Generative Artificial Intelligence in HRM Practice: Patterns, Profiles, and Theoretical Insights

Abstract

Although Generative Artificial Intelligence (GenAI) has the potential to transform Human Resource Management (HRM), empirical research on its actual use is still rare. This study aims to investigate how HR professionals use GenAI in HRM, the benefits and challenges they associate with it, and how these patterns vary with organizational context. An exploratory cross-sectional survey of 150 HR professionals in the UK (n = 70) and the US (n = 80) was conducted to investigate usage patterns. Results show that GenAI is mainly applied in job analysis and design, training and development, and recruitment and selection, but concerns persist around operational and technical difficulties, privacy and ethics, output accuracy, and employee resistance. Cluster analysis revealed four user profiles that represent different ways of reconciling efficiency gains and risks. Viewed through the lens of Diffusion of Innovation, Technology–Organization–Environment, and Task–Technology Fit, the results highlight ethical and legal compatibility as a relevant condition for sustained use, point to the potential importance of the organization’s GenAI governance environment, and reveal a boundary condition when tasks involve consequential decisions. This study provides insights into early patterns of GenAI use in HRM and advances theory with propositions that can guide future confirmatory research on responsible and effective use.

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