AI Displacement Risk in the Labor Market: Evidence, Exposure, and the Imperative for Adaptive Organizational Strategy

Abstract: Artificial intelligence—particularly generative large language models (LLMs)—presents organizations with a transformative technology whose labor market implications remain nascent yet consequential. This article synthesizes emerging empirical research on AI-driven job displacement and augmentation, focusing on the gap between theoretical automation potential and observed real-world implementation. Drawing on recent studies that combine task-level exposure metrics with employment and usage data, it examines which occupations face greatest risk, how demographic characteristics intersect with exposure, and the limited but suggestive early evidence of labor market disruption. The article then proposes evidence-based organizational responses—ranging from transparent workforce planning and skills investment to redesigned roles and adaptive governance—alongside long-term capability-building strategies. By grounding recommendations in validated research, this work offers leaders a framework for navigating AI's labor implications responsibly, mitigating harm, and preparing for an accelerating pace of workplace transformation.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.