Mapping the Intellectual Structure of Artificial Intelligence in HRM

Abstract

Artificial intelligence (AI) is reshaping how organisations create, validate, share and use workforce knowledge. Despite rapid growth, research on AI in human resource management (HRM) remains fragmented across HRM, information systems and knowledge management. This study maps the intellectual structure of the Scopus-indexed literature and interprets its major research streams through a knowledge management lens. Drawing on 693 peer-reviewed journal articles published between 1991 and 2025, it combines performance analysis, bibliographic coupling, keyword co-occurrence mapping and thematic evolution with a focused qualitative reading of influential works in each cluster. The findings show rapid growth since 2019 and three dominant but weakly integrated streams: AI adoption and HR analytics; algorithmic decision-making, fairness and governance; and human-AI collaboration. The qualitative interpretation shows that these streams differ not only in topic but also in their assumptions about workforce knowledge: whether it is treated as data to be codified and optimised, as a source of bias and ethical risk, or as a human capability that intelligent systems can augment. The study contributes to knowledge management by showing that AI-HRM research is organised around competing processes of knowledge codification, validation, transfer and use, while giving less attention to knowledge creation, tacit expertise and employee-side knowledge agency. It concludes with a research agenda on knowledge governance, employee experience and cross-database validation. The Scopus-only corpus is acknowledged as a limitation.