These leaders used their extensive work helping clients deploy gen AI over the past year to identify the use cases most likely to spark meaningful near-term productivity gains and economic value. Those numbers in hand, we tapped the experience and knowledge of McKinsey’s leaders in each domain. So we dug deeper into MGI’s data and modeling of 63 generative AI use cases in the life sciences and calculated the potential economic impact for five industry domains: research and early discovery, clinical development, operations, commercial, and medical affairs. With compelling new gen AI use cases emerging, we were eager to understand, in a more granular way, the most promising areas of potential value (see sidebar “A guide to our methodology”). AlphaFold2, ESMFold, and MoLeR, for example, all use deep learning to help predict the structures of nearly all known proteins, transforming our understanding of their underlying diseases. Even before last year’s explosion of interest, researchers were applying complex AI models to unlock the mechanisms of disease.

Pharmaceutical companies, of course, have long been in the vanguard of artificial intelligence.

This report is a collaborative effort by Chaitanya Adabala Viswa, Joachim Bleys, Eoin Leydon, Bhavik Shah, and Delphine Zurkiya, representing views from McKinsey’s Life Sciences Practice.

A company that used penetration pricing