Reskilling Most Common Strategy for Companies Sourcing AI Talent


To get around the shortage in AI talent, almost half of the organizations surveyed are reskilling or upskilling current employees.

The hiring process for most organizations with AI talent is still difficult, even after a year in which many large-scale tech companies fired thousands of employees and cut back on hiring. 

According to a report by McKinsey, most organizations are still finding it difficult to hire for most AI-related roles, with specialists such as AI data scientists and machine learning engineers becoming even harder to find over the past three years. 

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Software engineers were the most hired AI role over the past year. The role surpassed data engineers and AI data scientists in total hires, which seems to be an indication that organizations are shifting from experimenting with AI to embedding it into applications. 

To get around the shortage in AI talent, almost half of the organizations surveyed are reskilling or upskilling current employees. In the survey, McKinsey divided the results between AI high performers and all other respondents – both value reskilling as a solution for hiring shortages. 

“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,” said Bryce Hall, an associate partner at McKinsey. “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.”

AI high performers can therefore push employees into more technical or entirely new roles, whereas others are unable to as they don’t have the internal resources or mentoring to ensure that the employee is adequately trained for their new role. High performers are also more likely to upskill workers in non-technical roles to achieve certification, while other respondents do not see that same level of shift throughout the company. 

High performers have other routes to getting round the AI shortage, including hiring from top-tier technical universities and top-tier global technology companies. High performers do this at a 20 percent higher rate than others. As the leading-edge of AI research and development, these organizations are far more capable of poaching talent, and finding employees that are uniquely tied to a certain project or team. 

Even though there has been a small push in the right direction regarding diversity in technology, employees working on AI-related projects tend to still be white and male. According to the McKinsey report, 27 percent of employees developing AI solutions identify as women, and 25 percent identify as a racial or ethnic minority. About 29 percent of organizations surveyed said they did not have a single minority employee working on AI. 

Organizations are trying to draw in more women and minority workers, with 46 percent having programs aimed at bringing more women into the workforce and 33 percent having a program dedicated to improving ethnic diversity. Organizations that hire more women and ethnic minorities are 3.2 times more likely than others to be AI high performers.

David Curry

About David Curry

David is a technology writer with several years experience covering all aspects of IoT, from technology to networks to security.

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