Skill Deprioritization: Reorganizing in the Age of Generative Artificial Intelligence

How does generative artificial intelligence (GenAI) reshape the skills that organizations seek as they adapt to a new general-purpose technology? GenAI effectively retrieves data, performs analysis, and conveys information, so it can substitute for workers doing these activities and complement workers relying on them. A natural consequence is skill deprioritization, a systematic reduction in firms’ demand for human skills that GenAI can effectively address as organizations adjust the division of labor and integration of effort. We draw on a theoretically grounded classification of organizing skills—task division, task allocation, information provision, reward distribution, and exception management—and a queuing theory model of organizing efficiency to predict which skills firms will deprioritize first. Using a quasiexperimental design that leverages the introduction of ChatGPT as an exogenous shock, we analyze 1,820 publicly listed U.S. companies and track changes in their hiring demand patterns over a period of a ±12-month window surrounds the shock. We find significant declines in demand for monitoring (reward distribution), operational exceptions, and task division skills, with information provision also showing declines. Task allocation and conflict resolution showed greater stability, suggesting greater reliance on human judgment. These effects intensify following GPT-4’s release, indicating that capability improvements also drive adaptation. Our findings demonstrate that firms engage in immediate and selective skill deprioritization, raising questions about longer-term hollowing out of human expertise in automated domains. We contribute a novel taxonomy of organizing skills, extend queuing theory to the GenAI context, and provide early empirical evidence on how GenAI is reshaping organizational skill demands. This paper was accepted by Anita McGahan, strategy. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.01859 .