From Automation to Augmentation: A Bibliometric and Thematic Review of Artificial Intelligence in Human Resource Management

Abstract

This study reviews how artificial intelligence (AI) has been applied in Human Resource Management (HRM) research from 2019 to 2024.Using a systematic search procedure outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), eighty-five peer-reviewed articles were located and content analyzed using bibliometric mapping techniques in VOSviewer to identify publication trends, influence by key authors, and key topics. This study concentrates upon three primary HRM domains that demonstrate the greatest visibility of AI applications: recruitment and selection, performance appraisals, and employee training/learning and development. Overall, the literature demonstrates positive outcomes in the form of decision-making support, increased efficiencies, and greater analytic capabilities, as well as an increasing trend from automation to augmentation, in which AI supports rather than supplants human judgment. However, the literature identifies consistent concerns about risks associated with AI including; risk of algorithmic bias, risk of compromising data privacy, lack of transparency and accountability in high-stake HR decision making processes. Recent studies have highlighted the importance of developing Explainable AI, human-AI collaboration and developing more adaptive and personalized employee experiences. Through the combination of bibliometric analysis with thematic synthesis, this study identifies the current state of research on the relationship between AI and HRM, and provides direction regarding priority gaps that need to be addressed in order for the effective and ethically appropriate application of AI in HRM.

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