI/ML transforms IT automation efforts include:
Automated machine learning ( AutoML ) refers to automated processes that apply ML to real-world problems. AutoML can help data scientists in daily operations and increase efficiency. The autoML market is expected to grow by 43.7% until 2030.
( ) refers to automated processes that apply ML to real-world problems. AutoML can help data scientists in daily operations and increase efficiency. The autoML market is expected to grow by 43.7% until 2030. ML in test automation : ML algorithms and techniques can improve various aspects of test automation, such as test case generation, test case execution, and test data management.
ML algorithms and techniques can improve various aspects of test automation, such as test case generation, test case execution, and test data management. Intelligent document processing (IDP): allows businesses to automate document processes, including unstructured data such as PDFs and images, by leveraging natural language processing (NLP), optical character recognition (OCR), and robotic process automation (RPA).
2- Generative AI (GenAI)
The recent development in generative AI have also boosted interest and adoption in GenAI models. 97% of businesses are interested in developing GenAI models while 72% of businesses have already adopted data and ML pipelines to improve their GenAI strategies. The most preferred GenAI applications include:
Building integrations via API by 52%
Self-service automation for citizen users by 50%
Chat-based service support by 49%
Workflow creation by 45%
Error handling by 39%.
Figure 2: The most preferred GenAI capabilities across IT automation platforms. (Source 3)
3- Agentic AI
By 2028, 33% of enterprise software applications are expected to include agentic AI, allowing 15% of day-to-day work decisions to be made autonomously.
This growing adoption reflects how Agentic AI has been transforming IT automation by allowing systems to operate independently, make real-time decisions, and execute tasks without human intervention. Major players like Oracle and SAP have adopted Agentic AI in their platforms.
These intelligent agents process large volumes of data, detect anomalies, and take corrective actions, greatly enhancing efficiency and responsiveness in IT operations. A primary use of Agentic AI in IT automation is security monitoring and incident response.
For example, AI agents in financial institutions can detect fraudulent transactions, freeze affected accounts, and trigger investigations without manual oversight. Similarly, in IT operations, autonomous AI can monitor system performance, detect failures, and initiate recovery processes before issues escalate.
To maximize the benefits of Agentic AI, IT automation systems must be built on robust workflow engines that can integrate seamlessly across IT and business applications. This ensures AI agents operate with agility, precision, and adaptability, leading to more resilient and intelligent automation frameworks.
Explore more on agentic AI use cases and examples, such as agentic process automation to understand their impact on IT automation.
4- Hyperautomation
Every few years, vendors or analysts come up with new terms for existing concepts when the existing technologies fail to live up to the expectations. Hyperautomation is the new name for automation promoted by Gartner.
It specifically refers to automating end-to-end business processes, including structured and unstructured tasks, by combining AI/ML and multiple automation technologies, such as:
Business process automation (BPA)
RPA
Workflow automation
IT automation.
Almost all IT decision-makers consider automation a critical factor in their digital transformation strategy. Hyperautomation can accelerate digital transformation, ensuring higher efficiency, productivity, and agility levels.
5- No-code/Low-code automation
The low-code statistics show that 70% of new applications will use low-code or no-code technologies by 2025.
Low-code/ no code automation empowers non-technical users to create applications and automated processes without extensive programming knowledge. Low-code automation platforms provide visual interfaces and pre-built components that enable business users to drag and drop elements, define workflows, and configure automation logic.
No-code/ low-code automation helps mainly IT tasks and activities,such as:
Configuration by creating AI-driven workflows for business needs.
by creating AI-driven workflows for business needs. Monitoring and management by overseeing AI systems with a conversational interface.
These tools enable citizen developers to manage digital business initiatives, democratizing and streamlining the automation process with less automation spending.
6- Self-service automation
The widespread adoption of automation across IT, cloud, data, development, and business teams signifies that self-service automation is becoming a mainstream solution. Organizations are integrating automation deeply into their enterprise-wide operational strategies. In fact, 88% of a survey respondents claimed to offer self-service automation to their organisations in this year. (Source 3)
Figure 3: Self-service automation platforms usage by each functional role.(Source 3)
Previously, employees had to wait for IT requests to be completed, leading to delays, missed deadlines, and decreased productivity. However, with self-service automation, end-users can operate independently while IT operations teams retain centralized visibility and control.
7- Centralised management
The popularity of centralized scheduling and orchestration solutions is rising due to their ability to enhance speed, flexibility, scalability, and manageability in hybrid IT environments.
These tools orchestrate applications, infrastructure, and databases, including meta-orchestration of cloud, BPA, WLA, DevOps, and data/ML pipelines.
Below are the rates of automation solutions used for various data operations (e.g. data storage and ETL (Extract, Transform, Load)):
A combination of scripts and custom integrations managed in-house by 67%
An enterprise- grade scheduling / orchestration solution by 53%
Data tool-specific inbuilt job schedulers by 50%
An open-source scheduling/ orchestration solution by 44%.(Source 3)
It’s not just about using centrally managed tools; businesses are also forming centralized management teams. For example:
91% of organizations have a centralized IT automation team, up from 77% in the previous year, indicating substantial growth in adoption.
Centralized CloudOps teams increased to 74% from 53% in a year, enhancing cloud resource management and establishing enterprise-wide standards and innovation.(Source 3)
8- The rise of automation fabrics
Automation fabrics are emerging as a key trend in IT automation. These integrated frameworks unify applications, data, and workflows, eliminating the inefficiencies caused by disconnected automation tools. Instead of managing isolated systems that require constant maintenance, businesses are shifting toward seamless automation ecosystems that enable greater scalability and flexibility.
This shift aligns with the dissolution of automation islands, where enterprises consolidate disparate automation tools into unified platforms. Legacy applications, modern cloud solutions, and AI-driven automation can now coexist within a single, cohesive system. By adopting automation fabrics, businesses can ensure smoother operations, reduce downtime, and minimize technical debt.
Domain-specific IT automation trends
Figure 4: The image shows the most preferred capabilities in an IT automation platform by business leaders. (Source 3)
9- Cloud automation
Cloud automation tools are listed as top investment in this year by 50% of all companies. (Source 3)
IT automation can streamline the orchestration and management of complex cloud environments while scaling and monitoring resources across multiple cloud platforms. It can also enable hybrid cloud automation through data, IT operations and compliance management automation.
This is why other priority tools for this year’s investment include workload automation at 42% and service orchestration and automation platforms (SOAPs) at 38%. Since SOAPs encompass aspects of WLA, workload automation becomes the top priority investment in the IT automation domain, totalling 80%.(Source 3)
10- Container management
The rising adoption of cloud-native infrastructure will affect 75% of large enterprises to leverage container management by 2024. (Source 2) Container management refers to revising, adding, or changing large quantities of software containers.
IT automation streamlines container management by:
Providing infrastructure resource for container environments, including virtual machines and cloud foces.
Automating the creation, testing, and storage of container images, ensuring consistency and reproducibility.
Configuring and orchestrating containerized applications and their dependencies.
Enabling dynamic scaling of container r s based on predefined rules or resource utilization.
Configuring automated monitoring systems to detect issues and trigger alerts or remediation actions.
Automating lifecycle management processes like updating container images, version rollout, and canary/blue-green deployments.
Therefore, increasing container management will lead to adopting digital technologies like IT automation tools.
Resource availability
Workload priorities
Dependencies
Service Level Agreements (SLAs).
11- DevOps pipeline automation
DevOps pipelines refer to processes and strategies to build, test and deploy software applications. DevOps pipeline involves code compilation, testing, artefact creation, deployment, and monitoring.
According to estimates, 35% of this years’ automation efforts are allocated to DevOps automation. (Source 2) These efforts include adopting AIOps , MLOps and DataOps. For instance:
40% of IT teams will adopt AIOps and MLOps in these pipeline stages to reduce downtime by 20% this year. (Source 2)
DataOps has the potential to increase data output by 50%, shorten time to market by 30%, improve productivity by 10%, and lower IT expenses by 10%.
However, businesses need a systematic and automated approach to leverage AI and ML effectively. IT automation or RPA software can help scale these advanced technologies by providing the necessary infrastructure, tools, and workflows. For example, IT automation can:
Provide infrastructure resources required for DevOps pipelines Automate DevOps pipeline stages for a faster and error-free software delivery Enable the automated configuration management of infrastructure and application settings to increase consistency in the work environment. Automate testing activities to identify issues in application development Proactively set up monitoring configurations, define alerting rules, and trigger notifications.
If you are interested in DevOps terms and tools:
12- ITOps management
Organizations can reallocate 30% of ITOps management efforts into continuous engineering, such as automatic remediation and analytics by 2024. (Source 2) One way to reduce these efforts is to automate IT operations management with IT automation tools. Some of these tools include:
13- Workload automation and intelligent job scheduling
IT automation trends indicate that the adoption of workload automation has risen exponentially. For example, survey respondents reported that they:
Embraced workload automation by 82%
Consider automation as the first two to digital transformation by 47%.
Workload automation manages various tasks and workflows and executes batch jobs and business processes across different systems and applications to ensure the timely and accurate processing of workloads.
Despite the increasing interest, 82% of businesses also plan to add or replace a WLA platform since they need:
A more modern solution with more functionality by 67%
Better customer service by 53%
Reduced cost by 49%.(Source 3)
Intelligent job scheduling combines enterprise job scheduling capabilities with artificial intelligence and analytics to optimize task execution based on various factors, such as:
Discover WLA and Job scheduling tools in detail through our:
14- Service orchestration and automation platforms (SOAPs)
In the past, workload automation tools were just one of many isolated automation solutions used within organisations. Now, Service Orchestration and Automation Platforms (SOAPs) offer comprehensive integration across these disparate tools, enabling the orchestration of the entire IT stack from a single central point of control.
Figure 4: Service Orchestration and automation platforms past vs present.(Source 3)
SOAPs orchestrate a variety of specialised tools across diverse focus areas, including cloud management, data and machine learning pipelines, DevOps processes, and infrastructure automation. By the end of 2025, 80% of organizations that currently utilize workload automation are expected to switch to Service Orchestration and Automation Platforms (SOAPs) to orchestrate workloads across both IT and business domains. (Source 3)
15- Security automation
Cybersecurity becomes increasingly demanding for more organizations due to increasing cyberattacks. According to cybersecurity trends, at least 50% of companies will need to check the cybersecurity posture of their potential business partners by 2025.
IT automation can help enhance security automation through automated functionalities like:
Real-time threat detection
Incident response
Vulnerability management
Security policy enforcement.
16. Network Automation
McKinsey’s analysis of over 500 networking software projects reveals that rising software complexity is causing longer delivery times and increased effort despite improved quality. This challenge makes automation essential for managing and optimizing network operations effectively
Gartner forecasts that 30% of enterprises will automate more than half of their network activities by 2026, highlighting a major shift toward automated network management.
Network automation covers key activities such as:
Automated provisioning of network resources
Configuration management and compliance
Performance monitoring and fault management
Security policy enforcement and threat response.
AI has been integrated to improve network security use cases and other network operations like AI network monitoring. Explore what AI network security is and open source network security software.
Further reading
Explore more on IT automation types:
If you believe your business can benefit from IT automation, assess different vendors for each IT automation type by checking out our comprehensive and data-driven lists:
If you need more help, let us know:
| 2025-06-26T00:00:00 |
https://research.aimultiple.com/it-automation-trends/
|
[
{
"date": "2025/06/26",
"position": 25,
"query": "job automation statistics"
}
] |
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-0.030300993472337723
] |
|
Generational divide emerges on workplace AI
|
Generational divide emerges on workplace AI
|
https://workplaceinsight.net
|
[
"Neil Franklin"
] |
As artificial intelligence continues to reshape the world of work, younger employees are taking the lead in adopting and experimenting with new tools.
|
As artificial intelligence continues to reshape the world of work, younger employees are taking the lead in adopting and experimenting with new tools. According to a new survey by UKG and The Harris Poll, Gen Z workers are not only the most enthusiastic users of workplace AI but also the most likely to be self-taught. The research highlights a growing generational divide. While 84 percent of U.S. employees overall say they want AI to handle workplace processes, Gen Z – those aged 18 to 28 – are particularly eager. Ninety percent believe AI will save them time at work, with nearly a third expecting to reclaim up to 90 minutes a day. They’re also the most likely to have taken the initiative to learn AI skills independently, with 70 percent teaching themselves the tools they use.
Despite this enthusiasm, nearly half of Gen Z employees (49 percent) say their managers don’t understand the benefits of AI. That perception reflects a broader disconnect: just over a quarter of senior leaders in a 2023 UKG study believed Gen Z had the best grasp of AI at work. The new data suggests otherwise – and points to an opportunity for organisations to bridge that gap.
Across all generations, employees agree on one key principle: AI is a tool, not a colleague. Almost nine in ten (89 percent) say AI should support human work rather than replace it. Most want to see it used for repetitive, data-heavy, or error-prone tasks such as checking pay accuracy, creating schedules, or summarising policies – not for roles requiring judgment or empathy.
UKG’s chief product officer, Suresh Vittal, argues that this moment mirrors past technological shifts. “Every few decades, something changes everything – electricity, mobile phones, the internet,” he says. “AI is the next leap. The sooner organisations act, the more they’ll benefit.”
The research shows that while Gen Z may be the vanguard of AI adoption, their preferences align with wider employee sentiment. The majority want AI to enhance, not upend, the way they work. By learning from younger employees and promoting wider digital literacy, employers can use AI to free up time for more strategic and creative work – across every generation.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://workplaceinsight.net/generational-divide-emerges-on-workplace-ai/
|
[
{
"date": "2025/06/26",
"position": 76,
"query": "workplace AI adoption"
}
] |
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] |
Cybersecurity
|
Cybersecurity
|
https://www.fda.gov
|
[] |
This guidance provides recommendations on medical device cybersecurity considerations and what information to include in premarket submissions.
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On December 29, 2022, the Consolidated Appropriations Act, 2023 ("Omnibus") was signed into law. Section 3305 of the Omnibus -- "Ensuring Cybersecurity of Medical Devices" -- amended the Federal Food, Drug, and Cosmetic Act (FD&C Act) by adding section 524B, Ensuring Cybersecurity of Devices (section 3305). The Omnibus states that the amendments to the FD&C Act shall take effect 90 days after the enactment of this Act, on March 29, 2023.
To mark the FDA cyber team's 10-year anniversary, here is an infographic showing some of the ways they have contributed to patient safety over the past decade. Download (PDF - 252 KB)
June 27, 2025 - The FDA issued the final guidance Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. This guidance adds Section VII to address FDA’s recommendations regarding section 524B of the FD&C Act for cyber devices. This document supersedes the final guidance "Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions," issues September 27, 2023.
September 27, 2023 - The FDA issued the final guidance Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. This guidance provides recommendations on medical device cybersecurity considerations and what information to include in premarket submissions.
May 1, 2023 - The FDA has launched its third video: Tips for Health Care Facilities: Cybersecurity Incident Preparedness and Response. The video focuses on how to prepare for a cybersecurity event and help ensure patient safety during a prolonged cybersecurity event.
For more information about cybersecurity in medical devices, we recommend you consult our guidance documents and read Cybersecurity in Medical Devices Frequently Asked Questions (FAQs).
All legally-marketed medical devices have benefits and risks. The FDA clears, authorizes, and approves devices to be marketed when there is a reasonable assurance that the devices are safe and effective for their intended use.
Medical devices are increasingly connected to the Internet, hospital networks, and other medical devices to provide features that improve health care and increase the ability of health care providers to treat patients. These same features also increase potential cybersecurity risks. Medical devices, like other computer systems, can be vulnerable to security breaches, potentially impacting the safety and effectiveness of the device.
Threats and vulnerabilities cannot be eliminated and reducing cybersecurity risks is especially challenging. The health care environment is complex, and manufacturers, hospitals, and facilities must work together to manage cybersecurity risks.
On This Page
Cybersecurity News and General Resources
October 24, 2024 - New White Paper for Medical Device Manufacturers: Data Normalization Challenges and Mitigations in Software Bill of Materials (SBOM) Processing
October 12, 2024 - New Journal Article: Digital Certificate Management for Medical Devices in the Journal of Clinical Engineering
Legacy Medical Device Report: Next Steps Toward Managing Legacy Medical Device Cybersecurity Risks is a MITRE report that outlines practical approaches and recommendations that build on previous work and can further drive sector-wide legacy device cyber risk management efforts.
Incident Response Playbook: Medical Device Cybersecurity Regional Incident Preparedness and Response Playbook is a playbook that describes the types of readiness activities that will enable health delivery organizations (HDOs) to be better prepared for a cybersecurity incident involving their medical devices and gives product developers more opportunity to address the potential for large scale, multi-patient impacts that may raise patient safety concerns.
Threat Modeling Playbook: Playbook for Threat Modeling Medical Devices is an educational resource that discusses best practices for understanding basic threat modeling concepts and processes, and how to apply them to medical devices.
Speaker series: The joint FDA and University of California San Francisco-Stanford Center of Excellence in Regulatory Science and Innovation (CERSI) is a speaker series of virtual one-hour lectures to promote regulatory science –including innovative research, education, and scientific exchange. It takes place monthly, on the third Thursday of each month from noon to 1:00 p.m. EST
Videos:
Tips for Health Care Facilities – Cybersecurity Incident Preparedness and Response focuses on how to prepare for a cybersecurity event and helps ensure patient safety during a prolonged cybersecurity event.
Tips for Clinicians - Keeping Your Patients' Connected Medical Devices Safe is aimed to increase clinician comfort as they discuss cybersecurity in connected medical devices with patients.
Cybersecurity Awareness for Connected Medical Devices offers practical steps patients can take to protect their device and personal information.
Paper: Best Practices for Communicating Cybersecurity Vulnerabilities for Patients outlines information for the FDA, federal partners, and industry stakeholders to help thoughtfully inform patients and the public about cybersecurity vulnerabilities.
Mitigating Cybersecurity Risks
Medical device manufacturers (MDMs) and health care delivery organizations (HDOs) should take steps to ensure appropriate safeguards are in place.
Medical device manufacturers (MDMs) are responsible for remaining vigilant about identifying risks and hazards associated with their medical devices, including risks related to cybersecurity.
are responsible for remaining vigilant about identifying risks and hazards associated with their medical devices, including risks related to cybersecurity. Health care delivery organizations (HDOs) should evaluate their network security and protect their hospital systems.
should evaluate their network security and protect their hospital systems. Both MDMs and HDOs are responsible for putting appropriate mitigations in place to address patient safety risks and ensure proper device performance.
FDA Fact Sheet:
THE FDA'S ROLE IN MEDICAL DEVICE CYBERSECURITY
Dispelling Myths and Understanding Download the Fact Sheet (PDF - 175kb)
Cybersecurity Reports and White Papers
Date Title Description 11/30/2021 Playbook for Threat Modeling Medical Devices The Playbook for Threat Modeling is an educational resource for the medical device sector to learn how to effectively threat model. Many private and public sector organizations recommend threat modeling to help manage and respond to cyber threats and risks. 10/01/2021 Best Practices for Communicating Cybersecurity Vulnerabilities to Patients This paper outlines information for the FDA, federal partners, and industry stakeholders to help thoughtfully inform patients and the public about cybersecurity vulnerabilities. 05/10/2021 NIST Request on Presidential Executive Order: Comments Submitted by the FDA (PDF - 4MB) This paper provides relevant responses to the National Institute of Standards and Technology (NIST) call for position papers to fulfill the President's Executive Order (EO) on Improving the Cybersecurity of the Federal Government (EO 14028), issued on May 12, 2021. It highlights existing FDA guidance documents and international standards on the science of cybersecurity for the premarket review of medical devices and post-market surveillance of cybersecurity incidents and vulnerabilities. 06/17/2021 Strengthening Cybersecurity Practices Associated with Servicing of Medical Devices: Challenges and Opportunities The FDA released this discussion paper to consider cybersecurity issues that are unique to the servicing of medical devices and to seek input on these topics.
Cybersecurity Guidances
Date Title Description 6/27/2025 Final Guidance: Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions This guidance provides FDA’s recommendations to industry regarding cybersecurity device design, labeling, and the documentation that FDA recommends be included in premarket submissions for devices with cybersecurity risk. Additionally, this guidance has been updated to address FDA’s recommendations regarding section 524B of the FD&C Act for cyber devices (see Section VII). This document supersedes the guidance, of the same title, issued on September 27, 2023. 12/27/2016 Final Guidance: Postmarket Management of Cybersecurity in Medical Devices Provides recommendations to industry for structured and comprehensive management of postmarket cybersecurity vulnerabilities for marketed and distributed medical devices throughout the product lifecycle. 01/14/2005 Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software A growing number of medical devices are designed to be connected to computer networks. Many of these networked medical devices incorporate off-the-shelf software that is vulnerable to cybersecurity threats such as viruses and worms. These vulnerabilities may represent a risk to the safe and effective operation of networked medical devices and typically require an ongoing maintenance effort throughout the product life cycle to assure an adequate degree of protection. The FDA issued guidance to clarify how existing regulations, including the Quality System (QS) Regulation, apply to such cybersecurity maintenance activities.
Cybersecurity Safety Communications and Other Alerts
In each of the following cases, the FDA is not aware of any patient injuries or deaths associated with cybersecurity incidents, nor are we aware that any specific devices or systems in clinical use have been purposely targeted. However, left unpatched or otherwise mitigated, these vulnerabilities could allow unauthorized users to access, control, and issue commands to compromised devices, potentially leading to patient harm. Health care facilities can reduce the risk of unauthorized access by implementing recommendations in the safety communications and alerts listed below.
To receive safety communications on medical devices, including cybersecurity-related safety communications, subscribe to our Medical Devices Safety and Recalls emails.
Reporting Cybersecurity Issues to the FDA
As a part of our surveillance of medical devices on the market, the FDA monitors reports of cybersecurity issues with devices.
Manufacturers, Importers, and Device User Facilities: See Medical Device Reporting (MDR) for details on mandatory reporting requirements.
See Medical Device Reporting (MDR) for details on mandatory reporting requirements. Health care providers: Use the MedWatch voluntary report form for health professionals (Form 3500) to report a cybersecurity issue with a medical device.
Use the MedWatch voluntary report form for health professionals (Form 3500) to report a cybersecurity issue with a medical device. Patients and caregivers: Use the MedWatch voluntary report form for consumers/patients (Form 3500B) to report a cybersecurity issue with a medical device.
Memoranda of Understanding on Cybersecurity in Medical Devices
The table below provides an overview of the cybersecurity information sharing agreements that the FDA has with various stakeholders to help us further protect and promote the public health.
MOU/MOA Parties Description MOU 225-18-028 National Health Information Sharing & Analysis Center, Inc. (NHISAC) and MediSAO (information sharing analysis organization) The goal of these Information Sharing and Analysis Organizations (ISAOs) is to provide manufacturers with the opportunity to share information about potential vulnerabilities and emerging threats with the FDA and to help manufactures protect patients by addressing those issues earlier. MOU 225-18-030 Health Information Sharing & Analysis Center, Inc. (H-ISAC), formerly known as the National Health Information Sharing & Analysis Center, Inc. (NH-ISAC), and Sensato Critical Infrastructure ISAO The goal of these ISAOs is to provide manufacturers with the opportunity to share information about potential vulnerabilities and emerging threats with the FDA and to help manufacturers protect patients by addressing those issues earlier. MOA: DHS-FDA Medical Device Cybersecurity Collaboration Department of Homeland Security (DHS) The agreement implements a framework for greater coordination and information sharing about potential or confirmed medical device cybersecurity vulnerabilities and threats. This collaboration between the two agencies is intended to lead to better and more timely responses to potential threats to patient safety.
Workshops and Webinars on Cybersecurity
Other Collaborations on Cybersecurity in Medical Devices
International Medical Device Regulators Forum (IMDRF): The FDA serves as a co-chair of the IMDRF working group tasked with drafting a global medical device cybersecurity guide. The purpose of the guide is to promote a globally harmonized approach to medical device cybersecurity that at a fundamental level ensures the safety and performance of medical devices while encouraging innovation. The guide is thus intended to provide medical device cybersecurity advice for stakeholders across the device lifecycle on topics including but not limited to medical device cybersecurity terminology, stakeholders' shared responsibility, and information sharing. The finalized guide was published on March 18, 2020.
Healthcare and Public Health Sector Coordinating Council (HSCC): The FDA serves as a co-chair of the Government Coordinating Council (GCC) for the HPH HSCC. Specifically, this is a public-private partnership among healthcare industry leaders and the government to address the most pressing security and resiliency challenges to the healthcare sector as a whole including cybersecurity. As a co-chair of a task group within the HSCC cybersecurity working group, the FDA participated in the development of the Medical Device and Health IT Joint Security Plan (JSP). The JSP is a total product lifecycle reference guide to developing, deploying and supporting cybersecure technology solutions in the health care environment.
MITRE and MDIC Threat Modeling Bootcamps: In 2020, the FDA funded a series of threat modeling bootcamps, developed and hosted by MDIC and MITRE in partnership, to highlight the importance of threat modeling during the development, deployment, and maintenance of connected medical devices, and to provide training to industry representatives on threat modeling best practices and strategies. In addition to the bootcamps, the FDA has funded MDIC and MITRE to produce the Playbook for Threat Modeling Medical Devices, a threat modeling playbook that stakeholders throughout the sector may use to learn more about threat modeling best practices, and how to incorporate them into their own organizations and processes.
MITRE Corporation: In November 15, 2022, in collaboration with MITRE, the FDA updated the Medical Device Cybersecurity Regional Incident Preparedness and Response Playbook, a resource to help health care organizations prepare for cybersecurity incidents. The playbook focuses on preparedness and response for medical device cybersecurity issues that impact device functions.
Updates to the playbook include:
Emphasizing the need to have a diverse team participating in cybersecurity preparedness and response exercises – including clinicians, health care technology management professionals, IT, emergency response, and risk management and facilities staff.
Highlighting considerations for widespread impacts and extended downtimes during cybersecurity incidents which benefit from the use of regional response models and partners.
Adding a resource appendix making it easier to find tools, references, and other resources to help health care organizations prepare for and respond to medical device cybersecurity incidents (including ransomware).
A Playbook Quick Start Companion Guide is also available. The guide is a shorter version of the playbook that discusses preparedness and response activities health care organizations might want to start with as they are developing their medical device incident response program.
Medical Device Innovation Consortium (MDIC): In September 2018, as a member of an MDIC Steering Committee, the FDA supported the development of an MDIC's report Medical Device Cybersecurity Report: Advancing Coordinated Vulnerability Disclosure. The report encourages the adoption of coordinated vulnerability disclosure (CVD) policies by medical device manufacturers (MDMs) in an effort to promote medical device cybersecurity and patient safety. CVD policies establish formalized processes for obtaining cybersecurity vulnerability information, assessing vulnerabilities, developing remediation strategies, and disclosing the existence of vulnerabilities and remediation approaches to various stakeholders—often including peer companies, customers, government regulators, cybersecurity information sharing organizations, and the public. This report addresses the importance of CVD policies for MDMs and stakeholders across the medical device ecosystem.
FDA Cybersecurity News Releases
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.fda.gov/medical-devices/digital-health-center-excellence/cybersecurity
|
[
{
"date": "2025/01/30",
"position": 9,
"query": "technology regulation healthcare workers"
}
] |
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] |
When AI will come to kill whitecollar jobs?
|
When AI will come to kill whitecollar jobs?
|
https://medium.com
|
[
"Alexey Rubtsov"
] |
AI is reshaping rather than simply “killing” white-collar jobs, but the transformation is creating both opportunities and significant challenges that require ...
|
When AI will come to kill whitecollar jobs? Alexey Rubtsov 2 min read · May 27, 2025 -- Listen Share
There are many opinions on when or if AI will eliminate white-collar jobs. I personally think it’s not going to happen to all jobs.
AI is reshaping rather than simply “killing” white-collar jobs, but the transformation is creating both opportunities and significant challenges that require careful management.
The Nuanced Reality
Current AI technology excels at automating routine cognitive tasks — basic legal research, financial analysis, data entry, and standardized content creation. However, it struggles with complex reasoning, emotional intelligence, creative problem-solving, and strategic thinking that define many high-value white-collar roles.
Even when you cannot use AI at work due to restrictions or policies, you can explore it in your personal life if you are eager to try. Everyone can benefit, and businesses won’t miss the opportunity to achieve more with a smaller workforce.
Three-Tier Impact Pattern
1. Jobs at Risk (Next 3–7 years)
Routine analytical work (basic accounting, paralegal research, junior analyst roles)
Standardized content creation (basic journalism, marketing copy, technical documentation). And we already dying from precise but boring news reports
Middle-management coordination tasks that can be systematized. To be honest, nobody will miss those tasks.
2. Jobs Being Enhanced
Creative professionals using AI tools for productivity
Strategic roles requiring human judgment and relationship-building
Technical positions that involve human-AI collaboration, including software development
3. Jobs Likely to Remain Resilient
Leadership and executive functions
Complex client relationships and sales
Creative strategy and innovation
Jobs requiring physical presence and emotional intelligence
The Geographic and Social Divide
The benefits of AI transformation are concentrating in specific regions and worker classes. “Superstar cities” with strong tech ecosystems are capturing most of the productivity gains, while workers in other regions face displacement without comparable opportunities for transition. This creates a risk of extreme polarization between AI-enhanced “superstars” and displaced workers.
It’s new war. I wrote it twice. And this is third time.
Historical Context with Modern Urgency
While technological disruptions have historically created new job categories over time, the speed and scope of AI advancement may outpace traditional adaptation mechanisms. Unlike previous industrial revolutions that primarily affected manual labor, AI directly targets cognitive work that has been the foundation of middle-class employment.
Critical Success Factors
The outcome depends heavily on:
Institutional response : Education systems, retraining programs, and social safety nets
: Education systems, retraining programs, and social safety nets Policy intervention : Managing geographic inequality and supporting worker transitions
: Managing geographic inequality and supporting worker transitions Corporate responsibility: How companies implement AI — as a tool for augmentation versus replacement
I doubt institutions will keep up. The wave is too strong, and they’ll only understand what’s happening after it’s over. However, you can be ready. No, I don’t believe LLMs are smarter than most humans in the usual sense of “smart.” While they’re intelligent in their specific tasks, they’re tireless and follow instructions perfectly. Honestly, that’s more than you can expect from most people.
The bottom line: AI won’t simply “kill” white-collar jobs, but it will fundamentally restructure the white-collar landscape in ways that could either enhance human potential or exacerbate inequality, depending on how thoughtfully we manage the transition.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://medium.com/@alekseyrubtsov/when-ai-will-come-to-kill-whitecollar-jobs-2272e72b5723
|
[
{
"date": "2025/04/01",
"position": 84,
"query": "artificial intelligence blue collar workers"
},
{
"date": "2025/04/01",
"position": 50,
"query": "artificial intelligence white collar workers"
}
] |
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] |
15 HR Digital Transformation Use Cases in 2025
|
Top 15 HR Automation Case Study in Different Sectors ['25]
|
https://research.aimultiple.com
|
[] |
Companies using tools like AI-powered ATS platforms and employee chatbots are proving that smart digital strategies not only shorten hiring ...
|
Disruptive technologies such as artificial intelligence (AI) and robotic process automation (RPA) can dramatically reduce the time spent on repetitive and non-strategic HR tasks. However, according to PwC’s HR technology survey, one of the top reasons preventing HR leaders from adopting these technologies is the lack of compelling use cases.
Find out 15 HR automation case studies from companies that have implemented intelligent automation and tips for selecting a vendor:
Examples of HR automation case studies
Below is a list of HR automation case studies and examples that we’ve compiled from different vendors:
Updated at 07-04-2025 Customer Application Area/
Use Case Benefit Type Customer Industry Customer Country A multinational business consultant Recruitment Time saving Professional Services Global An electronics supply manufacturer Access management Time saving Manufacturing Global BAE Systems Payroll processing Time saving Aerospace United Kingdom Banque Internationale à Luxembourg Employee assistance Efficiency increase Financial Services Luxembourg Central England Co-operative Regulatory compliance Time saving Retail United Kingdom Coca-Cola HR audit Time saving Food and Beverages Global Dell Recruitment Efficiency increase Information Technology Global Health Service Executive (HSE) Employee vetting Time saving Healthcare Ireland IBM Employee promotions Time saving Information Technology Global Lenovo Payroll processing Time saving Manufacturing Global NGA Human Resources Access management Time saving Human Resources Services United Kingdom Provident Financial Romania Recruitment Time saving Financial Services Romania Santander Onboarding Time saving Financial Services Spain SSCL Recruitment Time saving Public Sector United Kingdom Swedish Public Employment Service Employee administration Efficiency increase Public Sector Sweden
1. A Global Consulting Firm
Problem
A multinational business consulting firm faced delays in its recruitment process. Long handling times slowed hiring, affecting productivity and responsiveness. The company needed a faster way to process applications and fill positions.
Solution
The firm partnered with a HR technology company to streamline recruitment.
Results
The average handling time in the recruitment process dropped by 55%. This led to quicker hiring and improved turnaround times, helping the firm fill roles more efficiently.
2. An electronics manufacturer
Problem
An electronics supply manufacturer struggled with slow and manual access management. Granting system access required tedious back-end processing. The company needed a faster, automated solution to save time.
Solution
The manufacturer partnered with a business provides HR automation technologies to automate access management. The project streamlined back-end processing and fully automated ticket generation.
Results
The company saved 5,000 hours across production systems. Automating ticket generation saved an additional 124 hours annually. The new system significantly reduced manual effort and improved productivity.
3. BAE Systems
Problem
BAE Systems faced slow and manual payroll processing. Uploading payroll data took too long, reducing performance. The company needed a faster, automated solution.
Solution
BAE Systems partnered with HR automation vendor to automate payroll processing. The project streamlined data uploads and reduced manual effort.
Results
Payroll data upload became 7 times faster. The company also automated 50 processes, saving over 2,600 hours annually. This improved productivity and reduced workload.
4. Banque Internationale à Luxembourg
Problem
Banque Internationale à Luxembourg needed a more efficient way to handle employee assistance tasks. Manual processes were slow and resource-intensive. The bank sought automation to improve efficiency.
Solution
The bank partnered with a HR automation platform to deploy 49 digital workers. These digital workers automated 60 processes across the organization.
Results
Automation streamlined operations and increased performance. The project delivered a 100% return on investment, reducing manual workload and improving productivity.
5. Central England Co-operative
Problem
Central England Co-operative needed to comply with UK regulations on employee record retention. After seven years, old records had to be deleted. Manually processing these records was time-consuming and inefficient.
Solution
The company partnered with HR automation platform to automate record management. Digital workers were deployed to handle the deletion process.
Results
Automation cleared over 10,000 old employee records. This saved time and allowed employees to focus on more productive tasks, improving overall performance.
6. Coca-Cola
Problem
Coca-Cola’s HR audit process was time-consuming and limited in scope. Manual work restricted the amount of data that could be reviewed. The company needed automation to improve performance.
Solution
Coca-Cola partnered with the HR audit automation platform to automate HR audits. More than 50 processes were automated across multiple SAP systems.
Results
Automation added 16 extra hours to every workday. The company also increased its audit coverage to 100%, improving accuracy and compliance.
7. Dell
Problem
Dell’s HR team spent too much time on manual tasks. Processes like recruiter assignments, onboarding reminders, and status updates were slow and repetitive. The company needed automation to improve productivity.
Solution
Dell partnered with a HR automation vendor to automate 30 HR processes. This removed transactional work and streamlined end-to-end operations.
Results
HR productivity increased by up to 85%. Automation reduced manual effort, allowing the team to focus on more strategic tasks.
8. Health Service Executive (HSE)
Problem
Health Service Executive (HSE) needed to vet 20,000 new employees annually. The manual process took five days per employee, causing delays in hiring. The organization needed a faster solution.
Solution
HSE partnered with a human resource automation vendor to automate the vetting process using Robotic Process Automation (RPA). This reduced manual effort and sped up processing.
Results
Automation cut vetting time from five days to one hour. This streamlined hiring and reduced delays in onboarding new employees.
9. IBM
Problem
IBM’s HR team manually collected and processed data for 15,000–17,000 employees during promotions. This was a time-consuming and labor-intensive task, delaying the process.
Solution
IBM implemented a HR automation software to automate promotion-related tasks. This reduced manual effort.
Results
Automation saved 12,000 hours in one quarter. The HR team also cut processing time from 10 weeks to 5 weeks, ensuring faster and fairer promotions.
10. Lenovo
Problem
Lenovo’s HR team handled manual and time-consuming tasks for income tax declarations, expense reimbursements, and payroll accounting. This process was slow and inefficient.
Solution
Lenovo partnered with a HR management system to automate these tasks. This reduced manual effort and improved payroll processing speed.
Results
Automation saved the HR team 6,000 hours per year. Employees also submitted expenses 90% faster than before, streamlining financial operations.
11. NGA Human Resources
Problem
NGA Human Resources relied on manual access management, which was slow and required many virtual machines.
Solution
The company implemented a HR management software to automate 23 processes, reducing manual effort.
Results
Automation cut the need for virtual machines by 50%, reducing costs.
12. Provident Financial Romania
Problem
Provident Financial Romania faced time-consuming recruitment processes in HR and Finance.
Solution
The company implemented robots automating business processes to automate 10 business processes in HR and Finance.
Results
Automation saved 10,000 hours each year, reducing manual effort.
13. Santander
Problem
Santander’s employee onboarding process took 6 weeks. With 50 to 100 new employees joining each month, this became a significant bottleneck.
Solution
The company implemented HR automation to streamline the onboarding process.
Results
Automation reduced onboarding time from 6 weeks to just 2 days, improving efficiency and saving valuable time.
14. Shared Services Connected Ltd (SSCL)
Problem
SSCL’s recruitment process was slow and manual. All applicants had to be processed through a central system, which required significant manual work. This caused delays in the hiring process.
Solution
SSCL implemented HR automation to extract data from key systems. The robot now generates reports and provides them to the recruitment team each morning.
Results
6 recruitment processes were automated within 3 weeks, speeding up the process and reducing manual work.
15. Swedish Public Employment Service
Problem
Before automation, the HR team could only manage 2% of employee tickets. This resulted in a slow and inefficient ticket handling process.
Solution
HR automation system was implemented to handle the employee ticket process. This allowed the HR team to manage all incoming tickets efficiently.
Results
The HR team now handles 100% of employee tickets, a significant increase from 2% before automation. This has greatly improved efficiency and response times.
Getting started with HR automation
You probably use several software systems for managing your HR processes. That forces you to consider the following factors when selecting an intelligent automation solution for your business:
Integration capabilities with all existing applications in your HR software stack,
Scalability,
Cognitive capabilities that you need, such as document processing or conversational AI.
For more on HR tasks
| 2025-06-26T00:00:00 |
https://research.aimultiple.com/digital-transformation-in-hr/
|
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] |
|
Earnings (CPS) : U.S. Bureau of Labor Statistics
|
Earnings (CPS)
|
https://www.bls.gov
|
[] |
This page contains information on the earnings of employed persons derived from the Current Population Survey (CPS).
|
Earnings (CPS)
This page contains information on the earnings of employed persons derived from the Current Population Survey (CPS). Data on employed and unemployed persons, hours of work, and other demographic and labor force characteristics also are available.
Earnings
Data measure usual hourly and weekly earnings of wage and salary workers. All self-employed persons are excluded, regardless of whether their businesses are incorporated. Data represent earnings before taxes and other deductions and include any overtime pay, commissions, or tips usually received. The earnings data are collected from one-fourth of the CPS total sample of approximately 60,000 households. Data are published quarterly. Learn more about CPS earnings concepts and definitions.
Earnings by demographics
Earnings data are available by demographic characteristics such as age, sex, race, and Hispanic or Latino ethnicity. See also Earnings by education and Employment and unemployment by demographics.
Earnings by education
Data measure usual hourly and weekly earnings of wage and salary workers 25 years and over by educational attainment. See also Employment and unemployment by educational attainment.
Hourly-paid workers
Data pertain only to workers who are paid hourly rates; salaried and other workers not paid by the hour are excluded. Hourly-paid workers make up approximately three-fifths of all wage and salary workers. See also Minimum wage workers.
Database: Retrieve historical data series
Statistics on hourly-paid workers and median hourly earnings are available in selected tables of the annual report, Highlights of Women's Earnings
Minimum wage workers
Statistics on hourly-paid workers with earnings at or below the prevailing Federal minimum wage.
Occupation and industry
Data measure usual hourly and weekly earnings of wage and salary workers by occupation and by industry. See also Employment by occupation and industry.
Union earnings
Median weekly earnings for union and non-union full-time wage and salary workers are published annually. Occupation and industry detail is available and, additionally, demographic characteristics such as age, sex, race, and Hispanic ethnicity. See also Union members.
Working poor
Annual report on persons who, during the year, spent 27 weeks or more in the labor force (working or looking for work), but whose incomes still fell below the official poverty level.
| 2025-06-26T00:00:00 |
https://www.bls.gov/cps/earnings.htm
|
[
{
"date": "2025/06/26",
"position": 5,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 3,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 6,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 3,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 5,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 4,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 3,
"query": "labor regulation wage distribution"
},
{
"date": "2025/06/26",
"position": 5,
"query": "labor regulation wage distribution"
}
] |
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] |
|
States Tackle Shifting Financial Landscape for Digital Equity
|
States Tackle Shifting Financial Landscape for Digital Equity
|
https://www.govtech.com
|
[
"Julia Edinger Is A Senior Staff Writer For"
] |
New federal funding cuts are impacting plans for high-speed Internet and digital inclusion work, leaving state broadband directors to explore alternate ...
|
Julia Edinger is a senior staff writer for Government Technology. She has a bachelor's degree in English from the University of Toledo and has since worked in publishing and media. She's currently located in Ohio.
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.govtech.com/spotlight/states-address-shifting-financial-landscape-for-digital-equity
|
[
{
"date": "2025/06/26",
"position": 77,
"query": "technology regulation equity in the workplace"
},
{
"date": "2025/06/26",
"position": 78,
"query": "technology regulation equity in the workplace"
},
{
"date": "2025/06/26",
"position": 78,
"query": "technology regulation equity in the workplace"
}
] |
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Slack: Why Daily AI Use Fuels Creativity
|
Why Daily AI Use Is Fueling More Creative and Fulfilled Workers
|
https://www.salesforce.com
|
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Daily AI usage has surged by 233%, with 60% of desk workers now incorporating AI and 40% utilizing AI agents, according to Slack's latest Workforce Index.
|
Daily AI usage has surged by 233%, with 60% of desk workers now incorporating AI and 40% utilizing AI agents, according to Slack’s latest Workforce Index. This new research also reveals an even more impactful insight: How people are using AI and its significant influence on creativity, collaboration, and job satisfaction. Lucas Puente, Head of Research at Slack who led this study, offers insights into several critical aspects of AI adoption. He discusses how AI is fueling a “virtuous cycle” of productivity and trust, what sets daily AI users (known as power users) apart, and the ways AI is reshaping how people learn, lead, and thrive professionally. Q: What’s behind the massive surge in AI use at work — and how are workers benefiting? We’re seeing a real inflection point. The fact that overall usage has jumped 50% since November 2024, and daily use has more than doubled, tells us this isn’t just hype or experimentation — it’s behavior change. AI is moving from the sidelines into people’s everyday workflows. And that’s where the real benefits emerge. Sure, AI still helps automate repetitive tasks, but more and more people are using the technology to unlock creativity, accelerate decision-making, and stretch into new types of work. We’re seeing folks doing things that used to take hours — like building presentations, analyzing complex data, brainstorming content — now done in minutes with AI as a creative partner.
A New Definition of Smart: Agentic AI’s Impact on Critical Thinking. GO DEEPER
Q: Why is daily AI usage so much more powerful than just dabbling?
Power users are seeing the biggest lift. Those using AI daily are 64% more likely to say they’re productive and 58% more likely to feel focused. Even more telling? They say they’re less burned out and more satisfied overall.
Consistent use unlocks exponential gains. You’re not just saving time — you’re reallocating it. That means more bandwidth for deep work, collaboration, and strategic thinking. And when AI becomes part of your everyday workflow, those benefits compound fast.
For daily users, AI isn’t just improving efficiency — it’s creating a better experience at work.
Consistent use unlocks exponential gains. You’re not just saving time — you’re reallocating it. That means more bandwidth for deep work, collaboration, and strategic thinking.
Q: Slack’s data show that the more people use AI, the more they trust it. What’s driving that flywheel effect?
As people use AI more frequently, they learn to leverage its strengths. That clarity leads to better results and builds user confidence. When you set AI up for success — just like a human teammate — it performs better, saving time and increasing productivity. This creates a powerful trust-usage flywheel: Greater trust inspires more sophisticated use, which in turn delivers real business impact.
For daily users, AI isn’t just improving efficiency — it’s creating a better experience at work
Q: Let’s talk about who’s leading this shift. Your research shows millennials outpacing Gen Z in AI adoption. Why is that?
While Gen Z is often seen as the “AI native” generation, it’s millennials who are using AI the most at work. They’re more likely to hold senior roles, and our research shows a clear correlation between seniority and AI usage. The higher up you are, the more likely you are to use AI to drive impact. They’re also hearing from leadership that AI is a priority, and they’re running with it.
Gen Z, on the other hand, tends to use AI more in personal projects. At work, they’re still early in their careers, and many are waiting for permission to use the technology. They want clear guardrails around what’s okay. That’s an opportunity for companies to step up: Give younger employees the right tools, connect those tools to real workflows and data, and make expectations around AI usage explicit. Doing so not only empowers Gen Z to contribute more confidently and creatively, but also helps shape a future-ready workforce that sees AI as a natural extension of their capabilities.
Building the AI Powered Workforce: Inside Salesforce’s Strategy for Agentic AI Adoption. READ THE STORY
Q: What can companies do to move from pilot to true AI adoption?
Encourage AI use as opposed to just permitting it. When workers are encouraged to use AI, they’re more than 200% more likely to become daily users. And that matters, because as we’ve shared, daily use is where the real benefits emerge.
The other key is embedding AI directly into the tools people already use every day. If AI is off to the side in some separate app, it’s not going to drive behavior change. But if it’s right there in your workflow — like inside Slack, where you’re already communicating, planning, and building — AI becomes a natural part of how work gets done. And once it’s part of the daily rhythm, that’s when you see the flywheel kick in: more trust, more productivity, more fulfillment — and ultimately, a workforce that’s not just using AI, but thriving because of it.
Go deeper:
| 2025-06-26T00:00:00 |
2025/06/26
|
https://www.salesforce.com/news/stories/daily-ai-use-fuels-creativity/
|
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"position": 4,
"query": "artificial intelligence creative workers"
},
{
"date": "2025/06/26",
"position": 1,
"query": "digital transformation creative workers"
}
] |
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