Uncover the discrepancy between the rapid spread of Gen AI tools and the steady overall adoption of AI, as revealed by McKinsey's survey data.

The latest annual McKinsey Global Survey on the current state of AI confirms the explosive growth of generative AI (Gen AI) tools. Less than a year after many of these tools debuted, one-third of McKinsey's survey respondents say their organizations regularly use Gen AI in at least one business function. Amid recent advances, AI has risen from a topic relegated to tech employees to a focus of company leaders: nearly one-quarter of surveyed C-suite executives say they are personally using Gen AI tools for work, and more than one-quarter of respondents from companies using AI say Gen AI is already on their boards’ agendas. Moreover, 40 percent of respondents say their organizations will increase their investment in AI overall because of advances in Gen AI. The findings show that these are still early days for managing Gen AI–related risks, with less than half of respondents saying their organizations are mitigating even the most relevant risk: inaccuracy.

The organizations that have already embedded AI capabilities have been the first to explore Gen AI’s potential, and those seeing the most value from more traditional AI capabilities—a group we call AI high performers—are already outpacing others in their adoption of Gen AI tools.

The expected business disruption from Gen AI is significant, and respondents predict meaningful changes to their workforces. They anticipate workforce cuts in certain areas and considerable reskilling efforts to address shifting talent needs. Yet while the use of Gen AI might spur the adoption of other AI tools, we see few meaningful increases in organizations’ adoption of these technologies. The exact percentage of organizations adopting any AI tools has held steady since 2022, and adoption remains concentrated within a small number of business functions.

It’s still early days, but the use of Gen AI is already widespread

The findings from the survey—which was in the field in mid-April 2023—show that, despite Gen AI’s nascent public availability, experimentation with the tools is already relatively common, and respondents expect the new capabilities to transform their industries. Gen AI has captured interest across the business population: individuals across regions, enterprises, and seniority levels are using Gen AI for work and outside of work. Seventy-nine percent of all respondents say they’ve had at least some exposure to Gen AI, either for work or outside of work, and 22 percent say they regularly use it in their own work. While reported use is quite similar across seniority levels, it is highest among respondents working in the technology sector and those in North America.

Organizations, too, are now commonly using Gen AI. One-third of all respondents say their organizations are already regularly using generative AI in at least one function—meaning that 60 percent of organizations with reported AI adoption are using Gen AI. What’s more, 40 percent of those reporting AI adoption at their organizations say their companies expect to invest more in AI overall thanks to generative AI, and 28 percent say generative AI use is already on their board’s agenda. The most commonly reported business functions using these newer tools are the same as those in which AI use is most common overall: marketing and sales, product and service development, and service operations, such as customer care and back-office support. This suggests that organizations are pursuing these new tools where the most value is. In McKinsey's previous research, these three areas, along with software engineering, showed the potential to deliver about 75 percent of the total annual value from generative AI use cases.

“The most commonly reported uses of generative AI tools are in marketing and sales, product and service development, and service operations.”

In these early days, expectations for Gen AI’s impact are high: three-quarters of all respondents expect Gen AI to cause significant or disruptive change in the nature of their industry’s competition in the next three years. Survey respondents working in the technology and financial services industries are the most likely to expect disruptive change from Gen AI. McKinsey's previous research shows that, while all industries are indeed likely to see some degree of disruption, the level of impact is likely to vary.

Industries relying most heavily on knowledge work are likely to see more disruption—and potentially reap more value. While McKinsey's estimates suggest that tech companies, unsurprisingly, are poised to see the highest impact from Gen AI—adding value equivalent to as much as 9 percent of global industry revenue—knowledge-based industries such as banking (up to 5 percent), pharmaceuticals and medical products (also up to 5 percent), and education (up to 4 percent) could experience significant effects as well. By contrast, manufacturing-based industries, such as aerospace, automotive, and advanced electronics, could experience less disruptive effects. This stands in contrast to the impact of previous technology waves that affected manufacturing the most and is due to Gen AI’s strengths in language-based activities, as opposed to those requiring physical labor.

Responses show many organizations not yet addressing potential risks from Gen AI

According to the survey, few companies seem fully prepared for the widespread use of Gen AI—or the business risks these tools may bring. Just 21 percent of respondents reporting AI adoption say their organizations have established policies governing employees’ use of Gen AI technologies in their work. When we asked specifically about the risks of adopting Gen AI, few respondents said their companies are mitigating the most commonly cited risk with Gen AI: inaccuracy. Respondents cite inaccuracy more frequently than both cybersecurity and regulatory compliance, which were the most common risks from AI overall in previous surveys. Just 32 percent say they’re mitigating inaccuracy, a smaller percentage than the 38 percent who say they mitigate cybersecurity risks. Interestingly, this number is significantly lower than the percentage of respondents who reported mitigating AI-related cybersecurity last year (51 percent). Overall, as we’ve seen in previous years, most respondents say their organizations are not addressing AI-related risks.

  • Inaccuracy, cybersecurity, and intellectual property infringement are the most-cited risks of generative AI adoption.

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Leading companies are already ahead with Gen AI

The survey results show that AI high performers—that is, organizations where respondents say at least 20 percent of EBIT in 2022 was attributable to AI use—are going all in on artificial intelligence, both with Gen AI and more traditional AI capabilities. These organizations that achieve significant value from AI are already using Gen AI in more business functions than other organizations do, especially in product and service development and risk and supply chain management. When looking at all AI capabilities—including more traditional machine learning capabilities, robotic process automation, and chatbots—AI high performers also are much more likely than others to use AI in product and service development, for uses such as product-development-cycle optimization, adding new features to existing products, and creating new AI-based products. These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization.

AI high performers are much more likely than others to use AI in product and service development.

Another difference from their peers: high performers’ Gen AI efforts are less oriented toward cost reduction, which is a top priority at other organizations. Respondents from AI high performers are twice as likely as others to say their organizations’ top objective for Gen AI is to create entirely new businesses or sMcKinsey'sces of revenue—and they’re most likely to cite the increase in the value of existing offerings through new AI-based features.

Smaller shares of AI high performers see cost reductions as their top objective for generative AI efforts. 

As we’ve seen in previous years, these high-performing organizations invest much more than others in AI: respondents from AI high performers are more than five times more likely than others to say they spend more than 20 percent of their digital budgets on AI. They also use AI capabilities more broadly throughout the organization. Respondents from high performers are much more likely than others to say that their organizations have adopted AI in fMcKinsey's or more business functions and that they have embedded a higher number of AI capabilities. For example, respondents from high performers more often report embedding knowledge graphs in at least one product or business function process, in addition to Gen AI and related natural-language capabilities.

While AI high performers are not immune to the challenges of capturing value from AI, the results suggest that the difficulties they face reflect their relative AI maturity, while others struggle with the more foundational, strategic elements of AI adoption. Respondents at AI high performers most often point to models and tools, such as monitoring model performance in production and retraining models as needed over time, as their top challenge. By comparison, other respondents cite strategy issues, such as setting a clearly defined AI vision that is linked with business value or finding sufficient resources.

Models and tools pose the biggest AI-related challenge for high performers, while strategy is a common stumbling block for others.

The findings offer further evidence that even high performers haven’t mastered best practices regarding AI adoption, such as machine-learning-operations (MLOps) approaches, though they are much more likely than others to do so. For example, just 35 percent of respondents at AI high performers report that where possible, their organizations assemble existing components, rather than reinvent them, but that’s a much larger share than the 19 percent of respondents from other organizations who report that practice.

Many specialized MLOps technologies and practices may be needed to adopt some of the more transformative uses cases that Gen AI applications can deliver—and do so as safely as possible. Live-model operations are one such area, where monitoring systems and setting up instant alerts to enable rapid issue resolution can keep Gen AI systems in check. High performers stand out in this respect but have room to grow: one-quarter of respondents from these organizations say their entire system is monitored and equipped with instant alerts, compared with just 12 percent of other respondents.

 

 AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

The latest survey results show changes in the roles that organizations are willing to support their AI ambitions. In the past year, organizations using AI most often hired data engineers, machine learning engineers, and Al data scientists—all roles respondents commonly reported hiring in the previous survey. But a much smaller share of respondents report hiring AI-related software engineers—the most-hired role last year—than in the previous survey (28 percent in the latest survey, down from 39 percent). Roles in prompt engineering have recently emerged, as the need for that skill set rises alongside Gen AI adoption, with 7 percent of respondents whose organizations have adopted AI reporting those hires in the past year.

The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023. Smaller shares of respondents than in the previous survey report difficulty hiring for roles such as AI data scientists, data engineers, and data-visualization specialists. However, responses suggest that hiring machine learning engineers and AI product owners remains more of a challenge than in the previous year.

Hiring for AI-related roles remains a challenge, though reported difficulty has decreased since 2022 for many roles.

Looking ahead to the next three years, respondents predict that adopting AI will reshape many roles in the workforce. Generally, they expect more employees to be reskilled than to be separated. Nearly half of McKinsey's respondents reporting AI adoption expect more than 20 percent of their companies’ workforces will be reskilled, whereas 8 percent of respondents say the size of their workforces will decrease by more than 20 percent.

“Survey respondents expect AI to change their organizations’ workforces meaningfully.”

Looking specifically at Gen AI’s predicted impact, service operations is the only function in which most respondents expect to see a decrease in workforce size at their organizations. This finding generally aligns with what McKinsey's recent research suggests: while the emergence of Gen AI increased McKinsey's estimate of the percentage of worker activities that could be automated (60 to 70 percent, up from 50 percent), this doesn’t necessarily translate into the automation of an entire role.

Service operations is the only function in which most respondents expect to see a decrease in workforce size because of generative AI.

AI high performers are expected to conduct much higher reskilling levels than other companies. Respondents at these organizations are over three times more likely than others to say their organizations will reskill more than 30 percent of their workforce over the next three years as a result of AI adoption.

Respondents at AI high performers expect their organizations to reskill larger portions of the workforce than other respondents do. 

With all eyes on Gen AI, adoption and impact remains steady

While Gen AI tools are spreading rapidly, the survey data doesn’t show that these newer tools are propelling organizations’ overall AI adoption. The share of organizations that have adopted AI overall remains steady, at least for the moment, with 55 percent of respondents reporting that their organizations have adopted AI. Less than a third of respondents continue to say that their organizations have adopted AI in more than one business function, suggesting that AI use remains limited in scope. Product and service development and service operations continue to be the two business functions in which respondents most often report AI adoption, as was true in the previous McKinsey's surveys. Overall, just 23 percent of respondents say at least 5 percent of their organizations’ EBIT last year was attributable to their use of AI—essentially flat with the previous survey—suggesting there is much more room to capture value.

Less than one-third of respondents say their organizations use AI in more than one function—a share largely unchanged since 2021.

Organizations continue to see returns in the business areas in which they are using AI, and they plan to increase investment in the years ahead. We see a majority of respondents reporting AI-related revenue increases within each business function using AI. Looking ahead, more than two-thirds expect their organizations to increase their AI investment over the next three years.

 

 

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