## Australia’s artificial intelligence ecosystem

Catalysing an AI industry

December 2023

CSIRO Australia’s National Science Agency


-----

**Citation**

Hajkowicz S, Bratanova A, Schleiger E, Naughtin C (2023)
Australia’s artificial intelligence ecosystem: Catalysing an
AI industry. CSIRO, Canberra.

The authors would like to thank the many experts and
stakeholders we interviewed from Australia’s artificial
intelligence companies and research institutes. We are also
grateful to staff within CSIRO who provided review and
feedback on early draft versions of this report.

**Copyright**

The contents of this report are subject to the
Creative Commons licence called “Attribution-Non
Commercial‑Share Alike 4.0 International (CC BY-NC-SA 4.0)”.

**Disclaimer**

CSIRO advises that the information contained in this
publication comprises general statements based on
scientific research. The reader is advised and needs to
be aware that such information may be incomplete or
unable to be used in any specific situation. No reliance
or actions must therefore be made on that information
without seeking prior expert professional, scientific and
technical advice. To the extent permitted by law, CSIRO
(including its employees and consultants) excludes all
liability to any person for any consequences, including
but not limited to all losses, damages, costs, expenses
and any other compensation, arising directly or indirectly
from using this publication (in part or in whole) and any
information or material contained in it.

**Acknowledgements**

The National AI Centre is funded by the Australian
Government and coordinated by CSIRO.

CSIRO acknowledges CEDA and Google as
Foundation Partners of the National AI Centre


**About this report**

This report was prepared by CSIRO for the National
Artificial Intelligence Centre. It presents information
about companies and research institutes active in
developing and applying artificial intelligence in Australia.
This provides investors, customers, policy-makers,
workers and students with information about Australia’s
capabilities, service offerings and research specialisations
in artificial intelligence.

**Accessibility**

CSIRO is committed to providing web accessible content
wherever possible. If you are having difficulties with
[accessing this document please contact csiro.au/contact.](http://csiro.au/contact)


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### Contents

Executive summary...................................................................................................................................2

Introduction................................................................................................................................................. 6

Section 1: Artificial intelligence companies................................................................................... 8

How we identify and define an AI company.....................................................................................................................9

Product and service offerings.........................................................................................................................................11

Geographic patterns of AI companies.............................................................................................................................15

Company formation and maturity...................................................................................................................................22

Jobs and skills demand.....................................................................................................................................................24

Investment trends.............................................................................................................................................................25

Section 2: Research and development.........................................................................................26

Methods and source data................................................................................................................................................27

Knowledge creation (research publishing trends).........................................................................................................29

Application domain specialisations................................................................................................................................31

Artificial intelligence technology specialisations..........................................................................................................33

Universities and research institutes................................................................................................................................35

Product innovation and patent applications..................................................................................................................38

Section 3: Industry stakeholder and expert perspectives...................................................42

Insights from our interviews............................................................................................................................................43

Australia’s AI ecosystem case studies..............................................................................................................................47

Key takeaways...................................................................................................................................................................52

Planned improvements....................................................................................................................................................54

References..................................................................................................................................................55


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### Executive summary

Australia has a growing and diversified AI industry


We identified 544 companies in Australia whose main
business activity is developing and selling AI products and
services. These companies are nestled within a broader
ecosystem of technology companies with >336,000 firms
in the professional, scientific and technical services
industry and close to 25,000 firms in information media
and telecommunications.

Data services

Finished solutions

Consulting

Skills and training

Hardware and infrastructure

Business transformation 61

395

356

240

210

156

123 **Why do they add up**

117 **to >544 companies?**

106 It’s because a single company

102 can have multiple product

61 and service offerings.


The number of AI companies
in Australia has increased
substantially over the past decade


Our initial search on company databases returned
1,606 AI companies. This was further screened, verified and
validated down to 544 active AI companies. We developed
a taxonomy to assign 10 AI product and service offering
categories to each company as follows.

395

356

240

210

**Why do they add up**

**to >544 companies?**

It’s because a single company

can have multiple product

and service offerings.

Australia’s AI companies
are showing patterns of
geographic clustering

Number of tightly clustered hotspots (precincts)

#### 8 of AI companies across parts of Sydney,

Melbourne, Brisbane, Adelaide and Perth


#### 396

 204

 7.7%


Number of Australian AI
companies opened for
business in the past 10 years

Number of Australian AI
companies opened for
business in the past 5 years

Year-on-year growth of company
count over the past 5 years


#### 54%

 80 m


Share of Australia’s 544 AI companies
located in these 8 clusters
(296 companies in total)

Average straight-line distance between
an AI company and its nearest
neighbouring AI company within
the same cluster


Australia’s AI companies are young and nimble


#### 6 yrs


Median age of
Australian AI companies 13 yrs


Median age of Australian information and
communications technology (ICT) sector companies


-----

Australia’s top 6 universities and research institutes
by AI publishing intensity

The share of AI research published by Australian universities is rising, with >10% of
the research published from the following six institutions being on AI topics in 2021.


18.8%


13.3%


12.9%


12.3%


12.2%


10.1%


University of

Technology

Sydney


University

of Southern
Queensland


Deakin

University


CSIRO


Swinburne

University of

Technology


University of

New South

Wales


Australia’s share of AI publishing and product innovation


#### 1.2%

Australia’s share of global
publishing on all topics


#### 1.6%

Australia’s share of global
publishing on AI topics


#### 0.2%

Share of global AI patent applications
with an Australian inventor

AI applications in
###### 4.8x
livestock production

AI applications in medical
###### 3.8x
laboratory technologies

AI applications
###### 3.2x
in horticulture

Self-organising map
###### 7.6x
(a type of neural network)

Ant colony optimisation (an algorithm
###### 5.3x
inspired by ant colony behaviours)

Gradient tree boosting (a machine
###### 5.2x


Australia’s top 3 AI application domains

Australia’s R&D sector (public and private) is publishing research
in AI application domains at a faster rate than the global average
in these areas. The revealed technology advantage (RTA) scores
show the rate at which we’re publishing, where a value of
1 would be equal to the global rate.

Australia’s top 3 AI technology sub‑fields

Australia’s R&D sector (public and private) is publishing research
in AI technology types at a higher rate than the global rate in
these areas. The RTA scores show we’re publishing at 7.6 times
the global average rate in the field of self-organising maps
(a type of neural network).


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Stakeholder perspectives

We held interviews with 28 stakeholders across Australia’s
AI ecosystem including AI companies (startups and small,
medium and large enterprises), academia and government
agencies. These consultations explored current and
emerging areas of competitive advantage across the
Australian AI ecosystem, the unique value proposition
of Australian‑developed AI and opportunities to support
the ecosystem in the future.


#### 1

**Challenge in separating the hype from reality.**

Leaps forward by open-access tools like ChatGPT
have accelerated interest in AI technologies. Business
leaders can better visualise the potential benefits AI could
bring to their organisations, but many remain risk-averse
and apprehensive about AI technologies.

**Need more awareness of local AI service**

#### 2

**providers. Australian companies are opting for**
international AI service providers when the capabilities
do not exist or cannot be identified locally. There are
opportunities to expand platforms like the Australian AI
Ecosystem Discoverability Platform to provide greater
coverage and promote awareness of Australia’s existing
AI ecosystem.


#### 3

**Deciding on what AI to build, buy or borrow.**

To be globally competitive, Australia needs to set
conditions that support AI adoption as well as create
unique AI capabilities. This could include addressing the
persisting challenge of scaling Australia’s strong academic
and research outputs into commercial innovations.

#### 4

**Prioritising being an AI specialist over an AI**


**generalist. Acknowledging our population, market**
and funding size can limit Australia’s capacity to compete
in the global AI arena, there could be opportunities to
grow world‑leading capabilities in strategic areas of AI
specialisation. These areas of AI specialisation should be
based on domains where Australia has a strategic and/or
comparative advantage.


-----

#### 5

**An opportunity for safe and responsible AI.**

There is a significant opportunity for Australia to
be a global leader in the development and use of safe and
responsible AI technologies. The ‘Australian brand’ is a
trusted asset, which puts us in a strong position to drive
future safe and responsible AI developments.

#### 6

**The benefits of socio-cultural diversity.**


Australia’s cultural and linguistic diversity provides
a unique comparative advantage when it comes
to developing novel AI technologies. Australia is
well‑placed to leverage this diversity in the creation
of novel AI technologies, as well as position itself as
an AI technology testbed.


#### 7

**Strengthening linkages across the AI ecosystem.**

Australia’s AI ecosystem requires strong leadership
and improved connectivity across the sector. Doing so
will maximise opportunities for Australia to define and
strengthen its global reputation in AI and mature as
an ecosystem.

#### 8

**Growing Australia’s AI talent and business**


**ecosystem. Talent shortages are a persisting**
struggle reported by Australian businesses, particularly
when it comes to sourcing specialised technical skills.
Australia could consider new approaches to inject talent
into the AI ecosystem and reduce friction for professionals
working in these areas.


-----

### Introduction

What is this report about?

This is the second report about Australia’s artificial
intelligence (AI) ecosystem with the first being published
in 2022 (NAIC, 2022). It has been commissioned by the
National Artificial Intelligence Centre (NAIC) and delivered
by CSIRO. Australia’s AI ecosystem is comprised of startups,
small-to-large-sized companies, universities, research
institutes, industry organisations and public sector agencies
engaged in developing and applying AI technologies.
This report aims to provide a snapshot of the current state
of Australia’s AI ecosystem and inform future strategy and
policy decisions about its growth and development.

Why read this report?

This report contains information about companies, product
innovation, investment, universities, research institutes and
specialised capabilities relating to AI in Australia. It also
contains information about the perceived challenges and
opportunities for future growth and development of AI
in Australia. The report aims to inform people in industry,
academia, government and community spheres making
decisions about AI capability uplift.

This report is useful to anyone wanting to understand
and develop AI capability in Australia. It could be used
by investors looking for opportunities in the Australian
marketplace. It could be used by a company wanting to
train and recruit AI talent. It could be used by someone
starting or developing an AI company. It could be used
by universities and research institutes planning for
AI capability development. It could be used by Local,
State/Territory and Federal government agencies aiming
to support the AI industry and job growth. And it could be
used by workers and students planning their AI careers.


What is the Australian
AI ecosystem?

The AI ecosystem comprises companies (startups, small,
medium and large enterprises), universities, education and
training institutes, research institutes, industry bodies and
professional associations concerned with the development
and application of AI in Australia. Whilst practically all
organisations use AI to some extent, this report focuses
on companies and research institutes that have a primary
or substantial focus on AI. We identify several sub-sectors
within the AI ecosystem:

1. Small, medium, large and startup companies that

make and sell AI products and services

2. Research institutes and universities significantly

engaged in developing and applying AI

3. Venture capital and other investors who inject

capital into the AI industry

4. Industry associations, professional bodies

and community organisations

5. Education and training institutions[1]

6. Government bodies that support and regulate

the AI industry[1]

7. Workers in AI occupations[1]


These sub-sectors were out of scope for the current report as we currently do not have datasets or standardised reporting frameworks for capturing


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Why is Australia’s AI
ecosystem significant?

Australia, along with most advanced economies,
is grappling with an ongoing productivity slump
(Australian Government, 2023). This causes lower rates
of economic growth and a reduced growth rate in living
standards. AI is a general-purpose technology that could
turn things around. AI is impacting every policy sphere,
industry sector and (practically) every career. This impact
will intensify in the future.

When used well, AI allows people to do things faster,
cheaper, safer and better. Just like electricity in the
1920s, AI has the capability to deliver an economy-wide
productivity boost in the 2020s and beyond. Many new
jobs, startups and corporations will be created through
AI over the coming years. Many existing companies and
workers will be impacted, which is part of the reason
why AI is attracting so much attention. It is both a critical
enabler of economic activity and a driver of disruption.

What’s changing in the world of AI?

The year 2023 is considered by some as a new phase in
the journey of AI. We’ve seen the rapid ascendancy of
foundation AI models and generative AI such as ChatGPT,
DALL-E 2 (OpenAI), GitHub Copilot, Imogen, Codey, Chirp
(Google), Llama 2 (Meta AI) and others. Many more such
models are likely to emerge over the coming months and
years. Foundation models such as these are typically trained
on vast quantities of data and can be used to perform a
wide variety of tasks and solve problems across virtually
all industries.

The global AI sector is racing to build new foundation
models and plug-ins to existing models. This will be
associated with the emergence and rapid scale-up of new AI
companies. It will also change what’s possible for existing
companies with the prospect of a significant productivity
uplift. There is much uncertainty about how the next
generation of foundation AI models will play out, but a
significant impact on Australia’s economy and industry is
certain. It’s already happening.


Where does our information
come from?

To describe the AI ecosystem, we draw upon large
commercial databases which are often used by investors
when searching for companies such as Crunchbase and
PitchBook. These databases contain detailed industry
classifications and wide-ranging company data. Our
analysis of AI product innovation and research activity
largely comes from The Lens, a comprehensive database
on Australian and global scholarly publishing and patent
applications. We also complemented these analyses
with structured qualitative interviews with experts
and stakeholders across the Australian AI ecosystem.

What’s in this report?

The report is broken up into three main sections. The first
section presents information on the AI industry in Australia
through the lens of startups and small, medium and large
companies whose primary business model involves making
and selling AI products and services. We examine the
temporal and spatial patterns of these AI companies and
classify their AI offerings. The next section focuses on the
research and development sector, examining patterns of
AI research publishing and patent applications in Australia
and areas of global specialisation. We also present
information about the AI research activities of Australia’s
leading universities and research institutes. The final
section presents insights from interviews with experts
across Australia’s AI ecosystem, shedding light on the
opportunities and barriers facing the ecosystem.


-----

### Artificial intelligence companies

Startups and small, medium and large
Australian‑headquartered companies
whose primary business activity is making
and selling AI products and services to customers.


-----

### How we identify and define an AI company

We define an Australian AI company as a privately owned or publicly listed company
headquartered in Australia whose primary business activities involve making and selling
AI products and services to customers. The list of Australian AI companies was constructed
using three datasets: Crunchbase (Crunchbase, 2023a), Pitchbook (Pitchbook, 2023) and the
Australian AI Ecosystem Discoverability Platform Beta (CSIRO, 2023).

531 privately owned
organisations

organisations


**OECD search phrases recommended**

for AI companies, goods and services

search: 105 keywords and phrases

##### Crunchbase 703

Over 2 million companies’ organisations
data including over 81,000 discovered
Australian companies

##### PitchBook 831

Over 3.6 million companies’ data organisations
including over 40,000 companies discovered
with offices in Australia

Australian AI Ecosystem 345
Discoverability
Platform Beta organisations

recorded

NAIC directory of Australian capabilities
to enable business adoption of AI
(126 companies) and NAIC company
list (219 companies)


**Validation**

Cross checking

of lists

**Verification**

Manual scan

of company

websites


-----

Limitations of our data
and methods

Identifying AI companies is a challenging task due to the
nature of AI as a general-purpose technology and the
range of ways it is used across all sectors of the economy.
Technology companies also make a large and dynamic
marketplace with company entries and exits occurring
frequently. The following limitations of this analysis need
to be considered when interpreting and using the results:

-  We do not include overseas technology corporations

with a presence in Australia because we cannot measure
the nature and extent of their Australian (versus global)
activity. However, they are likely to represent
a significant share of Australia’s AI activity.

-  While best efforts were made to collect data on as

many Australian AI companies as possible, the list is
not exhaustive. The companies with more general
descriptions of their activities on Crunchbase and
Pitchbook platforms could have been overlooked
in our search.

-  The coverage of the datasets used (Crunchbase,

Pitchbook) has been expanding over time, so trend
data should be interpreted with caution.

-  Data availability on the operations of private companies

and publicly listed companies varies, so any comparison
between these two groups should be performed
with caution.

-  The list of AI product and service offerings is sourced

from NAIC and complemented by the analysis of
Crunchbase industry classifications. This list is not
exhaustive and the classification categories are not
mutually exclusive.

-  For industry analysis, we applied the Crunchbase industry

taxonomy, which differs from the taxonomy used by the
Australian Bureau of Statistics.

Overall, the number of AI companies is likely to change
over time. Companies will grow, merge, enter and exit.
Definitions of AI will change as the technology changes.
Therefore, our estimate of AI companies in this report is
a snapshot for the time of publication.


We applied a set of 105 AI keywords developed by the OECD
(Nakazato and Squicciarini, 2021) to search the descriptions
of companies headquartered in Australia. Our aim was to
identify companies whose main product or service offering
was about AI. We recognise that many (arguably most)
companies use AI; but in this analysis, we’re after companies
who make and sell AI products or services as their main
business activity.

As a result of the search through three databases
(Crunchbase, Pitchbook, NAIC AI Ecosystem Discoverability
Platform Beta and company list), we identified a list of
1,606 Australian companies with unique names. The list
of companies was further validated and verified by
cross‑checking datasets and manual screening of company
web pages and LinkedIn profiles. Additional companies
identified through the stakeholder consultations were also
included in the final set, which totalled 544 Australian AI
companies, including 531 privately owned companies and
13 publicly listed companies.

To put these findings in perspective, Australia is home
to over 360,000 companies operating in the industries
where most AI companies operate; namely, professional,
scientific and technical services and information media and
telecommunications. According to the Australian Bureau
of Statistics (2023), at the end of June 2023, there were:

-  336,214 businesses in Australia in the professional,

scientific and technical services industry. The industry
demonstrated the third largest net increase in business
count in the past year (an increase of 4,960 businesses)
after healthcare and social assistance (10,721) and
transport, postal and warehousing (5,040)

-  24,757 businesses in the information media and

telecommunications industry in Australia. Compared
with the year before, the number of businesses in the
industry dropped by 0.3% or 75 companies.


-----

### Product and service offerings

Working closely with the NAIC, we identified a set of product and service offering categories for Australian AI companies,
noting that a company might provide multiple types of offerings. The categories are as follows:


**Cloud services**

Cloud services include software (an app or set of apps),
application development platforms and infrastructure
capabilities (memory, storage, networking). Cloud services
enable businesses to develop and implement AI capabilities.
Examples of AI cloud services capabilities include
development and implementation for SaaS (Software as a
Service), PaaS (Platform as a Service), IaaS (Infrastructure as
a Service).

**Consulting**

Consulting services assist with strategy, organisational
change processes and methods for optimal utilisation and
application of AI technologies. Consulting services enable
AI adoption and commercialisation and include services in
innovation consultancy, legal and financial services.

**Finished solutions**

Finished solutions providers create ready to use AI
applications, including platforms, enabling businesses
to adopt and implement AI technologies. Finished
solutions providers offer AI services to companies to help
solve business challenges, including end-to-end design,
implementation and maintenance of AI technologies for
specific business use cases.

**Hardware and infrastructure**

Hardware and infrastructure vendors provide physical
and network components of AI technologies to enable
computational capabilities and automation. Key
infrastructure for AI systems is high computing capacity
(CPU, GPU-based computing), storage capacity, networking
infrastructure (high bandwidth, low latency) and security.
This group of AI capability providers also include developers
of robotic solutions, communications and data centres
infrastructure, applications of sensor technology.

**Systems integration**

Systems integration providers develop solutions that
combine different computing operations components,
including hardware and software for a more coherent
and capable system. This includes developing software
components, such as speech synthesisers and
common‑sense knowledgebases, that are interoperable
with other components, and planning, coordinating,


**Data service**

Data services provide resources and expertise for the
utilisation of data to develop and optimise AI capabilities,
including datasets, data analysis and management.
This capability includes providers of DaaS (Data as a
Service), and web-delivered services that perform various
functions on data, such as data mining, visualisation,
image and speech recognition and others.

**Business transformation**

The business transformation AI capability includes
technology solutions and services in digitalisation,
digital transformation and broader business integration.
This category includes providers of business and
management information systems, and contract and
document management solutions.

**Research and innovation**

Research and innovation services provide expertise and
technical capabilities to enable cutting-edge progress on
emerging AI domains and solutions to complex challenges.
This category encompasses organisations seeking to
push the boundaries of knowledge and explore what is
possible in terms of AI applications, including the pursuit
of IP that can result in new products and services for
commercialisation.

**Governance and ethics**

The governance and ethics capability group includes
providers of advisory services that focus on the responsible
and ethical adoption of AI technologies in accordance
with ethical, privacy and security considerations and
requirements. Companies promoting inclusion and
diversity in the industry are also classified under this
capability group.

**Skills and training**

Skills and training providers enable individuals and
organisations to learn and build new workforce capabilities.
This category includes companies that provide online
training and courses along with tailored solutions for
workforce adoption and upskilling.


-----

Method for classifying companies

The table below shows the correspondences for the NAIC AI ecosystem taxonomy, Crunchbase Industry List and company
descriptions keyword search.


AI ECOSYSTEM
CAPABILITY
CATEGORY KEYWORDS

Cloud services Cloud technolog*, Cloud Comput*, Cloud Infrastructure,
Platform Service, Platform Vendor, Software as a Service,
SaaS, Platform as a Service, PaaS, Infrastructure as a
Service, IaaS, cloud service*

Finished solutions AI application*, AI as a Service, Artificial Intelligence as a
Service, Artificial Intelligence Development Service*, AI
Development Service*, Artificial Intelligence Solution*, AI
solution*, App*, platform*, game*, AI model*, software*


CORRESPONDING INDUSTRIES FROM
CRUNCHBASE INDUSTRY LIST

Cloud Infrastructure, Private Cloud, Cloud Data
Services, Cloud Management, Cloud Security,
Cloud Security, Cloud Computing, SaaS

App Discovery, App, Consumer Application,
Enterprise Application, Mobile App, Web
App, Software

Application Specific Integrated Circuit, ASIC,
Augmented Reality, Communication Hardware,
Communications Infrastructure, Computer Vision,
Data Center, Data Center Automation, GPS, GPU,
Network Hardware, Sensor, Virtual Reality, IT
Infrastructure, Network Security

Data Integration

Business Information Systems, Contact
Management, Document Management,
Management Information Systems

Application Performance Management, Business
Intelligence, Data Mining, Data Visualization,
Facial Recognition, Geospatial, Image Recognition,
Intelligent Systems, Location Based Services,
Predictive Analytics, Quantified Self, Speech
Recognition, Text Analytics, Usability Testing,
Big Data, Analytics

Consulting, Financial Services, FinTech, Trading
Platform, Market Research, Advertising, Marketing

GovTech, Information Services, Cyber Security,
Government, Military, National Security,
Network Security

Continuing Education, Corporate Training,
E-Learning, EdTech, Education, Edutainment,
Higher Education, MOOC, Personal Development,
Primary Education, Secondary Education, Skill
Assessment, STEM Education, Training, Tutoring,
Vocational Education


Hardware and
infrastructure


AI Infrastructure, AI Hardware, Artificial Intelligence
Infrastructure, Artificial Intelligence Hardware, GPU, CPU,
GPU-based computing, networking infrastructure, FPGA*,
on-chip memory, SRAM, automat*, robot*, sensor*


Systems integration Systems integrat*, speech synthesizer*, common sense
knowledgebase*, AI system*, Artificial Intelligence
system*, integrat*, design architect*, chatbot*, symbiosis,
AI standard*


Business
transformation


Business transform*, digital* transform*, digitali*, optim*
process, business solution*, solv* business, digital
product*, management tool*, business system*, growing
business*, digital ecosystem, business intelligence


Data services data analy*, data management, Data as a service, DaaS,
Training data, data aggregat*, data annotation, data
architecture, data transit, data storage, data analysis,
visuali*, voice*, speech*, vision*, data analy*, big data*,
video analy*, conversation*, art*, music*, image recogni*,
knowledge graph*, simulat*, interpret*

Consulting Consulting Service*, Consult*, Innovation Consult*,
innovation service, scal* up, legal*, decision*, insurance*,
financial report*, report* servic*, financial servic*,
business advis*, data-driven*, marketing


Research and
innovation

Governance
and ethics


research*, Innovation servic*, Artificial Intelligence
research, AI research, knowledge broker*, research
broker*, science, scientist*

Governance, ethic*, privacy, female*, woman*,
indigenous,*securit*, Data govern*, data privacy, cyber*


Skills and training skills, training*, workforce*, learning, upskill, workforce
capabilit*, educat*, employee assist*, recruit*


*Used as a common wildcard symbol to broaden the search to include the words that start with the same letters.


-----

Product and service offerings of Australian AI companies


To understand the capabilities of the Australian AI
ecosystem, we applied the NAIC taxonomy of AI
product and service offering categories to the company
descriptions. We incorporated the results of the NAIC
survey (CSIRO, 2023) and the Crunchbase industry group
classifications (Crunchbase, 2023b) to better define what
capabilities each company contributes to the Australian
AI ecosystem. Each Australian AI company was classified
into at least one of the ten AI product and service
offering categories.

Of the 544 Australian AI companies identified in this
analysis, 395 (73%) provide capabilities in data services.
Data services include expertise for the utilisation of data to
develop and optimise AI capabilities, including data analysis
and management. This component of AI capability includes
providers of DaaS, data mining, visualisation and image and
speech recognition, among others.

Business transformation 11

Governance and ethics 19

Skills and training

Consulting

Finished solutions

Data services


A large share of the companies (65%, 356 companies) are
also classified as providers of finished solutions, which
include ready to use AI applications (e.g., platforms) that
enable businesses to adopt and implement AI technologies.
Finished solutions providers also offer AI services to
companies to help solve business challenges, including
end-to-end design, implementation and maintenance of
AI technologies for specific business use cases.

Roughly every two out of five AI companies are engaged
in consulting and over 200 companies provide capabilities
in skills and training. Skills and training providers enable
individuals and organisations to learn and upskill for
new workforce capabilities in AI-related domains.
Another leading share of AI capabilities is in the domain of
AI governance and ethics (19%), which includes companies
actively promoting diversity and inclusion in the industry.


11

19

19

22

23

29

39

44

65

73


0 20 40 60 80

SHARE OF AUSTRALIAN AI COMPANIES (N = 544) (%)

**Capabilities each company contributes to the Australian AI ecosystem**


-----

AI companies by industry grouping

We used industry groupings available in commercial
databases to map AI companies by industry group.
Based on Crunchbase industry group taxonomy, 88%
of Australian AI companies are classified as software
providers and 79% of companies work in data and analytics.

Transportation 15

Commerce and shopping 15

Real estate 18

Administrative services 20

Privacy and Security 21

Mobile 21

Education 28

Media and entertainment 29

Apps 32

Design 41

Sales and marketing 45

Health care 50

Financial services 50

Hardware 60

Internet services 66

Professional services 96

Information technology

Science and engineering

Data and analytics

Software


This result is not surprising given the nature of AI
development and adoption. Importantly, 62% of AI
companies are also in the science and engineering domain,
which might reflect innovation, research and development
activities. Beyond this, the largest share of AI companies
are working in information technology (IT), professional
services, financial services and healthcare.


15

15

18

20

21

21

28

29

32

41

45

50

50

60

66

96

201

286

365

405


0 100 200 300 400 500

NUMBER OF AUSTRALIAN AI COMPANIES

**Top 20 industry groups for Australian AI companies (n = 461)**

Note: A total of 461 Australian AI companies had available data on industry group classification and were included in this analysis. One company can have
multiple industry groups, so it would be counted across multiple industry groups.


-----

### Geographic patterns of AI companies

We mapped the locations of AI company headquarters across Australia. Australian AI
companies are located in all States and Territories, with the majority in New South Wales,
Victoria and Queensland. The following heat maps show the intensity of clustering.
While the most intense clusters are in Australia’s capital cities, regional areas also feature.

Western Australia 24

South Australia 23

Australian Capital Territory 12

Northern Territory 2

Tasmania 2

241

178

62

24

23

12

2

2


0 50 100 150 200 250

NUMBER OF AUSTRALIAN AI COMPANIES

**Locations of AI company headquarters across Australia**


**Clustering intensity**

**LOW** **HIGH**

This map was created using the Google Geocoding API and data from Crunchbase on
the AI company name, short description, web address and general location (city/suburb and
State/Territory). We manually checked and edited the company addresses with web searches.
With the point (headquarters) data we used the “Folium HeatMap” Python plugin which uses
a density estimation algorithm to create a heatmap.


-----

While most Australian AI companies are located in
greater capital cities areas, 29 are in regional areas.

Greater Perth 24

Greater Adelaide 23

Greater Canberra 12

Rest of Queensland 12

Rest of New South Wales 10

Rest of Victoria 6

Greater Darwin 2

Greater Hobart 1

Rest of Tasmania 1

231

172

50

24

23

12

12

10

6

2

1

1


0 50 100 150 200 250

NUMBER OF AUSTRALIAN AI COMPANIES

**AI companies by location**


Greater Sydney (231)

Greater Melbourne (172)

Greater Brisbane (50)

Greater Adelaide (23)


Greater Perth (24)

Greater Canberra (12)

Greater Darwin (2)

Greater Hobart (1)


Rest of Queensland (12)

Rest of New South Wales (10)

Rest of Victoria (6)

Rest of Tasmania (1)


**AI companies by greater capital city** **AI companies by regional areas**


-----

**AI company heatmap – Greater Sydney**

**CITY CENTRE**

**LOW** **HIGH**


**AI company heatmap – Greater Melbourne**


**CITY CENTRE**

**LOW** **HIGH**


-----

**AI company heatmap – Greater Brisbane**

**CITY CENTRE**

**LOW** **HIGH**


**AI company heatmap – Greater Perth**


**CITY CENTRE**

**LOW** **HIGH**


-----

**AI company heatmap – Greater Adelaide**

**CITY CENTRE**

**LOW** **HIGH**


**Clustering intensity of AI companies in Australian cities – Nearest Neighbour Analysis**

Clusters are hotspots of concentrated industry activity and have been shown to be important in the formation, growth
and development of industries (Hajkowicz et al., 2023a; Cameron, 2022; Hajkowicz et al., 2021). We ran an unsupervised
machine learning clustering algorithm[2] to identify locations in Australia where AI companies are geographically clustered.
We found 8 clusters in Sydney, Melbourne, Brisbane, Perth and Adelaide containing 296 companies (54% of the total
companies identified). The AI companies are located within proximity to each other, indicating tight clustering, with an
average nearest neighbour distance of 80 m (range = 55 m to 158 m).

NUMBER OF AI COMPANIES IN CLUSTER AVERAGE (MEAN) DISTANCE METRES


160

140

120

100

80

60

40

20


180

160

140

120

100

80

60

40

20


146

158

147 149

136

88

96

89

77

55

15 14 11 8 8 6

George St Little Collins St Queen St Walker St Ann St Pirie St William St Cornwallis St
Sydney Melbourne Brisbane City North Sydney Fortitude Valley Adelaide Perth Eveleigh, Sydney

Number of AI companies in cluster Average (mean) distance to nearest neighbour (metres)


The clusters are centred on these locations and were identified by an unsupervised machine learning algorithm called DBSCAN – density-based spatial


-----

**Regional capability strengths**

The graphs below show the top five capability categories
for Australian States and Territories.[3] Except for the
Australian Capital Territory (ACT), the leading capability
categories across states were data services and
finished solutions.

In the ACT, 75% of AI companies had capabilities in skills
and training, which was greater than the national average
of 39%. The share of the companies contributing to AI
capability in consulting was 58% – also above the national
average. This is likely related to the service focus of ACT and
a higher concentration of AI companies providing training
and consulting for government and industry organisations.

SOUTH AUSTRALIA

Governance
and ethics
Skills and training 22

Consulting 30
Research and
innovation

Finished solutions 57

Data services 61


South Australia had a higher than national-average
concentration of research and innovation capabilities.
This is likely related to the concentration of world-renown
AI hub of research institutions in Adelaide, including
Defence Science and Technology Group, the Australian
Institute for Machine Learning, MITbigdata Living Lab
by Massachusetts Institute of Technology, the Australian
Research Centre for Interactive and Virtual Environments,
and the Australian Cyber Collaboration Centre
(Bratanova et al., 2022).

QUEENSLAND

Hardware and
infrastructure
Skills and training 34

Consulting 44

Finished solutions 69

Data services 79

29

34

44

69

79


NEW SOUTH WALES

Hardware and
infrastructure

Consulting 42

Skills and training 44

Finished solutions 72

Data services 72

29

42

44

72

72


AUSTRALIAN CAPITAL TERRITORY

Governance
and ethics

Finished solutions 58

Consulting 58

WESTERN AUSTRALIA Skills and training

Hardware and

33

infrastructure

Finished solutions

Skills and training 33

Consulting 71

Finished solutions 79

Data services 83

22

22

30

30

57

61

VICTORIA

Data services 67

Skills and training 75


Hardware and
infrastructure
Skills and training

Consulting

Finished solutions


Share of product and service
offering group (%)

National average


33

58

58

67

75


Data services 29


72

56

46

34

29


**Product and service offerings by State and Territory[3]**


-----

**Capability strengths of eight AI clusters**

The top five product and service offering categories for
the eight identified AI company clusters are demonstrated
below. Data services and finished solutions are among
the top three categories across all eight clusters.

**Greater Sydney clusters**


Consulting is relatively higher in Perth, Adelaide and
Brisbane central business district clusters, while the
Eveleigh cluster in Sydney has over half of member
companies offering products and services in research
and innovation.


**146 AI COMPANIES** **14 AI COMPANIES** **6 AI COMPANIES**


GEORGE ST, SYDNEY

Hardware and
infrastructure

Skills and training

Consulting

Finished solutions

Data services


WALKER ST, NORTH SYDNEY

Hardware and
infrastructure
Skills and training 43

Consulting 50

Data services 64

Finished solutions 71


CORNWALLIS ST, EVELEIGH, SYDNEY

Hardware and
infrastructure

Business transformation 17

Skills and training 17

Data services 50

Research and innovation 67

Finished solutions 67


27

40

41

70

73


29

43

50

64

71


17

17

17

50

67

67


**Greater Melbourne cluster** **Greater Perth cluster** **Greater Adelaide cluster**

**88 AI COMPANIES** **8 AI COMPANIES** **8 AI COMPANIES**

LITTLE COLLINS ST, MELBOURNE WILLIAM ST, PERTH PIRIE ST, ADELAIDE


Research and

38 Governance and ethics

innovation


Cloud services 30

Skills and training 31

Consulting 48

Finished solutions 56

Data services 77

30

31

48

56

77


**Greater Brisbane clusters**


Consulting

Data services


Consulting

Data services


38

38

63

88

100


25

38

38

50

88


**15 AI COMPANIES** **11 AI COMPANIES**

QUEEN ST, BRISBANE CITY ANN ST, FORTITUDE VALLEY


Systems integration

Research and innovation


Cloud services

Consulting



Governance and ethics


Hardware and infrastructure



Consulting

Data services


Data services


27

27

45

55

55

73


33

33

33

40

47

53

80


Share of product and service offering group (%) National average

**Product and service offerings by cluster**


-----

### Company formation and maturity


Australia is home to 544 AI companies, which is comparable
to other global leading AI countries. For example, having
a larger population and economy, Canada recently
reported 670 AI companies (University of Toronto, 2020).
Until the last decade, the number of AI companies founded
annually in Australia was growing at a slow pace, with an
average of 5.5 companies added to the ecosystem annually
between 1986 and 2013. This growth has since intensified,
with 74 companies founded in 2017, 61 in 2018 and 57 in
2019. A similar pattern has been observed internationally,
including the UK and Singapore.

NUMBER OF AUSTRALIAN AI COMPANIES

600

500

400

300

200

100

0

1962 2023

**The cumulative number of Australia AI companies has intensified**
**in the last decade**

Note: Foundation year data was only available for 531 of the total
544 Australian AI companies identified in this analysis.

SHARE OF AUSTRALIAN COMPANIES IN EACH SECTOR (%)

16

14

12

10


The Australian AI business ecosystem is young, with 76%
of companies (396 companies) founded in the last decade.
The emerging nature of Australian AI companies is evident
when compared to the broader Australian IT sector: the
median company age is 6 years for AI companies and
13 years for IT companies. The Australian IT sector has
been growing steadily, with growth intensifying in the
last decade. Growth in AI companies is skewed more
sharply towards the last 5 years, with 39.2% of Australian AI
companies founded in 2018–2022, compared with 14.8% of
Australian IT companies (Crunchbase, 2023a).

COUNT OF NEW AI COMPANIES

300

250

200

150

100

50

0

1985 2023

UK Singapore Australia

**The United Kingdom and Singapore have shown similar**
**temporal growth patterns to Australia**


1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 2019 2021 2023

IT companies AI companies

**Share of Australian IT and AI companies by founding year**


-----

Publicly listed Australian
AI companies

Our analysis revealed 13 Australian publicly listed AI
companies trading on the Australian Stock Exchange and
internationally (UK, USA). The total market capitalisation of
these companies is approaching $73 billion. All companies
operate in the IT sector with a specific industry focus.
Five out of 13 companies develop solutions for the
healthcare industry, two operate in the fields of energy and
the environment and one in transportation. While these
companies operate across multiple Australian offices
and internationally, their headquarters are in Victoria
(6 companies), New South Wales (4), Western Australia (2)
and the ACT (1).

A large majority of Australian AI companies are
headquartered in New South Wales, with almost every
second AI company founded in 2013–22 located there.
These companies are proportionally younger companies
too. Victoria hosts a larger proportion of more mature
AI companies, with 40% of Australian AI companies
founded in 1993–2012 and operating today headquartered
in Victoria.


NUMBER OF AI COMPANIES

250

200

150

100

50

0

211

173

35
4 5 5 21

1001+ 501– 251– 101– 51–100 11–50 1–10
1000 500 250

NUMBER OF EMPLOYEES

**Most Australian AI companies have 50 employees or less**

Note: The total count of Australian AI companies with data on the number
of employees was 454 (out of 544 companies included in this analysis).


47 13 3 29 5 2

38 10 4 41 4 4

44 8 5 38 3 3

0 10 20 30 40 50 60 70 80 90 100

AUSTRALIAN AI COMPANIES BY FOUNDATION YEAR AND LOCATION (%)

New South Wales Queensland South Australia Victoria Western Australia

Other States and Territories

**New South Wales has a larger share of younger AI companies, with Victoria home to more mature AI companies**


-----

### Jobs and skills demand


The demand for AI-related skills is growing in Australia
and internationally. In 2022, 2.1% of all job postings in the
USA were AI-related (up from 1.8% in 2021). Australia is
among the global leaders in terms of AI job postings,
with 1.2% of all job postings in 2022 being AI-related.

United States

Canada

Australia

United Kingdom


Demand for AI jobs has been going faster in Australia
relative to international comparisons, with the share of
AI-related job postings increasing by more than 7 times
between 2014 and 2022.


2.1

1.4

1.2

1.1

1.0

0.8

0.4


0.0 0.05 1.0 1.5 2.0 2.5

YEAR-ON-YEAR GROWTH RATE (%)

**Share of AI job postings as a percentage of all job postings (%), 2022**

Data source: Stanford AI Index report 2023 (Maslej et al., 2023).

GROWTH RATE IN AI JOB POSTINGS (2014 = 1)

8.9

7.9

Australia (7.3)

6.9

5.9

4.9


3.9

2.9

1.9

0.9


Canada (3.7)
United States (3.6)

United Kingdom (3.2)

New Zealand (3.0)

France (2.3)
Germany (2.1)

2014 2015 2016 2017 2018 2019 2020 2021 2022


**Growth rate in AI job postings relative as percentage of all job postings**

Data source: Stanford AI Index report 2023 (Maslej et al., 2023).


-----

### Investment trends


Venture capital (VC) investors
in Australian AI companies

According to funding deals recorded on Crunchbase, the
top five investors in Australian AI companies are as follows:

**Flying Fox Ventures is a Victoria-based venture capital**
investment firm founded in 2021. The company invests in
early-stage companies in technology, business-to‑business
and business-to-consumer sectors in Australia and
New Zealand.

**Artesian VC is a global company with offices in Sydney,**
Melbourne and Adelaide. The company was established
in 2004 and specialises in public and private debt, VC and
impact investment strategies.

**Blackbird is a VC firm that incubates and invests in**
early‑stage software technology companies.

**Main Sequence is a Sydney-based VC firm co-founded by**
CSIRO and the Australian Government in 2017. The firm
focuses on investment in deep tech and frontier technology
companies and leveraging world-leading research from
Australian institutions.

**Carthona Capital is a VC investment firm that is based in**
Sydney and invests globally. The firm was founded in 2014
and focuses on pre-seed, seed and Series A companies.


AI investment has seen substantial growth over the past
decade globally and in Australia. In 2022, private investment
in AI globally was $132.3 billion (US$91.9 billion) – 18 times
higher than it was in 2013 (Maslej et al., 2023). This growth
dropped off for the first time in 2022, with global private
investment in AI, declining by 26.7% from 2022 to 2021
(Maslej et al., 2023). Conversely, private investment in AI
in Australia spiked in 2021 and remained high in 2022.
Australia was ranked in 11th place for global ranking
by private investment in AI in 2022, which reflected an
improvement from 14th place over the period 2013–21.

MILLIONS OF DOLLARS

2500


1943


2000

1500

1000

500


1664

306
175 182
75

2017 2018 2019 2020 2021 2022


**Australian investment in AI**

Data source: Stanford AI Index report 2023 (Maslej et al., 2023), Global AI
Vibracy Tool (Stanford HCAI, 2023).


-----

### Research and development

Information about research publishing by Australia’s
universities and research institutes working on AI
and information about AI patent application trends.


-----

### Methods and source data

For our analysis of research publishing, intellectual property patents and Australian
research institutes, we draw upon data from The Lens, Scopus by Elsevier and AI
search phrases from the OECD. We use these datasets to identify AI-related scholarly
publishing (peer-reviewed books, book chapters, journal papers and conference
papers) using a bibliometric analysis approach. We apply a similar bibliometric
analysis as approach used in Hajkowicz et al. (2023b).


Patents
applications
in Australia

Total for 2000–23:
**5,281 about AI**
**by jurisdiction**
(893,759 in total),
representing
0.6% of patents

Scholarly
publications
in Australia

Total for 2000–23:
**92,940 about AI**
(1,679,922 in total),
representing 5.5%
of publications


##### OECD

AI search phrases from OECD
expert groups (225 unique
phrases/technologies).

##### The Lens Select patents

and scholarly

Comprehensive global database on scholarly

publications

publishing and patents. We extracted data over

from world

a 24-year period from 2000–23 using the API.

and Australia

##### Scopus

All Science Journal Classification Codes (ASJC)

-  4 First level categories

-  31 Second level categories

-  333 Third level categories.


-----

Revealed technology advantage metric and statistical test


We adopt the revealed technology advantage (RTA)
metric to assess the level of specialisation Australia has in
application domains of AI. The RTA is a metric proposed by
the OECD (2023) to measure technological specialisation
and assess the comparative advantages a country or
a jurisdiction might have in a particular technology.
Countries/jurisdictions will typically seek to invest in
technological capabilities where they have a comparative
advantage (i.e., those denoted by a high RTA).

An illustration of how the RTA is calculated is given below
for a hypothetical example where a country has 120 AI
papers in research field X and 1,000 papers in research field
X in total. This compares to the world which has 30,000 AI
papers in research field X and 1,000,000 papers in research
field X in total. This would yield a high RTA score of 4.
The calculations are as follows:

Number of AI papers in field_x in country

Number of papers in field_x in country

RTA =

Number of AI papers in field_x in world

Number of papers in field_x in world

120

1000

RTA =

30,000

1,000,000

12%

RTA =

3%

RTA = 4.0


While there is no clear cut-off about what represents a
significant RTA, a value of 4 or greater would typically
indicate an area of highly specialised technological
capability. In this hypothetical case we would probably
infer that the country has specialised AI capability in
research field X. RTAs are similar to location quotients
in economic geography where values above 1.2 to 1.5 are
often considered evidence of significant specialisation
(Crawley et al. 2013).

In our analysis, we apply a statistical technique developed
by Crawley et al. (2013) to identify ranges above and below
the estimated RTA score. Originally developed for use in the
calculation of location quotients in economic geography,
this statistical technique handles cases where a small
number of publications might yield a high score but also
carries high uncertainty and is therefore not significant.


-----

### Knowledge creation (research publishing trends)


Since 2000, Australian researchers have published
92,940 peer-reviewed journal articles, conference papers,
books and book chapters that refer to AI technologies in
the title, abstract or keywords. This represents 5.5% of our
total publishing. The intensity of AI publishing has increased
over time, with AI publishing accounting for 9.6% of all
publishing today compared with 2.5% in 2000.

AI RESEARCH PUBLISHING INTENSITY (%)

10

8

6

4

2

0

2000 2023

Australia World

**Share of all peer-reviewed publishing on the topic of AI**

NUMBER OF AUSTRALIAN AI PUBLICATIONS (’000)


Compared to other countries Australian researchers have
higher AI adoption rates, with 9.6% of Australian research
publications using or developing AI technology versus a
global average of 7.2%. Since 2000, Australia created 2.2%
of AI-related global publications, compared with 1.5% of
global research publications on all topics, demonstrating
Australia’s above-average contribution to global AI research.
While Australia’s publications grew in absolute terms from
2021 to 2022, Australia’s share of global publications declined
due to a rapid increase in global publishing driven by other
countries, such as China.

AUSTRALIAN SHARE OF GLOBAL PUBLISHED RESEARCH (%)

3.0

2.5

2.0

1.5

1.0

0.5

2000 2023

Artifcial intelligence All topics

**Australia’s share of global peer-reviewed publishing**

SHARE OF AUSTRALIAN PUBLISHING ON THE TOPIC OF AI (%)

16

8

4

Number of peer-reviewed

AI publications in field

AI publishing
intensity

Total number of all peer-reviewed

publications in field


0

2000 2023

Physical sciences Health sciences


12

10


10.8

9.6

8.6


0.6

0

2000 2023

**The volume of AI research publications with an Australian author**
**has been growing, except for the pandemic when there was a**
**downturn in publishing in most fields**

Note: According to The Lens, overall publishing in all fields saw a slowdown
during 2021–22 associated with the COVID-19 pandemic. Data for 2023 is an
incomplete year with data up until June 2023.


Life sciences


Social sciences and humanities


**AI publishing intensity has increased across all fields of research**
**(first-level categories) in Australia, with the strongest growth in**
**physical sciences**

Note: The last data point is for 2023 and is based on data only from January
to June 2023.


-----

The footprint of AI in Australia across more granular subject groups


Over the past two decades, AI has established a strong
position in practically all research fields in Australia,
suggesting that Australian researchers are making use of
AI to further innovation and problem-solving in their own
field. Across the second-level ASJC subject fields, the areas
that had the highest level of AI research publishing in 2023
were computer science, mathematics, decision sciences,
environmental science and engineering.


Whilst these are research fields, they’re likely to have
some connection to cutting-edge technology innovation
within associated Australian industries. For example,
the “health professions” field is likely to capture much
AI activity within the healthcare sector in Australia.


**Percent share of Australian research publishing by second-level ASJC field**

ALL SCIENCE JOURNAL CLASSIFICATION
(LEVEL 2) 2000 2005 2010 2012 2014 2016 2018 2019 2020 2021 2022 2023*

Agricultural and Biological Sciences 0.9% 1.3% 1.7% 2.0% 2.5% 3.0% 3.6% 3.9% 4.3% 4.4% 6.6% 6.0%

Arts and Humanities 2.2% 1.6% 1.7% 2.1% 1.4% 1.8% 1.6% 2.5% 2.5% 2.6% 3.9% 2.8%

Biochemistry Genetics and Molecular Biology 0.7% 1.3% 2.2% 2.7% 3.6% 4.0% 4.4% 4.6% 5.4% 5.9% 7.5% 7.7%

Business Management and Accounting 2.9% 1.1% 2.9% 2.9% 2.5% 4.5% 4.6% 4.9% 5.4% 7.0% 6.4% 7.4%

Chemical Engineering 2.0% 1.9% 1.1% 1.5% 2.3% 2.6% 1.6% 2.3% 2.2% 2.7% 5.1% 5.5%

Chemistry 0.8% 1.4% 1.0% 1.4% 1.5% 1.7% 1.9% 2.3% 2.6% 3.6% 4.8% 4.6%

Computer Science 20.2% 22.7% 21.4% 24.1% 24.5% 27.0% 31.1% 32.7% 35.0% 39.5% 39.2% 40.3%

Decision Sciences 7.4% 7.4% 11.0% 11.8% 8.3% 12.7% 13.6% 12.6% 16.6% 18.7% 20.1% 21.3%

Dentistry 1.3% 3.2% 3.0% 2.0% 5.7% 2.3% 3.6% 3.4% 3.4% 8.7% 6.7% 4.0%

Earth and Planetary Sciences 0.9% 1.8% 1.7% 2.0% 2.9% 3.2% 3.9% 4.4% 4.8% 5.8% 8.0% 8.1%

Economics Econometrics and Finance 1.3% 2.1% 1.1% 1.0% 1.0% 1.2% 1.6% 2.5% 2.2% 3.1% 5.2% 4.3%

Energy 0.5% 3.1% 2.6% 3.1% 3.7% 4.5% 3.1% 4.5% 5.2% 5.9% 7.0% 9.5%

Engineering 5.1% 4.8% 5.3% 6.9% 7.0% 7.8% 8.5% 10.8% 11.8% 14.0% 15.0% 19.8%

Environmental Science 1.5% 2.2% 2.0% 2.2% 2.8% 3.1% 4.1% 4.4% 4.5% 5.1% 6.3% 6.3%

Health Professions 2.3% 0.7% 1.8% 2.3% 3.5% 3.6% 4.1% 5.5% 5.5% 7.5% 9.4% 10.7%

Immunology and Microbiology 0.5% 1.1% 2.0% 2.4% 2.9% 3.0% 2.7% 4.1% 3.9% 4.5% 5.4% 3.3%

Materials Science 0.8% 0.7% 0.8% 1.1% 1.1% 1.6% 1.8% 3.8% 3.9% 4.2% 4.7% 5.3%

Mathematics 8.7% 13.5% 12.9% 13.8% 12.8% 15.4% 16.2% 17.7% 18.9% 20.7% 24.1% 25.1%

Medicine 1.4% 1.8% 2.5% 3.1% 3.5% 4.1% 4.7% 5.2% 5.7% 6.5% 8.0% 8.1%

Neuroscience 1.6% 2.8% 3.5% 4.9% 5.4% 6.0% 6.4% 6.5% 7.6% 8.2% 10.1% 10.4%

Nursing 1.6% 0.9% 2.3% 3.2% 2.8% 3.7% 4.6% 4.5% 4.7% 5.0% 6.3% 6.2%

Pharmacology Toxicology and Pharmaceutics 0.7% 1.1% 1.5% 1.6% 2.3% 1.5% 3.9% 3.5% 4.3% 4.7% 5.8% 4.4%

Physics and Astronomy 1.4% 1.5% 1.4% 1.4% 1.9% 2.3% 3.6% 4.8% 5.4% 7.6% 8.1% 10.1%

Psychology 2.8% 3.6% 3.6% 5.6% 4.1% 5.4% 4.9% 5.1% 5.3% 4.9% 6.1% 6.2%

Social Sciences 1.1% 1.5% 1.5% 1.8% 1.7% 2.3% 2.4% 3.2% 3.5% 3.6% 5.4% 5.2%

Veterinary 0.4% 1.0% 1.8% 3.7% 3.4% 4.8% 3.3% 4.2% 4.0% 5.1% 7.5% 3.9%


*Includes data for January to June 2023.


-----

### Application domain specialisations


Of the 333 third-level ASJC research fields where AI is
being applied, we identified 31 fields that reflect areas of
significant specialisation for Australia and likely areas of
comparative advantage. Areas of significant specialisation
were identified as research fields with Australian research
publishing RTA of 1.3 or greater and a lower bound of the
uncertainty range of 1.1 at the 95% confidence interval.

Food animals (livestock production)

Medical laboratory technology

Horticulture

Optometry

Dermatology

Decision sciences

Arts and humanities

Automotive engineering

Veterinary

Analysis

Ceramics and composites

Economics, econometrics and fnance

Earth and planetary sciences

Applied mathematics

Aerospace engineering

Animal science and zoology

Environmental science

Ophthalmology

Biophysics

Soil science

Analytical chemistry

Atomic and molecular physics, and optics

Aquatic science

Medicine

Computational mathematics

Biotechnology

Toxicology

Media technology

Infectious diseases

Psychiatry and mental health 1.32

Computational theory and mathematics 1.30

4.81

3.76

3.17

2.45

2.42

2.21

2.18

2.11

1.99

1.95

1.95

1.87

1.84

1.84

1.73

1.73

1.55

1.54

1.54

1.54

1.48

1.46

1.45

1.43

1.40

1.39

1.38

1.35

1.35

1.32

1.30


0 1

**Specialisation in AI application domains in 2022**


We note the cut-offs are assumed; in reality, the level of
specialisation is a continuously graded scale from high to
low. We use the cut-offs to create a shortlist of areas of
higher specialisation. There were 31 application domains
with an RTA score that met the criteria for significant
specialisation (above 1.3) and had statistical significance
at the 95% confidence interval.

4.81

3.76

3.17

2.45

2.42

2.21

2.18

2.11

1.99

1.95

1.95

1.87

1.84

1.84

1.73

1.73

2 3 4 5

REVEALED TECHNOLOGY ADVANTAGE SCORE


-----

For the 31 application domains with significant
specialisation for Australia, we calculated the share of
research in this field globally that is driven by an Australian
author. Australian authors account for approximately
26% of global AI publications in optometry, 8.5% in
livestock production and 6.8% in the decision sciences,
which is greater than the average Australian share
of global research publishing across all fields (1.3%).

Optometry

Food animals (livestock production)

Psychiatry and mental health 4.8

Aquatic science 4.7

Ophthalmology 4.6

Arts and humanities 4.5

Infectious diseases 3.9

Animal science and zoology 3.9

Medical laboratory technology 3.8

Veterinary 3.6

Ceramics and composites 3.5

Horticulture 3.5

Dermatology 3.5

Computational theory and mathematics 3.3

Automotive engineering 3.0

Toxicology 3.0

Computational mathematics 2.6

Biophysics 2.5

Economics, econometrics and fnance 2.5

Biotechnology 2.4

Applied mathematics 2.2

Media technology 2.1

Aerospace engineering 2.1

Analytical chemistry 2.0

Medicine 1.7

Atomic and molecular physics, and optics 1.6

Environmental science 1.5

Analysis 1.4

Earth and planetary sciences 1.4


Optometry, livestock production and decision sciences
therefore reflect AI application domains where Australian
research is highly represented.

26.3

8.5

6.8

4.9

4.8

4.7

4.6

4.5

3.9

3.9

3.8

3.6

3.5

3.5

3.5

3.3

3.0

3.0

2.6

2.5

2.5

2.4

2.2

2.1

2.1

2.0

1.7

1.6

1.5

1.4

1.4


10 15 20 25 30

AUSTRALIA’S SHARE OF GLOBAL PUBLISHING (%)


**Australia’s share of global AI publishing in application domains in 2022**


-----

### Artificial intelligence technology specialisations

We find numerous AI technologies where Australia has specialised capability on the global stage. Here we show
AI technologies with an RTA > 1.5 and lower bound above 1.0 at 95% Confidence Interval. Data is for the year 2022.


AUSTRALIAN
SHARE OF GLOBAL
PUBLISHING (%)


DESCRIPTION (TEXT IN THIS FIELD WAS INITIALLY GENERATED BY
CHATGPT 4 AND THEN CHECKED AND EDITED BY THE LEAD AUTHOR) RTA


AI TECHNOLOGY


Self-organising map A type of artificial neural network trained using unsupervised machine
learning to produce a low-dimensional representation of the input space,
typically a 2D grid.

Ant colony optimisation A type of optimisation inspired by the behaviour of ant colonies.
It is mainly used to find optimal paths through maps or charts.

Gradient tree boosting A machine learning technique that produces a prediction model in the form
of an ensemble of weak prediction models, typically decision trees.


7.63 14.0

5.27 9.7

5.19 9.5

3.03 5.6

2.58 4.7

2.50 4.6

1.96 3.6

1.95 3.6

1.89 3.5

1.86 3.4

1.68 3.1

1.59 2.9

1.58 2.9

1.54 2.8

1.51 2.8


Multi-objective
optimisation

Evolutionary
computation


An area of multiple criteria decision analysis concerned with mathematical
optimization problems involving more than one objective function to be
optimized simultaneously.

A family of algorithms for optimization inspired by biological evolution, such
as genetic algorithms, evolutionary strategies, genetic programming, etc.


Variational inference A method in machine learning that uses optimization techniques to estimate
the true probability distribution of data.

Social robot A type of autonomous robot that interacts and communicates with humans or
other autonomous physical agents by following social behaviours and rules.

Recommender system This is a type of machine learning that uses information provided by a user to
predict and/or prioritise the products/items they’re seeking.

Link prediction A problem in network science and social network analysis to predict the
existence of a link between two nodes, given a snapshot of a network.

Cognitive modelling A method used in AI to simulate human problem-solving and mental task
processes in a computerized model.

Evolutionary algorithm A subset of evolutionary computation, a generic population-based
metaheuristic optimization algorithm.


Human-robot
interaction


A field of study dedicated to understanding, designing and evaluating robotic
systems for use by or with humans.


Meta learning These are models that learn how to learn. The learning algorithm is adjusted
and improved in each iteration of problem solving. It helps identify which
algorithms perform best for a given problem.

Decision model This captures a wide range of AI-based tools and technologies which
are designed to help a decision model choose from a set of competing
alternatives.

Random field Random fields are used to infer the joint distribution of a set of variables
based on their interactions and dependencies. They’re used in image
processing, language processing and computational biology.


-----

Artificial intelligence technology specialisations – error margins

This graph shows the data on AI technology specialisation in Australia from the previous table but with
error margins around the RTA scores at the 95% confidence interval.

Self-organising map

Ant colony optimisation

Gradient tree boosting

Multi-objective optimisation

Evolutionary computation

Variational inference

Social robot

Recommender system

Link prediction

Cognitive modelling

Evolutionary algorithm

Human-robot interaction

Meta learning

Decision model

Random feld

0.0 5.0 10.0 15.0 20.0 25.0

REVEALED TECHNOLOGY ADVANTAGE SCORE

**Error margins at 95% confidence interval for AI technology specialisations in 2022**

Australia accounts for 1.3%

of global research publishing
on all topics in all felds

Evolutionary algorithm 3.1

Human-robot interaction 2.9

14.0

9.7

9.5

5.6

4.7

4.6

3.6

3.6

3.5

3.4

3.1

2.9

2.9

2.8

2.8


0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0

AUSTRALIAN SHARE OF GLOBAL AI PUBLISHING (%)


-----

### Universities and research institutes

Our datasets identify 167 research institutes in Australia with a unique global Research
Organisation Registry (ROR) code. Twenty-five of these institutes had published over
100 peer‑reviewed journal papers, books, book chapters or conference proceedings on
AI in 2021. Moreover, six institutes referred to AI technologies in over 10% of publications,
including the University of Technology Sydney, which had the highest AI publishing
intensity among Australian universities and research institutes.

University of Newcastle Australia 6.8

University of South Australia 6.1

University of Western Australia 5.7

18.8

13.3

12.9

12.3

12.2

10.1

9.7

9.6

8.4

8.4

8.4

8.4

7.9

7.6

7.5

7.3

7.3

7.1

6.8

6.5

6.5

6.1

5.8

5.7

5.6


0 5 10 15 20

SHARE OF RESEARCH PUBLICATIONS ON AI (%)

**Intensity of AI publishing by research institute as indexed by the share of total publishing on AI, 2021**

Note: This analysis was based on 2021 data from The Lens due to declines observed across many institutes in 2022, which was likely associated with the
COVID-19 pandemic. We assume that 2021 is a more representative year of AI publishing activity than 2022.


-----

When listed in descending order of volume of AI publishing
the ranking of institutes (with over 50 AI papers in 2021)
changes. The University of Sydney, University of New South
Wales, Monash University, University of Melbourne and
the University of Technology Sydney all contributed over
1000 peer reviewed AI publications in the year 2021.

University research centres,
industry and community groups
in the Australian AI ecosystem

Australian universities have research departments and
groups dedicated to AI research, many of which were
founded in partnerships with the Australian Government,
regional governments and industry organisations.

University of Sydney

UNSW Sydney

Monash University

University of Melbourne

University of Technology Sydney

University of Queensland

RMIT University

Deakin University

Queensland University of Technology 416

Australian National University 405

University of Western Australia 342

Grifth University 296

Swinburne University of Technology 295

Curtin University 294

University of Wollongong 255

La Trobe University 251

University of Newcastle Australia 249

University of Southern Queensland 149

Flinders University 149

James Cook University 130

University of South Australia 127

University of Tasmania 123


Research, innovation and commercialisation activities by
the Australian universities contribute to the development
and expansion of the AI ecosystem. The table below
provides examples of the university research groups and
departments that specialise in AI.

Australia is home to multiple industry and community
groups that actively contribute to the AI ecosystem,
especially in relation to skills and training, research and
innovation. The table below provides some examples.

1293

1237

1142

1045

1034

764

745

671

468

417

416

405

385

342

296

295

294

255

251

249

149

149

130

127

123


500 1000 1500

TOTAL NUMBER OF AI PUBLICATIONS


**Total number of AI publications in 2021 by research institute (institutes with over 100 AI publications)**

Note: This analysis was based on 2021 data from The Lens due to declines observed across many institutes in 2022, which was likely associated with the
COVID-19 pandemic. We assume that 2021 is a more representative year of AI publishing activity than 2022.


-----

**University research groups and departments that specialise in AI**

UNIVERSITY DEPARTMENT OR RESEARCH GROUP

University of Western Australia UWA Data Institute

Monash University Department of Data Science and Artificial Intelligence

Australian National University Intelligent Systems

University of Adelaide Australian Institute for Machine Learning

University of Melbourne Artificial Intelligence Assurance Lab and AI and Autonomy Lab
(School of Computing and Information Systems)

University of New South Wales UNSW AI Institute

University of Queensland UQ AI Collaboratory

The University of Sydney Sydney Artificial Intelligence Centre

Sydney Institute for Robotics and Intelligent Systems Australian Centre for Field Robotics

Macquarie University Centre for Applied Artificial Intelligence

Deakin University Centre for AI and Future of Business

La Trobe University, CISCO AI & Machine Learning at La Trobe University

RMIT Centre for Industrial AI Research & Innovation

University of Technology Sydney Australian Artificial Intelligence Institute

Torrens University Australia Centre for Artificial Intelligence Research and Optimisation

**Industry and community groups that actively contribute to the AI ecosystem**

NAME OF ORGANISATION OR INITIATIVE HEAD ORGANISATION OR FOUNDING ORGANISATIONS ESTABLISHED

National Artificial Intelligence Centre CSIRO, Google, Committee for Economic Development of Australia 2021

AcademicID CSIRO 2022

Artificial intelligence Ethics Committee Australian Computer Society NA


ARC Centre of Excellence for Automated
Decision‑Making and Society


Australian Research Council, Australian Government 2020


Advanced Robotics for Manufacturing Hub Queensland University of Technology, Urban Art Projects 2019

Australian Urban Research Infrastructure Network University of Melbourne 2022

Australian Council of Learned Academies Five Learned Academies 2010

Artificial Intelligence Laboratory University of Adelaide 2017

Advanced Analytics and AI Platform Intersect Australia NA

Gradient Institute IAG, The University of Sydney, CSIRO 2018


Queensland AI Hub Queensland Government, University of Queensland,
Queensland University of Technology


2020


Data Science and Ai Association of Australia NA 2018


-----

### Product innovation and patent applications


Intellectual property patent applications can be used as
an indicator that the applicant – an individual, company
or organisation – believes they have discovered a novel
technology with commercial value. Patent applications
are costly and time-consuming, so they have to be worth
the effort. Analysing AI patents provides insights into the
creation of cutting-edge commercially valuable inventions
in Australia.

However, we note that patents only capture part of what’s
happening. Research has shown that 36% of product
innovations result in patent applications (Arundel and
Kabla, 1998). So, whilst patent analyses provide some
insight into AI and product innovation, they likely
do not capture the bulk of activity, nor is there an
alternative comprehensive and consistent measure of
product innovation.

NUMBER OF PATENT APPLICATIONS

1000

800

600

400

200

0

2000 2022

AI patent applications lodged
in the Australian jurisdiction

AI patent applications where
inventor resides in Australia

AI patent applications where
the applicant resides in Australia

**AI patent applications in Australia**


With these caveats in mind, patents can still provide
insights into AI product innovation trends. In this section,
we analyse patent data relating to AI for Australia.
The headline graph on the number of AI patents by
Australian jurisdiction, applicant and inventor shows the
bulk of Australian AI patents had an overseas applicant or
inventor. All three trends show growth patterns, with more
rapid growth since 2016.

The share of global AI patent applicants and inventors
in Australia has fallen over time. This trend is likely to be
largely driven by increases in global patenting activity,
rather than a decline in patenting activity in Australia.
However, the number of global AI patent applications in the
Australian jurisdiction has grown sharply since 2010, which
is likely to be associated with the increasing presence of
overseas technology companies based in Australia.

PERCENT OF GLOBAL AI PATENT APPLICATIONS
MADE WITHIN AUSTRALIA (%)

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

2000 2023

By jurisdiction By applicant By inventor

**There has been a sharp increase in the share of global AI patent**
**applications submitted in the Australian jurisdiction**

Note: The last data point is for 2023 and is based on data only from January
to June 2023.


-----

There were 379 AI patent applications in the Australian
jurisdiction in 2022 (the last full year of data in The Lens),
compared with 73 submitted by Australian applicants and
119 by Australian inventors. The companies filing for the
largest number of AI patent applications in Australia have
their global headquarters in other countries. The top 10
companies submitting AI patent applications in Australia
account for 32% of all AI patent applications filed in the
Australian jurisdiction from 2000–22.


Examining AI patent applications by inventors (as opposed
to by applicant or jurisdiction) provides insights into the AI
product innovations being created by Australian inventors.
The data show a sharp rise in relative and absolute terms
since around 2015. In 2023, 3.6% of patents invented by
an Australian resident were about AI and 203 AI patents
were invented by Australians in 2022 (2.7% patents).
These temporal patterns show that AI technology is
playing an increasingly important role in Australian
product innovation.

|255 Lg Electronics INC Seoul, South Korea|185 Irobot Corp Bedford Massachusetts, US|140 Apple INC Cupertino California, US|Col4|139 Adobe INC San Jose California, US|Col6|
|---|---|---|---|---|---|
|||137 Covidien Lp Dublin, Ireland|128 Waymo LLC Mountian View California, US||113 Wing Aviation LLC Mountian View California, US|
|226 Sony Corp Minato City Tokyo, Japan|171 Accenture Global Solutions LTD Dublin, Ireland|||||
||||||105 Samsung Electronics Co LTD Suwon-si, South Korea|


**Top 10 companies that submitted AI patent applications in the Australian jurisdiction during 2000–22, all of which have international**
**headquarters**


SHARE OF AI PATENT APPLICATIONS IN AUSTRALIA
SUBMITTED BY AN AUSTRALIAN INVENTOR (%)

4.0

3.5

3.0

2.5

2.0

1.5

1.0

0.5

0.0

2000 2023

**Australian product innovation is increasingly incorporating**
**AI technology**


NUMBER OF AI PATENT APPLICATIONS IN AUSTRALIA
SUBMITTED BY AN AUSTRALIAN INVENTOR

250

200

150

100

50

0

2000

**Overall volume of AI product innovation has risen sharply**
**since 2015**


2023


Note: The last data point is for 2023 and is based on data only from January to June 2023.


-----

We analysed which of the 225 AI phrases identified by the OECD as relating to AI technologies
occurred most frequently in AI patent applications in Australia. Terms such as “robot”,
“image processing”, “machine learning”, “neural network” and “learning model” were most
frequently used in patent descriptions in patent applications with an Australian inventor.
The chart below shows the top 20 AI technology phrases that featured in patent applications
with an Australian inventor occurring during 2000 to 2023.

|328 robot|Col2|311 image processing|Col4|Col5|Col6|Col7|Col8|
|---|---|---|---|---|---|---|---|
|279 machine learning|145 learning model||||77 artificial intelligence|||
||71 unmanned aerial vehicle||47 natural language processing|||35 learning algorithm||
||||35 machine translation|34 autonomous vehicle|||32 artificial neural network|
||59 computer vision|||||||
||||29 expert system|26 chatbot|||26 object detection|


detection

###### 53
speech 28 25 24
recognition convolutional object feature

neural network recognition extraction


-----

Australia’s contribution to global AI patent applications
remains well below our contribution to AI research
publishing. From January to June 2023, Australia contributed
1.6% of the world’s peer-reviewed AI research publishing
but only 0.24% of global AI patent applications (with an
Australian inventor). This discrepancy suggests that we are
not converting AI research into AI product innovations and
commercialisation opportunities at the same rate as other
countries. We create much more knowledge about AI for
the world than we do commercial AI products.

AUSTRALIAN SHARE OF GLOBAL PATENTS
AND GLOBAL RESEARCH PUBLICATIONS (%)

3.0

2.5

2.0

1.5

1.0

0.5

0.0

2000 2023

Share of global research

Share of global patents


The share of global AI patents with an Australian inventor
has risen strongly in the last year, increasing from 0.11% in
2022 to 0.24% in 2023. This last data point captures only
January to June 2023; whether it’s an aberration or longer
term trend is unclear. This share of global AI patents is on
par with the share of global patents covering all topics
that have an Australian inventor. Australia’s share of global
patents (on all topics) has been on the decline due to
the rapid growth of patent production, especially from
emerging economies such as China.

SHARE OF GLOBAL PATENTS INVENTED
BY AN AUSTRALIAN RESIDENT (%)

0.6

0.5

0.4

0.3

0.2

0.1

|0.24% of global AI patent applications|Col2|
|---|---|
|had an Australian inventor during January to June 2023||
|||
|||
|||



0.0

2000 2023

Share of global patents (all topics)

Share of global patents (about AI)


**Share of global patents invented by Australian residents** **Australia makes up a larger share of global research outputs**
**than patent applications**

Note: The last data point is for 2023 and is based on data only from January to June 2023.


-----

### Industry stakeholder and expert perspectives

Qualitative analysis of interviews conducted by the
research team about opportunities and challenges
for Australia’s AI ecosystem.


-----

### Insights from our interviews


We conducted consultations with 28 stakeholders across
the AI ecosystem in Australia, including representatives
from companies developing and adopting AI technologies
and supporting services, government agencies and
academic institutions. These engagements sought
to better understand current and emerging areas of
competitive advantage across the Australian AI ecosystem,
the unique value proposition of Australian-developed AI
and opportunities to grow and support the ecosystem in
the future. This section details the key themes emerging
from these consultations and their implications for future
directions across the AI ecosystem in Australia.

The hype versus the reality
around AI

Interest in AI technologies has been accelerated by the
release of tools like OpenAI’s ChatGPT, Google’s Bard and
Microsoft’s Bing and there is a sense that we are still in
the beginning stages of the current AI hype cycle. These
developments have made it easier for business leaders to
visualise the potential benefits that AI technology could
provide for their organisations and Australian AI service
providers are routinely using ChatGPT as a useful reference
point in discussing AI opportunities with clients.

Despite this interest, there is a reasonable degree of
apprehension across government and businesses about
adopting and experimenting with AI technologies, with
some organisations more risk-averse than others. Moreover,
even if a business is open to implementing AI technologies,
many are held back due to fragmented data systems and/or
the investment needed to resolve these data issues. Further
work is needed to educate decision-makers about the types
of problems that AI can address or are best suited to and
the level of investment and development required to reach
an optimal solution.

The perceived value of local AI
service providers

When it comes to deciding whether to outsource AI
capabilities to an Australian or international provider,
there are several considerations. A key factor is access
to capabilities, with many companies opting for an
international AI service provider if the appropriate
capabilities do not exist or cannot be identified locally.


The NAIC has established the Australian AI Ecosystem
Discoverability Platform, a directory of Australian
capabilities that support the adoption of AI technologies
and a useful starting point in connecting local AI service
providers with businesses (CSIRO, 2023). There could be
future opportunities to expand the platform’s coverage of
the Australian AI ecosystem and awareness of the platform.

There are natural proximity advantages to doing business
with Australian AI service providers. These include
operating in the same time zone, having access to faceto-face support and an appreciation for the local industry
context. There is also an innate trust associated with
Australian-developed AI technologies (see An Opportunity
_for safe and responsible AI). On the other hand, there_
is a tendency to assume that AI models that have been
developed by larger international companies are more
reliable or scalable by virtue of being developed in bigger
markets or with greater resources. This perception, along
with existing business relationships with international
suppliers, can sway Australian companies to do business
with an international AI service provider.

Deciding on what AI to build,
buy or borrow

Will Australia be a leading creator of AI technologies
or adopt technologies developed elsewhere? There is a
general perception across the AI ecosystem that Australian
businesses will likely be a consumer of internationally
developed AI technologies. Despite being the 13th most
advanced economy, Australia is currently ranked as the
82nd most complex economy (Observatory of Economic
Complexity, 2023) and has low rates of adoption of AI and
data analytics relative to other OECD countries (Productivity
Commission, 2022). To be globally competitive, Australia
needs to set the conditions that support AI adoption as well
as create unique AI capabilities.

Australia has a strong network of universities and
research institutes working on AI technologies, but
there is a persisting challenge in commercialising and
scaling innovations. There are likely to be cases where
it is advantageous to outsource or adapt existing AI
technologies when there are cost and capabilities barriers
to developing from scratch. But there could be areas where
there is an imperative to develop sovereign foundational AI
models and technologies (e.g., applications based on data
resources that cannot be used to train international models
due to privacy restrictions). Some stakeholders were
supportive of using open-source models as a starting point
for building these foundational AI capabilities.


-----

An opportunity for safe and
responsible AI

Ensuring safe and responsible AI practices is important for
Australian businesses to mitigate potential risks associated
with AI, including legal and reputational risks (Reid et al.,
2023). It is acknowledged across the Australian AI ecosystem
that there is a significant opportunity for Australia to be
a global leader in the development and use of safe and
responsible AI technologies. This ambition aligns with
the Australian Government’s latest consultation on safe
and responsible AI in Australia which aims to identify
the optimal regulatory and policy approach to ensure AI
developed and used in Australia is done so in a safe and
responsible manner (DISR, 2023b).

Australia is not alone in its desire to be a global leader
in safe and responsible AI. Other countries, including
Singapore, the United States, the United Kingdom and the
European Union, are among a suite of jurisdictions that
are exploring both voluntary and regulatory approaches
safeguarding future AI developments and applications
(DISR, 2023b). Stakeholders across the Australian AI
ecosystem emphasised the need for a balanced, riskbased approach to regulating AI to ensure such policies
do not unnecessarily slow or disrupt future AI uptake and
developments.

The ‘Australian brand’ is a trusted asset, which puts
the nation in a strong position to drive future safe and
responsible AI developments. There is a current disconnect,
however, between best practice approaches to responsible
AI and actual business practices: the 2023 Responsible AI
Index found that

82% of Australian organisations believed they were
practising AI responsibly but only 24% had measures in
place to ensure this was the case (Fifth Quadrant, 2022).
To support Australian businesses in implementing AI
safely and responsibly, the NAIC and the Gradient Institute
have released a guide on implementing the Australian
Government’s eight AI ethics principles (Reid et al., 2023).


Australia’s modest culture and low appetite for risk
were also highlighted as factors that are holding the AI
ecosystem back from developing novel AI technologies.
This was exemplified in expatriates who had returned
to Australia after spending a substantial period working
in international technology clusters like Silicon Valley.
Showcasing AI success stories of innovative AI technologies
and applications across the Australian AI ecosystem, such as
those featured in this report, could help to create a cultural
shift around opportunities for the Australian AI ecosystem
to drive future technology developments.

Prioritising being an AI specialist
over an AI generalist

Australian businesses felt the current national AI approach
is too broad and aspirations to position Australia as a
global leader in AI technologies could be misplaced. This
sentiment is based on the acknowledgement that we do
not have the population, market or economy size and
level of investment in AI needed to compete with large
multinational technology companies. Instead, a more
desirable approach could be to focus on building and
growing our AI capabilities and reputation in specialised
areas where Australia has a strategic and/or comparative
advantage. Deciding on areas of AI specialisation could take
different forms.

For example, there could be opportunities to leverage
existing industry strengths in heavy industries, such as
mining and agriculture, where there are clear benefits for
the role of automation, computer vision and advanced
decision support tools. There could also be opportunities
to leverage Australia’s rich renewable energy resources to
sustainably power AI technologies. Australia’s large, open
spaces suitable for testing novel autonomous technologies
could also be attractive for international companies looking
to develop and test in a smaller market. Finally, in line with
the Australian Government’s ambition for Australia to be
a global leader in trusted, secure and responsible AI (DISR,
2023a), stakeholders acknowledged the unique advantage
of Australia in developing trusted AI technologies (see An
Opportunity for safe and responsible AI).


-----

Finding our strength through
our diversity

With every 3 in 10 people living in Australia born overseas,
Australia is one of the most culturally and linguistically
diverse countries in the world (AIHW, 2023). This provides
a unique comparative advantage when it comes to
developing novel AI technologies: AI models that are
trained on representative Australian data are likely to be
less vulnerable to population biases and be fit for purpose
as an exportable AI product or service in other countries.
This data diversity could be particularly powerful when
developing AI health applications or designing technologies
to mitigate biases against marginalised populations.

Conversely, a lack of diversity in training data used
in developing international AI technologies can be a
challenge for Australian businesses looking to apply these
technologies locally given they might not perform well
in an Australian context. There is an opportunity for the
Australian AI ecosystem to leverage this diversity in the
creation of novel AI technologies, as well as position itself
as the testbed for international companies looking to
improve the quality and representativeness of the training
data their models are built upon.

Strengthening linkages across the
AI ecosystem

Across the AI ecosystem in Australia, there is a perceived
lack of strong leadership and connectivity. In some cases,
this can lead to duplicated efforts across academia and
industry, with parallel streams of AI development on
similar technologies. In other cases, Australian businesses
can struggle to identify the right AI capabilities they need
and resort to sourcing these capabilities internationally.
While these barriers likely reflect the nascent nature of
Australia’s emerging AI sector, it nonetheless requires
attention to maximise opportunities for Australia to define
and strengthen its global reputation in AI and mature as
an ecosystem.


Some stakeholders pointed to opportunities to leverage
various national and state-based AI hubs and networks
across Australia as a mechanism for improving connectivity
across the ecosystem. Example AI hubs across Australia
include the NAIC (and its Responsible AI Network), the
Queensland AI Hub, and the university and research
institutions listed in this report (see Universities and
research institutes).

Partner programs offered by large technology companies,
such as Amazon Web Services Partner Programs and
Google’s Cloud Partner Advantage Program, were also
acknowledged as a useful channel for increasing the reach
of Australian AI service providers, particularly startups and
smaller providers.

Growing Australia’s AI talent and
business ecosystem

Talent shortages are a persisting struggle reported by
Australian businesses, particularly when it comes to
sourcing specialised technical skills. While it can be
challenging for Australian businesses to offer globally
competitive salary packages, the liveability benefits of
Australia can be attractive for skilled workers looking to
stay in Australia or relocate here. Beyond these lifestyle
perks, the stakeholders consulted in this project suggested
other potential mechanisms for growing Australia’s AI
talent pipeline and attracting and retaining AI companies
in Australia.

For instance, countries such as Singapore have introduced
several top-down measures designed to grow their AI
sector as part of their national AI strategy, including
streamlining its process for patenting AI technologies,
boosting research and development investment into AI
and fostering an attractive AI startup ecosystem (Goode
et al., 2023). Australian expatriate technology workers can
also face challenges returning home to work remotely for
an international technology company, with stakeholders
acknowledging the need to reduce the friction in this
process. Australia could also consider new approaches
for injecting talent into the AI ecosystem, such as hybrid
academic-industry positions, which could also help to
improve connectivity across sectors.


-----

-----

### Australia’s AI ecosystem case studies

The following case studies provide examples of Australian companies
at the coal-face of developing AI solutions for industry.

Streamlining the management of critical infrastructure


Image source: VAPAR.Solutions

The VAPAR platform improves the accuracy and timeliness
of pipe condition assessments, generates cost savings
and strengthens the consistency across assessments.
VAPAR processes over 200,000 metres of wastewater
infrastructure across Australia, New Zealand and the United
Kingdom. This case study highlights Australia’s ability to
develop AI solutions that solve tangible problems improving
the safety and efficiency of critical infrastructure.


There are thousands of kilometres of wastewater pipelines
beneath us that need to be regularly inspected for
maintenance or repair issues. Traditionally, engineers
would inspect the condition of these pipes manually using
CCTV footage, which is extremely challenging and costly
for utilities, councils and organisations. Sydney-based
VAPAR uses AI to eliminate the repetitive and manual
requirements of infrastructure management (VAPAR,
2023). Its cloud‑based platform uses machine learning to
automatically assess the condition of pipes and identify
defects from CCTV footage.


-----

Helping clinicians detect conditions better and faster


Image source: harrison.ai

In partnership with I-MED, harrison.ai launched annalise.
ai in 2020, a decision-support solution for chest X-rays.
The first product, Annalise CXR, provides decision support
for radiologists to detect conditions quickly and accurately
from chest X-rays (CXR). This solution outperforms similar
CXR systems and can detect more clinical findings (124)
than the next most comprehensive CXR product (75).
A peer‑reviewed study of Annalise CXR also found a
significant improvement in radiologist reporting, with the
AI model matching a radiologist’s decision in 86.5% of cases
(Jones et al., 2021). franklin.ai, a joint venture between Sonic
Healthcare and harrison.ai, aims to similarly augment and
improve pathologists’ decision-making using AI.


Sydney-based harrison.ai uses AI to address the healthcare
system’s wicked problem – the need to provide quality,
equitable and timely care in the face of growing workforce
shortages (Harrison AI, 2023). Already caring for over one
million patients a year in Australia, their goal is to raise
the standard of healthcare for one million patients a day
by 2025 and make equitable healthcare a reality for all.
harrison.ai is developing technologies that combine the
strengths of human intelligence and AI and equip clinicians
with decision-support tools that can improve their capacity
and quality of care.


-----

Taking a human-first approach to AI in recruitment


Image source: Sapia.ai

The platform has been used to interview more than
3 million people in 47 countries, including candidates
from industries such as retail, insurance, financial services,
healthcare and aviation. Sapia’s national and international
success provides an exemplar of the value of fair, trusted
and responsible AI technologies and lays the groundwork
for future opportunities to position Australia as a leader
in this domain.


Melbourne-based Sapia.ai has developed an AI-enabled
recruitment tool that aims to make hiring more inclusive,
efficient and effective (Sapia, 2023). Sapia.ai uses
proprietary algorithms and data to automate elements
of the hiring process, reducing time-to-hire for large
organisations, interrupting bias in hiring and enhancing
the candidate experience.

One of Sapia.ai’s key tenets is that the end-to-end system,
including the algorithms, must be fair and explainable.
Their platform was built to minimise bias in the hiring
process and excludes the use of video data, data scraped
from the web, metadata or any other third-party data.
The algorithms only use data provided by the candidate
with consent. They have also developed and published a
FAIR AI for Recruitment (FAIR) framework, which presents
a set of guidelines for fair, transparent and trustworthy
applications of AI in recruitment.


-----

Mining data for new mineral opportunities


Image source: DiUS and Datarock

The platform eliminates mundane human effort,
empowering the user to focus on data interpretation and
making informed, data-rich decisions that add additional
value to mining operations. Datarock has been embraced
by many of the world’s largest mining companies across
the globe.


Australia’s renowned mining sector is poised to harness the
potential of technological advancements, with AI playing
a central role. In 2018 an Australian technology company
and Solve Geosolutions, an Australian data science
consultancy, came together to create Datarock (DiUS, 2023).
They saw an opportunity to leverage AI technology and
provide the industry with a solution that transforms core
imagery into actionable data and delivers reliable insights
(Datarock, 2023; DiUS, 2023).

Traditionally, drill core is logged by manual inspection by
a geologist or geotechnical engineers, a labour‑intensive
approach susceptible to inconsistencies and errors.
Datarock technology – which utilises deep learning
models and computer vision – enables core imagery to
be processed via a cloud-hosted platform, automating
drill core analysis and extracting geoscientific insights
that are consistent, auditable, and can surpass the quality
and quantity of traditional human-based observations.


-----

Connecting remote communities with essential supplies and services


Swoop Aero has developed the world’s first end‑to‑end
drone logistics platform that is working to bridge
the tyranny of distance across Australia and beyond
(Swoop Aero, 2023). Using digital twins, sensor
technologies, machine learning and computer vision,
Swoop Aero’s drones are plugged into an integrated
logistics network to seamlessly deliver products of up to
5 kg, including medical supplies, with a range of 130 km
at speeds of 200 km/h. This technology has also been
used to map and respond to disaster-prone areas, patrol
coastal environments for incidents and monitor wildlife
and flora for conservation purposes.


In 2018, Swoop Aero was awarded the world’s first
commercial contract to provide medical drone logistics
services in Vanuatu. Healthcare workers previously
transported vaccinations in iceboxes and would have to
navigate difficult terrain to deliver these medical supplies.
Swoop Aero successfully piloted an integrated drone
network for delivering vaccines and other medical supplies
to remote locations across Vanuatu and has since operated
over 70 flights and delivered more than 25 kg of medical
supplies. Similar successful healthcare projects have been
conducted in Malawi, the Democratic Republic of Congo
and Mozambique, providing timely access to medical
supplies for communities living in remote locations.


-----

### Key takeaways

Australia’s AI companies have

#### 1

grown rapidly in the last five years

Until the last decade, the number of AI companies
founded annually in Australia was growing at a slow pace.
An average of 5.5 companies were added to the ecosystem
per year between 1986 and 2013. The last decade saw
intensified growth in the number of newly registered
Australian AI companies, with a spike of 74 companies
founded in 2017 followed by 61 and 57 over 2018–19.
We now have 544 AI companies, which is on par with other
global AI leaders. For example, with a larger population
and economy, Canada recently reported 670 AI companies
(University of Toronto, 2020).

Australia’s AI companies are

#### 2

young, vibrant and nimble

Three-quarters of Australia’s AI companies have been
operating for less than 10 years. This is comparatively
younger than both economy-wide companies and IT
sector companies. For example, around 40% of Australian
AI companies were founded in 2018–22 compared
to roughly 15% of Australian IT companies. Our AI
companies are also small, often employing 50 staff or less.
However, the youth and size of Australian AI companies
could be an advantage, enabling companies to pivot quickly
to changing market conditions and respond to early-stage
AI technology developments.


Geographic hotspots and

#### 3

clusters are forming – AI companies
like to be near other AI companies

We examined spatial clustering patterns of AI companies in
Australian cities and found that they are tightly clustered.
We identified eight clusters in Sydney, Melbourne,
Brisbane, Adelaide and Perth, with more forming in other
cities and regions. We found that 54% of AI companies
were in one of the clusters and that the average distance
from an AI company to its nearest neighbour AI company
was 80 m. This suggests we are likely to see the emergence
of place‑based AI hubs in Australia in the future.

Australia’s R&D sector

#### 4

has higher (and growing) rates
of AI adoption compared to
global averages

Australia has higher intensity of AI research than the global
average. In Australia, 9.6% of all R&D publications in 2022
referred to AI technologies compared to 7.2% globally.
The rate of research publications has also been increasing.
In 2023, AI R&D publishing by public and private sector
organisations accounted for 9.6% of all R&D publishing
which is up from 9.4% the year before, 4.9% in 2015 and
2.5% in the year 2000. Since 2000, the Australian R&D
sector has published 92,940 peer-reviewed books, book
chapters, conference papers and journal papers about AI.


-----

Australia creates much AI

#### 5

knowledge but relatively few
commercial products

We found that Australia contributes 1.6% of global
peer‑reviewed research publishing on AI. By comparison,
we found that Australia contributes only 0.2% of global
AI patent applications where the inventor resides within
Australia. This shows that we are not converting our
knowledge and understanding of cutting-edge AI science
into commercial products at the same rate as other
countries. Essentially, we give the world more knowledge
about AI than commercial AI products. This sentiment
was echoed in consultations with stakeholders across
the Australian AI ecosystem.

Australia has a mature

#### 6

AI ecosystem with clear
specialisations in AI technology
and applications domains

Stakeholders across Australia’s AI ecosystem identified a
desire to focus on specialised areas for AI development,
rather than positioning Australia as a “jack of all” AI
capabilities. Our analysis highlighted current areas of AI
specialisation when it comes to specific AI technologies
(e.g. self-organising maps, ant colony optimisation, gradient
tree boosting) and application domains (e.g. optometry,
livestock production and decision sciences). Beyond these
R&D-driven strengths, stakeholders also highlighted
opportunities for Australia to take a world-leading role in
areas complementary to existing industry and geographical
strengths, or AI technologies that emphasise Australia’s
trusted brand.


Australia is recognised as a

#### 7

place for trusted and responsible
AI innovation

The Australian brand is a key asset that can be leveraged
when it comes to the development of safe and responsible
AI. In addition to focusing on specialised areas of
strategic advantage, stakeholders from the AI ecosystem
identified the strength of Australia’s reputation as a
comparatively trustworthy and appropriately regulated
country. This global positioning, combined with Australia’s
diverse population which is beneficial when developing
AI technologies that are generalisable to diverse markets,
could be used to position Australia as a global leader
in the development and use of safe and responsible
AI technologies.


-----

### Planned improvements


Future AI ecosystem reports

This is the second AI ecosystem report released by
CSIRO’s National Artificial Intelligence Centre. It captures
a broader range of source data compared to last year’s
report. And there are plans to expand and improve the
metrics captured and communicated in future reports.
In this section we explore some of the main planned
improvements for future versions.

**1. Improved data on the AI workforce**
**(workers and skills demand profile)**

We know that most of Australia’s AI workforce is not in
one of the AI companies we identified. They’re in banks,
mining companies, software companies, supermarkets
and a wide range of other companies that are developing
AI capabilities to achieve improved business processes.
At the current time, there is no formal classification of
an AI worker in the Australian and New Zealand Standard
Classification of Occupations (ANZSCO). This means we
can’t get statistics from formal government sources
(e.g. The Australian Bureau of Statistics) on the number
of, and types of, AI workers in Australia. However, it may
be possible to use alternative online data about AI jobs
(e.g. job adverts). By meshing ANZSCO data from the census
with online data, we could build a more detailed picture
of Australia’s AI workforce.


**2. Improved data on AI training**
**and education providers**

Australia’s schools, universities and technical colleges
provide extensive education and training on AI-related
topics. These include skills and knowledge areas such
as machine learning, computer vision, natural language
processing, robotics, mathematics, statistics, software
engineering, computer coding and data science. It also
covers skills and knowledge such as AI business strategy,
technology foresight, government policy and planning,
ethics, user experience/design, human-computer interfaces
and organisational transformation. The AI training
ecosystem is likely to be expanding and diversifying
in Australia to meet the demand for these skills and
knowledge. Future versions of this report could examine
the size and structure of Australia’s AI training and
education sector.

**3. Analysis of AI adoption patterns**
**and productivity impacts**

There is an increasing body of evidence about productivity
uplift associated with AI adoption. For example, a recent
study by Stanford University (Brynjolfsson et al., 2023)
found that customer support staff were able to solve 14%
more complex customer questions per hour when they
used generative AI chatbots. It was also found to increase
customer satisfaction and the productivity gains were more
than double for new and inexperienced staff. However, not
all AI adoption stories are about success. There is much
complexity and challenge about how AI is adopted, which
tools are used and how they are applied (Hajkowicz and
Whittle, 2023). Future versions of the AI Ecosystem report
could explore the extent of adoption, patterns of adoption
and productivity impacts.


-----

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-----

-----

The National AI Centre is
building Australia’s responsible
and inclusive AI future.

**For further information**
**National AI Centre**
1300 363 400
+61 3 9545 2176
[naic@csiro.au](https://forrester-my.sharepoint.com/personal/sbriggs_forrester_com/Documents/Microsoft Teams Chat Files/naic@csiro.au)
csiro.au/naic

Dr Stefan Hajkowicz
Chief Research Consultant
Analytics and Decision Sciences


-----

