AI-Driven Operational Intelligence and Workforce Reskilling in U.S. Hotels

Abstract

This paper examines the relationship between AI-driven operational intelligence adoption as the independent variable and workforce reskilling outcomes as the dependent variable in U.S. hotel operations. AI-driven operational intelligence systems are being deployed across hotel operations at a pace that is structurally transforming what hotel managers are required to know and do. Conducting a systematic narrative literature review of peer-reviewed research and verified industry evidence, the paper identifies five AI deployment domains generating the most consequential reskilling demands: revenue management analytics, workforce scheduling analytics, procurement and inventory analytics, guest data and personalization systems, and business intelligence dashboards. Drawing on Human Capital Theory (Becker, 1964; Schultz, 1961) and the Technology-Organization-Environment framework (Tornatzky and Fleischer, 1990), the review documents that the reskilling response to AI adoption in U.S. hotels is constrained primarily by organizational and environmental barriers rather than by the unavailability of reskilling tools or programs. The paper proposes a three-level reskilling framework calibrated to the operational realities of U.S. hotel properties at different scales, identifies five barriers that currently limit reskilling effectiveness, and argues that the workforce consequences of failing to close the analytical skills gap extend beyond individual property performance to national workforce development outcomes across an industry employing 2.17 million workers (AHLA, 2025a).