Artificial Intelligence in Human Resource Management: A Systematic Literature Review (SLR)

Intan Rohimatul Alawiyah; Suharto Suharto; Umar Burhan

doi:10.31004/riggs.v5i2.10011

Authors

Intan Rohimatul Alawiyah Universitas Gresik
Suharto Suharto Universitas Gresik
Umar Burhan Universitas Gresik

DOI:

https://doi.org/10.31004/riggs.v5i2.10011

Keywords:

Artificial Intelegence, AI Adoption, Human Resource Management, Ethical Challenges, Employee Outcomes, Challenges, Systematic Literature Review (SLR)

Abstract

Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping Human Resource Management (HRM) practices across organizations. Despite the growing body of research on AI-enabled HRM, the existing literature remains fragmented, particularly regarding AI adoption, ethical challenges, employee outcomes, and future research directions. Therefore, this study aims to systematically review and synthesize the current state of knowledge on AI in HRM. A Systematic Literature Review (SLR) was conducted following the PRISMA 2020 guidelines. The literature search was performed in the Scopus database using predefined search strings related to AI and HRM. After applying a series of inclusion and exclusion criteria, including subject area, document type, open-access status, citation threshold, and relevance screening, 28 highly cited articles were selected for the final review. The findings reveal a substantial increase in AI-HRM research in recent years, with recruitment and selection, talent management, workforce analytics, and employee development emerging as the most prominent application areas. The review also identifies key ethical concerns, including algorithmic bias, transparency, privacy, and accountability. Furthermore, AI adoption generates both positive employee outcomes, such as enhanced engagement and productivity, and negative outcomes, including job insecurity and technostress. This study contributes to the literature by providing an integrated synthesis of AI-HRM research and proposing future directions related to Generative AI, explainable AI, and human–AI collaboration.

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