Artificial IntelligenceIndex Report 2023

Artificial IntelligenceIndex Report 2023

CHAPTER 4:

The Economy

Chapter 4 Preview

1Artificial IntelligenceIndex Report 2023

CHAPTER 4 PREVIEW:

The Economy
Overview

3

Chapter Highlights

4

4.1 Jobs

6

AI Labor Demand

6

Perceived Importance of AI

43

Global AI Labor Demand

6

U.S. AI Labor Demand by Skill Cluster
and Specialized Skill

AI Investments and Investment
Outcomes

44

7

U.S. AI Labor Demand by Sector

9

Challenges in Starting and Scaling
AI Projects

46

U.S. AI Labor Demand by State

10

AI Hiring

13

AI Skill Penetration

15

Global Comparison: Aggregate

15

Global Comparison: By Gender16

4.2 Investment

17

Corporate Investment

17

Startup Activity

20

Global Trend

20

Regional Comparison by Funding Amount 22
Regional Comparison by
Newly Funded AI Companies

26

Focus Area Analysis

28

4.3 Corporate Activity

31

Industry Adoption

31

Adoption of AI Capabilities

Industry Motivation

Earnings Calls

41
43

48

Aggregate Trends

48

Specific Themes

49

Narrative Highlight: What Are Business
Leaders Actually Saying About AI?
50
Sentiment Analysis

52

4.4 Robot Installations

53

Aggregate Trends

53

Industrial Robots:
Traditional Vs. Collaborative Robots
By Geographic Area
Narrative Highlight: Country-Level
Data on Service Robotics

55
56
60

Sectors and Application Types

63

China Vs. United States

65

Appendix

67

31

Consideration and Mitigation of Risks
From Adopting AI
39

Chapter 4 Preview

Narrative Highlight: The Effects of
GitHub’s Copilot on Developer
Productivity and Happiness

ACCESS THE PUBLIC DATA

2

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy

Overview
Increases in the technical capabilities of AI systems have led to greater rates of AI
deployment in businesses, governments, and other organizations. The heightening
integration of AI and the economy comes with both excitement and concern. Will
AI increase productivity or be a dud? Will it boost wages or lead to the widespread
replacement of workers? To what degree are businesses embracing new AI
technologies and willing to hire AI-skilled workers? How has investment in AI
changed over time, and what particular industries, regions, and fields of AI haveattracted the greatest amount of investor interest?
This chapter examines AI-related economic trends by using data from Lightcast,
LinkedIn, McKinsey, Deloitte, and NetBase Quid, as well as the International
Federation of Robotics (IFR). This chapter begins by looking at data on AI-related
occupations and then moves on to analyses of AI investment, corporate adoption ofAI, and robot installations.

Chapter 4 Preview

3Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy

Chapter Highlights
The demand for AI-related
professional skills is increasing
across virtually every
American industrial sector.
Across every sector in the United States forwhich there is data (with the exception ofagriculture, forestry, fishing, and hunting), thenumber of AI-related job postings has increasedon average from 1.7% in 2021 to 1.9% in 2022.
Employers in the United States are increasingly
looking for workers with AI-related skills.

For the first time in the last
decade, year-over-year private
investment in AI decreased.
Global AI private investment was $91.9 billion
in 2022, which represented a 26.7% decreasesince 2021. The total number of AI-related
funding events as well as the number of newly
funded AI companies likewise decreased.
Still, during the last decade as a whole, AI
investment has significantly increased. In 2022
the amount of private investment in AI was 18
times greater than it was in 2013.

Once again, the United States leads in investment in AI.
The U.S. led the world in terms of total amount of AI private investment. In 2022, the $47.4 billion
invested in the U.S. was roughly 3.5 times the amount invested in the next highest country, China($13.4 billion). The U.S. also continues to lead in terms of total number of newly funded AI companies,
seeing 1.9 times more than the European Union and the United Kingdom combined, and 3.4 times
more than China.

In 2022, the AI focus area with the most investment was medical
and healthcare ($6.1 billion); followed by data management,
processing, and cloud ($5.9 billion); and Fintech ($5.5 billion).
However, mirroring the broader trend in AI private investment, most AI focus areas saw less investment
in 2022 than in 2021. In the last year, the three largest AI private investment events were: (1) a $2.5 billion
funding event for GAC Aion New Energy Automobile, a Chinese manufacturer of electric vehicles; (2) a$1.5 billion Series E funding round for Anduril Industries, a U.S. defense products company that builds
technology for military agencies and border surveillance; and (3) a $1.2 billion investment in Celonis, abusiness-data consulting company based in Germany.

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4Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy

Chapter Highlights (cont’d)
While the proportion ofcompanies adopting AI has
plateaued, the companies
that have adopted AI
continue to pull ahead.
The proportion of companies adopting AI
in 2022 has more than doubled since 2017,
though it has plateaued in recent years
between 50% and 60%, according to theresults of McKinsey’s annual research
survey. Organizations that have adopted AI
report realizing meaningful cost decreases
and revenue increases.

AI is being deployedby businesses in
multifaceted ways.
The AI capabilities most likely to have been
embedded in businesses include robotic
process automation (39%), computer vision(34%), NL text understanding (33%), and virtual
agents (33%). Moreover, the most commonly
adopted AI use case in 2022 was service
operations optimization (24%), followed by
the creation of new AI-based products (20%),
customer segmentation (19%), customer
service analytics (19%), and new AI-basedenhancement of products (19%).

AI tools like Copilot are
tangibly helping workers.

China dominates industrial
robot installations.

Results of a GitHub survey on the use of
Copilot, a text-to-code AI system, findthat 88% of surveyed respondents feel
more productive when using the system,
74% feel they are able to focus on moresatisfying work, and 88% feel they are able
to complete tasks more quickly.

In 2013, China overtook Japan as the nation
installing the most industrial robots. Since
then, the gap between the total number ofindustrial robots installed by China and thenext-nearest nation has widened. In 2021,
China installed more industrial robots than
the rest of the world combined.

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5Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

4.1 Jobs
AI Labor Demand

Global AI Labor Demand
Figure 4.1.1 highlights the percentage of all job

This section reports demand for AI-related skills

postings that require some kind of AI skill. In 2022,

in labor markets. The data comes from Lightcast,which mined millions of job postings collected fromover 51,000 websites since 2010 and flagged listingscalling for AI skills.

the top three countries according to this metric were
the United States (2.1%), Canada (1.5%), and Spain(1.3%). For every country included in the sample, thenumber of AI-related job postings was higher in 2022
than in 2014.1

AI Job Postings (% of All Job Postings) by Geographic Area, 2014–22
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

2.05%, United States

AI Job Postings (% of All Job Postings)

2.00%

1.45%, Canada
1.33%, Spain
1.23%, Australia
1.20%, Sweden
1.16%, Switzerland
1.14%, United Kingdom
1.01%, Netherlands
0.98%, Germany
0.89%, Austria
0.86%, Belgium
0.84%, France

1.50%

1.00%

0.72%, Italy
0.50%

0.00%

0.45%, New Zealand

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.1.1

1 In 2022, Lightcast slightly changed their methodology for determining AI-related job postings from that which was used in previous versions of the AI Index Report. As such, some of thenumbers in this chart do not completely align with those featured in last year’s report.

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6Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

U.S. AI Labor Demand by Skill Cluster and Specialized Skill
Figure 4.1.2 showcases the most in-demand AI skill clusters in the U.S. labor market since 2010. The mostin-demand skill cluster was machine learning (1.0%), followed by artificial intelligence (0.6%) and natural
language processing (0.2%). Every listed AI skill cluster is now more in demand than it was 10 years ago.

AI Job Postings (% of All Job Postings) in the United States by Skill Cluster, 2010–22
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

1.03%, Machine Learning

AI Job Postings (% of All Job Postings)

1.00%

0.80%

0.61%, Arti cial Intelligence

0.60%

0.40%

0.20%, Natural Language Processing
0.16%, Neural Networks
0.15%, Autonomous Driving
0.13%, Visual Image Recognition

0.20%

0.06%, Robotics
0.00%
2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.1.2

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7Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

Figures 4.1.3 and 4.1.4 showcase the top ten specialized skills that were demanded in AI job postings in 2022 compared
to 2010–20122. On an absolute level, virtually every specialized skill is more in demand now than a decade ago. The
growth in demand for Python is particularly notable, evidence of its growing popularity as an AI coding language.

Top Ten Specialized Skills in 2022 AI Job Postings in the United States, 2010–12 Vs. 2022
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

296,662

Python (Programming Language)

12,884
260,333

Computer Science

48,001
185,807

SQL (Programming Language)

22,037
159,801

Data Analysis

16,571
157,855

Data Science

1,227
155,615

Amazon Web Services

962
152,956

Agile Methodology

7,549
138,791

Automation

13,207
133,856

Java (Programming Language)

26,5572022
133,286

Software Engineering

2010–12

22,384050,000100,000

150,000
200,000
Number of AI Job Postings

250,000

300,000
Figure 4.1.3

Top Ten Specialized Skills in 2022 AI Job Postings in the United States by Skill Share, 2010–12 Vs. 2022
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

37.13% (+592%)

Python (Programming Language)

5.36%
32.58% (+63%)

Computer Science

19.98%
23.25% (+153%)

SQL (Programming Language)

9.17%
20.00% (+190%)

Data Analysis
Data Science
Amazon Web Services
Agile Methodology

6.90%
19.75% (+3,767%)
0.51%
19.47% (+4,763%)
0.40%
19.14% (+509%)
3.14%
17.37% (+216%)

Automation

5.50%
16.75% (+52%)

Java (Programming Language)

11.06%

Software Engineering
0%2022
16.68% (+79%)

2010–12

9.32%

5%

10%

15%

20%
25%
Skill Share in AI Job Postings (%)

30%

35%

40%
Figure 4.1.4

2 The point of comparison of 2010–2012 was selected because some data at the jobs/skills level is quite sparse in earlier years. Lightcast therefore used thewhole set of years 2010–2012 to get a larger sample size for a benchmark from 10 years ago to compare.

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8Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

U.S. AI Labor Demand by Sector

fishing, and hunting), the number of AI job postings

Figure 4.1.5 shows the percentage of U.S. job

was notably higher in 2022 than in 2021, with the top

postings that required AI skills by industry sector

three sectors being information (5.3%); professional,

from 2021 to 2022. Across virtually every included

scientific, and technical services (4.1%); and financesector (with the exception of agriculture, forestry,

and insurance (3.3%).

AI Job Postings (% of All Job Postings) in the United States by Sector, 2021 Vs. 2022
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

Information

4.85%
4.07%
3.86%

Professional, Scienti c, and Technical Services
Finance and Insurance

2.94%

Manufacturing

2.86%

Management of Companies and Enterprises

1.08%

Public Administration

0.98%

Waste Management and Administrative Support Services
0%

1.32%

1.27%
1.10%
1.19%
1.00%

Mining, Quarrying, and Oil and Gas Extraction

Transportation and Warehousing

1.37%

1.28%

0.82%

Utilities

Real Estate and Rental and Leasing

3.26%

1.53%
1.41%

Educational Services

Wholesale Trade

3.33%

1.64%
1.66%

Agriculture, Forestry, Fishing, and Hunting

Retail Trade

5.30%

0.98%
0.82%
0.89%
0.65%

2022

0.67%
0.59%

2021

0.58%
0.56%

1%

2%
3%
4%
AI Job Postings (% of All Job Postings)

5%
Figure 4.1.5

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9Artificial IntelligenceIndex Report 2023

U.S. AI Labor Demand by State
Figure 4.1.6 highlights the numberof AI job postings in the UnitedStates by state. The top three

Chapter 4: The Economy
4.1 Jobs

Number of AI Job Postings in the United States by State, 2022
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

AK

ME

970

2,227
1,571

VT

2,719 34,603

NH

MA

NY

CT

RI

OH

PA

NJ

DC

MD

DE

states in terms of postings were
California (142,154), followed byWA

Texas (66,624) and New York(43,899).

MT

SD

MN

WI

MI

NE

IA

IL

IN

KS

MO

KY

WV
887

9,606 16,769 3,503

833

1,227

OR

ID

WY
769

4,032 5,670 31,569 9,247 19,208 20,397 23,447

UT

CO

AZ

NM

10,811 6,109

CA

ND

31,284

NV

142,154 6,813

2,195 11,808 8,879 25,366

6,885 20,421 7,683 10,990 4,536

OKAR

43,899 8,960 2,965

19,514 3,357

5,719

7,247

11,173 34,221 23,854

TN

VA

NC

TX

LA

MS

AL

GA

SC

66,624 4,806 2,548 7,866 26,620 4,928

HI

FL

2,550

33,585Figure 4.1.6

Figure 4.1.7 demonstrates whatpercentage of a state’s total job
postings were AI-related. The top
states according to this metric

Percentage of U.S. States Job Postings in AI, 2022
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

AK
0.88%

ME
1.64%
NH MA
VT
1.34% 1.20% 2.26%

were the District of Columbia(3.0%), followed by Delaware

MI
WA MT NDSD MN WI
2.48% 0.72% 1.04% 1.83% 1.22% 0.90% 1.77%(2.7%), Washington (2.5%), and

CT
NY
RI
2.07% 1.66% 1.84%

ID
IA
IL
IN
OR
OH
PA
NJ
WY NE
1.43% 1.89% 1.18% 1.14% 1.14% 1.63% 0.88% 1.07% 1.30% 2.04%

Virginia (2.4%).

CA
NV
CO
KS MO KYUTWV DC MD DE
2.21% 1.23% 1.54% 1.46% 1.43% 1.15% 0.85% 0.99% 2.95% 1.96% 2.66%
AZ NM OK
AR
TN
NC
VA
1.40% 1.36% 1.07% 2.03% 1.11% 2.42% 1.44%
LA
MS
AL
GA
TX
SC
1.52% 0.87% 1.15% 1.31% 1.64% 0.87%
HI
1.46%

FL
1%
Figure 4.1.7

Chapter 4 Preview

10Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

Percentage of United States AI Job Postings by State, 2022

Which states had the greatest

Source: Lightcast, 2022 | Chart: 2023 AI Index Reportshare of AI job postings as ashare of all AI job postings in

AK
0.12%

the U.S. in 2022? California was

ME
0.28%
VT
NH MA
0.20% 0.34% 4.35%

first: Last year 17.9% of all AI job
postings in the United StatesMI
WA MT NDSD MN WI
3.93% 0.10% 0.15% 0.28% 1.48% 1.12% 3.19%

were for jobs based in California,

CT
NY
RI
5.52% 1.13% 0.37%

OR
ID
NE
IA
IL
IN
OH
PA
NJ
WY
1.36% 0.77% 0.10% 0.51% 0.71% 3.97% 1.16% 2.41% 2.56% 2.95%

followed by Texas (8.4%) and
New York (5.5%) (Figure 4.1.8).

CA
NV
CO
KS MO KYUTWV DC MD DE
17.87% 0.86% 0.87% 2.57% 0.97% 1.38% 0.57% 0.11% 1.21% 2.11% 0.44%
AZ NM OK
AR
TN
VA
2.45% 0.42% 0.72% 0.91% 1.40% 4.30%

NC
3%

LA
MS
AL
GA
TX
SC
8.37% 0.60% 0.32% 0.99% 3.35% 0.62%
HI
0.32%

FL
4.22%
Figure 4.1.8

Figure 4.1.9 highlights the trends over time in AI job postings for four select states that annually report a highnumber of AI-related jobs: Washington, California, New York, and Texas. For all four, there was a significant
increase in the number of total AI-related job postings from 2021 to 2022, suggesting that across these states,
employers are increasingly looking for AI-related workers.

Percentage of U.S. States’ Job Postings in AI by Select U.S. State, 2010–22
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

Percentage of U.S. States’ Job Postings in AI

2.50%

2.48%, Washington
2.21%, California
2.07%, New York

2.00%

1.52%, Texas

1.50%

1.00%

0.50%

0.00%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.1.9

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11Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

Figure 4.1.10 highlights the degree to which AI-related job postings have been subdivided among the top
four states over time. California’s share of all AI job postings has decreased steadily since 2019 while Texas’has marginally increased. The fact that California no longer commands one-quarter of all AI-related jobs
suggests that AI jobs are becoming more equally distributed among U.S. states.

Percentage of United States AI Job Postings by Select U.S. State, 2010–22
Source: Lightcast, 2022 | Chart: 2023 AI Index Report

Percentage of United States AI Job Postings

25%

20%
17.87%, California
15%10%
8.37%, Texas
5.52%, New York

5%

3.93%, Washington

0%

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.1.10

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12Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

AI Hiringin the same period the job began, divided by the total

Our AI hiring data is based on a LinkedIn dataset of skills
and jobs that appear on their platform. The countries

number of LinkedIn members in the corresponding
location. This rate is then indexed to the averagemonth in 2016; for example, an index of 1.1 in December

included in the sample make at least 10 AI hires each2021 points to a hiring rate that is 10% higher than themonth and have LinkedIn covering at least 40% of

average month in 2016. LinkedIn makes month-to-

their labor force. India is also included in the sample

month comparisons to account for any potential lags in

given their increasing significance in the AI landscape,members updating their profiles. The index for a year is

although LinkedIn does not cover 40% of their labor

the number in December of that year.

force. Therefore, the insights drawn about India should
be interpreted with particular caution.

The relative AI hiring index measures the degree to which
the hiring of AI talent is changing, more specificallyFigure 4.1.11 highlights the 15 geographic areas thatwhether the hiring of AI talent is growing faster than,have the highest relative AI hiring index for 2022. The
AI hiring rate is calculated as the percentage of LinkedIn
members with AI skills on their profile or working in
AI-related occupations who added a new employer

equal to, or more slowly than overall hiring in a particulargeographic region. In 2022, Hong Kong posted thegreatest growth in AI hiring at 1.4, followed by Spain, Italy
and the United Kingdom, and the United Arab Emirates.

Relative AI Hiring Index by Geographic Area, 2022
Source: LinkedIn, 2022 | Chart: 2023 AI Index Report

Hong Kong

1.37

Spain

1.19

Italy

1.18

United Kingdom

1.18

United Arab Emirates

1.15

South Africa

1.13

New Zealand

1.06

Denmark

1.06

Belgium

1.05

Netherlands

1.03

South Korea

1.02

Sweden1.01

Canada0.99

Switzerland

0.99

Singapore

0.99

0.00

0.20

0.40

0.60
0.80
Relative AI Hiring Index

1.00

1.20

1.40
Figure 4.1.11

Figure 4.1.12 highlights how the AI hiring index changes over time for a wide range of countries3. Overall, themajority of countries included in the sample have seen meaningful increases in their AI hiring rates since 2016.
This trend suggests that those countries are now hiring more AI talent than in 2016. However, for many countries,
AI hiring rates seem to have peaked around 2020, then dropped, and have since stabilized.
3 Both Figure 4.1.11 and Figure 4.1.12 report the Relative AI Hiring Index. Figure 4.1.11 reports the Index value at the end of December 2022, while Figure 4.1.12 reports a twelve-month rolling average.

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13Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

Relative AI Hiring Index by Geographic Area, 2016–22
Source: LinkedIn, 2022 | Chart: 2023 AI Index Report

Australia

2.00
1.50

0.50

1.00

2020

0.00

2022

Chile

1.00

1.00

2020

0.00

2022

2018

1.50

0.00

2022

1.50
1.14

1.00
0.50

0.50

0.50

0.00

0.00

Germany

2.00

2020

2022

Hong Kong

2.00

1.50

1.50
1.13

1.00
0.50
0.00

2018

0.50
2018

2020

0.00

2022

2018

Israel2020

1.00

0.94 1.00

0.50

0.50
2018

1.50

0.50

0.50

0.50

0.00

0.00

0.00

2022

New Zealand

2.00

2020

2022

Norway

2.00

1.50

0.50
2020

0.00

2022

2018

2020

Portugal

2018

2020

0.00

2022

1.50

0.50

0.50
0.00

Switzerland

2.00

2.00

1.50

United Arab Emirates

0.50
2020

Chapter 4 Preview

2022

0.00

2020

2022

United Kingdom

1.50
1.08

1.00
0.50

2018

2018

2.00

1.50
1.09

1.00

0.00

2022

0.50
2018

2020

2022

0.00

2018

2020

2022

United States

2.00
1.25

1.00

1.09

1.00

0.00

2020

2022

1.50
1.12

1.00

0.50
2018

2020
Sweden

0.00

2022

2018

2.00

0.50
2020

1.13

1.00

0.00

2018

2022

Singapore

Spain

1.05

1.00

2020

0.50

2.00

1.50
1.10

2018

1.50
1.01

South Korea

1.50

2022

1.21

2.00

1.00

0.00

2022

2.00

1.00

2022

0.50

South Africa
2.00

2020

1.50
1.10

1.00
0.50

2018

2018

2.00

1.50
1.09

1.00

0.00

2018

2020

1.00

0.50
2020

2018

1.50
1.03

1.00

0.00

2018

1.08

2.00

1.50
1.11

1.00

2022

Netherlands

2.00

1.50
1.00

0.00

2022

2020
Ireland

Luxembourg

2.00

1.00

2020

2018

2.00
1.50

Italy

2.00

2022

1.50

0.00

2022

2020
India

2.00
1.21

1.00

2018

1.19

1.00

0.00

2022

2022

1.50
1.08

1.00

0.50
2020

2020

2.00

0.00

2018

2018

France

2.00

1.50
1.05

2020
Finland

2.00

1.00

1.17

1.00
0.50

Denmark

2.00

Relative AI Hiring Index

1.50

0.50
2018

Canada

2.00

1.50
1.11

0.50
2018

Brazil

2.00

1.50
1.12

1.00

0.00

Belgium

2.00

1.50
1.15

1.00
0.50

2018

2020

2022

0.00

2018

2020

2022
Figure 4.1.12

14

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

rate of various countries or regions from 2015 to

AI Skill Penetration

2022. In this case, the relative AI skill penetration rate

The AI skill penetration rate is a metric created bycan be understood as the sum of the penetration of

LinkedIn that measures the prevalence of various

each AI skill across occupations in a given country or

AI-related skills across occupations. LinkedIn

region, divided by the global average across the samegenerates this metric by calculating the frequencies

occupation. For instance, a relative skill penetrationof LinkedIn users’ self-added skills in a given area

rate of 1.5 means that the average penetration of AI

from 2015 to 2022, then reweighting those numbers

skills in that country or region is 1.5 times the global

with a statistical model to create the top 50

average across the same set of occupations.

representative skills in that select occupation.

As of 2022, the three countries or regions with the

Global Comparison: Aggregate

highest AI skill penetration rates were India (3.2),

Figure 4.1.13 shows the relative AI skill penetration

the United States (2.2), and Germany (1.7).

Relative AI Skill Penetration Rate by Geographic Area, 2015–22
Source: LinkedIn, 2022 | Chart: 2023 AI Index Report

India

3.23

United States

2.23

Germany

1.72

Israel

1.65

Canada

1.54

United Kingdom

1.54South Korea

1.44

Singapore

1.37

France1.13

Brazil

0.99

Spain

0.98

Netherlands

0.95

Italy

0.95

Switzerland

0.91

Australia
0.00

0.89

0.50

1.00

1.50
2.00
Relative AI Skill Penetration Rate

2.50

3.00

Figure 4.1.13

Chapter 4 Preview

15Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.1 Jobs

Global Comparison: By Gender
Figure 4.1.14 disaggregates AI skill penetration rates

pooled together across the same set of occupations

by gender across different countries or regions.

in the country. For all countries in the sample, theA country’s “Relative AI skill penetration rate

relative AI skill penetration rate is greater for men

across genders” for women of 1.5 means that female

than women. India (2.0), the United States (1.3), andmembers in that country are 1.5 times more likely to

Israel (0.9) have the highest reported relative AI skill

list AI skills than the average member in all countries

penetration rates for women.

Relative AI Skill Penetration Rate Across Gender, 2015–22
Source: LinkedIn, 2022 | Chart: 2023 AI Index Report
3.27

India

1.99
2.36

United States

1.28
2.05

Israel0.87

Canada

0.86

1.59
1.91

Germany

0.71

Singapore

0.68

1.37
1.46

United Kingdom

0.57
1.13

France0.39

Netherlands

0.38

0.98
0.82

United Arab Emirates

0.31

Italy

0.31

Brazil

0.30

Spain

0.30

Finland

0.29

Australia

0.29

0.00

0.85
0.98
1.03

Male

1.08

Female
0.88

0.50

1.00

1.50
2.00
Relative AI Skill Penetration Rate

2.50

3.00
Figure 4.1.14

Chapter 4 Preview

16Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Using data from NetBase Quid, this section tracks trends in AI-related investments. NetBase Quid tracks data on the investments of over
8 million global public and private companies. NetBase Quid also uses natural language processing techniques to search, analyze, andidentify patterns in large, unstructured datasets, like aggregated news and blogs, and company and patent databases. NetBase Quid
continuously broadens the set of companies for which it tracks data, so that in this year’s AI Index, the reported investment volume forcertain years is larger than that of previous reports.

4.2 Investment
Corporate Investment
As AI becomes more and more integrated into the

For the first time since 2013, year-over-year global

economy, it becomes increasingly important to track

corporate investment in AI has decreased. In 2022,

AI-related corporate investment. Figure 4.2.1 shows

total global corporate AI investment was $189.6

overall global corporate investment in AI from 2013

billion, roughly a third lower than it was in 2021.

to 2022. Corporate investment includes mergers andStill, in the last decade, AI-related investment has

acquisitions, minority stakes, private investment, andincreased thirteenfold.

public offerings.

Global Corporate Investment in AI by Investment Activity, 2013–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report300

Merger/Acquisition
276.14

Minority Stake
Private Investment
Total Investment (in Billions of U.S. Dollars)

250

Public O ering

119.66200

189.59146.74

150

83.35

26.06
95.57

100
79.62
53.72

50
14.57
0

2013

19.04
2014

25.43

33.82
13.05

24.68

13.35

17.13

25.72

2015

2016

2017

46.06

125.36

31.91

21.89

43.1

55.09

12.62

2018

13.01

2019

91.86

61.61

2020

29.1
2021

2022
Figure 4.2.1

Chapter 4 Preview

17Artificial IntelligenceIndex Report 2023

To provide a fuller context for thenature of AI investment in the last year,
Figures 4.2.2 through 4.2.5 highlightthe top merger/acquisition, minority
stake, private investment, and public

Chapter 4: The Economy
4.2 Investment

Top Five AI Merger/Acquisition Investment Activities, 2022
Source: NetBase Quid, 2022 | Table: 2023 AI Index ReportCompany Name

Headquarters
Country

Focus Area

NuanceCommunications, Inc.

United States

Arti cial Intelligence;Enterprise Software;
Healthcare; Machine
Learning

19.80

Citrix Systems, Inc.

United States

Data Management,
Processing, and Cloud;HR Tech

17.18

Avast Limited

Czech Republic

Data Management,
Processing, and Cloud;Fintech; Cybersecurity,
Data Protection

8.02

AspenTech
Corporation

United States

Manufacturing;
Software; Supply
Chain Management

6.34

Vivint Smart Home,Inc.

United States

Cybersecurity, Data
Protection; Sales
Enablement

5.54

offering events in the last year. The
greatest single AI investment event
was the merger/acquisition of Nuance
Communications, valued at $19.8 billion(Figure 4.2.2). The largest minority
stake event was for the British company
Aveva Group ($4.7 billion) (Figure 4.2.3).
The greatest private investment event
was GAC Aion New Energy Automobile($2.5 billion), a Chinese clean energy

Funding Amount(in Billions USD)

and automotive company (Figure 4.2.4).
Finally, the largest public offering was

Figure 4.2.2

ASR Microelectronics ($1.1 billion),a Chinese semiconductor company(Figure 4.2.5).

Top Five AI Minority Stake Investment Activities, 2022
Source: NetBase Quid, 2022 | Table: 2023 AI Index ReportCompany Name

Headquarters
Country

Focus Area

Funding Amount(in Billions USD)AVEVA Group, PLC

UnitedKingdom

Chemical; Computer;
Data Mining; Electronics;
Industrial Manufacturing;
Information Technology;
Simulation; Software

4.68

Grupo deInversiones
Suramericana, SA

Colombia

Financial Services; Impact
Investing; Insurance

1.48

Fractal Analytics
Private Limited

India

Analytics; Arti cialIntelligence; Big Data;
Business Intelligence;
Consulting; Machine
Learning

0.35

Atrys Health, SA

Spain

Medical and Healthcare

0.28

R Systems
International, Ltd.

India

Analytics; Information
Technology; IT
Management; Software

0.17

Figure 4.2.3

Chapter 4 Preview

18Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Top Five AI Private Investment Activities, 2022
Source: NetBase Quid, 2022 | Table: 2023 AI Index ReportCompany Name

Headquarters
Country

Focus Area

Funding Amount(in Billions USD)GAC Ai¬¥an New
Energy Automobile
Co., Ltd.

China

Automotive; Clean
Energy; Electric Vehicle;
Manufacturing

2.54

Idience Co., Ltd.

South Korea

Emergency Medicine;
Healthcare;
Pharmaceutical

2.15

Uali

Argentina

Drones; Cloud Computing

1.50

Anduril Industries,
Inc.

United States

Cybersecurity, Data
Protection; AR/VR;
Drones

1.50

Celonis, GmbHGermany

Retail; Industrial
Automation, Network; HR
Tech; Insurtech

1.22

Figure 4.2.4

Top Five AI Public O ering Investment Activities, 2022
Source: NetBase Quid, 2022 | Table: 2023 AI Index ReportCompany Name

Headquarters Focus Area
Country

Funding Amount(in Billions USD)

ASR Microelectronics
Co., Ltd.

China

Semiconductor; VC

1.08

iSoftStone Information
Technology (Group)Co., Ltd.

China

Data Management,
Processing, and Cloud;Cybersecurity, Data
Protection

0.73

Jahez International
Company for
Information Systems
Technology

Saudi Arabia

Arti cial Intelligence;
E-Commerce; Food andBeverage; Food Delivery;
Information Technology;
Logistics

0.43

Fortior Technology(Shenzhen) Co., Ltd.

China

Electronics; Machine
Manufacturing;
Semiconductor

0.30

Beijing Deep Glint
Technology Co., Ltd.

China

Cybersecurity, Data
Protection; Music, Video
Content

0.29

Figure 4.2.5

Chapter 4 Preview

19Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Startup Activity

Global Trend

The next section analyzes private investment trends in

The global private AI investment trend reveals that

artificial intelligence startups that have received overwhile investment activity has decreased since 2021, it$1.5 million in investment since 2013.

is still 18 times higher than it was in 2013 (Figure 4.2.6).

Private Investment in AI, 2013–22

Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

Total Investment (in Billions of U.S. Dollars)

120

100
91.86

80

60

40

20

0

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.2.6

Chapter 4 Preview

20Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

A similar trend, of short-term decreases but longer-

decrease from 2021 but a sixfold increase since 2013

term growth, is evident in data on total private(Figure 4.2.7). Similarly, the number of newly funded AI

investment events. In 2022 there were 3,538 AI-

companies dropped to 1,392 from 1,669 last year, while

related private investment events, representing a 12%

having increased from 495 in 2013 (Figure 4.2.8).

Number of Private Investment Events in AI, 2013–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

4,000
3,538

Number of Private Investment Events

3,500

3,000

2,500

2,000

1,500

1,000

500

0

2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.2.7

Number of Newly Funded AI Companies in the World, 2013–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

1,600
1,392

1,400

Number of Companies

1,200

1,000

800

600

400

200

0

2013

Chapter 4 Preview

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.2.8

21

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

The year-over-year decrease in AI-related
funding is also evident when the funding events

AI Private Investment Events by Funding Size,
2021 Vs. 2022
Source: NetBase Quid, 2022 | Table: 2023 AI Index Report

are disaggregated by size. Across all size

Funding Size

categories, with the exception of ones overOver $1 Billion

4

6$1 billion, the total number of AI funding events$500 Million – $1 Billion

13

5

18

$100 Million – $500 Million

277

164

441

decreased (Figure 4.2.9).

2021$50 Million – $100 Million
Under $50 Million
Undisclosed
Total

2022

Total
10

277

238

515

2,851

2,585

5,436

598

540

1,138

4,020

3,538

7,558

Figure 4.2.9

Regional Comparison by Funding AmountOnce again, the United States led the world in terms of total AI private investment. In 2022, the $47.4 billion
invested in the United States was roughly 3.5 times the amount invested in the next highest country, China($13.4 billion), and 11 times the amount invested in the United Kingdom ($4.4 billion) (Figure 4.2.10).

Private Investment in AI by Geographic Area, 2022
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

United States

47.36

China

13.41

United Kingdom

4.37

Israel

3.24

India

3.24

South Korea

3.10

Germany

2.35

Canada1.83

France1.77

Argentina

1.52

Australia

1.35

Singapore

1.13

Switzerland

1.04

Japan

0.72

Finland0.61

0Chapter 4 Preview

5

10

15

20
25
30
Total Investment (in Billions of U.S. Dollars)

35

40

45
Figure 4.2.10

22

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

When private AI investments are aggregated since 2013, the same ranking of countries applies:
The United States is first with $248.9 billion invested, followed by China ($95.1 billion) and theUnited Kingdom ($18.2 billion) (Figure 4.2.11).

Private Investment in AI by Geographic Area, 2013–22 (Sum)
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

United States

248.90

China

95.11

United Kingdom

18.24

Israel10.83

Canada

8.83

India

7.73

Germany

6.99

France

6.59

South Korea

5.57

Singapore

4.72

Japan

3.99

Hong Kong

3.10

Switzerland

3.04

Australia

3.04

Spain 1.81
0Chapter 4 Preview

20

40

60

80

100
120
140
160
Total Investment (in Billions of U.S. Dollars)

180

200

220

240
Figure 4.2.11

23

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

While the United States continues to outpace

The top five American AI private investment events

other nations in terms of private AI investment,

are highlighted in Figure 4.2.13, the top five European

the country experienced a sharp 35.5% decrease

Union and British investments in Figure 4.2.14, and thein AI private investment within the last year (Figure

top five Chinese investments in Figure 4.2.15.

4.2.12). Chinese investment experienced a similarly
sharp decline (41.3%).

Private Investment in AI by Geographic Area, 2013–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

Total Investment (in Billions of U.S. Dollars)

70

60

50

47.36, United States

40

30

20
13.41, China
11.04, European Union and United Kingdom

10

0
2013

2014

Chapter 4 Preview

2015

2016

2017

2018

2019

2020

2021

2022

Figure 4.2.12

24

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Top AI Private Investment Events in the UnitedStates, 2022

Top AI Private Investment Events in the European
Union and United Kingdom, 2022

Company Name

Focus Area

Funding Amount(in Billions USD)

Company Name

Focus Area

Anduril Industries, Inc.

Cybersecurity, Data
Protection; AR/VR;
Drones

1.50

Celonis, GmbH

Retail; Industrial
Automation, Network;
HR Tech; Insurtech

1.22

Faire Wholesale, Inc.

Fintech; Retail; Sales
Enablement

0.82

Content Square, SAS

0.60

Anthropic, PBC

Arti cial Intelligence;
Information
Technology; Machine
Learning

0.58

Analytics; Arti cialIntelligence: CRM:
Data Visualization;
Digital Marketing;
SaaS
Retail

0.57

Data Management,
Processing, and Cloud;Cybersecurity, Data
Protection

0.40

Retail Logistics Excellence- RELEX Oy
Cera Care Limited

Medical and
Healthcare

0.32Babylon Holdings Limited

0.40

Medical and
Healthcare; Music,
Video Content

0.30

Data Management,
Processing, and Cloud;
AV; AR/VR

Source: NetBase Quid, 2022 | Table: 2023 AI Index Report

Arctic Wolf Networks, Inc.

JingChi, Inc.

Source: NetBase Quid, 2022 | Table: 2023 AI Index Report

Funding Amount(in Billions USD)

Figure 4.2.14
Figure 4.2.13

Top AI Private Investment Events in China, 2022

Source: NetBase Quid, 2022 | Table: 2023 AI Index ReportCompany Name

Focus Area

Funding Amount(in Billions USD)GAC Ai¬¥an New Energy
Automobile Co., Ltd.

Automotive; Clean
Energy; Electric
Vehicle;Manufacturing

2.54

GAC Ai¬¥an New Energy
Automobile Co., Ltd.

Automotive; Clean
Energy; Electric
Vehicle;Manufacturing

1.11

Beijing ESWIN
Technology Group Co.,Ltd.

Data Management,
Processing, and Cloud;Industrial Automation,
Network;
Semiconductor;
Marketing, Digital Ads;
Sales Enablement

0.58

Zhejiang Hozon New
Energy Automobile Co.,Ltd.

Data Management,
Processing, and Cloud;Cybersecurity, Data
Protection; Sales
Enablement

0.44

Zhejiang Hozon New
Energy Automobile Co.,Ltd.

Data Management,
Processing, and Cloud;Cybersecurity, Data
Protection; Sales
Enablement

0.32

Figure 4.2.15

Chapter 4 Preview

25Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Regional Comparison by Newly Funded
AI Companies
This subsection studies the number of newly funded

United States led all regions with the largest number of

AI companies across various geographic areas.

newly funded AI companies at 542, followed by China

As was the case with private investment, theat 160 and the United Kingdom at 99 (Figure 4.2.16).

Number of Newly Funded AI Companies by Geographic Area, 2022
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

United States

542

China

160

United Kingdom

99

Israel

73

India

57

Canada47

France44

Germany

41

Singapore

36

Japan

32

Switzerland

26

Australia

23

South Korea

22

Sweden12

Netherlands

120

Chapter 4 Preview

100

200

300
Number of Companies

400

500
Figure 4.2.16

26

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

A similar trend is evident in the aggregate data since 2013. In the last decade, the number of newly funded
AI companies in the United States is around 3.5 times the amount in China, and 7.4 times the amount in the
United Kingdom (Figure 4.2.17).

Number of Newly Funded AI Companies by Geographic Area, 2013–22 (Sum)
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

United States

4,643

China

1,337

United Kingdom

630

Israel402

Canada341

France

338

India

296

Japan

294

Germany

245

Singapore

165

South Korea

145

Australia

126

Switzerland

108

Sweden83

Netherlands

78

0

500

Figure 4.2.18 breaks
down data on newly

1,000

1,500

2,000
2,500
3,000
Number of Companies

3,500

4,000

4,500
Figure 4.2.17

Number of Newly Funded AI Companies by Geographic Area, 2013–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

700

funded AI companies
within select600

geographic regions.
In a trend that goesthe United States
continues to outpace
both the European
Union and the United
Kingdom, as well as

500
Number of Companies

back a decade,542,
United States

400

300

293,
European
Union and
United Kingdom

200

160,
China

China. However, thegrowth rates of the100

different regions are
relatively similar.

0
2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 4.2.18

Chapter 4 Preview

27Artificial IntelligenceIndex Report 2023

Focus Area Analysis
Private AI investment can also be disaggregated byfocus area. Figure 4.2.19 compares global private
AI investment by focus area in 2022 versus 2021.
The focus areas that attracted the most investment
in 2022 were medical and healthcare ($6.1 billion);
data management, processing, and cloud ($5.9
billion); fintech ($5.5 billion); cybersecurity anddata protection ($5.4 billion); and retail ($4.2
billion). Mirroring the pattern seen in total AI private
investment, the total investment across most focus
areas declined in the last year.

Chapter 4: The Economy
4.2 Investment

Private Investment in AI by Focus Area, 2021 Vs. 2022
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

Medical and Healthcare
Data Management, Processing, Cloud
FintechCybersecurity, Data Protection
RetailIndustrial Automation, Network
Sales Enablement
Marketing, Digital Ads
AR/VR
DronesInsurtechMusic, Video Content
Semiconductor
HR TechEnergy, Oil, and Gas
AV
NLP, Customer Support
AgritechEntertainment
Legal Tech
Geospatial
Fitness and Wellness
Ed TechFacial Recognition
VC

2022
2021

0

2

4

6

8

10

Total Investment (in Billions of U.S. Dollars)

Figure 4.2.19

Figure 4.2.20 presents trends in AI focus area

cybersecurity and data protection, drones, marketinginvestments. As noted earlier, most focus areas saw

and digital ads, HR tech, AR/VR, and legal tech. Still,

declining investments in the last year. However, somemirroring a broader trend in AI private investment,of the focus areas that saw increased investments aremost focus areas saw greater amounts of AI private

semiconductor, industrial automation and network,

investment in 2022 than they did in 2017.

Chapter 4 Preview

28Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Private Investment in AI by Focus Area, 2017–22

Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

Data Management, Processing, Cloud
8
6
4
2
0

5.86

2018

2020

2022

Medical and Healthcare
8
6
4
2
0

Semiconductor
8
6
4
2
0

2020

2022

8
6
4
2
0

Total Investment (in Billions of U.S. Dollars)

NLP, Customer Support
8
6
4
2
0

1.01
2018

2020

2018

2022

2020

2018

2022

2020

8
6
4
2
0

2022

8
6
4
2
0

2020

2022

2020

8
6
4
2
0

2022

8
6
4
2
0

2020

2022

8
6
4
2
0

2022

2020

8
6
4
2
0

2020

8
6
4
2
0

2022

0.53
2018

2020

8
6
4
2
0

2022

2020

2022

8
6
4
2
0

2022

8
6
4
2
0

1.88
2018

2020

2020

2022

8
6
4
2
0

1.74
2018

2020

2022

8
6
4
2
0

0.37
2018

2020

2022

Music, Video Content

0.87
2018

2022

Ed Tech

2.39
2018

2020

Insurtech

0.07
2018

2022

DronesEntertainment

0.83
2018

2022

5.38

2018

2020

Fitness and Wellness

AR/VR

3.18
2018

2020

1.34
2018

Facial Recognition

1.63
2018

2018

Legal Tech

0.71
2018

2020

2022

Cybersecurity, Data Protection

1.61
2018

Geospatial
8
6
4
2
0

2022

2020

4.20

Sales Enablement

0.87
2018

2020

8
6
4
2
0

Energy, Oil, and Gas

Agritech
8
6
4
2
0

2018

8
6
4
2
0

RetailHR Tech

3.05
2018

2022

3.92

Marketing, Digital Ads
8
6
4
2
0

2020

AV
5.52

Industrial Automation, Network

1.65
2018

6.05

Fintech
8
6
4
2
0

2022

8
6
4
2
0

1.72
2018

2020

2022

VC
8
6
4
2
0

0.02
2018

2020

Chapter 4 Preview

2022

Figure 4.2.20

29

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.2 Investment

Finally, 4.2.21 shows private investment in AI by focus areabillion), and 40 times more than that in the European

over time within select geographic regions, highlighting

Union and the United Kingdom ($0.04 billion). Chinese

how private investment priorities in AI differ acrossprivate investment in AI-related semiconductors ($1.02

geographies. For example, in 2022, private investment

billion) was 1.75 times more than that in the United

in AI-related drone technology in the United States ($1.6States ($0.58 billion), and 102 times more than that in thebillion) was nearly 53 times more than that in China ($0.03

European Union and the United Kingdom ($0.01 billion).

Private Investment in AI by Focus Area and Geographic Area, 2017–22

Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

Data Management, Processing, Cloud
Medical and Healthcare
US, 3.13
US, 4.19
CN, 1.87
CN, 0.25
4
4
EU/UK, 0.24
EU/UK, 0.76 4
2
0

2
2018

2020

2022

Semiconductor
4

0

2018

2020

2022

0

2018

2020

2022

Retail0

US, 1.52
CN, 0.01
EU/UK, 2.07 4

2

2

2

2

0

0

0

2020

2022

2
0

2018

2022

Energy, Oil, and Gas

2018

2020

2022

0

2
2020
Agritech
4

2022

0

US, 0.55
CN, 0.10
EU/UK, 0.08 4

2020

2022

2
2018

2020

2022

HR Tech0

US, 0.24
CN, 0.00
EU/UK, 1.28 4

2
2018

2018

2020

2022

Sales Enablement

0

2018

2020

2022

Facial Recognition

0

2018

2020
AR/VR

US, 1.12
CN, 1.68
EU/UK, 0.16 4

2022

0

2

2

2

0

0

Geospatial
4

US, 0.55
CN, 0.03
EU/UK, 0.01 4

2

2

0

2018

2020
VC

4

2022

0

2018

2020
Legal Tech

2022

US, 0.71
CN, 0.05
EU/UK, 0.06 4

2018

2020

Entertainment

2022

US, 0.47
CN, 0.18
EU/UK, 0.17 4

2
2018

2020

2022

0

2020
Drones2018

2020
Insurtech

2018

2020
Ed TechUS, 2.07
CN, 0.01
EU/UK, 0.06 4

0

2022

2018

US, 0.23
CN, 0.00
EU/UK, 0.142022
US, 1.60
CN, 0.03
EU/UK, 0.04

2022
US, 0.39
CN, 0.00
EU/UK, 1.29

2

2
2020

2022

Fitness and Wellness

US, 0.07
CN, 0.00
EU/UK, 0.00 4

0

2018

2020

2

2
2018

2018

Cybersecurity, Data Protection
US, 0.80
US, 3.87
CN, 0.34
CN, 1.07
EU/UK, 0.20 4
EU/UK, 0.23 42

Marketing, Digital Ads
US, 1.14
CN, 0.88
4
EU/UK, 0.76 4

0

2020

US, 0.69
CN, 0.49
EU/UK, 0.02

2

0

2018

AV

US, 3.23
CN, 0.03
EU/UK, 0.94 4

2

Industrial Automation, Network
US, 0.58
US, 0.87
CN, 1.02
CN, 1.06
4
EU/UK, 0.01
EU/UK, 1.65 4

NLP, Customer Support
US, 0.69
CN, 0.13
4
EU/UK, 0.04 4

Total Investment (in Billions of U.S. Dollars)Fintech

2018

2020

2022
US, 0.12
CN, 0.01
EU/UK, 0.10

2022Music, Video Content
US, 1.10
CN, 0.03
EU/UK, 0.44

2
2018

2020

2022

0

2018

2020

2022

US, 0.00
CN, 0.00
EU/UK, 0.02

2
0

2018

2020

Chapter 4 Preview

2022

Figure 4.2.21

30

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

This section explores how corporations tangibly use AI. First, it highlights industry adoption trends and asks how businesses adopt
AI and what particular AI technologies they find most useful, and identifies how AI adoption affects their bottom line. Second, thesection considers industry motivations and explores what questions industry leaders consider when thinking about incorporating AI
technologies. Finally, it paints a qualitative picture of business AI use by examining trends in AI-related earnings calls.

4.3 Corporate Activity
Industry Adoption

Adoption of AI Capabilities

The following subsection on the industry adoption

According to the most recent McKinsey report, as ofof AI borrows data from McKinsey’s “The State of2022, 50% of surveyed organizations reported having

AI in 2022—and a Half Decade in Review,” as welladopted AI in at least one business unit or function

as previous years’ editions. The 2022 report drew on(Figure 4.3.1). This total is down slightly from 56% indata from a survey of 1,492 participants representing

2021, although up significantly from 20% in 2017. AI

a wide range of regions, industries, company sizes,

usage has rapidly grown in the past half-decade, butfunctional specialties, and tenures.

leveled off since 2020.

Share of Respondents Who Say Their Organizations Have Adopted AI in at Least One Function, 2017–22
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

60%

50%

50%

% of Respondents

40%

30%

20%

10%

0%

2017

2018

2019

2020

2021

2022
Figure 4.3.1

Chapter 4 Preview

31Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

In the last half-decade, the average number of AI capabilities that organizations have embedded
has doubled from 1.9 in 2018 to 3.8 in 2022 (Figure 4.3.2). Some of the AI capabilities that McKinsey
features in their survey include recommender systems, NL text understanding, and facial recognition.4

Average Number of AI Capabilities That Respondents’ Organizations Have Embedded Within at Least One
Function or Business Unit, 2018–22
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

4.00
3.80

Number of AI Capabilities (Average)

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

2018

2019

2020

2021

2022
Figure 4.3.2

4 In the 2022 edition of the McKinsey survey, 16 total AI capabilities are considered: computer vision, deep learning, digital twins, facial recognition, GAN, knowledge graphs,
NL generation, NL speech understanding, NL text understanding, physical robotics, recommender systems, reinforcement learning, robotic process automation, transfer
learning, transformers, and virtual agents.

Chapter 4 Preview

32Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

The most commonly adopted AI use case in 2022 was service operations optimization (24%), followedby the creation of new AI-based products (20%), customer segmentation (19%), customer service
analytics (19%), and new AI-based enhancement of products (19%) (Figure 4.3.3).

Most Commonly Adopted AI Use Cases by Function, 2022
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

Service Operations Optimization

24%

Creation of New AI-Based Products

20%

Customer Segmentation

19%

Customer Service Analytics

19%

New AI-Based Enhancements of Products

19%

Customer Acquisition and Lead Generation

17%

Product Feature Optimization

16%

Contact-Center Automation

16%

Risk Modeling and Analytics

15%

Service Operations
Product and/or
Service Development
Marketing and Sales

Predictive Service and Intervention
0%

Risk

14%
4%

8%

12%
% of Respondents

16%

20%

24%

Figure 4.3.3

Chapter 4 Preview

33Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

With respect to the type of AI capabilities embedded

rates of embedding were 48%, 47%, and 46%. Acrossin at least one function or business unit, as indicated

all industries, the most embedded AI technologies

by Figure 4.3.4, robotic process automation had

were robotic process automation (39%), computerthe highest rate of embedding within high tech/vision (34%), NL text understanding (33%), and virtual

telecom, financial services and business, and legal

agents (33%).

and professional services industries—the respective

AI Capabilities Embedded in at Least One Function or Business Unit, 2022

20%

39%

16%

11%

33%

Business, Legal, and32%
Professional Services

37%

31%

11%

8%

26%

12%

22%

34%

19%

23%

26%

46%

16%

11%

30%

Consumer Goods/
33%
Retail

36%

25%

19%

13%

18%

20%

11%

22%

24%

32%

19%

25%

7%

11%

40%

Financial Services 24%

22%

18%

24%

13%

29%

20%

30%

42%

14%

30%

19%

47%

17%

12%

33%

Healthcare Systems/
Pharma and 32%
Med. Products

18%

16%

5%

5%

14%

5%

12%

29%

11%

16%

13%

16%

9%

6%

14%

High Tech/Telecom 37%

45%

24%

16%

15%

23%

24%

29%

40%

15%

34%

23%

48%

22%

15%

43%

ow
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All Industries 34%

G

Industry

Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report% of Respondents (AI Capability)
Figure 4.3.4

Chapter 4 Preview

34Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Figure 4.3.5 shows AI adoption by industry and AI function in 2022. The greatest adoption was in risk for hightech/telecom (38%), followed by service operations for consumer goods/retail (31%) and product and/or service
development for financial services (31%).

AI Adoption by Industry and Function, 2022

Industry

Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

All Industries

11%

8%

5%

10%

19%

19%

21%

9%

Business, Legal, andProfessional Services

11%

10%

9%

8%

16%

20%

19%

12%

Consumer Goods/
Retail

14%

4%

3%

4%

15%

31%

29%

11%

Financial Services

1%

8%

7%

31%

17%

24%

23%

2%

Healthcare Systems/
Pharma andMed. Products

15%

7%

2%

4%

22%

12%

8%

8%

High Tech/Telecom

6%

6%

4%

7%

38%

21%

25%

8%

Ris
Pro
k
Se ductrviancane D d/o
dS
r
ev
ale
elo
s
pm
en
t

Se
rv

Hu

ma

nR
eso
urc
es

Ma
nu
fa

ctu

rin

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rke
ti
g

ng

ice

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Co ateg
rpo y aera
rat nd
eF
tio
ns
ina
n

Op

ce

Su
p
Ma plyna Ch
a
ge
me in
nt

% of Respondents (Function)
Figure 4.3.5

Chapter 4 Preview

35Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Figure 4.3.6 shows how rates of AI adoption by

points); followed by high tech/telecom, for risk

industry and AI function vary from 2021 to 2022

(22 percentage points). The most significant

in order to demonstrate how rates of AI adoption

decreases were in high tech/telecom, for producthave changed over the last year. The greatest year-

and/or service development (38 percentage points);

over-year increases were in consumer goods/retail,and healthcare systems, also for product and/or

for strategy and corporate finance (25 percentage

service development (25 percentage points).

Percentage Point Change in Responses of AI Adoption by Industry and Function 2021 Vs. 2022

Industry

Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

All Industries

2%

-4%

-15%

-13%

6%

-6%

12%

-4%

Business, Legal, andProfessional Services

-3%

2%

-19%

-7%

3%

-6%

11%

-1%

Consumer Goods/
Retail

12%

-14%

-19%

-13%

14%

16%

25%

-7%

Financial Services

-9%

4%

-17%

11%

-15%

-16%

10%

-6%

Healthcare Systems/
Pharma andMed. Products

6%

-4%

-12%

-25%

9%

-5%

-4%

-1%

High Tech/Telecom

-6%

-5%

-24%

-38%

22%

-13%

15%

-8%

Ris
Pro
k
Se duct
rvi
a
nd
ce
an
De /or
dS
ve
ale
lop
s
me
nt

Se
rv

Hu

ma

nR
eso
urc
es

Ma
nu
fa

ctu

rin

Ma
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ti
g

ng

ice

Str
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rpo y aera
rat nd
eF
tio
ns
ina
n

Op

ce

Su
p
Ma plyna Ch
a
ge
me in
nt

Percentage Point Change in Responses (Function)
Figure 4.3.6

Chapter 4 Preview

36Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Organizations report AI adoption leading to both

(Figure 4.3.7). On the revenue side, the functions thatcost decreases and revenue increases. On the costmost respondents saw increases in as a result of AI

side, the functions that most respondents saw

adoption were marketing and sales (70%), productdecreases in as a result of AI adoption were supply

and/or service development (70%), and strategy andchain management (52%), service operations (45%),

corporate finance (65%).

strategy and corporate finance (43%), and risk (43%)

Cost Decrease and Revenue Increase From AI Adoption by Function, 2021
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

Decrease by <10%

Decrease by 10–19%

Service Operations

Decrease by ≥20%

Increase by >10%45%

10% 6%

10%

7%

10%

18%

33%
31%

29%

42%

Manufacturing

32%

Increase by 6–10%
10%

25%

14%

13%

Marketing and Sales

28%

21%

9%

20%

Function29%

43%

Supply Chain Management

52%

41%

30%

Product and/or Service Development
Strategy and Corporate Finance
Average Across All Activities

30%

43%

31%

32%

10%

7%

14%

17%

13%

24%

6%
8%

23%

61%
58%
70%

41%

8%

20%

57%

37%

Human Resources

Risk

Increase by ≤5%

11%

48%

27%

59%

28%

70%

33%

8%

16%

41%

65%

8%

19%

36%

63%

% of Respondents
Figure 4.3.7

Chapter 4 Preview

37Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Figure 4.3.8 shows AI adoption by organizations

across all geographies was 50%, down 6% from 2021.

globally, broken out by regions of the world. In 2022,

Notably, “Greater China” registered a 20 percentage

North America led (59%), followed by Asia-Pacificpoint decrease from 2021.

(55%) and Europe (48%). The average adoption rate

AI Adoption by Organizations in the World, 2021 Vs. 2022
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report50%

All Geographies

56%
55%

Asia-Paci c

64%
48%

Europe51%
59%

North America

55%

Greater China(incl. Hong Kong,
Taiwan)41%
61%

Developing Markets(incl. India,
Latin America,
MENA)
0%

2022

44%

2021
52%

10%

20%

30%
40%
% of Respondents

50%

60%
Figure 4.3.8

Chapter 4 Preview

38Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Consideration and Mitigation of Risks From
Adopting AI

risks were regulatory compliance (45%), personal/

As has been the case in the last few iterations of the

The least salient risks identified by organizations were

McKinsey report, in 2022 respondents identified

national security (13%) and political stability (9%).

individual privacy (40%), and explainability (37%).

cybersecurity as the most relevant risk when adopting
AI technology (59%) (Figure 4.3.9). The next most cited

Risks From Adopting AI That Organizations Consider Relevant, 2019–22
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

60%

59%, Cybersecurity50%% of Respondents

45%, Regulatory Compliance40%

40%, Personal/Individual Privacy
37%, Explainability

30%

32%, Organizational Reputation
30%, Equity and Fairness
28%, Workforce/Labor Displacement

20%

20%, Physical Safety
13%, National Security

10%

0%

9%, Political Stability

2019

2020

2021

2022
Figure 4.3.9

Chapter 4 Preview

39Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Figure 4.3.10 highlights the AI risks that organizations

have taken steps to mitigate. For instance, there isare taking steps to mitigate. The top three responses

a gap of 8 percentage points for cybersecurity, 9were cybersecurity (51%), followed by regulatory

percentage points for regulatory compliance, and 12

compliance (36%) and personal/individual privacy

percentage points for personal/individual privacy.

(28%). As was the case in previous years, there are

These differences suggest there is a gap between

meaningful gaps between the risks organizations

the awareness organizations have of various risks andcite as relevant and those which organizations

their steps taken to mitigate such risks.

Risks From Adopting AI That Organizations Take Steps to Mitigate, 2019–22
Source: McKinsey & Company Survey, 2022 | Chart: 2023 AI Index Report

51%, Cybersecurity

50%

40%

% of Respondents

36%, Regulatory Compliance30%

28%, Personal/Individual Privacy
22%, Organizational Reputation
22%, Explainability

20%

18%, Workforce/Labor Displacement
17%, Equity and Fairness
15%, Physical Safety

10%7%, National Security
4%, Political Stability
0%
2019

2020

2021

2022
Figure 4.3.10

Chapter 4 Preview

40Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Narrative Highlight:

The Effects of GitHub’s Copilot on Developer
Productivity and HappinessIn 2021, launched a technical preview of Copilot,
a generative AI tool that enables developers andcoders to present a coding problem in natural
language and then have Copilot generate asolution in code. Copilot can also translate
between various programming languages. In
2022, GitHub surveyed over 2,000 developers
who were using the tool to determine its effect on
their productivity, well-being, and workflow.5
Figure 4.3.11 summarizes the results of the survey.
Developers overwhelmingly reported feelingmore productive, satisfied, and efficient whenworking with Copilot. More specifically, 88% of
surveyed respondents commented feeling moreproductive, 74% reported being able to focus on
more satisfying work, and 88% claimed to havecompleted tasks more quickly. One software
engineer stated, “[With Copilot] I have to think

It took the developers
using Copilot only 71
minutes to complete theirtask—56% less time thanthe developers who did not
use Copilot (161 minutes).
reported a completion rate of 78%, 8 percentage
points higher than those who did not use Copilot.
Likewise, it only took the developers using Copilot
71 minutes to complete their task, which was 56%
less time than the developers who did not use
Copilot (161 minutes). These survey and experimentresults are evidence of the tangible ways in which
AI tools improve worker productivity.

less, and when I have to think, it’s the fun stuff. It
sets off a little spark that makes coding more fun
and more efficient.”6
As part of the same survey, GitHub recruited
95 developers and randomly split them into twogroups, one of which used Copilot as part of acoding task and the other which did not. The
results of this experiment are summarized in
Figure 4.3.12. The developers who used Copilot

5 Most of the developers surveyed, around 60%, were professional developers; 30% were students and 7% were hobbyists.
6 The quote is taken from this source.

Chapter 4 Preview

41Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Narrative Highlight:

The Effects of GitHub’s Copilot on Developer
Productivity and Happiness (cont’d)
Measuring Dimensions of Developer Productivity When Using Copilot: Survey Responses, 2022
Source: GitHub Survey, 2022 | Chart: 2023 AI Index Report

I Am More Productive

88%

Focus on More Satisfying Work74%

More Ful�lled With My Job

60%

Less Frustrated When Coding

59%

Perceived Productivity
Satisfaction and Well-BeingE�ciency and Flow

Faster With Repetitive Tasks

96%

Faster Completion

88%

Less Mental E�orts on Repetitive Tasks

87%

Less Time Searching

77%

More In The Flow
0%

73%
20%

40%
60%
% of Participants That Agreed or Strongly Agreed

80%

100%

Figure 4.3.11

Summary of the Experiment Process and Results
Source: GitHub Survey, 2022 | Table: 2023 AI Index Report

Used
GitHub Copilot

Did Not Use
GitHub Copilot

Number of Developers

45

50

Completion Rate (%)

78

70

Average Time Taken toComplete the Task (Minutes)

71

161

Figure 4.3.12

Chapter 4 Preview

42Artificial IntelligenceIndex Report 2023

Industry Motivation
This section explores the motivations industry

Chapter 4: The Economy
4.3 Corporate Activity

Importance of AI Solutions for Organizations’Overall Success
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Reportleaders have in deploying AI and examines the
1%, Not Important
5%, Somewhat Importantdegree to which they feel AI is important, thereasons they are eager to embrace AI, and thefactors that have hindered further scaling ofAI solutions. The data from this section comesfrom Deloitte’s “State of AI in Enterprise” report,
which has surveyed companies about their use
of AI since 2017. This year’s survey polled 2,620

94%, Importantbusiness leaders from a wide range of countries,
industries, and corporate levels.

Figure 4.3.13

Perceived Importance of AI
Figures 4.3.13 and 4.3.14 suggest that an
overwhelming majority of business leaders
perceive AI to be important for their businesses.
More specifically, when asked how important
AI solutions were for their organization’s overall
success, 94% responded “important,” 5% said
“somewhat important,” and 1% answered “notimportant” (Figure 4.3.13).

Believe AI Enhances Performance and Job
Satisfaction, 2022
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Report

2%, Strongly Disagree / Disagree
1%, Unsure

16%, Neither Agree nor Disagree

Similarly, when asked whether they believe that AI
enhances performance and job satisfaction, 82%
responded “strongly agree/agree,” 16% said they“neither agree nor disagree,” and only 2% selected“strongly disagree/disagree” (Figure 4.3.14).
82%, Strongly Agree / Agree

Chapter 4 Preview

Figure 4.3.14

43

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

AI Investments and Implementation
Outcomes
In 2022, 76% of surveyed leaders reported
expecting to increase AI investments in the next
fiscal year (Figure 4.3.15). Although this represents

a 9 percentage point decrease since 2021 and a 12
percentage point decrease since 2018, a significantly
large portion of business leaders continue to express
interest in AI investment.

Expected AI Investment Increase in the Next Fiscal Year
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Report80%% of Respondents

76%

60%

40%

20%

0%

2018

2019

2020

2021

2022
Figure 4.3.15

Chapter 4 Preview

44Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Figure 4.3.16 highlights the main outcomes that business leaders achieved by embracing AI solutions.7
The top outcome was lowered costs (37%), followed by improved collaboration across business
functions/organizations (34%) and having discovered valuable insights (34%).

Main Outcomes of AI Implementation, 2022
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Report

37%

Lower Costs
Improve Collaboration AcrossBusiness Functions/Organizations

34%

Discover Valuable Insights

34%

Customize or Improve Product/Programs,
Services, or Offers

33%Enter New Markets/Expand Services to
New Constituents

33%

Make Organizational Processes More E�cient

33%

Improve Decision-Making

32%

Create New Products/
Programs and Services

32%

Predict Demand

32%

Enable New Business/
Service Models

32%

Increase Revenue

31%

Activate the Potential of Existing Headcountand/or Improve Talent Management

30%

Improve Constituent Engagement30%

Anticipate Constituent Needs
0%

28%

10%

20%
% of Respondents

30%
Figure 4.3.16

7 Figure 4.3.16 is drawn from the chart in the Deloitte survey: “Outcomes—‘Achieved to a high degree.’”

Chapter 4 Preview

45Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Challenges in Starting and Scaling AI Projects

were proving business value (37%), lack of executive

The top three challenges that business leaders

commitment (34%), and choosing the right AI

identified in terms of starting AI-related projects

technologies (33%) (Figure 4.3.17).

Top Three Challenges in Starting AI Projects, 2022
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index Report

Proving Business
Value

37%

Lack of Executive
Commitment

34%

Choosing the Right
AI Technologies

0%

33%

5%

10%

15%

20%
% of Respondents

25%

30%

35%
Figure 4.3.17

Chapter 4 Preview

46Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

The main barrier leaders faced in scaling existing AI initiatives was managing AI-related risks (50%), obtaining
more data or inputs to train a model (44%), and implementing AI technologies (42%) (Figure 4.3.18).

Main Barriers in Scaling AI Initiatives, 2022
Source: Deloitte Survey, 2022 | Chart: 2023 AI Index ReportManaging
AI-Related Risks

50%

Obtaining Needed Data
or Input to Train Model

44%

Implementing
AI Technologies

42%

Proving Business Value

0%

40%

10%

20%

30%
% of Respondents

40%

50%

Figure 4.3.18

Chapter 4 Preview

47Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Earnings Calls

Aggregate Trends

The following subsection presents data fromfrom Fortune 500 companies that mentioned AI-related

NetBase Quid, which uses natural language

keywords (Figure 4.3.19). The number of such mentions

processing tools to analyze trends in corporate

dropped from the previous year, when there were 306,

earnings calls. NetBase Quid analyzed all 2022

but has increased since 2018 when there were 225.

In the 2022 fiscal year, there were 268 earnings calls

earnings calls from Fortune 500 companies,
identifying all mentions of “Artificial Intelligence,”“AI,” “Machine Learning,” “ML,” and “deep learning.”

Number of Fortune 500 Earnings Calls Mentioning AI, 2018–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

300

Number of Earnings Calls

268

200

100

0

2018

2019

2020

2021

2022
Figure 4.3.19

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48Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Specific Themes

advertising and marketing (8.8%) (Figure 4.3.20).

Mentions of AI in Fortune 500 earnings calls wereCompared to 2018, some of the less prevalentassociated with a wide range of themes. In 2022, theAI-related themes in 2022 included deep learning

most cited themes were business integration (10.0%);(4.8%), autonomous vehicles (3.1%), and data

pricing and inventory management (8.8%); andstorage and management (3.0%).

Themes for AI Mentions in Fortune 500 Earnings Calls, 2018 Vs. 2022
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report

9.96% (-15%)

Business Integration

11.74%
8.82% (+48%)

Pricing and Inventory Management

5.94%
8.82% (+204%)

Advertising and Marketing

2.90%
8.39% (+23%)

Process Automation

6.81%
7.40% (-7%)
7.97%

Support Decision-Making

7.11% (+69%)

Healthcare and Medical Practices

4.20%
6.26% (+73%)

Cloud Platforms

3.62%
5.26% (-21%)

Personalizing Customer Experience

6.67%

4.84% (-41%)

Deep Learning

8.26%
4.13% (+24%)

Edge Intelligence

3.33%
3.84% (+121%)

Nvidia AI Use Cases

1.74%
3.27% (+33%)

Revenue Growth

2.46%
3.13% (-47%)

Autonomous Vehicles

5.94%
2.99% (+37%)

Data Processing

2.17%
2.99% (-55%)

Data Storage and Management

6.67%
2.70% (+10%)
2.46%

Adobe Experience
Customer Support

2.42% (+734%)
0.29%
2.13% (-59%)

Azure Cognitive Services

5.22%
1.85% (-20%)
2.32%

Data Center GPU
Investments
Nvidia RTX
Digital Transformation
0%

1.28% (+47%)
0.87%

2022

1.00% (-62%)

2018
2.61%

0.71% (-87%)
5.36%

1%

2%

3%

4%

5%

6%
7%
8%
9%
Theme Mentioned (% of Total)

10%

11%

12%

13%

14%

Figure 4.3.20

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49Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Narrative Highlight:

What Are Business Leaders Actually Saying About AI?
To better understand business attitudes that surround AI, it is worth
looking at AI-related excerpts from the Fortune 500 earnings calls.
For example, on the topic of business integration, companies often
cite AI and machine learning (ML) use cases to reassure businessaudiences of safer business practices, growing opportunities,
streamlining processes, and capability expansion.

“We spent $100 million building
certain risk and fraud systems
so that when we process
payments on the consumer side,
losses are down $100 million to$200 million. Volume is way up.
That’s a huge benefit.”– Jamie Dimon, CEO, JP Morgan
Chase & Co. (Q2 2022)“Especially in the last year or so, thefield of robotics itself has actuallychanged because with AI and
ML coming to the picture, there’s
significant developments in the
robotics field. So we think it’s ahuge opportunity for us.”– Raj Subramaniam, CEO, FedEx(Q3 2022)“We spent a ton of moneyon Cloud. We spend a ton ofmoney on adding capabilities.
And over time, as you do it on
one platform, it all becomes
more efficient. So, I think it’sa lot of little things, but it adds
up with our base of people
and fixed cost, it adds up
significantly over time. We’ve
been able to maintain ourheadcount at a level we feel
good about, and we think
we can grow massively on
top of that without having to
add lots of bodies to be able
to do it.” – Peter Kern, CEO,
Expedia Group (Q4 2022)

In terms of process automation, business leaders emphasize the ability of AI tools to accelerate
productivity gains and to deliver a better customer experience.
“We continue to drive
the use of automation
and artificial
intelligence to drive
productivity gains to
help offset inflationarypressures.” – Jim Davis,
CEO, Quest Diagnostics(Q4 2022)

Chapter 4 Preview“We have improved theexperience for customers byapplying artificial intelligence
to match them with an expert
who is right for their specificsituation and to deliver insights
to experts so they can provide
excellent service.” – Sasan
Goodarzi, CEO, Intuit (Q2 2022)“In September, we opened a nextgen fulfillment center in Illinois.
This 1.1 million square foot facility
features robotics, machine learning,
and automated storage, resulting inincreased productivity and a better
service for our customers at fasterdelivery times.” – John David, CFO,
Walmart (Q3 2022)

50

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Narrative Highlight:

What Are Business Leaders Actually Saying About AI?
(cont’d)
The conversation surrounding pricing and inventory management saw companies reassuring businessaudiences on how their use of AI would improve their operational strength, especially in environments ofhigh inflation and supply chain challenges.

“We are … continuing to refine and invest
in machine learning tools that will allow for
more sophisticated competitive pricing
and greater automation at scale.”– Adrian Mitchell, CFO, Macy’s (Q3 2022)“Our teams are utilizing technology, innovative data analytics
and AI to forecast supply chain lead times and changes
in market demand to ensure optimal levels. These actions
along with our pricing initiatives positively impacted ourgross margin in the second quarter.”– Bert Nappier, CFO, Genuine Parts Company (Q3 2022)

There is also a vibrant discussion about the ways in which AI can change healthcare and medical
practices, more specifically to reduce costs, improve the patient experience, and better serve clinicians.
“[Using] machinelearning and robotics,
we can now resolvea wide range ofprescription drug
claims which previously
required the attention of
our pharmacists, freeing
them up to spend timewith patients. Thisadvanced approach
reduces overall costand improves thepatient experience.”– Karen Lynch, CEO,CVS Health (Q2 2022)

Chapter 4 Preview“I’d like to highlight productivity efforts in our preauthorization process where
we’re leveraging an in-house artificial intelligence solution to automaticallymatch incoming faxes to the correct authorization requests. This solution
creates administrative efficiencies across millions of inbound images. We arealso scaling this solution to multiple business units such as pharmacy andare also expanding the application of this type of AI to provide decision
support to clinicians, which will result in improvements to authorization
turnaround times, reduction in friction for providers and creating a better
member experience.” – Bruce Broussard, CEO, Humana (Q3 2022)“We continue to see opportunities across [the software and analytics] segment
as payers, providers, and partners take advantage of our high ROI solutions andrealize the benefits of our data, AI models, and workflow capabilities.”– Neil de Crescenzo, CEO, UnitedHealth Group (Q2 2022)

51

Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.3 Corporate Activity

Sentiment Analysis

sentiment associated with mentions of AI has been

NetBase Quid also runs the AI-related text of Fortune

overwhelmingly positive (Figure 4.3.21). Mentions

500 earnings calls through a sentiment analysis

of AI were rarely negative, suggesting that large

machine-learning algorithm that identifies whether

businesses tend to have positive associations when it

the sentiment associated with the mention of AI iscomes to AI tools.

positive, mixed, or negative . Overall, since 2018, the8

Sentiment Summary Distribution for AI Mentions in Fortune 500 Earnings Calls by Publication Date, 2018–22
Source: NetBase Quid, 2022 | Chart: 2023 AI Index Report
1%%

2%%

3%%

17%%

19%%

18%%

80%%

79%%

81%%

84%%

Sentiment Summary

1%%

16%%

79%%

86%%

1%%

84%%

12%%

85%%

1%%

20%%

2%%

16%%

1%%

14%%

77%%

0%%

87%%

1%%

13%%

1%%

84%%

80%%

77%%

81%%

1%%

22%%

3%%

17%%

18%%

81%%

84%%

17%%

81%%

15%%

18%%

81%%

3%%

21%%

2%%

76%%

1%%
2%%

23%%

0%%

15%%

1%%

19%%

86%%

80%

1%%

13%%

100%

60%

40%

Negative
20%

Mixed
Positive

0%

Q1

Q2
Q3
2018

Q4

Q1

Q2
Q3
2019

Q4

Q1

Q2
Q3
2020

Q4

Q1

Q2
Q3
2021

Q4

Q1

Q2
Q3
2022

Q4

Figure 4.3.21

8 Chapter 2 of the 2023 AI Index highlights trends in the performance of sentiment analysis algorithms.

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52Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

Given that robots are frequently deployed with AI-based software technologies, it is possible to gain insights on AI-ready infrastructure
being deployed in the real world by tracking the installation of industrial robots. Data in this section comes from the International
Federation of Robotics (IFR), an international nonprofit organization that works to promote, strengthen, and protect the robotics
industry. Every year the IFR releases the World Robotics Report, which tracks global trends in installations of robots.9

4.4 Robot Installations
Aggregate Trends

2021 saw a rebound in the total number of worldwide

The following subsection includes data on the

robot installations. The 517,000 industrial robots

installation and operation of industrial robots,

installed in 2021 represented a 31.3% increase fromwhich are defined as an “automatically controlled,

2020 and a 211.5% increase since 2011 (Figure 4.4.1).

reprogrammable, multipurpose manipulator,
programmable in three or more axes, which can beeither fixed in place or mobile for use in industrial
automation applications.”

Number of Industrial Robots Installed in the World, 2011–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

517

Number of Industrial Robots Installed (in Thousands)

500

400

300

200

100

0

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.4.1

9 Due to the timing of the IFR’s survey, the most recent data is from 2021.

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53Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

The worldwide operational stock of industrial robots

from 3,035,000 in 2020. In the last decade, thealso continues to steadily increase year over year

number of industrial robots being installed and the(Figure 4.4.2). The total number of operational

number being used have both steadily increased.

industrial robots jumped 14.6% to 3,477,000 in 2021,

Operational Stock of Industrial Robots in the World, 2011–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

3,477

3,500

Number of Industrial Robots (in Thousands)

3,000

2,500

2,000

1,500

1,000

500

0

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.4.2

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Chapter 4: The Economy
4.4 Robot Installations

Industrial Robots: Traditional Vs.
Collaborative Robots

scalable than traditional robots, and are capable ofiterative learning.

A distinction can be drawn between traditional
robots that work for humans and collaborative

In 2017, only 2.8% of all newly installed industrial

robots that are designed to work with humans.

robots were collaborative (Figure 4.4.3). As of 2021,

Recently, the robotics community has been excitedthat number increased to 7.5%. Although traditional

about the potential of collaborative robots given

industrial robots still lead new installations, thethat they can be safer, more flexible, and morenumber of collaborative robots is slowly increasing.

Number of Industrial Robots Installed in the World by Type, 2017–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

Number of Industrial Robots Installed (in Thousands)

500

Traditional

517

Collaborative

39

424
400

400

391

394

300

478
200

389

405

2017

2018

370

368

2019

2020

100

0

2021
Figure 4.4.3

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55Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

By Geographic Area

the amount installed by Japan (47,200) and 7.7

Country-level data on robot installations can illustratetimes the amount installed by the United States

which countries are prioritizing the integration of(35,000) (Figure 4.4.4). The countries with the next

robots into their economy. In 2021, China installed

most installations were South Korea (31,100) andthe most industrial robots, with 268,200, 5.7 times

Germany (23,800).

Number of Industrial Robots Installed by Country, 2021
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

China

268.20

Japan

47.20

United States

35.00

South Korea

31.10

Germany

23.80

Italy

14.10

Taiwan9.60

France5.90

Mexico

5.40

India

4.90

Canada4.30

Thailand

3.90

Singapore

3.50

Spain

3.40

Poland

3.30

0

30

60

90
120
150
180
Number of Industrial Robots Installed (in Thousands)

210

240

270
Figure 4.4.4

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Chapter 4: The Economy
4.4 Robot Installations

In 2013, China overtook Japan as the nation installingthe most industrial robots (Figure 4.4.5). Since then,the gap between the total number of industrial robots
installed by China and the next-nearest nation has

only widened. In 2013, Chinese industrial robot
installations represented 20.8% of the world’s share,
whereas in 2021, they represented 51.8%.

Number of New Industrial Robots Installed in Top Five Countries, 2011–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

268, China

Number of Industrial Robots Installed (in Thousands)

250

200

150

100

47, Japan
35, United States
31, South Korea
24, Germany

50

0

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 4.4.5

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Chapter 4: The Economy
4.4 Robot Installations

China consolidated its dominance in industrial robotics in 2021, the first year in which the country installed
more industrial robots than the rest of the world combined (Figure 4.4.6).

Number of Industrial Robots Installed (China Vs. Rest of the World), 2016–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

Number of Industrial Robots Installed (in Thousands)

268, China
250

249, Rest of the World

200

150

100

50

0

2016

2017

2018

2019

2020

2021
Figure 4.4.6

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58Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

Figure 4.4.7 shows the annual growth rate ofof industrial robot installations. The countries thatindustrial robot installations from 2020 to 2021 byreported the highest growth rates were Canada

country. Virtually every country surveyed by the(66%), Italy (65%), and Mexico (61%).

IFR reported a yearly increase in the total number

Annual Growth Rate of Industrial Robots Installed by Country, 2020 Vs. 2021
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

Canada

66%

Italy

65%

Mexico61%

Poland

56%

India

54%

China

51%

Thailand36%

Taiwan

31%

Japan

22%

United States

14%

France11%

Germany

6%South Korea

2%

Spain
Singapore

1%
-35%

−40%

−30%

−20%

−10%

0%
10%
20%
30%
Annual Growth Rate of Industrial Robots Installed

40%

50%

60%

70%
Figure 4.4.7

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Chapter 4: The Economy
4.4 Robot Installations

Narrative Highlight:

Country-Level Data on Service Robotics
Another important class of robots are service robots,
which the ISO defines as a robot “that performs

Service Robots in Medicine
Source: UL Solutions, 2022

useful tasks for humans or equipment excluding
industrial automation applications.”10 Figure 4.4.8
is an example of a robot being used in medicine,
Figure 4.4.9 illustrates how a robot can help withprofessional cleaning, and Figure 4.4.10 shows arobot designed for maintenance and inspection.

Figure 4.4.8

Service Robots in Professional Cleaning
Source: This Week in FM, 2021

Figure 4.4.9

Service Robots in Maintenance and Inspection
Source: Robotnik, 2022

Figure 4.4.10

10 A more detailed definition can be accessed here.

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Chapter 4: The Economy
4.4 Robot Installations

Narrative Highlight:

Country-Level Data on Service Robotics (cont’d)Compared to 2020, 2021 saw a higher number of professional service robots installed in the world
for several key application areas, including hospitality, medical robotics, professional cleaning, andtransportation and logistics (Figure 4.4.11). The category that registered the greatest year-over-yearincrease was transportation and logistics: In 2021, 1.5 times the number of such service robots were
installed as in 2020.

Number of Professional Service Robots Installed in the World by Application Area, 2020 Vs. 2021
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

8

Agriculture

8

20

Hospitality

11

15

Medical Robotics

12

13

Professional Cleaning

2020
50

Transportation and Logistics

0

2021

10

34
5

10

15
20
25
30
35
40
Number of Professional Service Robots Installed (in Thousands)

45

50

Figure 4.4.11

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Chapter 4: The Economy
4.4 Robot Installations

Narrative Highlight:

Country-Level Data on Service Robotics (cont’d)
As of 2022, the United States has the greatest number of professional service robot manufacturers,
roughly 2.16 times as many as the next nation, China. Other nations with significant numbers of robot
manufacturers include Germany (91), Japan (66), and France (54) (Figure 4.4.12).

Number of Professional Service Robot Manufacturers in Top Countries by Type of Company, 2022
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

225

Number of Professional Service Robot Manufacturers

29
200

150

100

104
194

Startups

91

Incumbents
Unknown
66
54

50

94

52

47

79
61

China

FranceRussia44

Germany

Japan

39

34

35

1

1

United States

44
3

2

0

49

39

South Korea

Switzerland

CanadaFigure 4.4.12

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62Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

Sectors and Application Types(137,000), followed by automotive (119,000) (Figure

On a global level, the sector that saw the greatest

increases in the total number of industrial robot

amount of robot installations was electrical/electronics

installations since 2019.

4.4.13). Each of the highlighted sectors has recorded

Number of Industrial Robots Installed in the World by Sector, 2019–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report52

All Others

37
30

119

Automotive

84
102
137

Electrical/Electronics

110
89
15

Food

12
11
64

Metal and Machinery

44
52
24

Plastic and Chemical Products

19
18

2021
2020

107

Unspeci ed

87
87

0

20

40

60
80
100
Number of Industrial Robots Installed (in Thousands)

2019
120

140
Figure 4.4.13

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63Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

Robots can also be deployed in a wide range of

230,000 industrial robots were installed for handling

applications, from assembling to dispensing andfunctions, 2.4 times more than for welding (96,000)

handling. Figure 4.4.14 illustrates how the applicationand 3.7 times more than for assembling (62,000).

of industrial robots has changed since 2021. HandlingEvery application category, with the exception

continues to be the application case toward whichof dispensing and processing, saw more robot

the most industrial robots are deployed. In 2021,

installations in 2021 than in 2019.

Number of Industrial Robots Installed in the World by Application, 2019–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

62

Assembling

50
40

32
32

Cleanroom
26
11

Dispensing

8
12
230

Handling

169
177
7

Processing

5

2021

7

2020

80

Unspeci ed

2019

60

55
96

Welding

70
74

0

20

40

60

80
100
120
140
160
Number of Industrial Robots Installed (in Thousands)

180

200

220

240
Figure 4.4.14

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64Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

China Vs. United Statesand metal and machinery (34,000) (Figure 4.4.15).

The Chinese industrial sectors that installed theEvery industrial sector in China recorded a greater

greatest number of industrial robots in 2022 were

number of robot installations in 2021 than in 2019.

electrical/electronics (88,000), automotive (62,000),

Number of Industrial Robots Installed in China by Sector, 2019–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report29

All Others

21
12
62

Automotive

31
32
88

Electrical/Electronics

64
42
4

Food

3
3
34

Metal and Machinery

Pharma/Cosmetics

22
22
1
1
1
6

Rubber and Plastics

5

2021

4

2020

43

Unspeci ed
0

2019

30
31

10

20

30
40
50
60
Number of Industrial Robots Installed (in Thousands)

70

80

90
Figure 4.4.15

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65Artificial IntelligenceIndex Report 2023

Chapter 4: The Economy
4.4 Robot Installations

The automotive industry installed the greatest number of industrial robots in the United States in 2021,
although installation rates for that sector decreased year over year (Figure 4.4.16). However, other sectors likefood, along with plastic and chemical products, saw year-over-year increases in robot installations.

Number of Industrial Robots Installed in the United States by Sector, 2019–21
Source: International Federation of Robotics (IFR), 2022 | Chart: 2023 AI Index Report

4.50

All Others

2.60
3.50
9.80

Automotive

10.50
13.00
2.90

Electrical/Electronics

3.70
3.50
3.40

Food

2.70

2.20
3.80

Metal and Machinery

2.30
3.80
3.50

Plastic and Chemical Products

2.60
2.50

2021
2020

7.10

Unspeci ed

6.30

2019

5.00

0

2

3

5
6
8
9
Number of Industrial Robots Installed (in Thousands)

11

12

14

Figure 4.4.16

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66Artificial IntelligenceIndex Report 2023

Appendix
Chapter 4: The Economy

Appendix
Lightcastjob postings is the Job Openings and Labor TurnoverPrepared by Scott Bingham, Julia Nania, Layla O’Kane,of Labor Statistics. Based on comparisons between

and Bledi Taska

Survey (JOLTS) program, conducted by the Bureau
JOLTS and Lightcast, the labor market demandLightcast delivers job market analytics that empower

captured by Lightcast data represents over 99% of

employers, workers, and educators to make data-

the total labor demand. Jobs not posted online are

driven decisions. The company’s artificial intelligence

usually in small businesses (the classic example being

technology analyzes hundreds of millions of job postings

the “Help Wanted” sign in a restaurant window) andand real-life career transitions to provide insightunion hiring halls.

into labor market patterns. This real-time strategic

Measuring Demand for AI

intelligence offers crucial insights, such as what jobs are

In order to measure the demand by employers of AI

most in demand, the specific skills employers need, andskills, Lightcast uses its skills taxonomy of over 31,000

the career directions that offer the highest potential forskills. The list of AI skills from Lightcast data are shownworkers. For more information, visit www.lightcast.io.

below, with associated skill clusters. While some skills

Job Posting Data

are considered to be in the AI cluster specifically,

To support these analyses, Lightcast mined its dataset

for the purposes of this report, all skills below wereof millions of job postings collected since 2010.

considered AI skills. A job posting was considered an

Lightcast collects postings from over 51,000 online job

AI job if it mentioned any of these skills in the job text.

sites to develop a comprehensive, real-time portrait

Artificial Intelligence: AIOps (Artificial Intelligence forof labor market demand. It aggregates job postings,
removes duplicates, and extracts data from job postings
text. This includes information on job title, employer,industry, and region, as well as required experience,education, and skills.

IT Operations), Applications of Artificial Intelligence,
Artificial General Intelligence, Artificial Intelligence,
Artificial Intelligence Development, Artificial
Intelligence Markup Language (AIML), Artificial
Intelligence Systems, Azure Cognitive Services,

Job postings are useful for understanding trends in

Baidu, Cognitive Automation, Cognitive Computing,the labor market because they allow for a detailed,Computational Intelligence, Cortana, Expert Systems,

real-time look at the skills employers seek. To assessIntelligent Control, Intelligent Systems, Interactive

the representativeness of job postings data, Lightcast

Kiosk, IPSoft Amelia, Knowledge-Based Configuration,

conducts a number of analyses to compare theKnowledge-Based Systems, Multi-Agent Systems,

distribution of job postings to the distribution of official

Open Neural Network Exchange (ONNX), OpenAI

government and other third-party sources in the UnitedGym, Reasoning Systems, Soft Computing, Syman,

States. The primary source of government data on U.S.

Watson Conversation, Watson Studio, Weka

Chapter 4 Preview

67Artificial IntelligenceIndex Report 2023

Appendix
Chapter 4: The Economy

Autonomous Driving: Advanced Driver Assistance

Machine Learning: AdaBoost, Apache MADlib,

Systems, Autonomous Cruise Control Systems,

Apache Mahout, Apache SINGA, Apache Spark,

Autonomous System, Autonomous Vehicles, Guidance

Association Rule Learning, Automated Machine

Navigation and Control Systems, Light Detection andLearning, Autonomic Computing, AWS SageMaker,

Ranging (LiDAR), OpenCV, Path Analysis, Path Finding,Azure Machine Learning, Boosting, CHi-Squared

Remote Sensing, Unmanned Aerial Systems (UAS)

Automatic Interaction Detection (CHAID),

Natural Language Processing (NLP): Amazon Textract,
ANTLR, BERT (NLP Model), Chatbot, Computational
Linguistics, DeepSpeech, Dialog Systems, fastText,
Fuzzy Logic, Handwriting Recognition, Hugging
Face (NLP Framework), HuggingFace Transformers,
Intelligent Agent, Intelligent Software Assistant,Intelligent Virtual Assistant, Kaldi, Latent Dirichlet
Allocation, Lexalytics, Machine Translation, Microsoft
LUIS, Natural Language Generation, Natural Language
Processing, Natural Language Processing Systems,
Natural Language Programming, Natural Language
Toolkits, Natural Language Understanding, Natural
Language User Interface, Nearest Neighbour
Algorithm, OpenNLP, Optical Character Recognition(OCR), Screen Reader, Semantic Analysis, Semantic
Interpretation for Speech Recognition, Semantic
Parsing, Semantic Search, Sentiment Analysis,
Seq2Seq, Speech Recognition, Speech Recognition
Software, Statistical Language Acquisition, Text Mining,
Tokenization, Voice Interaction, Voice User Interface,
Word Embedding, Word2Vec Models

Classification And Regression Tree (CART), Cluster
Analysis, Collaborative Filtering, Confusion Matrix,
Cyber-Physical Systems, Dask (Software), DataClassification, DBSCAN, Decision Models, Decision
Tree Learning, Dimensionality Reduction, Dlib
(C++ Library), Ensemble Methods, Evolutionary
Programming, Expectation Maximization Algorithm,
Feature Engineering, Feature Extraction, Feature
Learning, Feature Selection, Gaussian Process,
Genetic Algorithm, Google AutoML, Google Cloud
ML Engine, Gradient Boosting, H2O.ai, Hidden
Markov Model, Hyperparameter Optimization,
Inference Engine, K-Means Clustering, Kernel
Methods, Kubeflow, LIBSVM, Machine Learning,
Machine Learning Algorithms, Markov Chain, Matrix
Factorization, Meta Learning, Microsoft Cognitive
Toolkit (CNTK), MLflow, MLOps (Machine Learning
Operations), mlpack (C++ Library), Naive Bayes,
Perceptron, Predictionio, PyTorch (Machine Learning
Library), Random Forest Algorithm, Recommendation
Engine, Recommender Systems, Reinforcement
Learning, Scikit-learn (Machine Learning Library),

Neural Networks: Apache MXNet, Artificial NeuralSemi-Supervised Learning, Soft Computing, Sorting

Networks, Autoencoders, Caffe, Caffe2, Chainer,

Algorithm, Supervised Learning, Support Vector

Convolutional Neural Networks, Cudnn, Deep Learning,

Machine, Test Datasets, Torch (Machine Learning),

Deeplearning4j, Keras (Neural Network Library), Long

Training Datasets, Transfer Learning, Unsupervised

Short-Term Memory (LSTM), OpenVINO, PaddlePaddle,

Learning, Vowpal Wabbit, Xgboost

Pybrain, Recurrent Neural Network (RNN), TensorFlow

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Robotics: Advanced Robotics, Cognitive Robotics,

intelligence, computer vision, image processing,

Motion Planning, Nvidia Jetson, Robot Framework,

deep learning, TensorFlow, Pandas (software), andRobot Operating Systems, Robotic Automation

OpenCV, among others.

Software, Robotic Liquid Handling Systems, Robotic
Programming, Robotic Systems, Servomotor, SLAM
Algorithms (Simultaneous Localization and Mapping)

Skill groupings are derived by expert taxonomists
through a similarity-index methodology thatmeasures skill composition at the industry level.

Visual Image Recognition: 3D Reconstruction, Activity

LinkedIn’s industry taxonomy and their corresponding

Recognition, Computer Vision, Contextual Image

NAICS codes can be found here.

Classification, Digital Image Processing, Eye Tracking,

Skills Genome

Face Detection, Facial Recognition, Image Analysis,Image Matching, Image Processing, Image Recognition,
Image Segmentation, Image Sensor, Imagenet,
Machine Vision, Motion Analysis, Object Recognition,
OmniPage, Pose Estimation, RealSense

For any entity (occupation or job, country, sector,etc.), the skill genome is an ordered list (a vector) of
the 50 “most characteristic skills” of that entity. Thesemost characteristic skills are identified using a TF-IDF
algorithm to identify the most representative skills ofLinkedIn

the target entity, while down-ranking ubiquitous skills

Prepared by Murat Erer and Akash Kaura(e.g., Microsoft Word).

Country Sample

TF-IDF is a statistical measure that evaluates how

Included countries represent a select sample of eligible

representative a word (in this case a skill) is to a

countries with at least 40% labor force coverage by

selected entity). This is done by multiplying two

LinkedIn and at least 10 AI hires in any given month.

metrics:

China and India were included in this sample becauseof their increasing importance in the global economy,
but LinkedIn coverage in these countries does notthat add little information about that specific entity

1. The term frequency of a skill in an entity (TF).
	2. The logarithmic inverse entity frequency of thereach 40% of the workforce. Insights for these countries

skill across a set of entities (IDF). This indicates

may not provide as full a picture as other countries, andhow common or rare a word is in the entire entity

should be interpreted accordingly.

set. The closer IDF is to 0, the more common the

Skills (and AI Skills)

word.

LinkedIn members self-report their skills on their

So if the skill is very common across LinkedIn entities,

LinkedIn profiles. Currently, more than 38,000 distinct,

and appears in many job or member descriptions, thestandardized skills are identified by LinkedIn. These

IDF will approach 0. If, on the other hand, the skill

have been coded and classified by taxonomists at

is unique to specific entities, the IDF will approach

LinkedIn into 249 skill groupings, which are the skill

1. More details are available at LinkedIn’s Skills

groups represented in the dataset. The top skills that

Genome and LinkedIn-World Bank Methodology.

make up the AI skill grouping are machine learning,
natural language processing, data structures, artificial
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AI Skills Penetration

perform the job. Skills penetration is used as a signal

The aim of this indicator is to measure the intensity of AI

for whether AI skills are prevalent in an occupation

skills in an entity (a particular country, industry, gender,

representative in any sector where the occupationetc.) through the following methodology:

representative may exist. Examples of such

• Compute frequencies for all self-added skills by
LinkedIn members in a given entity (occupation,industry, etc.) in 2015–2021.
• Re-weight skill frequencies using a TF-IDF model
to get the top 50 most representative skills inthat entity. These 50 skills compose the “skill
genome” of that entity.
• Compute the share of skills that belong to theoccupations include (but are not limited to): machinelearning engineer, artificial intelligence specialist,
data scientist, computer vision engineer, etc.

AI Talent
A LinkedIn member is considered AI talent if they
have explicitly added AI skills to their profile and/or
they are occupied in an AI occupation representative.
The counts of AI talent are used to calculate talentconcentration metrics. For example, to calculate

AI skill group out of the top skills in the selected

the country level AI talent concentration, we use

entity.

the counts of AI talent at the country level vis-a-vis

Interpretation: The AI skill penetration rate signals
the prevalence of AI skills across occupations, or thethe counts of LinkedIn members in the respective
countries.

intensity with which LinkedIn members utilize AI skills

Relative AI Skills Penetrationin their jobs. For example, the top 50 skills for the

To allow for skills penetration comparisons acrossoccupation of engineer are calculated based on the

countries, the skills genomes are calculated and aweighted frequency with which they appear in LinkedIn

relevant benchmark is selected (e.g., global average).

members’ profiles. If four of the skills that engineers

A ratio is then constructed between a country’s andpossess belong to the AI skill group, this measure

the benchmark’s AI skills penetrations, controlling forindicates that the penetration of AI skills is estimated tooccupations.

be 8% among engineers (i.e., 4/50).

Interpretation: A country’s relative AI skills

Jobs or Occupations

penetration of 1.5 indicates that AI skills are 1.5 times

LinkedIn member titles are standardized and grouped

as frequent as in the benchmark, for an overlapping

into approximately 15,000 occupations. These areset of occupations.

not sector- or country-specific. These occupations

Global Comparison

are further standardized into approximately
3,600 occupation representatives. Occupation
representatives group occupations with a common roleand specialty, regardless of seniority.

For cross-country comparison, we present therelative penetration rate of AI skills, measured as
the sum of the penetration of each AI skill acrossoccupations in a given country, divided by the

AI Jobs and Occupations

average global penetration of AI skills across the

An “AI” job (technically, occupation representative)

overlapping occupations in a sample of countries.

is an occupation representative that requires AI skills to
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Interpretation: A relative penetration rate of 2 means

Interpretation: A country’s “Relative AI Skills

that the average penetration of AI skills in that country

Penetration Across Genders” for women of 1.5 means

is two times the global average across the same set ofthat female members in that country are 1.5 times

occupations.

more likely to list AI skills than the average member in

Global Comparison: By Industry

all countries pooled together across the same set of

The relative AI skills penetration by country for industry

occupations that exist in the country.

provides an in-depth sectoral decomposition of AI skill

Relative AI Hiring Index

penetration across industries and sample countries.

	
LinkedIn Hiring Rate or Overall Hiring Rate

Interpretation: A country’s relative AI skill penetration
rate of 2 in the education sector means that the average
penetration of AI skills in that country is two times theglobal average across the same set of occupations inthat sector.

Global Comparison: By Gender
The “Relative AI Skills Penetration by Gender” metricprovides a cross-country comparison of AI skill
penetrations within each gender, comparing countries’
male or female AI skill penetrations to the global

is a measure of hires normalized by LinkedIn
membership. It is computed as the percentage of
LinkedIn members who added a new employer
in the same period the job began, divided by
the total number of LinkedIn members in thecorresponding location.
AI Hiring Rate is computed following the overall
hiring rate methodology, but only considering
members classified as AI talent.
	
Relative AI Hiring Index is the pace of change inaverage of the same gender. Since the global averages

AI Hiring Rate normalized by the pace of change

are distinct for each gender, this metric should only bein Overall Hiring Rate, providing a picture ofused to compare country rankings within each gender,whether hiring of AI talent is growing at a higher,

and not for cross-gender comparisons within countries.

equal, or lower rate than overall hiring in a

Interpretation: A country’s AI skills penetration for
women of 1.5 means that female members in thatcountry are 1.5 times more likely to list AI skills than themarket. The relative AI Hiring Index is equal to
1.0 when AI hiring and overall hiring are growing
at the same rate year on year.

average female member in all countries pooled together

Interpretation: Relative AI Hiring Index shows how

across the same set of occupations that exist in thefast each country is experiencing growth in AI talentcountry/gender combination.

hiring relative to growth in overall hiring in the country.

Global Comparison: Across Gender

A ratio of 1.2 means the growth in AI talent hiring has

The “Relative AI Skills Penetration Across Genders”

outpaced the growth in overall hiring by 20%.

metric allows for cross-gender comparisons within
and across countries globally, since we compare thecountries’ male and female AI skill penetrations to thesame global average regardless of gender.

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Changelog From Methodology Included in Last Year’s AI Index	1. LinkedIn ramped a new version of its industry taxonomy (see details here).
		a. This has resulted in changes to our top level five key industries. We have made the full-time series
available for each industry (as with prior years).
			i. “Software & IT Services” industry evolved into a wider “Technology, Information and Media,” which
encompasses media and telecommunications as well as other sub-industries.
			ii. Former “Hardware & Networking” industry does not exist in the new taxonomy, so we introduced“Professional Services” industry as the fifth industry in scope which contains a high concentration of AI
talent.
			iii. Remaining “Education,” “Manufacturing,” and “Financial Services” (formerly known as “Finance”)
also had updates in their coverage resulting from the inclusion of more granular sub-industries.
		b. This also resulted in minor changes in magnitudes for some metrics since the distinct number ofindustries, as well as the distinct number of AI occupations defined within each country-industry pair havechanged:			i. We define AI occupations (occupation representatives that require AI skills to perform the job) andthe respective definition of AI Talent at Country-Industry level. For example, data engineers working
in the technology, information, and media industry in Germany may be identified as holding an AI
occupation, whereas data engineers working in the construction industry in the United Arab Emirates
may not be identified as AI Talent. Following the introduction of a more granular industry taxonomy
with improved accuracy, our AI Talent identifications have been improved, and results have beenreflected to the entirety of time series for each relevant metric.
			ii. The following metrics have been impacted by this change in industry taxonomy: AI Talent
Concentrations, and Relative AI Hiring Rates. No directional changes were observed, only minorchanges in magnitudes.
	2. We introduced a methodology change into Relative Skills Penetration metrics:
		a. In the past, the data used to calculate these metrics were limited to top five industries with the highest
AI skill penetration globally: “Software & IT Services,” “Hardware & Networking,” “Manufacturing,”“Education,” and “Finance” industries. This year we updated our coverage to all industries.

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NetBase Quid

subsidiary, out of business) throughout the world.

Prepared by Bill Valle and Nicole Seredenko

M&A, public offerings, minority stakes made by PE/

The investment data includes private investments,

NetBase Quid delivers AI-powered consumer and

VCs, corporate venture arms, governments, andmarket intelligence to enable business reinvention

institutions both within and outside the United States.

in a noisy and unpredictable world. The software

Some data is simply unreachable—for instance, whenapplies artificial intelligence to reveal patterns in large,

investors’ names or funding amounts are undisclosed.

unstructured datasets and to generate visualizations

NetBase Quid embeds Capital IQ data as a default

that enable users to make smart, data-driven decisions
accurately, quickly, and efficiently. NetBase Quid uses
Boolean query to search for focus areas, topics, andkeywords within social media, news, forums and blogs,
companies, and patents data sources, as well as othercustom datasets. NetBase Quid then visualizes thesedata points based on the semantic similarity.

Search, Data Sources, and Scope
Over 8 million global public and private company profiles
from multiple data sources are indexed in order tosearch across company descriptions, while filtering andincluding metadata ranging from investment information
to firmographic information, such as founded year, HQ
location, and more. Company information is updated
on a weekly basis. The NetBase Quid algorithm reads
a big amount of text data from each document tomake links between different documents based on
their similar language. This process is repeated at animmense scale, which produces a network with different
clusters identifying distinct topics or focus areas. Trends
are identified based on keywords, phrases, people,
companies, and institutions that NetBase Quid identifies,
and the other metadata that is put into the software.

and adds in data from Crunchbase for the data
points that are not captured in Capital IQ. This not
only yields comprehensive and accurate data on
all global organizations, but it also captures earlystage startups and funding events data. Company
information is updated on a weekly basis.
Earnings Calls
NetBase Quid leverages earnings call transcript
data embedded from Seeking Alpha. For this report,
NetBase Quid has analyzed mentions of AI-related
keywords across all earnings call transcripts from
Fortune 500 companies from January 2018 throughDecember 2022. New earnings call transcript data is
updated in NetBase Quid on the 1st and 15th of every
month.

Search Parameters
Boolean query is used to search for focus areas,
topics, and keywords within the archived company
database, within their business descriptions andwebsites. We can filter out the search results by
HQ regions, investment amount, operating status,organization type (private/public), and founding
year. NetBase Quid then visualizes these companies

Databy semantic similarity. If there are more than 7,000

Companies

companies from the search result, NetBase Quid

Organization data is embedded from Capital IQ andselects the 7,000 most relevant companies for

Crunchbase. These companies include all types ofvisualization based on the language algorithm.

companies (private, public, operating, operating as a

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Boolean Search: “artificial intelligence” or “AI” or“machine learning” or “deep learning”
Companies:• Global AI and ML companies that have received
investments (private, IPO, M&A) from January 1,
2013, to December 31, 2022.
• Global AI and ML companies that have received

Appendix
Chapter 4: The Economy

McKinsey & Company
Data used in the Corporate Activity-Industry
Adoption section was sourced from the McKinsey
Global Survey “The State of AI in 2022—and a Half
Decade in Review.”
The online survey was in the field from May 3, 2022,
to May 27, 2022, and from August 15, 2022, toover $1.5M for the last 10 years (January 1, 2013,

August 17, 2022, and garnered responses from 1,492

to December 31, 2022): 7,000 out of 7,500

participants representing a full range of regions,companies have been selected through NetBase

industries, company sizes, functional specialties,

Quid’s relevance algorithm.

and tenures. Of those respondents, 744 said theirTarget Event Definitions• Private investments: A private placement is aorganization had adopted AI in at least one function
and were asked questions about their organization’s
AI use. To adjust for differences in response rates,

private sale of newly issued securities (equity or

the data is weighted by the contribution of eachdebt) by a company to a selected investor or a

respondent’s nation to global GDP.

selected group of investors. The stakes that buyers
take in private placements are often minority
stakes (under 50%), although it is possible to takecontrol of a company through a private placement

The AI Index also considered data from previous
iterations of the survey. More specifically, the AI
index made use of data from:as well, in which case the private placement would

The State of AI in 2021

be a majority stake investment.

The State of AI in 2020

• Minority investment: These refer to minority
stake acquisitions in NetBase Quid, which takeplace when the buyer acquires less than 50% of
the existing ownership stake in entities, asset

Global AI Survey: AI Proves Its Worth,
But Few Scale Impact (2019)
AI Adoption Advances, But Foundational Barriers
Remain (2018)

products, and business divisions.
• M&A: This refers to a buyer acquiring more than
50% of the existing ownership stake in entities,
asset products, and business divisions.

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GitHub

To obtain a global view of how AI is transforming

Data on the effects of GitHub’s Copilot on developerbusiness leaders between April 2022 and May 2022.

productivity and happiness was sourced from theGitHub Copilot Survey conducted in 2022.

organizations, Deloitte surveyed 2,620 global
Thirteen countries were represented: Australia (100respondents), Brazil (115 respondents), Canada (175The survey was emailed to 17,420 users who had opted

respondents), China (200 respondents), France (130in to receive communications and were using GitHub

respondents), Germany (150 respondents), India

Copilot for their daily programming activities. Between(200 respondents), Israel (75 respondents), Japan

February 10, 2022, and March 6, 2022, the authors(100 respondents), Singapore (100 respondents),

received 2,047 responses that could be matched with

South Africa (75 respondents), the United Kingdom

usage measurements during the four-week period(200 respondents), and the United States (1,000

leading up to March 12, 2022. The survey contained

respondents). All participating companies havemultiple-choice questions on demographic information

adopted AI technologies and are AI users.

and Likert-type questions on different aspects of

Respondents were required to meet one of the

productivity, which were randomized in the order offollowing criteria: responsible for AI technology

appearance to the user.

spending or approval of AI investments, developing

More details can be found in Ziegler at al., 2022.

AI technology strategies, managing or overseeing
AI technology implementation, serving as an AI

Deloitte

technology subject matter specialist, or making

Data used in the Corporate Activity-Industry Motivation

complement the blind survey, Deloitte conducted

section was sourced from Deloitte’s “State of AI in thequalitative telephone interviews with 15 AI specialists

Enterprise” surveys.

from various industries. More details are available onMore specifically, the AI Index made use of the following
sources of information:
Deloitte’s State of AI in the Enterprise,
5th Edition Report (2022)

or influencing decisions around AI technology. To

Deloitte’s website.

International Federation of
Robotics (IFR)

State of AI in the Enterprise, 4th Edition (2021)

Data presented in the Robot Installations section was

Deloitte’s State of AI in the Enterprise, 3rd Edition (2020)

sourced from the “World Robotics 2022” report.

State of AI in the Enterprise, 2nd Edition (2018)
The 2017 Deloitte State of Cognitive Survey (2017)

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