Systematic Review and Bibliometric Analysis of Artificial Intelligence Adoption in Human Resource Management

Objective: This study conducts a systematic and bibliometric review of scientific research on the adoption of artificial intelligence (AI) in human resource management (HRM). It examines how the literature conceptualizes determinants, obstacles, and paradoxical tensions linked to AI adoption, while proposing an integrative interpretation that goes beyond descriptive approaches. Design/Methodology/ approach: A systematic review was carried out following the PRISMA protocol. The corpus was extracted from Scopus using precise search equations. Bibliometric techniques (Bibliometrix, VOSviewer) enabled co-occurrence analysis, author-network mapping, and the identification of thematic clusters and emerging trends. Results: Findings show rapid growth of AI-related studies in HRM, mainly from the United States, Europe, and Asia. Four major research areas emerge: technological optimization, strategic transformation of the HR function, employee experience, and paradoxical tensions associated with algorithmic systems. A recurrent gap appears between AI’s technical promises and organizational realities, particularly in emerging contexts. Practical implications: The study highlights limits of linear adoption models and emphasizes the importance of aligning strategy, culture, data governance, and change management. Socially, it underscores ethical concerns such as algorithmic bias, transparency, and employee trust. Originality / Value: By combining PRISMA and bibliometric analysis, this review proposes an innovative interpretive model and identifies future research directions centered on ethics, emerging contexts, and the evolving role of HR professionals in the AI era.