Artificial IntelligenceIndex Report 2021

CHAPTER 3:

The Economy

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CHAPTER 3:

Chapter Preview
Overview

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3.3 CORPORATE ACTIVITY

19

Chapter Highlights

4

Industry Adoption

19

Global Adoption of AI

19

3.1 JOBS

5

AI Adoption by Industry and Function

20

AI Hiring5

Type of AI Capabilities Adopted

20

AI Labor Demand

7

Consideration and Mitigation of
Risks from Adopting AI

22

The Effect of COVID-19

24

Industrial Robot Installations

25

Global AI Labor Demand

7

U.S. AI Labor Demand: By Skill Cluster 8
U.S. Labor Demand: By Industry

9

U.S. Labor Demand: By State11

AI Skill Penetration

12

Global Comparison

12

Global Comparison: By Industry

13

3.2 INVESTMENT

14

Corporate Investment

14

Startup Activity

15

Global Trend

15

Regional Comparison

16

Focus Area Analysis

18

Global Trend

25

Regional Comparison

26

Earnings Calls

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APPENDIX

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CHAPTER 3:
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Overview
The rise of artificial intelligence (AI) inevitably raises the question of how muchthe technologies will impact businesses, labor, and the economy more generally.
Considering the recent progress and numerous breakthroughs in AI, the field offers
substantial benefits and opportunities for businesses, from increasing productivity
gains with automation to tailoring products to consumers using algorithms,
analyzing data at scale, and more.
However, the boost in efficiency and productivity promised by AI also presents
great challenges: Companies must scramble to find and retain skilled talent tomeet their production needs while being mindful about implementing measures tomitigate the risks of using AI. Moreover, the COVID-19 pandemic has caused chaosand continued uncertainty for the global economy. How have private companies
relied on and scaled AI technologies to help their business navigate through thismost difficult time?
This chapter looks at the increasingly intertwined relationship between AI and theglobal economy from the perspective of jobs, investment, and corporate activity.
It first analyzes the worldwide demand for AI talent using data on hiring rates
and skill penetration rates from LinkedIn as well as AI job postings from Burning
Glass Technologies. It then looks at trends in private AI investment using statistics
from S&P Capital IQ (CapIQ), Crunchbase, and Quid. The third, final section
analyzes trends in the adoption of AI capabilities across companies, trends in robot
installations across countries, and mentions of AI in corporate earnings, drawing
from McKinsey’s Global Survey on AI, the International Federation of Robotics(IFR), and Prattle, respectively.

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CHAPTERHIGHLIGHTS

CHAPTER HIGHLIGHTS• “Drugs, Cancer, Molecular, Drug Discovery” received the greatest amount of private AI
investment in 2020, with more than USD 13.8 billion, 4.5 times higher than 2019.
• Brazil, India, Canada, Singapore, and South Africa are the countries with the highest
growth in AI hiring from 2016 to 2020. Despite the COVID-19 pandemic, the AI hiringcontinued to grow across sample countries in 2020.
• More private investment in AI is being funneled into fewer startups. Despite the pandemic,
2020 saw a 9.3% increase in the amount of private AI investment from 2019—a higher
percentage increase than in 2019 (5.7%), though the number of newly funded companies
decreased for the third year in a row.
• Despite growing calls to address ethical concerns associated with using AI, efforts toaddress these concerns in the industry are limited, according to a McKinsey survey. For
example, issues such as equity and fairness in AI continue to receive comparatively little
attention from companies. Moreover, fewer companies in 2020 view personal or individual
privacy risks as relevant, compared with in 2019, and there was no change in the percentageof respondents whose companies are taking steps to mitigate these particular risks.
• Despite the economic downturn caused by the pandemic, half the respondents in a
McKinsey survey said that the coronavirus had no effect on their investment in AI, while
27% actually reported increasing their investment. Less than a fourth of businesses
decreased their investment in AI.
• The United States recorded a decrease in its share of AI job postings from 2019 to 2020—
the first drop in six years. The total number of AI jobs posted in the United States also
decreased by 8.2%, from 325,724 in 2019 to 300,999 in 2020.

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3.1 J O B S

Attracting and retaining skilled AI talent is challenging. This section examines the latest trend in AI hiring, labor demand,and skill penetration, with data from LinkedIn and Burning Glass.

3.1 JOBSAI HIRING
How rapidly is the growth of AI jobs in different countries?
This section first looks at LinkedIn data that gives theAI hiring rate for different countries. The AI hiring rateis calculated as the number of LinkedIn members who
include AI skills on their profile or work in AI-related
occupations and who added a new employer in the samemonth their new job began, divided by the total number ofLinkedIn members in the country. This rate is then indexed
to the average month in 2016; for example, an index of 1.05
in December 2020 points to a hiring rate that is 5% higher
than the average month in 2016. LinkedIn makes monthto-month comparisons to account for any potential lags
in members updating their profiles. The index for a year isthe average index over all months within that year.

This data suggests that the hiring rate has been
increasing across all sample countries in 2020. Brazil,
India, Canada, Singapore, and South Africa are thecountries with the highest growth in AI hiring from 2016
to 2020 (Figure 3.1.1). Across the 14 countries analyzed,
the AI hiring rate in 2020 was 2.2 times higher, on
average, than that in 2016. For the top country, Brazil,
the hiring index grew by more than 3.5 times. Moreover,despite the COVID-19 pandemic, AI hiring continued its
growth across the 14 sampled countries in 2020 (Figure
3.1.2).
For more explorations of cross-country comparisons, see
the AI Index Global AI Vibrancy Tool.

AI HIRING INDEX by COUNTRY, 2020
Source: LinkedIn, 2020 | Chart: 2021 AI Index Report
Brazil
India
CanadaSingapore
South Africa
Germany
Australia
United States
Argentina
United Kingdom
TurkeyItaly
France
China

0

1

2
AI Hiring Index

3
Figure 3.1.1

1 Countries included are a sample of eligible countries with at least 40% labor force coverage by LinkedIn and at least 10 AI hires in any given month. China and India were also included in this sample
because of their increasing importance in the global economy, but LinkedIn coverage in these countries does not reach 40% of the workforce. Insights for these countries may not provide as full a picture
as in other countries, and should be interpreted accordingly.

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3.1 J O B S

AI HIRING INDEX by COUNTRY, 2016-20
Source: LinkedIn, 2020 | Chart: 2021 AI Index Report

3.4 Brazil

3

2.8 India

2.7 Canada

2.3 South Africa

2.2 Germany

2.1 Australia

2.1 United States

2.0 Argentina

1.8 United Kingdom

1.8 Turkey

1.7 Italy

2.5 Singapore

2
1
0
3

AI Hiring Index

2
1
0
3
2
1
0
3
1.6 France

2

1.3 China

1
0

2016

2020

2016

2020

2016

2020

2016

2020
Figure 3.1.2

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This section analyzes the AI labor demand based ondata from Burning Glass, an analytics firm that collects
postings from over 45,000 online job sites. To develop
a comprehensive, real-time portrait of labor market
demand, Burning Glass aggregated job postings,
removed duplicates, and extracted data from job posting
text. Note that Burning Glass updated the data coveragein 2020 with more job sites; as a result, the numbers inthis report should not be directly compared with data inthe 2019 report.

Global AI Labor Demand
Demand for AI labor in six countries covered by Burning
Glass data—the United States, the United Kingdom,
Canada, Australia, New Zealand, and Singapore—has

3.1 J O B S

grown significantly in the last seven years (Figure 3.1.3).
On average, the share of AI job postings among all job
postings in 2020 is more than five times larger than in
2013. Of the six countries, Singapore exhibits the largest
growth, as its percentage of AI job postings across all job
roles in 2020 is 13.5 times larger than in 2013.
The United States is the only country among the six thatrecorded a decrease in its share of AI job postings from2019 to 2020—the first drop in six years. This may be dueto the coronavirus pandemic or the country’s relatively
more mature AI labor market. The total number of AI jobs
posted in the United States also decreased by 8.2%, from
325,724 in 2019 to 300,999 in 2020.

AI JOB POSTINGS (% of ALL JOB POSTINGS) by COUNTRY, 2013-20
Source: Burning Glass, 2020 | Chart: 2021 AI Index Report

AI Job Postings (% of All Job Postings)

2.5%

2.4% Singapore

2.0%

1.5%

1.0%
0.8% United States
0.8% United Kingdom
0.7% Canada

0.5%

0.5% Australia
0.2% New Zealand

0.0%
2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 3.1.3

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U.S. AI Labor Demand: By Skill Cluster
Taking a closer look at the AI labor demand in the UnitedStates between 2013 and 2020, Figure 3.1.4 breaks down
demand during that period year by year according to skill
cluster. Each skill cluster consists of a list of AI-related
skills; for example, the neural network skill cluster includes
skills like deep learning and convolutional neural network.
The Economy chapter appendix provides a complete list of
AI skills under each skill cluster.
Between 2013 and 2020, AI jobs related to machinelearning and artificial intelligence experienced the fastest
growth in online AI job postings in the United States,
increasing from 0.1% of total jobs to 0.5% and 0.03% to
0.3%, respectively. As noted earlier, 2020 shows a decreasein the share of AI jobs among overall job postings across all
skill clusters.

3.1 J O B S

Between 2013 and2020, AI jobs related tomachine learning andartificial intelligenceexperienced the fastest
growth in online AI job
postings in the UnitedStates, increasing from
0.1% of total jobs to
0.5% and 0.03% to 0.3%,
respectively.

AI JOB POSTINGS (% of ALL JOB POSTINGS) in the UNITED STATES by SKILL CLUSTER, 2013-20
Source: Burning Glass, 2020 | Chart: 2021 AI Index Report

0.5% Machine Learning

AI Job Postings (% of All Job Postings)

0.5%

0.4%

0.3%

0.3% Artificial Intelligence

0.2%
0.1% Neural Networks
0.1% Natural Language Processing

0.1%

0.1% Robotics
0.1% Visual Image Recognition
0.1% Autonomous Driving

0.0%
2013

2014

2015

2016

2017

2018

2019

2020

2021

2022
Figure 3.1.4

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3.1 J O B S

In 2020, industries
focused on information(2.8%); professional,
scientific, and technicalservices (2.5%); andagriculture, forestry,
fishing, and hunting(2.1%) had the highest
share of AI job postings
among all job postings in
the United States.

U.S. Labor Demand: By Industry
To dive deeper into how AI job demand in the U.S. labormarket varies across industries, this section looks at theshare of AI job postings across all jobs posted in the UnitedStates by industry in 2020 (Figure 3.1.5) as well as thetrend in the past 10 years (Figure 3.1.6).
In 2020, industries focused on information (2.8%);
professional, scientific, and technical services (2.5%); andagriculture, forestry, fishing, and hunting (2.1%) had thehighest share of AI job postings among all job postings
in the United States. While the first two have always
dominated demand for AI jobs, the agriculture, forestry,
fishing, and hunting industry saw the biggest jump—byalmost 1 percentage point—in the share of AI jobs from2019 to 2020.

AI JOB POSTINGS (% of ALL JOB POSTINGS) in the UNITED STATES by INDUSTRY, 2020
Source: Burning Glass, 2020 | Chart: 2021 AI Index Report

Information
Professional, Scientific, Tech Services
Agriculture, Forestry, Fishing, Hunting
Manufacturing
Finance and Insurance
Public Administration
Mining, Quarrying, Oil/Gas Extraction
Management of Companies/Enterprises
Educational Services
Wholesale Trade
Utilities
Real Estate and Rental and Leasing
Other Services
Retail Trade
Transportation and Warehousing
Health Care and Social AssistanceConstruction
Arts, Entertainment, and Recreation
Accommodation and Food Services
0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

AI Job Postings (% of All Job Postings)
Figure 3.1.5

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3.1 J O B S

AI JOB POSTINGS (% of ALL JOB POSTINGS) in the UNITED STATES by INDUSTRY, 2013-20
Source: Burning Glass, 2020 | Chart: 2021 AI Index Report
2.8%
Information

3.0%

2.5%
Professional, Scientific, Tech Services

2.1%
Agriculture, Forestry, Fishing, Hunting

1.8%
Manufacturing

2.0%
1.0%
0.0%
3.0%
1.4%
Finance and Insurance

AI Job Postings (% of All Job Postings)

2.0%

0.9%
Public Administration

1.0%

0.8%
Mining, Quarrying, Oil/Gas Extraction

0.8%
Management of Companies/Enterprises

0.0%
3.0%
2.0%

0.8%
Educational Services

1.0%

0.7%
Wholesale Trade

0.6%
Utilities
0.5%
Other Services

0.0%
3.0%
2.0%
1.0%

0.5%
Real Estate and Rental and Leasing

0.4%
Retail Trade

0.2%
Transportation and Warehousing

0.2%
Health Care and Social Assistance

0.2%
Construction

0.2%
Arts, Entertainment, and Recreation

0.0%
3.0%
2.0%
1.0%
0.0%

2013

2020

2013

2020

0.1%
Accommodation and Food Services

2013

2020

2013

2020
Figure 3.1.6

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3.1 J O B S

U.S. Labor Demand: By StateAs the competition for AI talent intensifies, where arecompanies seeking employees with machine learning, data
science, and other AI-related skills within the United States?
Figure 3.1.7 examines the labor demand by U.S. state in2020, plotting the share of AI job postings across all job
postings on the y-axis and the total number of AI jobs
posted on a log scale on the x-axis. The chart shows thatthe District of Columbia has the highest share of AI jobs
posted (1.88%), overtaking Washington state in 2019; andCalifornia remains the state with the highest number of AI
job postings (63,433).
In addition to Washington, D.C., six states registeredover 1% of AI job postings among all job postings—
Washington, Virginia, Massachusetts, California, New York,and Maryland—compared with five last year. California
also has more AI job postings than the next three states
combined, which are Texas (22,539), New York (18,580),
and Virginia (17,718).

AI JOB POSTINGS (TOTAL and % of ALL JOB POSTINGS) by U.S. STATE and DISTRICT, 2020
Source: Burning Glass, 2020 | Chart: 2021 AI Index Report

2.0%
District of Columbia

AI Job Postings (% of All Job Postings)

Washington
Virginia

1.5%

Massachusetts
California
New York

1.0%

MarylandNew Jersey
Delaware
New Mexico

North Carolina

Arizona

Connecticut

Illinois
Pennsylvania

South Dakota
HawaiiVermont

0.5%

Maine

Wyoming

Wisconsin
Indiana

Arkansas

Texas

Michigan Ohio

Alabama

Idaho

Montana
AlaskaOregonMissouri

FloridaLouisiana

0.0%
100

200

500

1,000

2,000

5,000

10,00020,00050,000 100,000

Number of AI Job Postings (Log Scale)Figure 3.1.7

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A I S K I L L P E N E T R AT I O N
How prevalent are AI skills across occupations? The AI skill
penetration metric shows the average share of AI skills
among the top 50 skills in each occupation, using LinkedIn
data that includes skills listed on a member’s profile,positions held, and the locations of the positions.

Global Comparison
For cross-country comparison, the relative penetration
rate of AI skills is measured as the sum of the penetrationof each AI skill across occupations in a given country,
divided by the average global penetration of AI skills
across the same occupations. For example, a relative

3.1 J O B S

penetration rate of 2 means that the average penetrationof AI skills in that country is 2 times the global average
across the same set of occupations.
Among the sample countries shown in Figure 3.1.8, theaggregated data from 2015 to 2020 shows that India (2.83
times the global average) has the highest relative AI skill
penetration rate, followed by the United States (1.99 times
the global average), China (1.40 times the global average),
Germany (1.27 times the global average), and Canada (1.13
times the global average).2

RELATIVE AI SKILL PENETRATION RATE by COUNTRY, 2015-20
Source: LinkedIn, 2020 | Chart: 2021 AI Index Report

India
United States
China
Germany
CanadaSouth Korea
Singapore
United Kingdom
FranceAustralia
Brazil
Italy
South Africa
0

1

2

3

Relative AI Skill Penetration Rate
Figure 3.1.8

2 Countries included are a select sample of eligible countries with at least 40% labor force coverage by LinkedIn and at least 10 AI hires in any given month. China and India were included in this sample
because of their increasing importance in the global economy, but LinkedIn coverage in these countries does not reach 40% of the workforce. Insights for these countries may not provide as full a picture
as other countries, and should be interpreted accordingly.

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Global Comparison: By Industry

3.1 J O B S

industries, while the United States and China frequently
appear high up on the list. Other pockets of specialization
worth highlighting with relative skill penetration rates ofmore than 1 include Germany in hardware and networking
as well as manufacturing; and Israel in manufacturing andeducation.

To provide an in-depth sectoral decomposition of AI skill
penetration across industries and sample countries,
Figure 3.1.9 includes the aggregated data of the top fiveindustries with the highest AI skill penetration globallyin the last five years: education, finance, hardware andnetworking, manufacturing, and software and IT.3 India
has the highest relative AI skill penetration across all five

RELATIVE AI SKILL PENETRATION RATE by INDUSTRY, 2015-20
Source: LinkedIn, 2020 | Chart: 2021 AI Index Report

Education

Finance

Hardware & Networking

India

India

India

United States

United States

United States

South Korea

CanadaGermany

IsraelSwitzerland

Germany
United Kingdom

China
IsraelGermanySouth Africa

FranceChina

Netherlands

Singapore

0

1

2

3

Manufacturing

0

India

India
United States

Germany

China

IsraelSouth Korea

United Kingdom

Germany

Spain

CanadaChina

Israel
1

2

3

2

3

0

1

2

3

Software & IT Services

United States

0

1

2

3

0

1

Relative AI Skill Penetration Rate
Figure 3.1.9

3 Countries included are a select sample of eligible countries with at least 40% labor force coverage by LinkedIn and at least 10 AI hires in any given month. China and India were included in this sample
because of their increasing importance in the global economy, but LinkedIn coverage in these countries does not reach 40% of the workforce. Insights for these countries may not provide as full a picture
as other countries, and should be interpreted accordingly.

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3. 2 I N V E S T M E N T

This section explores the investment activity of private companies by NetBase Quid based on data from CapIQ and
Crunchbase. Specifically, it looks at the latest trends in corporate AI investment, such as private investment, public
offerings, mergers and acquisitions (M&A), and minority stakes related to AI. The section then focuses on the private
investment in AI, or how much private funding goes into AI startups and which sectors are attracting significant
investment and in which countries.

3.2 INVESTMENTC O R P O R AT E I N V E S T M E N T
The total global investment in AI, including private
investment, public offerings, M&A, and minority stakes,
increased by 40% in 2020 relative to 2019 for a total of
USD 67.9 billion (Figure 3.2.1). Given the pandemic, manysmall businesses have suffered disproportionately. As aresult, industry consolidation and increased M&A activity

in 2020 are driving up the total corporate investment in
AI. M&A made up the majority of the total investment
amount in 2020, increasing by 121.7% relative to 2019.
Several high-profile acquisitions related to AI took
place in 2020, including NVIDIA’s acquisition of Mellanox
Technologies and Capgemini’s of Altran Technologies.

GLOBAL CORPORATE INVESTMENT in AI by INVESTMENT ACTIVITY, 2015-20
Source: CapIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report

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

70,00067,854

Private Investment
60,000Public O ering
Merger/Acquisition

50,000Minority Stake

48,85144,07542,23843,81140,00023,00230,00038,65936,57620,0004,140

17,69912,751

10,000

7,952

0

4,328

2015

13,0972016

18,932

19,849

2017

4,493

8,541

2018

2019

2020
Figure 3.2.1

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3. 2 I N V E S T M E N T

S TA R T U P ACT I V I T Y

40,00030,00020,00010,000

2020

2019

2018

2017

0
2016

More private investment in AI is being funneledinto fewer startups. Despite the pandemic,
2020 saw a 9.3% increase in the amount ofprivate AI investment from 2019—a higher
percentage than the 5.7% increase in 2019
(Figure 3.2.2), though the number of companies
funded decreased for the third year in a row(Figure 3.2.3). While there was a record high of
more than USD 40 billion in private investment
in 2020, that represents only a 9.3% increase
from 2019—compared with the largest increaseof 59.0%, observed between 2017 and 2018.
Moreover, the number of funded AI startups
continued a sharp decline from its 2017 peak.

Source: CapIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report

2015

Global Trend

PRIVATE INVESTMENT in FUNDED AI COMPANIES, 2015-20

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

The following section analyzed the trend ofprivate investment in AI startups that havereceived investments of over USD 400,000 in
the last 10 years. While the amount of private
investment in AI has soared dramatically inrecent years, the rate of growth has slowed.

Figure 3.2.2

NUMBER OF NEWLY FUNDED AI COMPANIES in the WORLD, 2015-20
Source: CapIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report

Number of Companies

4,000

3,000

2,000

1,000

0
2015

2016

2017

2018

2019

2020
Figure 3.2.3

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3. 2 I N V E S T M E N T

Regional Comparison
As shown in Figure 3.2.4, the United States remains the leading destination for private investment, with over USD 23.6
billion in funding in 2020, followed by China (USD 9.9 billion) and the United Kingdom (USD 1.9 billion).
PRIVATE INVESTMENT in AI by COUNTRY, 2020
Source: CapIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report

United States
China
United Kingdom
IsraelCanadaGermany
FranceIndia
Japan
Singapore
Australia
0

5,000

10,00015,00020,00025,000Total Investment (in Millions of U.S. Dollars)
Figure 3.2.4

A closer examination of the three contenders leading
the AI race—the United States, China, and the European
Union—further validates the United States’ dominantposition in private AI investment. While China saw an
exceptionally high amount of private AI investment in

2018, its investment level in 2020 is less than half that of
the United States (Figure 3.2.5). It is important to note,however, that China has strong public investments in
AI. Both the central and local governments in China are
spending heavily on AI R&D.4

4 See “A Brief Examination of Chinese Government Expenditures on Artificial Intelligence R&D” (2020) by the Institute for Defense Analyses for more details.

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3. 2 I N V E S T M E N T

PRIVATE INVESTMENT in AI by GEOGRAPHIC AREA, 2015-20
Source: CAPIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report
23,597
US

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

25,00020,00015,000

9,933
China

10,000

6,662
Rest of the World

5,000
2,044
EU

0
2015

2016

2017

2018

2019

2020
Figure 3.2.5

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3. 2 I N V E S T M E N T

Focus Area Analysis
Figure 3.2.6 shows the ranking of the top 10 focus areasthat receive the greatest amount of private investment
in 2020 as well as their respective investment amountin 2019. The “Drugs, Cancer, Molecular, Drug Discovery”
area tops the list, with more than USD 13.8 billion in
private AI investment—4.5 times higher than 2019—
followed by “Autonomous Vehicles, Fleet, Autonomous
Driving, Road” (USD 4.5 billion), and “Students, Courses,
Edtech, English Language” (USD 4.1 billion).

In addition to Drugs, Cancer, Molecular, Drug Discovery,”both “Games, Fans, Gaming, Football” and “Students,
Courses, Edtech, English Language” saw a significant
increase in the amount of private AI investment from2019 to 2020. The former is largely driven by several
financing rounds to gaming and sports startups in
the United States and South Korea, while the latter isboosted by investments in an online education platform
in China.

GLOBAL PRIVATE INVESTMENT in AI by FOCUS AREA, 2019 vs 2020
Source: CapIQ, Crunchbase, and NetBase Quid, 2020 | Chart: 2021 AI Index Report

Drugs, Cancer, Molecular, Drug Discovery
Autonomous Vehicles, Fleet, Autonomous
driving, Road
Students, Courses, Edtech, English language
Open Source, Compute, Hadoop, Devops
Speech Recognition, Computer interaction,
Dialogue, Machine translation
Money Laundering, Anti-fraud, Fraud Detection,
Fraud Prevention
Fashion, Shopping Experience, Beauty, Visual
SearchGames, Fans, Gaming, Football
Semiconductor, Chip, Data Centers, Processor
2019
2020

Bank, Card, Credit Cards, Gift

0

5,000

10,00015,000Total Investment (in Millions of U.S. Dollars)
Figure 3.2.6

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3. 3 C O R P O R AT E
AC T I V I T YThis section reviews how corporations have capitalized on the advances in AI, using AI and automation to their advantage
and generating value at scale. While the number of corporations starting to deploy AI technologies has surged in recent
years, the economic turmoil and impact of COVID-19 in 2020 have slowed that rate of adoption. The latest trends incorporate AI activities are examined through data on the adoption of AI capabilities by McKinsey’s Global Survey on
AI, trends in robot installations across the globe by the International Federation of Robotics (IFR), and mentions of AI incorporate earnings calls by Prattle.

3.3 CORPORATE ACTIVITYI N D U S T RY A D O P T I O NGlobal Adoption of AI

This section shares the results of a McKinsey & Company
survey of 2,395 respondents: individuals representing
companies from a range of regions, industries, sizes,
functional specialties, and tenures.

The 2020 survey results suggest no increase in AI
adoption relative to 2019. Over 50% of respondents
say that their organizations have adopted AI in at least
one business function (Figure 3.3.1). In 2019, 58% of
respondents said their companies adopted AI in at least
one function, although the 2019 survey asked aboutcompanies’ AI adoption differently.

McKinsey & Company’s “The State of AI in 2020”
report contains the full results of this survey, including
insights on how different companies have adopted AI
across functions, core best practices shared among thecompanies that are generating the greatest value fromAI, and the impacts of the COVID-19 pandemic on thesecompanies’ AI investment plans.

In 2020, companies in developed Asia-Pacific countries
led in AI adoptions, followed by those in India and North
America. While AI adoption was about equal acrossregions in 2019, this year’s respondents working forcompanies in Latin America and in other developing
countries are much less likely to report adopting AI in atleast one business function.

AI ADOPTION by ORGANIZATIONS GLOBALLY, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report

Developed Asia-Pacific
India
All Geographies

North America
EuropeDeveloping Markets (incl.
China, MENA)
Latin America
0%

10%

20%

30%

40%

50%

60%

% of Respondents
Figure 3.3.1

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3. 3 C O R P O R AT E
AC T I V I T Y

AI Adoption by Industry and Function

Type of AI Capabilities Adopted

Respondents representing companies in high tech andtelecom were most likely to report AI adoption in 2020,
similar to the 2019 results, followed in second place byboth financial services and automotive and assembly(Figure 3.3.2).

By industry, the type of AI capabilities adopted varies(Figure 3.3.4). Across industries, companies in 2020
were most likely to identify other machine learningtechniques, robotic process automation, and computer
vision as capabilities adopted in at least one business
function.

In another repeat from 2019 (and 2018), the 2020 survey
suggests that the functions where companies are most
likely to adopt AI vary by industry (Figure 3.3.3). For
example, respondents in the automotive and assembly
industry report greater AI adoption for manufacturingrelated tasks than any other; respondents in financial
services report greater AI adoption for risk functions; andrespondents in high tech and telecom report greater AI
adoption for product and service development functions.
Across industries, companies in 2020 are most likely
to report using AI for service operations (such as field
services, customer care, back office), product and service
development, and marketing and sales, similar to thesurvey results in 2019.

Industries tend to adopt AI capabilities that best serve
their core functions. For example, physical robotics,
as well as autonomous vehicles, are most frequently
adopted by industries where manufacturing anddistribution play a large role—such as automotive
and assembly, and consumer goods and retail.
Natural language processing capabilities, such as
text understanding, speech understanding, and text
generation, are frequently adopted by industries withhigh volumes of customer or operational data in text
forms; these include business, legal, and professional
services, financial services, healthcare, and high techand telecom.

AI ADOPTION by INDUSTRY, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index ReportHigh tech/Telecom
Automotive and Assembly
Financial Services
Business, Legal, andProfessional Services
Healthcare/Pharma
Consumer Goods/Retail
0%

10%

20%

30%

40%

50%

60%

70%

% of Respondents

Figure 3.3.2

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3. 3 C O R P O R AT E
AC T I V I T Y

AI ADOPTION by INDUSTRY & FUNCTION, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report
Human
Resources

Industry

Manufacturing

Marketing And
Sales

Product and/or
Service
Development

Service
Operations

Risk

Strategy andCorporate
Finance

Supply-Chain
Management

All Industries

8%

12%

15%

21%

10%

21%

7%

9%

Automotive and Assembly

13%

29%

10%

21%

2%

16%

8%

18%

Business, Legal, and Professional
Services

13%

9%

16%

21%

13%

20%

10%

9%

Consumer Goods/Retail

1%

19%

20%

14%

3%

10%

2%

10%

Financial Services

5%

5%

21%

15%

32%

34%

7%

2%

Healthcare/Pharma

3%

12%

16%

15%

4%

11%

2%

6%

High Tech/Telecom

14%

11%

26%

37%

14%

39%

9%

12%

% of Respondents
Figure 3.3.3

AI CAPABILITIES EMBEDDED in STANDARD BUSINESS PROCESSES, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report

Deep
Learning

NL
NL Speech U NL Text Unde
Generation nderstanding rstanding

Other
MachineLearning
Techniques

PhysicalRobotics

Robotic
Process
Automation

Industry

Autonomous
VehiclesComputer
VisionConversation
al Interfaces

All Industries

7%

18%

15%

16%

11%

12%

13%

23%

13%

22%

Automotive and Assembly

20%

33%

16%

19%

12%

14%

19%

27%

31%

33%

Business, Legal, and Professional
Services

7%

13%

17%

19%

14%

15%

18%

25%

11%

13%

Consumer Goods/Retail

13%

10%

9%

6%

6%

6%

9%

12%

23%

14%

Financial Services

6%

18%

24%

19%

18%

19%

26%

32%

8%

37%

Healthcare/Pharma

1%

15%

10%

14%

12%

11%

15%

19%

10%

18%

High Tech/Telecom

9%

34%

32%

30%

18%

25%

33%

37%

14%

34%

% of Respondents
Figure 3.3.4

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3. 3 C O R P O R AT E
AC T I V I T YConsideration and Mitigation of Risks from
Adopting AI
Only a minority of companies acknowledge the risks
associated with AI, and even fewer report taking steps
to mitigate those risks (Figure 3.3.5 and Figure 3.3.6).
Relative to 2019, the share of survey respondents
citing each risk as relevant has largely remained flat;that is, most changes were not statistically significant.
Cybersecurity remains the only risk a majority of
respondents say their organizations consider relevant.
A number of less commonly cited risks, such as national
security and political stability, were more likely to be
seen as relevant by companies in 2020 than in 2019.
Despite growing calls to attend to ethical concerns
associated with the use of AI, efforts to address theseconcerns in the industry are limited. For example,
concerns such as equity and fairness in AI use continue
to receive comparatively little attention from companies.
Moreover, fewer companies in 2020 view personal orindividual privacy as a risk from adopting AI comparedwith in 2019, and there is no change in the percentageof respondents whose companies are taking steps tomitigate this particular risk.

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Relative to 2019, the shareof survey respondents
citing each risk as
relevant has largely
remained flat; that is,most changes were notstatistically significant.
Cybersecurity remains
the only risk a majorityof respondents say theirorganizations consider
relevant.

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THE ECONOMY

3. 3 C O R P O R AT E
AC T I V I T Y

RISKS from ADOPTING AI THAT ORGANIZATIONS CONSIDER RELEVANT, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report

Cybersecurity
Regulatory
Compliance
Explainability
Personal/Individual
PrivacyOrganizational
Reputation
Workforce/Labor
Displacement
Equity And Fairness
Physical Safety
2019

National Security

2020

Political Stability
0%

10%

20%

30%

40%

50%

60%

% of Respondents
Figure 3.3.5

RISKS from ADOPTING AI THAT ORGANIZATIONS TAKE STEPS to MITGATE, 2020
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report

Cybersecurity
Regulatory
Compliance
Personal/Individual
Privacy
Explainability
Workforce/Labor
Displacement
Organizational
Reputation
Physical Safety
Equity And Fairness
National Security

2019

Political Stability

2020
0%

10%

20%

30%

40%

50%

% of Respondents
Figure 3.3.6

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The Effect of COVID-19

3. 3 C O R P O R AT E
AC T I V I T Y

Despite the economic
downturn caused by
the pandemic, half of
respondents said the
pandemic had no effecton their investment inAI, while 27% actuallyreported increasing
their investment.

Despite the economic downturn caused by the pandemic,
half of respondents said the pandemic had no effecton their investment in AI, while 27% actually reported
increasing their investment. Less than a fourth ofbusinesses decreased their investment in AI (Figure
3.3.7).5 By industry, respondents in healthcare andpharma as well as automotive and assembly were themost likely to report that their companies had increased
investment in AI.

CHANGES in AI INVESTMENTS AMID the COVID-19 PANDEMIC
Source: McKinsey & Company, 2020 | Chart: 2021 AI Index Report

All Industries

23%

Automotive and Assembly

22%

Business, Legal, andProfessional Services

50%

27%

36%

42%

27%

48%

25%

Consumer Goods/Retail

23%

50%

26%

Financial Services

22%

50%

28%

11%

Healthcare/Pharma

44%

44%

23%

High Tech/Telecom
0%10%Decreased

52%
20%

30%
No Effect

40%

50%

24%
60%

70%

80%

90%

100%

Increased
Figure 3.3.7

5 Figures may not sum to 100% because of rounding.

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Artificial IntelligenceIndex Report 2021

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3. 3 C O R P O R AT E
AC T I V I T Y

INDUSTRIAL ROBOT INSTALLATIONS

Global Trend

Right now, AI is being deployed widely onto consumer
devices like smartphones and personal vehicles(e.g., self-driving technology). But relatively little AI
is deployed on actual robots.6 That may change as
researchers develop software to integrate AI-based
approaches with contemporary robots. For now, it ispossible to measure global sales of industrial robots
to draw conclusions about the amount of AI-readyinfrastructure being bought worldwide. While the COVID19-induced economic crisis will lead to a decline in robot
sales in the short term, the International Federation of
Robotics (IFR) expects the pandemic to generate global
growth opportunities for the robotics industry in themedium term.

After six years of growth, the number of new industrial
robots installed worldwide decreased by 12%, from422,271 units in 2018 to 373,240 units in 2019 (Figure
3.3.8). The decline is a product of trade tensions between
the United States and China as well as challenges facedby the two primary customer industries: automotive andelectrical/electronics.
With the automotive industry taking the lead (28% of
total installations), followed by electrical/electronics(24%), metal and machinery (12%), plastics and chemical
products (5%), and food and beverages (3%).7 It is
important to note that these metrics are a measurementof installed infrastructure that is susceptible to adopting
new AI technologies and does not indicate whether every
new robot used a significant amount of AI.

GLOBAL INDUSTRIAL ROBOT INSTALLATIONS, 2012-19
Source: International Federation of Robotics, 2020 | Chart: 2021 AI Index Report

Number of Units (in Thousands)

400

300

200

100

0
2012

2013

2014

2015

2016

2017

2018

2019
Figure 3.3.8

6 For more insights on the adoption of AI and robots by the industry, read the National Bureau of Economic Research working paper based on the 2018 Annual Business Survey by the U.S. Census Bureau,“Advancing Technologies Adoption and Use by U.S. Firms: Evidence From the Annual Business Survey” (2020).
7 Note that there is no information on the customer industry for approximately 20% of robots installed.

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Regional Comparison

3. 3 C O R P O R AT E
AC T I V I T Y

Asia, Europe, and North
America—three of thelargest industrial robot
markets—all witnessed
the end of a six-year
growth period in robot
installations. North
America experienced the
sharpest decline, of 16%, in
2019, compared with 5% in
Europe and 13% in Asia.

Asia, Europe, and North America—three of the largest
industrial robot markets—all witnessed the end of a sixyear growth period in robot installations (Figure 3.3.9).
North America experienced the sharpest decline, of 16%,
in 2019, compared with 5% in Europe and 13% in Asia.
Figure 3.3.10 shows the number of installations in thefive major markets for industrial robot markets. All five—accounting for 73% of global robot installations—saw
roughly the same decline, except for Germany, which saw
a slight bump in installations between 2018 and 2019.
Despite the downward trend in China, it is worth notingthat the country had more industrial robots in 2019 thanthe other four countries combined.

NEW INDUSTRIAL ROBOT INSTALLATIONS by REGION, 2017-19
Source: International Federation of Robotics, 2020 | Chart: 2021 AI Index Report

Asia

EuropeNorth Amercia

OthersSouth & Central America

2017
2018
2019

Africa
0

100

200

300

Number of Units (in Thousands)
Figure 3.3.9

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Artificial IntelligenceIndex Report 2021

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3. 3 C O R P O R AT E
AC T I V I T Y

NEW INDUSTRIAL ROBOT INSTALLATIONS in FIVE MAJOR MARKETS, 2017-19
Source: International Federation of Robotics, 2020 | Chart: 2021 AI Index Report

China

Japan

United States

2017
2018

South Korea

2019

Germany
Rest of the
World
0

50

100

150

200

Number of Units (in Thousands)
Figure 3.3.10

E A R N I N G S CA L L S
Mentions of AI in corporate earnings calls have increased substantially since 2013, as Figure 3.3.11 shows. In 2020, the numberof mentions of AI in earning calls was two times higher than mentions of big data, cloud, and machine learning combined,though that figure declined by 8.5% from 2019. The mentions of big data peaked in 2017 and have since declined by 57%.
MENTIONS of AI in CORPORATE EARNINGS CALLS, 2011-20
Source: Prattle & Liquidnet, 2020 | Chart: 2021 AI Index Report

5,000
4,734 Arti cial Intelligence

Number of AI Mentions

4,000

3,000

2,000
1,356 Maching Learning

1,000
652 Big Data
310 Cloud

0
2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021
Figure 3.3.11

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Artificial IntelligenceIndex Report 2021

APPENDIXC H A P T E R 3:
ECONOMYAPPENDIX
LINKEDIN
Prepared by Mar Carpanelli, Ramanujam MV, and Nathan
Williams

Country Sample
Included countries represent a select sample of eligible
countries with at least 40% labor force coverage byLinkedIn and at least 10 AI hires in any given month.
China and India were included in this sample because of
their increasing importance in the global economy, but
LinkedIn coverage in these countries does not reach 40%of the workforce. Insights for these countries may notprovide as full a picture as other countries, and should beinterpreted accordingly.

SkillsLinkedIn members self-report their skills on their LinkedIn
profiles. Currently, more than 35,000 distinct, standardized
skills are identified by LinkedIn. These have been codedand classified by taxonomists at LinkedIn into 249 skill
groupings, which are the skill groups represented in thedataset. The top skills that make up the AI skill grouping
are machine learning, natural language processing, data
structures, artificial intelligence, computer vision, image
processing, deep learning, TensorFlow, Pandas (software),and OpenCV, among others.
Skill groupings are derived by expert taxonomists througha similarity-index methodology that measures skill
composition at the industry level. Industries are classified
according to the ISIC 4 industry classification (Zhu et al.,2018).

AI Skills Penetration
The aim of this indicator is to measure the intensity of AI
skills in an entity (in a particular country, industry, gender,etc.) through the following methodology:• Compute frequencies for all self-added skills by LinkedIn
members in a given entity (occupation, industry, etc.) in
2015–2020.

CHAPTER 3 PREVIEW• Re-weight skill frequencies using a TF-IDF model to get
the top 50 most representative skills in that entity. These
50 skills compose the “skill genome” of that entity.
• Compute the share of skills that belong to the AI skill
group out of the top skills in the selected entity.
Interpretation: The AI skill penetration rate signals theprevalence of AI skills across occupations, or the intensitywith which LinkedIn members utilize AI skills in theirjobs. For example, the top 50 skills for the occupation ofengineer are calculated based on the weighted frequency
with which they appear in LinkedIn members’ profiles. If
four of the skills that engineers possess belong to the AI
skill group, this measure indicates that the penetration of
AI skills is estimated to be 8% among engineers (e.g., 4/50).

Relative AI Skills Penetration
To allow for skills penetration comparisons acrosscountries, the skills genomes are calculated and a relevantbenchmark is selected (e.g., global average). A ratio is thenconstructed between a country’s and the benchmark’s AI
skills penetrations, controlling for occupations.
Interpretation: A country’s relative AI skills penetrationof 1.5 indicates that AI skills are 1.5 times as frequent as in
the benchmark, for an overlapping set of occupations.

Global Comparison
For cross-country comparison, we present the relative
penetration rate of AI skills, measured as the sum of thepenetration of each AI skill across occupations in a given
country, divided by the average global penetration of AI
skills across the overlapping occupations in a sample ofcountries.
Interpretation: A relative penetration rate of 2 meansthat the average penetration of AI skills in that country
is two times the global average across the same set ofoccupations.

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APPENDIXC H A P T E R 3:
ECONOMY

Global Comparison: By Industry

B U R N I N G G L A S S T E C H N O LO G I E S

The relative AI skills penetration by country for industry
provides an in-depth sectoral decomposition of AI skill
penetration across industries and sample countries.

Prepared by Bledi Taska, Layla O’Kane, and Zhou Zhou

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 in that
sector.

LinkedIn AI Hiring Index
The LinkedIn AI hiring rate is calculated as the total
number of LinkedIn members who are identified as AI
talent and added a new employer in the same month the
new job began, divided by the total number of LinkedIn
members in the country. By analyzing only the timeliest
data, it is possible to make month-to-month comparisonsand account for any potential lags in members updating
their profiles.
The baseline time period is typically a year, and it isindexed to the average month/period of interest duringthat year. The AI hiring rate is indexed against the averageannual hiring in 2016; for example, an index of 3.5 for
Brazil in 2020 indicates that the AI hiring rate is 3.5 times
higher in 2020 than the average in 2016.
Interpretation: The hiring index means the rate ofhiring in the AI field, specifically how fast each country is
experiencing growth in AI hiring.

Top AI Skills
AI skills most frequently added by members during
2015–2020 period.

Burning Glass Technologies delivers job market analytics
that empower employers, workers, and educators tomake data-driven decisions. The company’s artificial
intelligence technology analyzes hundreds of millions
of job postings and real-life career transitions toprovide insight into labor market patterns. This realtime strategic intelligence offers crucial insights, suchas what jobs are most in demand, the specific skills
employers need, and the career directions that offer thehighest potential for workers. For more information, visit
burning-glass.com.

Job Posting Data
To support these analyses, Burning Glass mined its
dataset of millions of job postings collected since 2010.
Burning Glass collects postings from over 45,000 online
job sites to develop a comprehensive, real-time portraitof 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.
Job postings are useful for understanding trends inthe labor market because they allow for a detailed,real-time look at the skills employers seek. To assess
the representativeness of job postings data, Burning
Glass conducts a number of analyses to compare the
distribution of job postings to the distribution of official
government and other third-party sources in the UnitedStates. The primary source of government data on U.S.
job postings is the Job Openings and Labor Turnover
Survey (JOLTS) program, conducted by the Bureau ofLabor Statistics.
To understand the share of job openings captured by
Burning Glass data, it is important to first note that
Burning Glass and JOLTS collect data on job postings
differently. Burning Glass data captures new postings:
A posting appears in the data only on the first month

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Artificial IntelligenceIndex Report 2021

it is found and is considered a duplicate and removed
in subsequent months. JOLTS data captures active
postings: A posting appears in the data for every monththat it is still actively posted, meaning the same posting
can be counted in two or more consecutive months if it
has not been filled. To allow for apples-to-apples volume
comparison in postings, the Burning Glass data needs
to be inflated to account for active postings, not only
new postings. The number of postings from Burning
Glass can be inflated using the ratio of new jobs to active
jobs in Help Wanted OnLine™ (HWOL), a method usedin Carnevale, Jayasundera and Repnikov (2014). Basedon this calculation, the share of jobs online as captured
by Burning Glass is roughly 85% of the jobs captured in
JOLTS in 2016.
The labor market demand captured by Burning Glassdata represents over 85% of the total labor demand.
Jobs not posted online are usually in small businesses(the classic example being the “Help Wanted” sign in therestaurant window) and union hiring halls.

Measuring Demand for AI
In order to measure the demand by employers of AI skills,
Burning Glass uses its skills taxonomy of over 17,000
skills. The list of AI skills from Burning Glass data areshown below, with associated skill clusters. While someskills are considered to be in the AI cluster specifically,
for the purposes of this report, all skills below wereconsidered AI skills. A job posting was considered an AI
job if it requested one or more of these skills.
Artificial Intelligence: Expert System, IBM Watson,
IPSoft Amelia, Ithink, Virtual Agents, Autonomous
Systems, Lidar, OpenCV, Path Planning, Remote Sensing

APPENDIXC H A P T E R 3:
ECONOMY

Language Processing, Natural Language Toolkit (NLTK),
Nearest Neighbor Algorithm, OpenNLP, Sentiment
Analysis/Opinion Mining, Speech Recognition, Text
Mining, Text to Speech (TTS), Tokenization, Word2Vec
Neural Networks: Caffe Deep Learning Framework,
Convolutional Neural Network (CNN), Deep Learning,
Deeplearning4j, Keras, Long Short-Term Memory (LSTM),
MXNet, Neural Networks, Pybrain, Recurrent Neural
Network (RNN), TensorFlow
Machine Learning: AdaBoost algorithm, Boosting(Machine Learning), Chi Square Automatic Interaction
Detection (CHAID), Classification Algorithms, Clustering
Algorithms, Decision Trees, Dimensionality Reduction,
Google Cloud Machine Learning Platform, Gradientboosting, H2O (software), Libsvm, Machine Learning,
Madlib, Mahout, Microsoft Cognitive Toolkit, MLPACK(C++ library), Mlpy, Random Forests, Recommender
Systems, Scikit-learn, Semi-Supervised Learning,
Supervised Learning (Machine Learning), Support
Vector Machines (SVM), Semantic Driven Subtractive
Clustering Method (SDSCM), Torch (Machine Learning),
Unsupervised Learning, Vowpal, Xgboost
Robotics: Blue Prism, Electromechanical Systems,
Motion Planning, Motoman Robot Programming, Robot
Framework, Robotic Systems, Robot Operating System(ROS), Robot Programming, Servo Drives / Motors,
Simultaneous Localization and Mapping (SLAM)
Visual Image Recognition: Computer Vision, Image
Processing, Image Recognition, Machine Vision, Object
Recognition

Natural Language Processing (NLP): ANTLR, Automatic
Speech Recognition (ASR), Chatbot, Computational
Linguistics, Distinguo, Latent Dirichlet Allocation, Latent
Semantic Analysis, Lexalytics, Lexical Acquisition,
Lexical Semantics, Machine Translation (MT), Modular
Audio Recognition Framework (MARF), MoSes, Natural

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N E T BA S E Q U I D
Prepared by Julie Kim
NetBase Quid is a big data analytics platform thatinspires full-picture thinking by drawing connections
across massive amounts of unstructured data. The
software applies advanced natural language processing
technology, semantic analysis, and artificial intelligencealgorithms to reveal patterns in large, unstructured
datasets and to generate visualizations that allow users
to gain actionable insights. NetBase Quid uses Boolean
query to search for focus areas, topics, and keywords
within the archived news and blogs, companies, andpatents database, as well as any custom uploaded
datasets. This can filter out the search by publisheddate time frame, source regions, source categories,
or industry categories on the news—and by regions,investment amount, operating status, organization type(private/public), and founding year within the companies
database. NetBase Quid then visualizes these data points
based on the semantic similarity.

Search, Data Sources, and Scope
Here 3.6 million 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. Quid algorithm reads a big amount oftext data from each document (news article, company
descriptions, etc.) to make links between different
documents based on their similar language. This process isrepeated at an immense scale, which produces a networkwith different clusters identifying distinct topics or focus
areas. Trends are identified based on keywords, phrases,
people, companies, institutions that Quid identifies, andthe other metadata that is put into the software.

Data
Organization data is embedded from CapIQ and
Crunchbase. These companies include all types of

CHAPTER 3 PREVIEWAPPENDIXC H A P T E R 3:
ECONOMYcompanies (private, public, operating, operating asa subsidiary, out of business) throughout the world.
The investment data includes private investments,
M&A, public offerings, minority stakes made by PE/VCs,
corporate venture arms, governments, and institutions
both within and outside the United States. Some data
is simply unreachable—for instance, when the investors
are undisclosed or the funding amounts by investors are
undisclosed. Quid also embeds firmographic informationsuch as founded year and HQ location.
NetBase Quid embeds CapIQ data as a default andadds in data from Crunchbase for the ones that are notcaptured in CapIQ. This yields not only comprehensive
and accurate data on all global organizations, but it also
captures early-stage startups and funding events data.
Company information is uploaded on a weekly basis.

Methodology
Boolean query is used to search for focus areas, topics,
and keywords within the archived company database,within their business descriptions and websites. We canfilter out the search results by HQ regions, investment
amount, operating status, organization type (private/public), and founding year. Quid then visualizes thesecompanies. If there are more than 7,000 companies fromthe search result, Quid selects the 7,000 most relevant
companies for visualization based on the language
algorithm.
Boolean Search: “artificial intelligence” or “AI” or“machine learning” or “deep learning”
Companies:• Chart 3.2.1: Global AI & ML companies that havebeen invested (private, IPO, M&A) from 01/01/2011 to
12/31/2020.
• Chart 3.2.2–3.2.6: Global AI & ML companies that haveinvested over USD 400,000 for the last 10 years (January
1, 2011 to December 31, 2020)—7,000 companies outof 7,500 companies have been selected through Quid’s
relevance algorithm.

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Artificial IntelligenceIndex Report 2021

Target Event Definitions• Private investments: A private placement is a private
sale of newly issued securities (equity or debt) by acompany to a selected investor or a selected groupof investors. The stakes that buyers take in private
placements are often minority stakes (under 50%),although it is possible to take control of a company
through a private placement as well, in which casethe private placement would be a majority stake
investment.
• Minority investment: These refer to minority stakeacquisitions in Quid, which take place when the buyer
acquires less than 50% of the existing ownership stakein entities, asset product, and business divisions.
• M&A: This refers to a buyer acquiring more than 50% of
the existing ownership stake in entities, asset product,
and business divisions.

APPENDIXC H A P T E R 3:
ECONOMYI N T E R N AT I O N A L F E D E R AT I O N O F
R O B OT I C S

SourceData was received directly from the International
Federation of Robotics’ (IFR) 2020 World Robotics Report.
Learn more about IFR.

Methodology
The data displayed is the number of industrial robots
installed by country. Industrial robots are defined by theISO 8373:2012 standard. See more information on IFR’s
methodology.

NuanceM C K I N S E Y & C O M PA N Y• It is unclear how to identify what percentage of robot
units run software that would be classified as “AI,” and it
is unclear to what extent AI development contributes toindustrial robot usage.
• This metric was called “robot imports” in the 2017 AI
Index Report.

This survey was written, filled, and analyzed by McKinsey &Company. You can find additional results from the Global
AI Survey here.

Prepared by Jeffrey Banner and Steven Nichols

Methodology

SourceSOURCEThe survey was conducted online and was in the field from
June 9, 2020, to June 19, 2020, and garnered responses
from 2,395 participants representing the full range ofregions, industries, company sizes, functional specialties,
and tenures. Of those respondents, 1,151 said theirorganizations had adopted AI in at least one function andwere asked questions about their organizations’ AI use.
To adjust for differences in response rates, the data areweighted by the contribution of each respondent’s nation
to global GDP. McKinsey also conducted interviews withexecutives between May and August 2020 about theircompanies’ use of AI. All quotations from executives weregathered during those interviews.

P R AT T L E ( E A R N I N G CA L L S O N LY )Liquidnet provides sentiment data that predicts
the market impact of central bank and corporate
communications. Learn more about Liquidnet here.

Note
Survey respondents are limited by their perception of theirorganization’s AI adoption.

CHAPTER 3 PREVIEW

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