The GenAI Wall Effect: Examining the Limits to Horizontal Expertise Transfer Between Occupational Insiders and Outsiders

As firms continue to seek efficiency by deploying Generative AI (GenAI) tools across various organizational functions, a critical question emerges: When and why does GenAI enable (versus constrain) individuals from one occupational group (i.e., outsiders) to perform tasks assigned to another occupational group (i.e., insiders), with equivalent speed and quality? And does GenAI’s effect diminish as the “knowledge distance” between occupational groups increases?

In an experiment conducted at a large UK firm, we examine these questions. Three groups—occupational insiders, adjacent outsiders, and distant outsiders—attempted to both conceptualize as well as execute tasks that are “core” to occupational insiders and were randomly assigned to receive support from a bespoke GenAI tool. We found that a “GenAI wall”—that is, the point at which GenAI can no longer meaningfully reduce the expertise gap between occupational insiders and outsiders—emerged for the joint effect of knowledge distance and task characteristics. Specifically, we found that GenAI is more effective at bridging expertise gaps between near (rather than distant) occupations, and more so for conceptualization (as opposed to execution) tasks. We discuss the implications of these findings for scholarship on occupations, learning, and division of labor in the wake of emerging technologies such as GenAI.