Integrating Generative Artificial Intelligence Into Hrm Practice: Usage Patterns Across Hrm Functions

Generative artificial intelligence (GenAI) is increasingly used in Human Resource Management (HRM), but empirical studies on how practitioners apply it within HRM functions are limited. This study explores GenAI use in HRM through an affordance-actualization perspective, using an exploratory cross-sectional survey of 150 HRM professionals in the United Kingdom and the United States. Following a person-centered logic, we conducted an agglomerative hierarchical cluster analysis. Results reveal two distinct usage profiles: a smaller group characterized by very frequent use and broad application across multiple HRM functions (including performance management, strategic HRM, job analysis and design, training and development, workforce planning, and HRM analytics), and a larger group exhibiting more moderate, selective use concentrated in a narrower subset of activities. Hierarchical position distinguished profiles, whereas other demographics and contextual variables did not. The findings provide an empirically derived typology of GenAI use in HRM and suggest that role-related discretion and task scope may be important factors influencing the realization of GenAI affordances in practice.