After five years of steady, sometimes heady, growth, AI adoption has plateaued, according to The state of AI in 2022, our annual survey of 1,500 companies. Use cases are stable, and the market for tech talent is tight, with new “hot jobs” surfacing every year. But much remains to be done about managing risk and building inclusive teams.
“After a period of initial exuberance, we have reached a plateau, a course we’ve observed with other technologies in their early years of adoption,” says Partner Michael Chui. He authored the research with Senior Partners and QuantumBlack, AI by McKinsey leaders Alex Singla and Alex Sukharevsky; Partner Helen Mayhew; and Associate Partner Bryce Hall. “We might be seeing the reality sinking in about the level of organizational change it takes to successfully embed this technology,” Michael adds. In 2017, 20 percent of respondents reported adopting AI in at least one business area. After peaking at 58 percent in 2019, it’s dropped to 50 percent today.
This tailing off is not about saturation; most organizations haven’t yet maximized the opportunity of the technology, according to our research. It’s that companies aren’t investing in the resources needed for the organizational change required to effectively implement AI. One reason is that there’s a talent crunch.
A typical AI project requires a highly-skilled team including a data scientist, data engineer, machine-learning engineer, product manager, and designer—and there simply aren’t enough skilled professionals available, even with the recent contraction across the technology industry.
Companies are increasingly hiring directly from bootcamps and training academies, regional tech companies, and professional organizations, according to the research. “As employers,” says Helen Mayhew, “we have to think creatively about how we find talent and develop a meaningful apprenticeship journey to help them develop skills as role requirements change, which can be as frequently as every six months.”
That’s exactly what Smaranda Gosa-Mensing does at QuantumBlack, AI by McKinsey, where she leads the people team, designs career paths for our tech professionals, and works closely with leaders to build an environment to help AI talent to flourish.
“We've talked to a lot of HR leaders in the tech industry to understand what really motivates this talent. Understandably, people want to have a sense of belonging,” Smaranda says. Our guilds—organized by expertise including data engineering, data science, and software engineering—play an important role in providing AI practitioners with an engaged community, best practices, and skills development.
McKinsey offers tech talent opportunities to work on real and challenging problems for clients such as decarbonization, business building, and AI implementations—in an environment that encourages innovation. “Our senior experts gain leadership experience and a chance to shape the future landscape of technology,” Smaranda points out.
According to our research, the AI space is evolving quickly with a greater specialization in roles. One example is the machine learning engineer who designs, builds, and productionizes predictive models and AI systems for automation, performance, and scalability.
"Our senior experts gain a seat at the leadership table and a chance to shape the future landscape of technology."
— Smaranda Gosa-Mensing, Quantum Black, AI at McKinsey
“When it comes to sourcing AI talent, the most popular strategy among all respondents is reskilling existing employees. Nearly half of the companies we surveyed are doing so,” says Alex Sukharevsky. According to Alex, we invest in up to 200 hours of learning per year per technologist and have apprenticeships that blend on-the-job learning of business skills with tech-training programs. As they progress to more senior levels, McKinsey technologists often specialize in an industry or function.
The new report shines a light on the industry's challenges with diversity. Addressing them will be a critical factor to long-term success. “With the scarcity of talent, needless to say, the research findings on diversity are concerning,” observes Helen. The average share of employees on AI teams at respondents’ organizations who identify as women is just 27 percent; the share is similar among the average proportion of racial or ethnic minorities: 25 percent. Diverse and inclusive perspectives are especially critical in AI to prevent issues of bias in datasets and models, and distrust in outcomes.
Looking ahead, as companies evolve their strategies for developing AI tech talent, they may find lessons that are applicable to other parts of their business; the newest wave of generative AI models, for example, promises to reinvent functions such as communications, sales, and human resources. “As individual AI capabilities, such as natural-language processing and generation, continue to improve and democratize,” says Bryce Hall in the report, “we’re excited to see a wave of new applications emerge and more companies capture value from AI at scale.”