After five years of steady, sometimes dizzying growth, AI adoption has plateaued. State of AI in 2022, our annual survey of 1,500 companies. Use cases are stable, the market for technical talent is tight, and new “hot jobs” emerge every year. However, much remains to be done when it comes to risk management and building inclusive teams.
“After an early period of excitement, we reached a plateau, a trajectory observed early in the adoption of other technologies,” says partner Michael Chui. He co-authored the study with his senior partner and his AI QuantumBlack by McKinsey leaders Alex Singla and Alex Skalevski. His partner is Helen Mayhew. And associate partner Bryce Hall. “Maybe we are starting to see the reality of the level of organizational change required to successfully incorporate this technology,” Michael added. In 2017, 20% of his respondents reported that he was implementing AI in at least one business area. After peaking at 58% in 2019, he’s now down to 50%.
This taper is not about saturation. Our research shows that most organizations are still not taking full advantage of technology opportunities. It’s that companies aren’t investing in the resources needed for the organizational transformation needed to effectively deploy AI. One reason is the lack of manpower.
A typical AI project requires a highly skilled team of data scientists, data engineers, machine learning engineers, product managers, designers, etc. Despite the recent shrinking of the tech industry as a whole, There is a total lack of skilled professionals available.
Research shows that more companies are hiring directly from bootcamps, training academies, local tech companies and professional organizations. Helen Mayhew says, “As employers, we must think creatively about how we find talent and develop meaningful apprenticeships to build skills as role requirements change. Hmm, sometimes this happens on a semi-annual basis.”
QuantumBlack team in London
QuantumBlack team in London
That’s exactly what Smaranda Gosa Mensing is doing with McKinsey’s AI, QuantumBlack. There, she leads the talent team, designs her career path for technology professionals, and works closely with leaders to create environments that support her AI talent flourish.
“We have spoken to many HR leaders in the tech industry to understand the true motivations of this talent. It is natural for people to want to feel a sense of belonging,” says Smaranda. . Organized by expertise in data engineering, data science, software engineering and more, our guild plays a key role in providing AI practitioners with a passionate community, the best of his practices, and skill development.
McKinsey offers technology talent the opportunity to tackle clients’ real and challenging problems, such as decarbonization, business building and AI adoption, in an environment that fosters innovation. “Our senior professionals gain leadership experience and have the opportunity to shape the future landscape of technology,” Smaranda notes.
Our research shows that the AI space is evolving rapidly, with more specialized roles. One example is a machine learning engineer who designs, builds, and puts into production predictive models and AI systems for automation, performance, and scalability.
Our senior professionals take a seat at the leadership table and have the opportunity to shape the future landscape of technology.
McKinsey Quantum Black, AI, Smaranda Gosa-Mensing
“Reskilling existing employees is the most popular strategy among all respondents when it comes to sourcing AI talent. Nearly half of the companies we surveyed do so,” said Alex Sukharevsky. say. According to Alex, the company invests up to 200 hours of learning per year for each technician and has an apprenticeship that combines on-the-job learning of business skills with technical training programs. Technologists at McKinsey often specialize in a particular industry or sector as they progress to senior levels.
A new report sheds light on the industry’s diversity challenges. Addressing them will be a key factor in your long-term success. “Of course, talent is scarce, so the findings on diversity are worrying,” says Helen. The average percentage of employees on her AI team at the respondent’s organization who identify as female is just 27%. The percentage is similar for the average percentage of racial or ethnic minorities, which is 25 percent. Diverse and comprehensive perspectives are especially important in AI to prevent issues of dataset and model bias and distrust of results.
Going forward, as companies evolve their AI talent development strategies, they may find lessons that can be applied to other parts of their business. For example, the latest wave of generative AI models promises to reinvent functions such as communication, sales, and human resources. Bryce Hall said in the report, “As individual AI capabilities such as natural language processing and generation improve and become more democratized, a wave of new applications will emerge and more companies will see value from AI. I look forward to earning it.” scale. “
