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Adoption has more than doubled since 2017, though the proportion of organizations using AI1In the survey, we defined AI as the ability of a machine to perform cognitive functions that we associate with human minds (for example, natural-language understanding and generation) and to perform physical tasks using cognitive functions (for example, physical robotics, autonomous driving, and manufacturing work). has plateaued between 50 and 60 percent for the past few years. A set of companies seeing the highest financial returns from AI continue to pull ahead of competitors. The results show these leaders making larger investments in AI, engaging in increasingly advanced practices known to enable scale and faster AI development, and showing signs of faring better in the tight market for AI talent. On talent, for the first time, we looked closely at AI hiring and upskilling. The data show that there is significant room to improve diversity on AI teams, and, consistent with other studies, diverse teams correlate with outstanding performance.
Table of Contents
- Five years in review: AI adoption, impact, and spend
- Mind the gap: AI leaders pulling ahead
- AI talent tales: New hot roles, continued diversity woes
- About the research
1. Five years in review: AI adoption, impact, and spend
This marks the fifth consecutive year we’ve conducted research globally on AI’s role in business, and we have seen shifts over this period.
First, AI adoption has more than doubled.1In 2017, the definition for AI adoption was using AI in a core part of the organization’s business or at scale. In 2018 and 2019, the definition was embedding at least one AI capability in business processes or products. In 2020, 2021, and 2022, the definition was that the organization has adopted AI in at least one function. In 2017, 20 percent of respondents reported adopting AI in at least one business area, whereas today, that figure stands at 50 percent, though it peaked higher in 2019 at 58 percent.
Meanwhile, the average number of AI capabilities that organizations use, such as natural-language generation and computer vision, has also doubled—from 1.9 in 2018 to 3.8 in 2022. Among these capabilities, robotic process automation and computer vision have remained the most commonly deployed each year, while natural-language text understanding has advanced from the middle of the pack in 2018 to the front of the list just behind computer vision.
The top use cases, however, have remained relatively stable: optimization of service operations has taken the top spot each of the past four years.
Second, the level of investment in AI has increased alongside its rising adoption. For example, five years ago, 40 percent of respondents at organizations using AI reported more than 5 percent of their digital budgets went to AI, whereas now more than half of respondents report that level of investment. Going forward, 63 percent of respondents say they expect their organizations’ investment to increase over the next three years.
Third, the specific areas in which companies see value from AI have evolved. In 2018, manufacturing and risk were the two functions in which the largest shares of respondents reported seeing value from AI use. Today, the biggest reported revenue effects are found in marketing and sales, product and service development, and strategy and corporate finance, and respondents report the highest cost benefits from AI in supply chain management. The bottom-line value realized from AI remains strong and largely consistent. About a quarter of respondents report this year that at least 5 percent of their organizations’ EBIT was attributable to AI in 2021, in line with findings from the previous two years, when we’ve also tracked this metric.
Lastly, one thing that has remained concerningly consistent is the level of risk mitigation organizations engage in to bolster digital trust. While AI use has increased, there have been no substantial increases in reported mitigation of any AI-related risks from 2019—when we first began capturing this data—to now.
2. Mind the gap: AI leaders pulling ahead
Over the past five years we have tracked the leaders in AI—we refer to them as AI high performers—and examined what they do differently. We see more indications that these leaders are expanding their competitive advantage than we find evidence that others are catching up.
First, we haven’t seen an expansion in the size of the leader group. For the past three years, we have defined AI high performers as those organizations that respondents say are seeing the biggest bottom-line impact from AI adoption—that is, 20 percent or more of EBIT from AI use. The proportion of respondents falling into that group has remained steady at about 8 percent. The findings indicate that this group is achieving its superior results mainly from AI boosting top-line gains, as they’re more likely to report that AI is driving revenues rather than reducing costs, though they do report AI decreasing costs as well.
Next, high performers are more likely than others to follow core practices that unlock value, such as linking their AI strategy to business outcomes(Exhibit 1).2All questions about AI-related strengths and practices were asked only of the 744 respondents who said their organizations had adopted AI in at least one function, n = 744. Also important, they are engaging more often in “frontier” practices that enable AI development and deployment at scale, or what some call the “industrialization of AI.” For example, leaders are more likely to have a data architecture that is modular enough to accommodate new AI applications rapidly. They also often automate most data-related processes, which can both improve efficiency in AI development and expand the number of applications they can develop by providing more high-quality data to feed into AI algorithms. And AI high performers are 1.6 times more likely than other organizations to engage nontechnical employees in creating AI applications by using emerging low-code or no-code programs, which allow companies to speed up the creation of AI applications. In the past year, high performers have become even more likely than other organizations to follow certain advanced scaling practices, such as using standardized tool sets to create production-ready data pipelines and using an end-to-end platform for AI-related data science, data engineering, and application development that they’ve developed in-house.
High performers might also have a head start on managing potential AI-related risks, such as personal privacy and equity and fairness, that other organizations have not addressed yet. While overall, we have seen little change in organizations reporting recognition and mitigation of AI-related risks since we began asking about them four years ago, respondents from AI high performers are more likely than others to report that they engage in practices that are known to help mitigate risk. These include ensuring AI and data governance, standardizing processes and protocols, automating processes such as data quality control to remove errors introduced through manual work, and testing the validity of models and monitoring them over time for potential issues.
AI use and sustainability efforts
The survey findings suggest that many organizations that have adopted AI are integrating AI capabilities into their sustainability efforts and are also actively seeking ways to reduce the environmental impact of their AI use (exhibit). Of respondents from organizations that have adopted AI, 43 percent say their organizations are using AI to assist in sustainability efforts, and 40 percent say their organizations are working to reduce the environmental impact of their AI use by minimizing the energy used to train and run AI models. As companies that have invested more in AI and have more mature AI efforts than others, high performers are 1.4 times more likely than others to report AI-enabled sustainability efforts as well as to say their organizations are working to decrease AI-related emissions. Both efforts are more commonly seen at organizations based in Greater China, Asia–Pacific, and developing markets, while respondents in North America are least likely to report them.
When asked about the types of sustainability efforts using AI, respondents most often mention initiatives to improve environmental impact, such as optimization of energy efficiency or waste reduction. AI use is least common in efforts to improve organizations’ social impact (for example, sourcing of ethically made products), though respondents working for North American organizations are more likely than their peers to report that use.
Investment is yet another area that could contribute to the widening of the gap: AI high performers are poised to continue outspending other organizations on AI efforts. Even though respondents at those leading organizations are just as likely as others to say they’ll increase investments in the future, they’re spending more than others now, meaning they’ll be increasing from a base that is a higher percentage of revenues. Respondents at AI high performers are nearly eight times more likely than their peers to say their organizations spend at least 20 percent of their digital-technology budgets on AI-related technologies. And these digital budgets make up a much larger proportion of their enterprise spend: respondents at AI high performers are over five times more likely than other respondents to report that their organizations spend more than 20 percent of their enterprise-wide revenue on digital technologies.
Finally, all of this may be giving AI high performers a leg up in attracting AI talent. There are indications that these organizations have less difficulty hiring for roles such as AI data scientist and data engineer. Respondents from organizations that are not AI high performers say filling those roles has been “very difficult” much more often than respondents from AI high performers do.
The bottom line: high performers are already well positioned for sustained AI success, improved efficiency in new AI development, and a resultingly more attractive environment for talent. The good news for organizations outside the leader group is that there’s a clear blueprint of best practices for success.
3. AI talent tales: New hot roles, continued diversity woes
Our first detailed look at the AI talent picture signals the maturation of AI, surfaces the most common strategies organizations employ for talent sourcing and upskilling, and shines a light on AI’s diversity problem—while showing yet again a link between diversity and success.
Hiring is a challenge, but less so for high performers
All organizations report that hiring AI talent, particularly data scientists, remains difficult. AI high performers report slightly less difficulty and hired some roles, like machine learning engineers, more often than other organizations.
Software engineers emerged as the AI role that survey responses show organizations hired most often in the past year, more often than data engineers and AI data scientists. This is another clear sign that many organizations have largely shifted from experimenting with AI to actively embedding it in enterprise applications.
Unfortunately, the tech talent shortage shows no sign of easing, threatening to slow that shift for some companies. A majority of respondents report difficulty in hiring for each AI-related role in the past year, and most say it either wasn’t any easier or was more difficult to acquire this talent than in years past. AI data scientists remain particularly scarce, with the largest share of respondents rating data scientist as a role that has been difficult to fill, out of the roles we asked about.
As mentioned earlier, we see some signs that AI high performers have a slightly easier time hiring than other organizations, but they still report difficulty more often than not. What’s more evident from the survey findings is their focus on hiring for AI industrialization and business value optimization. For example, they’re more than twice as likely to have hired a machine learning (ML) engineer in the past year—a role focused on optimizing the ML models built by data scientists for performance and scalability, as well as automating the ML pipeline, from data ingestion to prediction generation. Respondents at high performers are also nearly twice as likely as others to say they have hired an AI product manager to oversee AI application development and adoption and more than three times as likely to have hired an analytics translator, two roles that ensure that AI applications deliver business value.
Reskilling and upskilling are common alternatives to hiring
When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half are doing so. Recruiting from top-tier universities as well as from technology companies that aren’t in the top tier, such as regional leaders, are also common strategies. But a look at the strategies of high performers suggests organizations might be best served by tapping as many recruiting channels as possible (Exhibit 2). These companies are doing more than others to recruit AI-related talent from various sources. The findings show that while they’re more likely to recruit from top-tier technical universities and tech companies, they’re also more likely to source talent from other universities, training academies, and diversity-focused programs or professional organizations.
2
Responses suggest that both AI high performers and other organizations are upskilling technical and nontechnical employees on AI, with nearly half of respondents at both AI high performers and other organizations saying they are reskilling as a way of gaining more AI talent. However, high performers are taking more steps than other organizations to build employees’ AI-related skills.
Respondents at high performers are nearly three times more likely than other respondents to say their organizations have capability-building programs to develop technology personnel’s AI skills. The most common approaches they use are experiential learning, self-directed online courses, and certification programs, whereas other organizations most often lean on self-directed online courses.
High performers are also much more likely than other organizations to go beyond providing access to self-directed online course work to upskill nontechnical employees on AI. Respondents at high performers are nearly twice as likely as others to report offering peer-to-peer learning and certification programs to nontechnical personnel.
Increasing diversity on AI teams is a work in progress
We also explored the level of diversity within organizations’ AI-focused teams, and we see that there is significant room for improvement at most organizations. The average share of employees on these teams at respondents’ organizations who identify as women is just 27 percent (Exhibit 3). The share is similar when looking at the average proportion of racial or ethnic minorities developing AI solutions: just 25 percent. What’s more, 29 percent of respondents say their organizations have no minority employees working on their AI solutions.
3
Some companies are working to improve the diversity of their AI talent, though there’s more being done to improve gender diversity than ethnic diversity. Forty-six percent of respondents say their organizations have active programs to increase gender diversity within the teams that are developing AI solutions, through steps such as partnering with diversity-focused professional associations to recruit candidates. One-third say their organizations have programs to increase racial and ethnic diversity. We also see that organizations with women or minorities working on AI solutions often have programs in place to address these employees’ experiences.
In line with previous McKinsey studies, the research shows a correlation between diversity and outperformance. Organizations at which respondents say at least 25 percent of AI development employees identify as women are 3.2 times more likely than others to be AI high performers. Those at which at least one-quarter of AI development employees are racial or ethnic minorities are more than twice as likely to be AI high performers.
About the research
The online survey was in the field from May 3 to May 27, 2022, and from August 15 to August 17, 2022, and garnered responses from 1,492 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 744 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.
The survey content and analysis were developed by Michael Chui, a partner at the McKinsey Global Institute and a partner in McKinsey’s Bay Area office; Bryce Hall, an associate partner in the Washington, DC, office; Helen Mayhew, a partner in the Sydney office; and Alex Singla, a senior partner in the Chicago office, and Alex Sukharevsky, a senior partner in the London office, global leaders of QuantumBlack, AI by McKinsey.
The authors wish to thank Sanath Angalakudati, Medha Bankhwal, David DeLallo, Heather Hanselman, Vishan Patel, and Wilbur Wang for their contributions to this work.
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