We are in the early innings of generative AI, and companies already anticipate a meaningful impact on talent—from opening up new work opportunities and transforming how work gets done to introducing whole new job categories such as prompt engineering. One of the benefits of generative AI is that it can help nearly everyone with their jobs, and this is also its greatest challenge.
This scale differs from traditional AI, which affected a fairly small—though no less important—portion of the workforce who had deep skills in technical areas like machine learning, data science, or robotics. Given the highly specialized capabilities required, AI talent always seemed in short supply. Our survey highlights that hiring for these roles is still a challenge. Generative AI, in contrast, will still need highly skilled people to build large language models and train generative models, but users can be nearly anyone, and they won’t need data science degrees or machine learning expertise to be effective. The analogy is similar to the move from mainframe computers—large machines operated by highly technical experts—to the personal computer, which anyone could use. It’s a revolutionary shift in terms of how people can use technology as a power tool.
This view of generative AI as a tool is reflected in our survey. In most instances companies see generative AI as a tool to augment human activities, not necessarily replace them. So far, we’re mainly seeing companies that are leaning forward with generative AI, focusing on pragmatic areas where the routes to improvements in top-line growth or productivity are clearest. Examples include using generative AI tools to help modernize legacy code or speed up research and discovery time in the sciences. We’re still just scratching the surface of these augmentation capabilities, and we can anticipate that their use will accelerate.
Lareina Yee
McKinsey commentary
Senior partner, McKinsey; chair, McKinsey Technology Council
