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The state of AI in 2021
December 8, 2021 | Survey
The results of our latest McKinsey Global Survey on AI indicate that AI adoption continues to grow and that the benefits remain significant. As AI’s use in business becomes more common, the tools and best practices to make the most out of AI have also become more sophisticated. We looked at the practices of the companies seeing the biggest earnings boost from AI and found that they are not only following more of both the core and advanced practices, including machine-learning operations (MLOps), that underpin success but also spending more efficiently on AI and taking more advantage of cloud technologies. Additionally, they are more likely than other organizations to engage in a range of activities to mitigate their AI-related risks—an area that continues to be a shortcoming for many companies’ AI efforts.
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Table of Contents
1. AI adoption and impact
2. The differentiators of AI outperformance
3. Managing AI risks
1
AI adoption and impact
A majority of survey respondents now say their organizations have adopted AI capabilities, as AI’s impact on the bottom line is growing.
Findings from the 2021 survey indicate that AI adoption is continuing its steady rise: 56 percent of all respondents report AI adoption in at least one function, up from 50 percent in 2020. The newest results suggest that AI adoption since last year has increased most at companies headquartered in emerging economies, which includes China, the Middle East and North Africa: 57 percent of respondents report adoption, up from 45 percent in 2020. And across regions, the adoption rate is highest at Indian companies, followed closely by those in Asia–Pacific. As we saw in the past two surveys, the business functions where AI adoption is most common are service operations, product and service development, and marketing and sales, though the most popular use cases span a range of functions. The top three use cases are service-operations optimization, AI-based enhancement of products, and contact-center automation, with the biggest percentage-point increase in the use of AI being in companies’ marketing-budget allocation and spending effectiveness.
57%
of respondents in emerging economies report adoption, up from 45 percent in 2020
The most popular AI use cases span a range of functional activities.
Top use cases
Use cases by function
Top use cases
Most commonly adopted AI use cases,¹ by function, % of respondents
27
Service operations
Product and/or service development
Marketing and sales
Risk
Service-operations optimization
New AI-based enhancements of products
Contact-center automation
Product-feature optimization
Predictive service and intervention
Customer-service analytics
Creation of new AI-based products
Customer segmentation
Risk modeling and analytics
Fraud and debt analytics
22
22
20
18
17
17
16
16
14
¹Out of 39 use cases. Question was asked only of respondents who said their organizations have adopted AI in at least 1 business function.
The results also suggest that AI’s impact on the bottom line is growing. The share of respondents reporting at least 5 percent of earnings before interest and taxes (EBIT) that’s attributable to AI has increased year over year to 27 percent, up from 22 percent in the previous survey. Meanwhile AI’s revenue and cost saving benefits have held steady or even decreased since the previous survey—especially for supply-chain management, where AI was unlikely to compensate for the pandemic era’s global supply-chain challenges.
27%
of respondents report at least 5% of EBIT attributable to AI
Finally, respondents say AI’s prospects remain strong. Nearly two-thirds say their companies’ investments in AI will continue to increase over the next three years, similar to the results from the 2020 survey.
In certain functions, respondents report lower levels of cost decreases from AI adoption in the pandemic’s first year, while revenue increases held steady.
Revenue increase
Cost decrease
Cost decrease
Cost decrease from AI adoption by function, % of respondents
¹
Decrease by <10%
Decrease by 10–19%
Decrease ≥20%
30
17
7
28
16
8
33
8
11
25
9
7
16
18
12
44
6
6
12
7
7
20
3
12
25
11
8
36
14
5
29
9
3
28
9
8
24
5
2
28
13
5
34
4
4
18
7
2
30
7
1
27
8
4
55
41
45
31
46
42
27
38
39
54
52
52
41
46
56
26
35
44
¹Question was asked only of respondents who said their organizations have adopted AI in a given function. Respondents who said “no change,” “cost increase,” “not applicable,” or “don’t know” are not shown.
Fiscal year 2019
Fiscal year 2020
Service operations Manufacturing Human resources Marketing and sales Risk Supply-chain management Product and/or service development Strategy and corporate finance Average across all activities
McKinsey commentary
AI adoption trends and impact
Michael Chui
2
The differentiators of AI outperformance
The companies seeing the biggest bottom-line impact from AI adoption are more likely to follow both core and advanced AI best practices, including MLOps; move their AI work to the cloud; and spend on AI more efficiently and effectively than their peers.
We sought to understand more about the factors and practices that differentiate the best AI programs from all others: specifically, at the organizations at which respondents attribute at least 20 percent of EBIT to their use of AI—our “AI high performers.” With adoption becoming ever more commonplace, we asked new questions about more advanced AI practices, particularly those involved in MLOps, a best-practice approach to building and deploying machine-learning-based AI that has emerged over the past few years. While organizations seeing lower returns from AI are increasingly engaging in core AI practices, AI high performers are still more likely to engage in most of the core practices. High performers also engage in most of the advanced AI practices more often than others do.
Organizations seeing the highest returns from AI are more likely to follow both core and more advanced best practices.
Share of respondents reporting their organizations engage in each practice,
¹
% of respondents
Core
Use design thinking when developing AI tools
Test the performance of our AI models internally before deployment
Track the performance of AI models to ensure that process outcomes and/or models improve over time
Have well-defined processes for data governance
Have protocols in place to ensure good data quality
Have a clear framework for AI governance that covers the model-development process
AI-development teams follow standard protocols for building and delivering AI tools
Have well-defined capability-building programs to develop technology personnel’s AI skills
60
57
46
45
40
38
36
36
46
43
35
37
42
20
33
20
Core
Advanced data
Advanced models, tools, and technology
User enablement
AI high performers²
All other respondents
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown. ²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
There’s evidence that engaging in such practices is helping high performers industrialize and professionalize their AI work, which leads to better results and greater efficiency and predictability in their AI spending. Three-quarters of AI high performers say the cost to produce AI models has been on par with or even less than they expected, whereas half of all other respondents say their companies’ AI project costs were higher than expected. Going forward, the AI high performers’ work could push them farther ahead of the pack, since both groups plan to increase their spending on AI by roughly the same amount.
Compared with their peers, the high performers’ AI spending is more efficient and predictable.
Typical costs for AI model production, compared with expected,
¹
% of respondents
Don’t know
Less than expected
More than expected
About the same
23
55
20
2
51
34
8
8
AI high performers
²
All other respondents
¹Figures may not sum to 100%, because of rounding. ²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
The survey results also suggest that AI high performers could be gaining some of their efficiency by using the cloud. Most companies—whether they are high performers or not—tend to use a mix of cloud and on-premises platforms for AI similar to what they use for overall IT workloads. But the high performers use cloud infrastructure much more than their peers do: 64 percent of their AI workloads run on public or hybrid cloud, compared with 44 percent at other companies. This group is also accessing a wider range of AI capabilities and techniques on a public cloud. For example, they are twice as likely as the rest to say they tap the cloud for natural-language-speech understanding and facial-recognition capabilities.
64%
of high performers’ AI workloads run on public or hybrid cloud, compared with 44 percent at other companies.
McKinsey commentary
Keys to maximizing returns from AI
Michael Chui, Jacomo Corbo, Kia Javanmardian
McKinsey commentary
The growing role of the cloud
Jacomo Corbo, Kia Javanmardian
3
Managing AI risks
Risk management remains a shortcoming for most companies’ AI efforts, but a set of emerging best practices can help.
No matter a company’s AI performance, risk management remains an area where many have room to improve—which we have seen in previous survey results. Cybersecurity remains the most recognized risk among respondents, yet a smaller share says so than did in 2020, despite the rising threat of cyberincidents seen throughout the COVID-19 pandemic. On a positive note, respondents report increasing focus on equity and fairness as a relevant risk and one that their companies are mitigating. Across regions, survey respondents report some notable changes since the previous survey and very different opinions on cybersecurity risks. In developed economies, their views on the biggest risks have held relatively steady since 2020, though 57 percent (versus 63 percent last year) cite cybersecurity as a relevant AI risk. In emerging economies, respondents report a more dramatic decline in the relevance and mitigation of several of the top risks. Yet, they also report personal and individual privacy as a relevant AI risk more often.
The management of AI risks remains an area for significant improvement, as respondents report a waning focus on cyber—especially in emerging economies.
Relevant risks
Mitigated risks
Relevant risks
AI risks that organizations consider relevant, % of respondents by headquarters¹
59
63
37
51
31
43
33
41
26
32
22
24
35
29
19
19
12
16
11
8
47
57
40
50
34
44
45
41
24
37
30
30
31
24
18
22
18
12
16
7
In emerging economies
In developed economies
2020
2021
Cybersecurity Regulatory compliance Explainability² Personal/individual privacy Organizational reputation Equity and fairness Workforce/labor displacement Physical safety National security Political stability
¹
“Emerging economies” includes respondents in Association of Southeast Asian Nations, China, India, Latin America, Middle East, North Africa, South Asia, and sub-Saharan Africa, and “developed economies” includes respondents in developed Asia, Europe, and North America. Question was asked only of respondents who said their organizations have adopted AI in ≥1 business function. Those who answered “don’t know” are not shown. That is, the ability to explain how AI models come to their decisions.
²
When asked why companies aren’t mitigating all relevant risks, respondents most often say it’s because they lack capacity to address the full range of risks they face and have had to prioritize. Notably, the second-most common response from those seeing lower returns from AI adoption is that they are unclear on the extent of their exposure to AI risks (29 percent versus only 17 percent of AI high performers). And by geography, respondents in emerging economies are more likely than others to report that they are waiting until clearer regulations for risk mitigation are in place, and that they do not have the leadership buy-in to dedicate resources toward AI risk mitigation. Additional survey results suggest a way forward for companies that continue to struggle with risk management in AI. We asked about a range of risk-mitigation practices related to model documentation, data validation, and checks on bias. And in most cases, the AI high performers are more likely than other organizations to engage in these practices.
Organizations seeing the highest returns from AI engage in risk-mitigation practices more often than others.
Share of respondents reporting their organizations engage in each practice,
¹
% of respondents
Model documentation
Training and testing data
Measuring model bias and accuracy
Training and testing data
Scan training and testing data to detect the underrepresentation of protected characteristics and/or attributes
Data professionals actively check for skewed or biased data during data ingestion
Increase the representation of protected characteristics and/or attributes in our training and testing data as needed
Data professionals actively check for skewed or biased data at several stages of model development
Legal and risk professionals work with data-science teams to help them understand definitions of bias and protected classes
Have a dedicated governance committee that includes risk and legal professionals
47
47
43
36
24
23
33
27
23
24
26
17
AI high performers²
All other respondents
¹Practices shown here are representative of those with the highest deltas between AI high performers and other respondents. Not all practices are shown. ²Respondents who said that at least 20 percent of their organizations’ earnings before interest and taxes (EBIT) in 2020 was attributable to their use of AI.
McKinsey commentary
The state of AI risk management
Michael Chui, Liz Grennan
About the research The online survey was in the field from May 18 to June 29, 2021, and garnered responses from 1,843 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 1,013 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. * Some data and analyses were updated in September 2022.
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ABOUT THE AUTHOR(S)
The survey content and analysis were developed by Michael Chui, a partner of the McKinsey Global Institute and a partner in McKinsey’s Bay Area office; Bryce Hall, an associate partner in the Washington, DC, office; Alex Singla, a senior partner in the Chicago office; and Alex Sukharevsky, a senior partner in the Moscow office. The authors wish to thank Jacomo Corbo, David DeLallo, Liz Grennan, Heather Hanselman, and Kia Javanmardian for their contributions to this article. This article was edited by Daniella Seiler, a senior editor in the New York office.