**Now decides next:**
Insights from the leading
edge of generative AI adoption

Deloitte’s State of Generative AI in the Enterprise
Quarter one report

**January 2024**


-----

Table of contents

Foreword

Introduction

**Now: Key findings**

**1** **Excitement about generative AI remains** **4**

high, and transformative impacts are

expected in the next three years.

**2** Many leaders are confident about their  **5**

organization’s generative AI expertise.

**3** **Organizations that report very high** **6**

expertise in generative AI tend to feel more

positive about it—but also more pressured

**7**

and threatened.

**8**

**Next: Looking ahead**

Authorship & Acknowledgments


**Current generative AI efforts remain more**

focused on efficiency, productivity and cost

reduction than on innovation and growth.

**Most organizations are still primarily relying**

on off-the-shelf generative AI solutions.

**Talent, governance and risk are critical areas**

where generative AI preparedness is lacking.

**Leaders see significant societal impacts on**
the horizon.

**Leaders are looking for more regulation and**

collaboration globally.


About the Deloitte AI Institute
About the Deloitte Center for Integrated Research
About the Deloitte Center for Technology, Media & Telecommunications

Methodology


-----

Foreword
Now decides next

The arrival of generative AI heralds disruption and

opportunity across industries. Organizations are

exploring how generative AI can be used to unlock

business value, supercharge efficiency and productivity,

and open the door to entirely new products, services

and business models. As business leaders contend

with this new technology and make decisions about the

future of the enterprise with generative AI, it is helpful

to keep one’s finger on the pulse of adoption.

To that end, The State of Generative AI in the Enterprise:

_Now decides next, captures the sentiments of 2,835_

business and technology leaders involved in piloting or

implementing generative AI in their organizations. In this

inaugural release of the quarterly report series, leaders

indicated persistent excitement for using generative

AI and many expect substantial transformative

impacts in the short term. Yet, they also acknowledged

uncertainty about generative AI’s potential implications

on workforces and society as the technology is

widely scaled, calling for greater investment in talent,

governance and global collaboration.


prepare their enterprise for what comes next.

future with generative AI.

[deloitte.com/us/state-of-generative-ai.](http://deloitte.com/us/state-of-generative-ai)

_Brenna Sniderman_


From these wave one insights, we can gain a clearer

picture of how leaders are using generative AI,

challenges, and lessons learned thus far. This helps

reveal some of the essential questions leaders should

be asking now and actions they should be taking to

There is still much to discover with generative AI.

As it matures and is deployed at scale for a litany of

applications, new questions and challenges will become

clearer. Our quarterly reports will be available to help

you make sense of this fast-moving space, consider

practical guidance based on what we have learned,

and take a forward-looking view in your business

Learn more about the series and sign up for updates at

_Deborshi Dutt, Beena Ammanath, Costi Perricos and_


-----

Introduction

**Now decides next: Insights from the leading edge of generative AI adoption**

Will generative AI (gen AI) be the greatest, most impactful technology innovation in Generative AI seems to be following the same pattern, only much, much faster. ChatGPT

history? Will it completely transform how humans live and work? Or will it turn out to was publicly released on November 30, 2022, largely as a technology demonstration.

be just another technology du jour that promised revolutionary change but ultimately Two months later, it had already attracted an estimated 100 million active users—

delivered only incremental improvement? Right now, we can’t be certain. making it the fastest-growing consumer application in history.1

What we do know is that many breakthrough technologies of the past have followed Since then, generative AI has continued to advance by leaps and bounds and many new

a common adoption pattern: initial awareness; excitement that led to hype; mild tools and use cases have emerged—providing a powerful glimpse at the technology’s

disappointment as hype met reality; and then explosive growth once the technology vast potential to transform how people live and work.

reached critical mass and proved its worth.


-----

Introduction

Insights from the leading edge (cont.)

During this frenzied period of generative AI advancement

and adoption, leaders in business, technology and

the public sector are under tremendous pressure to

understand generative AI—and to figure out how to harness

its capabilities most effectively (or at least avoid being

disrupted). They also sense that now decides next; that their

decisions and actions today will significantly affect how

generative AI unfolds in the future, for better or worse.

It’s been said that people tend to overestimate the effect of

a technology in the short run and underestimate its effect in

the long run. This phenomenon has occurred many times in

the past and could very well happen again with generative AI.

Note here that given generative AI’s dizzying pace of change,

the gap between the short run and long run might be

measured in days, weeks or months—not years or decades.

All statistics noted in this report and its graphics are derived from Deloitte’s

first quarterly survey, conducted October – December 2023; The State of

_Generative AI in the Enterprise: Now decides next, a report series. N (Total_

leader survey responses) = 2,835

Generative AI is an area of artificial intelligence and refers to AI that in

response to a query can create text, images, video and other assets.

Generative AI systems can interact with humans and are often built using

large language models (LLMs). Also referred to as “gen AI.”


**About The State of**
**Generative AI in**
**the Enterprise**

To help leaders in business, technology and the
public sector track the rapid pace of generative
AI change and adoption, Deloitte is conducting a
series of quarterly surveys. The series is based
on Deloitte’s State of AI in the Enterprise reports,
which have been released annually five years
running. The wave one survey was fielded to more
than 2,800 director- to C-suite-level respondents
across six industries and 16 countries between
October and December 2023. Industries included:
Consumer; Energy, Resources & Industrials;


To help make smart decisions, leaders need objective, timely

information about current generative AI developments—

and where things are headed. Which is why Deloitte is

conducting this ongoing quarterly survey. Our goal is to take

the pulse of generative AI adoption, offer a view of what’s

happening, track evolving attitudes and activities, and deliver

practical, actionable insights that can help leaders like you

make informed and confident decisions about AI, strategy,

investment and deployment.


In this report, we examine our first quarterly survey findings

Financial Services; Life Sciences & Health Care;

in detail, supported by insights from Deloitte’s AI-related Technology, Media & Telecom; and Government

& Public Services. Learn more at deloitte.com/us/

work with organizations across every major industry and

state-of-generative-ai.

many geographic regions. We also offer a forward-looking

view to help you decide what generative AI actions may make

sense for your own organization and situation.

_The State of_

a report series. N (Total


-----

##### Now: Key findings

This first pulse of our generative AI quarterly surveys was

completed in December 2023, and included more than

2,800 AI-savvy business and technology leaders directly

involved in piloting or implementing gen AI at major

organizations around the world. Here’s what they had to

say about sentiment, use cases, challenges and more.


-----

Generative AI elicits a range
of strong emotions

Excitement

Fascination

Uncertainty

Trust

Surprise

Anxiety

Confusion

**62[%]**

**46[%]**

**30[%]**

**17[%]**

**16[%]**

**10[%]**

**8[%]**


Fear

**6[%]**


Exhaustion

Anger **1[%]**

**Figure 1**

**4[%]**

**1[%]**

_Q: Thinking about generative AI, what emotions do you feel most about the technology?_


_(Oct./Dec. 2023) N (Total) = 2,835_


**Now: Key findings**

Excitement about generative AI remains high, and
transformative impacts are expected in the next three years.

Nearly two-thirds (62%) of the business and technology leaders surveyed reported excitement as a top

sentiment with regard to generative AI; however, that excitement was tinged with uncertainty (30%) (figure 1).

The vast majority of respondents (79%) said they expect generative AI to drive substantial transformation

within their organization and industry over the next three years—with nearly a third expecting substantial

transformation to occur now (14%) or in less than one year (17%) (figure 2).

The survey results suggest that many AI-fueled organizations are on the verge of scaling up their efforts

and embracing generative AI in a more substantial way. This aligns with what we’re seeing in the marketplace,

where organizations around the world are racing to move from experimentation and proofs-of-concept

to larger-scale deployments across a variety of use cases and data types—pursuing both speed and

value capture while managing potential downside risks and societal impacts.

In future surveys, we will be closely monitoring progress in this area—particularly with regard to

organizations’ expertise, capabilities, tangible outcomes, and responses to rapidly emerging advances in

generative AI technology.


of the leaders we surveyed expect substantial transformation
in less than one year; 48% expect it in one to three years.

### 31%


-----

**Now: Key findings**

When is generative AI likely
to transform your organization?

**1%**

**14%**

Never

Now

**20%**
Beyond

**17%**

three years

Less than
one year

**48%**
In one to
three years

**Figure 2**

_Q: When is generative AI likely to substantially transform your organization and your industry, if at all?_

_(Oct./Dec. 2023) N (Total) = 2,835_


-----

###### 44%


rate their organization’s generative AI expertise as

high or very high, but is such expertise even possible

given the pace of the technology’s advancement?


**Now: Key findings**

Many leaders are confident about their organization’s
generative AI expertise.

A large percentage of our survey respondents (44%) said they believe their organizations currently have

high (35%) or very high (9%) levels of expertise with generative AI. This result is somewhat surprising given

how rapidly generative AI is evolving (figure 3).

But within the specific context of our survey, high levels of confidence seem entirely reasonable since

we deliberately chose experienced leaders with direct involvement in AI initiatives at large organizations

already piloting or implementing generative AI solutions. However, given how rapidly the field is unfolding, it

may be worth questioning the extent to which any leader should feel highly confident in their organization’s

expertise and preparedness. In fact, even today’s foremost AI experts who are personally developing


Self-assessed expertise with
generative AI runs high


**1%**

No expertise



**35%**

High
expertise


**9%**

**10%**

Very high

Little

expertise

expertise

**45%**

Some
expertise


**Figure 3**

_Q: How would you assess your organization’s current level of overall expertise_
_regarding generative AI?_

_(Oct./Dec. 2023) N (Total) = 2,835_


generative AI technologies at times seem genuinely surprised by their own creations’ capabilities.

Do some leaders consider their organizations to have high expertise based largely on the knowledge

and experience gained from small-scale pilots with a small number of generative AI tools? If so, leaders

and organizations might actually become less confident over time as they gain experience with the larger

challenges of deploying generative AI at scale. In other words, the more they know, the more they might

realize how much they don’t know. This is a trend we’ve seen time and again with other technological

advancements, and one we’ll be watching closely in our future surveys.


-----

Expertise with generative AI drives
attitudes toward adoption


**Now: Key findings**

Organizations that report very high expertise in generative
AI tend to feel more positive about it—but also more
pressured and threatened.

Relative to other respondents, leaders who rated their organization’s overall generative AI expertise as “very

high” tended to feel much more positive about the technology; however, they also feel more pressure to

adopt it—and see it as more of a threat to their business and operating models (figure 4).

Analysis showed this group using more modalities, deploying generative AI across more enterprise functions,

and pursuing more use cases. As you can see in the figure 4, leaders who reported very high levels of

expertise were also more likely to report higher levels of trust and lower levels of uncertainty. They also

tended to show broader interest in generative AI and expected faster transformation for their organizations.

At the same time, these respondents’ greater understanding of generative AI appears to be shaping their

perspective on potential impacts—positive and negative. Many reported they viewed widespread adoption of

the technology as a threat to how their organizations operate and conduct business, amplifying the pressure

and urgency they felt to adopt generative AI and scale it.


**Very high**
**expertise**


**Some**
**expertise**


**Trust prevails** **9%**
**over​ uncertainty**

**[39%]**

**11% 38%**


Rank trust
among top
emotions felt​

Rank uncertainty
among top
emotions felt

Say employees
show high interest
in gen AI​

Say gen AI
is already
**transformative​**

Feel widespread
adoption is a
**threat to business​**

Feel greater
**pressure to**
adopt gen AI​


**78% 38%**

**31%** **9%**

**33% 16%**

**44% 25%**


**Broad interest**
**sparks**
**transformation**

**Widespread**
**adoption**
**generates**
**pressure**

**Figure 4**


**Leaders of organizations with very high expertise are more likely to**
view generative AI as a threat to their business and operating models.


_(Oct./Dec. 2023) N (Total) = 2,835,_
_N (Very high) = 267; N (Some) = 1,273_


-----

Key benefits organizations hope
to achieve with generative AI


**Now: Key findings**

Current generative AI efforts remain more focused
on efficiency, productivity and cost reduction than on
innovation and growth.

The majority of organizations surveyed are currently targeting tactical benefits such as improving

efficiency / productivity (56%) and/or reducing costs (35%). Also, 91% said they expect generative AI to

improve their organization’s productivity, and 27% expect productivity to increase significantly. A smaller

percentage of organizations reported targeting strategic benefits such as innovation and growth (29%)

(figure 5).

This is consistent with past technology adoption patterns. Initially, most organizations logically focus on

incrementally improving their existing processes and capabilities—capturing value from low-hanging fruit

while building knowledge, experience and confidence with the new technology. Later, they expand or shift

their focus to improvements that are more innovative, strategic and transformational—using the new

technology to drive growth and competitive differentiation and advantage through capabilities that simply

weren’t possible before.

Surveyed leaders that cited higher levels of AI expertise show earlier signs of moving up this curve. They

are more focused on uncovering new ideas and insights (23% vs. 19% for the overall respondent pool),

with less emphasis on efficiency and productivity (44% vs. 61% for the overall respondent pool) and cost

reduction (26% vs. 38% for the overall respondent pool)—although those tactical benefits continue to be


efficiency and
productivity



Improve existing **29%**
products and
services
innovation
and growth


Shift workers from **26%**
lower to higher
value tasks

**56%**

**35%**

**29%**

**29%**

**26%**

**26%**


revenue


**26%** Increase speed
and/or ease of
developing new
systems / software


**25%**


relationships

**23%**

with clients /
customers

Uncover new **19%**
ideas and

**19%**

insights


**18%** Detect fraud and
manage risk


**Figure 5**


**18%**


_Q: What are the key benefits you hope to achieve through your generative AI efforts?_

_(Oct./Dec. 2023) N (Total) = 2,835_


-----

**Now: Key findings**

their bigger focus. In addition, nearly three-quarters of organizations that cited very high generative

AI expertise had already begun integrating the technology into their product development and R&D

activities, which are key drivers of innovation and growth.

As more organizations gain expertise and experience with generative AI, will they reinvest their dividends

from improving efficiency and productivity toward pursuing more strategic benefits such as innovation

and growth? Or will they use those dividends in other ways? This is another area we’ll be monitoring

closely in future pulse surveys.

Certainly, productivity and efficiency can be transformational, especially given the massive scale

generative AI has the potential to enable. However, the greatest value and strategic differentiation will

likely come from using the technology to innovate. First, by helping to generate new products, services

and capabilities that wouldn’t be possible otherwise. And, second, by enabling new business models and

ways of working across an enterprise.

In addition, organizations that cited very high generative AI expertise were already taking a much more

comprehensive approach than average, with significantly higher adoption levels across a broad range

of functional areas. In specific areas such as HR, and legal, risk and compliance, those organizations’

generative AI adoption rates were nearly three times higher than for the total respondent pool (figure 6).

of all organizations expect their productivity to increase due
to generative AI.

### 91%


-----

|T / cybersecurity|Col2|Col3|Col4|
|---|---|---|---|
|46|22 %|% 38|% 57|
|arketing, sales and customer service 41 41|% 16 %|% 34|% 50 57|
|roduct development / R&D|14|% 28|%|
|trategy and operations 35|% 10|% 26|% 47 37|
|upply chain / manufacturing 29 inance|% 9|% 21|% 37|
|25 23|% 5 %|% 14|%|
|uman resources|6|% 13|% 29 28|


% of those who are using generative AI

**Now: Key findings**

in a limited or at-scale implementation **Total** **Little expertise** **Some expertise** **High expertise** **Very high expertise**

Level of generative AI
adoption

IT / cybersecurity **22%** **38%** **57%** **71%**

**46%**

Marketing, sales and customer service **41%** **16%** **34%** **50%** **73%**

**57%**

Product development / R&D **41%** **14%** **28%** **73%**

Strategy and operations **35%** **10%** **26%** **47%** **62%**

**37%**

Supply chain / manufacturing **29%** **9%** **21%** **61%**

Finance **25%** **5%** **14%** **37%** **63%**

**Figure 6**

Human resources **23%** **6%** **13%** **29%** **64%**

_Q: What is your organization’s_
_current adoption level of generative AI across_
_the following functions?_

**28%**

**60%**

_(Oct./Dec. 2023) N (Total) = 2,835;_ Legal, risk and compliance **21%** **7%** **10%**
_N (Very high) = 267; N (High) = 1,003;_
_N (Some) = 1,273; N (Little) = 274_


-----

**Generative AI: Have we seen this movie before?**


The term “unprecedented” is often thrown around

when talking about business and technology, to the

point of being cliché. However, in describing the pace of

generative AI’s emergence and advancement—and its


Generative AI’s speed factor may give organizations less

time to ruminate or dabble with small-scale pilots—

while reducing the margin for error—and increasing the

consequences of inaction. It also creates opportunities


help the workforce get accustomed to using generative

AI, and will show people how it can help make their

jobs easier. Also, early wins will likely help produce cost

savings and momentum that then can be channeled into


massive potential impact on business (and humanity as a to generate extraordinary business value very quickly. higher value opportunities that are more strategic and

whole)—unprecedented could be an understatement. differentiated in nature, such as enabling new products,

Despite generative AI ’s greatly accelerated pace,

services, business models and ways of working that

Generative AI is already widely available to the public understanding typical adoption patterns based on

simply weren’t possible before generative AI.

and has a running start toward critical mass. Also, similar previous breakthrough technologies can provide

to smartphones, it’s easy for an average person to use valuable lessons that leaders can use to help them

without much training—and can help with activities they understand and fully capitalize on the technology’s rapid

already engage in every day—so the barriers to adoption advancement.

are low. What’s more, generative AI has the strong

As in the past, organizations’ initial efforts will likely

potential to assist with its own future development,

center around efficiency, productivity, cost savings and

which could trigger a cycle of exponential improvement

other incremental improvements. This is expected to

at exponential speed.


-----

**Now: Key findings**

Most organizations are primarily relying on off-the-shelf
generative AI solutions.

In line with their current emphasis on tactical benefits from generative AI, the vast majority of respondents

were currently relying on off-the-shelf solutions. These included productivity applications with integrated

generative AI (71%); enterprise platforms with integrated generative AI (61%); standard generative AI

applications (68%); and publicly available large language models (LLMs) (56%), such as ChatGPT.

Relatively few reported using more narrowly focused and differentiated generative AI solutions, such as

industry-specific software applications (23%), private LLMs (32%), and/or open-source LLMs (customized to

their business) (25%).

Reliance on standard, off-the-shelf solutions is consistent with the current early phase of generative AI

adoption, which is primarily focused on improving the efficiency and productivity of existing activities.

However, as use cases for generative AI become more specialized, differentiated and strategic, the

associated development approaches and technology infrastructure will likely follow suit.

When will we see complex, high-value use cases that are truly differentiated and tailored to the specialized

needs of specific companies, functions and industries? How will organizations combine internal and

external resources to create customized generative AI tools that enable such strategic differentiation? In

particular, will we see off-the-shelf technology offerings be supplemented by private or hybrid public/private

development approaches and technology infrastructures capable of delivering and supporting those

differentiated solutions?


Where off-the-shelf generative AI
is used most

Productivity applications

#### 71%

Standard applications

#### 68%

Enterprise platforms

#### 61%

Public LLMs

#### 56%


-----

**Now: Key findings**


Key findings

Talent, governance and risk are critical areas where
generative AI preparedness is lacking.

In this initial quarterly survey, 41% of leaders reported their organizations were only slightly or not at

all prepared to address talent concerns related to generative AI adoption, while 22% considered their

organizations highly or very highly prepared. Similarly, 41% of leaders reported their organizations were only

slightly or not at all prepared to address governance and risk concerns related to generative AI adoption,

while 25% considered their organizations highly or very highly prepared (figure 7).

Larger percentages of leaders reported high to very high levels of preparedness in technology infrastructure

(40%) and strategy (34%); however, the survey results show there is still significant room for improvement.

**Generative AI barriers related to risk and governance**

When it comes to risk and governance, generative AI is definitely not “just another technology.” The

fundamental challenge is how to capitalize on artificial intelligence’s power without losing control of it. After

all, the capability people seem to find most enthralling about generative AI is its ability to so convincingly

simulate human thinking and behavior. Of course, human thinking and behavior aren’t always perfect,

predictable or socially acceptable—and the same is true for the technology, itself.


-----

**Now: Key findings**

Preparedness for generative AI

Respondents claimed the highest levels of preparation in technology
**and strategy, while feeling far less prepared in risk and talent.**


Technology infrastructure **4%** **17%** **38%** **30%** **10%**

Strategy **5%** **20%** **41%** **26%** **8%**

Risk & governance **13%** **28%** **34%** **18%** **7%**


Not prepared

Slightly prepared

Moderately prepared

Highly prepared


Talent **13%** **28%** **37%** **17%** **5%** Very highly prepared

**Figure 7**

_Q: Consider the following areas. For each, rate your organization’s level of preparedness with respect to broadly adopting generative AI tools / applications?_

_(Oct./Dec. 2023) N (Total) = 2,835_


-----

Managing generative AI
implementation risk

Establishing a governance
framework for the use
of generative AI tools /
applications


Monitoring regulatory
requirements and
ensuring compliance


Conducting internal
audits and testing
on generative AI
tools / applications

Training practitioners
how to recognize and
mitigate potential risks

Ensuring a human
validates all generative
AI content


Using a formal group
or board to advise
on generative

**47[%]**

**46[%]**

**42[%]**

**37[%]**

**36[%]**

**34[%]**

**32[%]**

implementations


Single executive
responsible for managing

**21[%]**

generative AI-related risks


Keeping a formal inventory
of all generative AI
implementations

Using outside vendors
to conduct independent
audits and testing

**Figure 8**


**26[%]**


_Q: What is your organization currently doing to actively manage the risks around_

_your generative AI implementations?_

_(Oct./Dec. 2023) N (Total) = 2,835_


**Now: Key findings**

Specific generative AI risks and concerns include inaccurate results and information (i.e., “hallucinations”);

legal risks such as plagiarism, copyright infringement, and liability for errors; privacy and data ownership

challenges; lack of transparency, explainability and accountability; and systemic bias. The latter

exemplifies another category of risk in which AI amplifies and exacerbates a problem that already exists,

such as propagating and systematizing existing social biases, facilitating and accelerating the spread of

misinformation, helping criminals commit crimes, or fanning the flames of political divisiveness.

According to the business and technology leaders we surveyed during fourth quarter 2023, the biggest

concerns related to governance were: lack of confidence in results (36%), intellectual property issues

(35%), misuse of client or customer data (34%), ability to comply with regulations (33%), and lack of

explainability / transparency (31%).

Some of the surveyed organizations were already actively managing generative AI implementation

risks through actions such as monitoring regulatory requirements and ensuring compliance (47%),

establishing a governance framework for generative AI (46%), and conducting internal audits and testing

on generative AI tools and applications (42%) (figure 8). However, such organizations are in the minority

and their actions barely scratch the surface of the challenge. This is especially true given that regulatory

requirements typically lag behind the pace of technology innovation—although a US presidential

executive order and the European Union’s ambitious Artificial Intelligence Act are clear signs government

leaders in many parts of the world are taking the issue of AI risk very seriously.


-----

Generative AI is impacting talent
strategies now

**2%**

Never

**10%** **17%**

No formal Now
time frame

**24%**

**16%**

**Now: Key findings** 2+ years

**Generative AI barriers related to talent and workforce**

Generative AI has the potential to supplement human workers across a vast array of activities traditionally

thought of as uniquely human. As such, its impact on talent and workforce strategies could be immense.

How will it affect organizations and their workers in the short and long runs? Which types of skills will be

most affected, and when?

**31%**


1-2 years


**24%**

Within 1 year


The vast majority of leaders we surveyed (72%) said they expect generative AI to drive changes in their

talent strategies sometime within the next two years: now (17%), within 1 year (24%), or in 1-2 years (31%)

(figure 9).

However, less than half (47%) reported that they are sufficiently educating their employees on the

capabilities, benefits and value of generative AI—survey respondents also cited a lack of technical talent and

skills as the biggest barriers to adoption.


**Figure 9**

_Q: When do you expect to make changes to your talent strategies because of_
_generative AI?_

_(Oct./Dec. 2023) N (Total) = 2,835_


-----

**Now: Key findings**

Against this backdrop, some respondents reported making a high or very high effort to:

recruit and hire technical talent to drive their generative AI initiatives (42%), educate the

workforce about generative AI (40%), and reskill workers impacted by generative AI (36%).

Those numbers are much higher for leaders who viewed their organization’s generative AI

expertise as very high (74%, 74% and 73%, respectively) (figure 10).


It should be noted, however, that these reported workforce-related efforts might be limited

in scope. Deloitte’s experience suggests that most organizations have yet to substantially

address the talent and workforce challenges likely to arise from large-scale generative AI

adoption. A likely reason for this is that many leaders don’t yet know what generative AI’s

talent impacts will be, particularly with regard to which skills and roles will be needed most.


Preparing workforces for generative AI: Respondents making a high or very high effort in the following areas.

**74%** **74%**
**73%**

All respondents **55%** **55%**


**42%**
**40%**

**36%**

**30%**
**27%**
**24%**

**16%**
**14%**
**10%**


**50%**


Very high expertise Recruiting and hiring technical talent to drive Educating our broader workforce to raise

our generative AI initiatives overall generative AI fluency


Reskilling workers because of the impact
of generative AI to their roles


All respondents

Little expertise

Some expertise

High expertise

Very high expertise


_Q: What level of effort is your organization taking regarding the following workforce-related areas?_

_(Oct./Dec. 2023) N (Total) = 2,835_


**Figure 10**


-----

“Generating confidence
in workers’ abilities to
collaborate with generative AI,
now, could elevate creativity
and job satisfaction, next.”


-----

**Now: Key findings**

Leaders see significant societal impacts on the horizon.

Although the leaders we surveyed were generally excited and enthusiastic about generative AI’s potential

business benefits, they were less optimistic about its broader societal impacts. Specifically, 52% of

respondents said they expected widespread use of generative AI to centralize power in the global economy,

while 30% expected it to more evenly distribute global power. Similarly, 51% expected generative AI to

increase economic inequality, while 22% expected it to reduce inequality (figure 11).

What’s more, 49% of respondents believe the rise of generative AI tools / applications will erode the overall

level of trust in national and global institutions.

Is this pessimism or realism? Our survey results appear to reflect the broader moral and ethical debates

about artificial intelligence that are occurring in every corner of society—even in the boardrooms of the

technology companies driving AI development, where AI’s commercial value is being weighed against its

potential value to serve humanity and AI’s potential benefits are being weighed against its potential risks.

The challenges that generative AI poses in corporate governance and risk parallel those in societal

governance and risk. In both domains, the technology’s potential benefits and potential harms are high.

National and supranational organizations and governments will likely need to walk the tightrope of helping

to ensure that generative AI benefits are broadly and fairly distributed, without overly hindering innovation

or providing an unfair advantage to countries with different rules.


**expect generative AI to**
**increase economic inequality.**

## 51%


-----

**Now: Key findings**

Expected societal impacts of generative AI

**Distribution of economic power**

**30%**

|5%|25%|18%|42%|Col5|10%|
|---|---|---|---|---|---|
|||||||
|||||||
|||||||

distribute

**Levels of economic inequality**

**22%**

|3%|19%|27%|41%|Col5|140%%|
|---|---|---|---|---|---|
|||||||
|||||||
|||||||

decrease inequality

_Q: How will widespread use of generative AI shift the overall distribution of power in the global economy?_
**Figure 11**

_Q: How will widespread use of generative AI tools / applications impact global levels of economic inequality?_

_(Oct./Dec. 2023) N (Total) = 2,835_



**42%** **10%**

**52%**
centralize

**41%** **10%4%**

**51%**
increase inequality


-----

Support for increased regulation
and global collaboration

**78% more regulation**

Agree the widespread

proliferation of generative

AI tools / applications will

require more regulation

**of AI by governments**

**72% more collaboration**

Agree there is not enough

**global collaboration with**

respect to ensuring the

responsible development

of all AI-powered systems

**Figure 12**

_(Oct./Dec. 2023) N (Total) = 2,835_


**Now: Key findings**

Leaders are looking for more regulation and
collaboration globally.

In a break from traditional business norms, the unique risks associated with generative AI are prompting

many business leaders to call for increased government regulation and increased global collaboration

around AI technologies.

Among the leaders in our survey, 78% said that more governmental regulation of AI is needed, while 72%

said there is currently not enough global collaboration to ensure the responsible development of

AI-powered systems (figure 12).

These results seem to reflect an understanding that generative AI could be too powerful, far-reaching and

impactful for individual organizations to regulate themselves. This isn’t meant to suggest that individual

organizations be absolved from behaving responsibly; however, relying on them to be the primary

gatekeepers for containing AI risk could potentially be dangerous.


**Could generative AI be too powerful and far-reaching for individual**
organizations to regulate themselves?


-----

##### Next: Looking ahead

As the first in an ongoing series of quarterly surveys to track the pulse

of generative AI, this initial effort was designed to establish a strong

baseline to build on. Moving forward, our objective is to understand how

generative AI adoption is unfolding—and to anticipate where it’s headed.

Throughout this report we posed a number of strategic questions to

help organizations think critically about how the actions they take now

will best set the stage for what comes next. We don’t have definitive

answers to every question yet, nor would we claim to. However, we can

offer some questions to spark thinking and some practical guidance

based on what we’ve learned so far. We also note that given the pace at

which AI and specifically generative AI is moving, definitive answers that

make sense today may not be relevant in a few months.


-----

In the race to deploy generative AI solutions, organizational attributes such as adaptation, experimentation

and agility will be critical as new models, capabilities and use cases emerge. The key is to maintain a beginner’s

mindset—the belief that no matter how expert you think you are, there will always be much more to learn—

even as your experience grows. Careful coordination across your organization will be needed to successfully

shepherd generative AI transformation in the face of rapid change. Work to improve generative AI literacy

throughout your organization, and lead using a cross-disciplinary approach. Actively collaborate with partners


**Next: Looking ahead**

How can my organization build generative AI
**expertise**

and third-party organizations. Also, gain experience with a variety of generative AI technologies—with

innovation happening so quickly, it’s nearly impossible to pick a clear winner today.


-----

**Next: Looking ahead**

How can we best scale up and build a
foundation for longer-term value creation?

Experimentation is essential when deploying generative AI. But if you can’t scale up your

efforts, the high expectations for transformation revealed by our survey likely won’t be

met. It’s fine to focus on a few use cases at first. However, the most valuable use cases

will likely change over time, so it’s important to focus on improving end-to-end processes,

not just narrow tasks. Also, follow the example of organizations that report high

generative AI expertise and consider deploying AI broadly across your enterprise as part

of a holistic strategy, rather than focusing narrowly on point solutions and silos. Strive to

build platform capabilities that can enable multiple use cases, accommodate new and

improved generative AI models, and provide consistent governance and risk management

to ensure models produce safe and trustworthy outputs and content.


How should we invest the dividends gained
from improving efficiency and productivity?

Generative AI projects can potentially be expensive, so leaders are naturally looking for

rapid ways to achieve a compelling return on investment (ROI). Benefits might at first

arrive slowly, but then ramp up quickly as your organization reaches a critical mass

of experience and proficiency. Most generative AI efforts are currently focused on

improving efficiency and productivity, and reducing costs. Once you achieve those goals,

what will you do with the time and money saved? Will you strategically reinvest in more

generative AI projects? Will you invest in training and reskilling your workforce? Will you

improve your technology infrastructure? Or will you simply boost your bottom line? A

deliberate reinvestment strategy for generative AI dividends will help lay the path for

continued success.


-----

Where companies
reinvest the gains they
make in efficiency from
generative AI, now, may
decide their level of
transformation, next.


-----

**Next: Looking ahead**

How can our organization use generative AI
to create strategic differentiation and a
competitive edge?

As generative AI adoption rises and the technology becomes a standard commodity—

with increased integration into common enterprise software, broader availability of

specialized tools and models, and standardized data requirements—will first movers

lose their advantage? To maximize the value of the technology, organizations should

consciously focus on innovation and differentiation—customizing their generative AI

solutions to fit their unique needs and data assets, with the goal of building capabilities

that create sustainable competitive advantage. Pursuing easy opportunities and quick

wins is smart, but not to the exclusion of more strategic opportunities (even though the

latter will require more time and money to achieve and may take longer to achieve ROI).


How should we balance buying vs. building?

When developing and deploying generative AI solutions, should you buy or build? The

answer depends on many factors, including your overall goals and the scale, complexity

and uniqueness of your solution and use case. Are you looking to monetize your model?

What is your approach to open source? How much control over training datasets do you

want? Questions like these will help you choose from the broad spectrum of approaches,

which include: building large language models or LLMs from scratch, fine-tuning vendor
provided models with your own data, or using enterprise software with generative AI built

in. Each approach has its benefits and drawbacks, and you might end up choosing more

than one. When deciding, be sure to consider your business strategy, desired investment

level, risk tolerance and data readiness.


-----

**Next: Looking ahead**

How can we best invest in our people and
reinvent how they work with generative AI?

Survey respondents cited talent as their biggest barrier to generative AI adoption. To

surmount it, you will likely need to recruit new talent, empower your existing workforce,

and build organizational trust. Although the talent market is highly competitive, don’t let

that deter you from pursuing people with the technical skills to develop and maintain

generative AI solutions (e.g., prompt engineers, AI solutions architects, data scientists /

engineers, LLM operators). At the same time, invest in training to help your people get the

maximum value from generative AI tools and to improve their productivity. Also, prioritize

broad workforce education to help allay fears and misconceptions about AI technology.


What guardrails does our organization
need to ensure responsible use of generative
AI, and how do we stay aligned with shifting
societal guardrails?

Respondents expressed a variety of concerns about generative AI risks, including the

need to manage hallucinations and model bias, assess potential intellectual property

issues, and ensure transparency and explainability. These issues underscore the

importance of keeping humans in the loop to work with AI, check its accuracy, and

address any problems that arise. Additionally, there are open questions about how

various regulatory and legal challenges will affect development of the overall market.

A large percentage of organizations (47%) reported they were monitoring regulatory

requirements as part of their risk management efforts. Many respondents noted their

concerns that the widespread use of generative AI will concentrate power and increase

economic disparity. As a leader, you will need to consider how your organization’s

generative AI decisions and actions fit into the larger picture—and will likely need to do so

prospectively, instead of waiting for official guidance from lawmakers and regulators.


-----

Authorship and Acknowledgments


**Acknowledgments**

The authors would like to thank the many talented professionals who

brought this research to life: Joe Ucuzoglu, Nitin Mittal, Kevin Westcott,

Lynne Sterrett, Rod Sides, Dina Tallarico, David Jarvis, Jeff Loucks, Ahmed

Alibage, Natasha Buckley, Jonathan Holdowsky, Siri Anderson, David Levin,

Joe Mariani, Sandeep Vellanki, Rajesh Medisetti, Shubham Oza, Gerson

Lehrman Group (GLG), Lena La, Kate Schmidt, Ivana Vucenovic, Sharonjeet

Meht, Bryan Furman, Lesley Stephen, Stephanie Anderson, Steve Dutton,

Justin Joyner, Jordan Garrick, Karen Hogger, Matt Lennert, Maria Fernanda

Castro, Tracy Fulham, Jose Porras, Jonathan Pryce, Sourabh Yaduvanshi,

Jessi Hendon, Jamie Palmeroni-Lavis, Melissa Neumann, Tatum Hoehn, Sean

Benton, Eric Alons-Cruz, Lancy Jiang, Amber Bushnell, Brandon Gomez, Judy

Mills, Marianne Wilkinson, Lou Ghaddar, Lisa Iliff and Michael Lim.

We would also like to thank additional Deloitte subject matter specialists

who contributed to the development of the survey and report: Rohit Tandon,

Mike Segala, Bjoern Bringmann, Kellie Nuttal, Ed Bowen, Oz Karan, Lou

DiLorenzo, Ed Van Buren, Amelia Dunlop, Ashley Reichheld, Maggie Fletcher,

Elizabeth Powers, Baris Sarer, Dany Rifkin and Laura Shact.


**Deborshi Dutt**

US AI Strategic Growth Offering Leader

Deloitte Consulting LLP

[debdutt@deloitte.com](mailto:debdutt%40deloitte.com?subject=)

**Costi Perricos**

Global Office of Generative AI Leader

Deloitte UK

[cperricos@deloitte.co.uk](mailto:cperricos%40deloitte.co.uk?subject=)


**Beena Ammanath**

Global Deloitte AI Institute Executive Director

Deloitte LLP

[bammanath@deloitte.com](mailto:bammanath%40deloitte.com?subject=)

**Brenna Sniderman**

Deloitte Center for Integrated Research

Executive Director

Deloitte Services LLP

[bsniderman@deloitte.com](mailto:bsniderman%40deloitte.com?subject=)


-----

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-----

Methodology

maker, or manage or oversee AI technology implementations.


To obtain a global view of how generative AI is being adopted by organizations on the leading edge of AI, Deloitte surveyed 2,835

leaders between October and December 2023. Respondents were senior leaders in their organization and included board and

-suite members, and those at the president, vice president and director level. The survey sample was split equally between IT and

line of business leaders. Sixteen countries were represented: Australia (100 respondents), Brazil (115 respondents), Canada (175

respondents), France (130 respondents), Germany (150 respondents), India (200 respondents), Italy (50 respondents), Japan (100

respondents), Korea (11 respondents), Mexico (101 respondents), Netherlands (75 respondents), Singapore (76 respondents), Spain

(101 respondents), Switzerland (50 respondents), the United Kingdom (200 respondents), and the United States (1,201 respondents).

All participating organizations have one or more working implementations of AI being used daily. Plus, they have pilots in place to

explore generative AI or have one or more working implementations of generative AI being used daily. Respondents were required

to meet one of the following criteria with respect to their organization’s AI and data science strategy, investments, implementation

approach, and value measurement. They: influence decision-making, are part of a team that makes decisions, are the final decision-

All statistics noted in this report and its graphics are derived from Deloitte’s first quarterly survey, conducted October – December

, a report series. N (Total leader survey responses) = 2,835


-----

**Endnotes:**

[1. Krystal Hu, “ChatGPT sets record for fastest-growing user base – analyst note,” Reuters, February 2, 2023, https://www.reuters.com/](https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/)
[technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/, accessed January 3, 2023.](https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/)

2. Will Douglas Heaven, “Geoffrey Hinton tells us why he’s now scared of the tech he helped build,” MIT Technology Review, May 2, 2023,

[https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/, accessed January 3, 2023.](https://www.technologyreview.com/2023/05/02/1072528/geoffrey-hinton-google-why-scared-ai/)

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