Artificial Intelligence in Human Resource Management: A Systematic Literature Review of Drivers, Barriers, and Outcomes

This review systematically maps the determinants, constraints, and consequences of artificial intelligence (AI) use in human resource management (HRM). A Scopus search for 2021-2026 publications produced 608 records. Sequential screening of titles, abstracts, and available full texts resulted in 44 eligible studies, including 33 empirical investigations and 11 secondary or conceptual works. The synthesis shows that leadership commitment, strategic alignment, employee trust, human capabilities, digital preparedness, resources, perceived usefulness, and institutional conditions support AI adoption and integration. Major obstacles include implementation difficulty, ethical and fairness issues, algorithmic bias, privacy and security exposure, employee anxiety or resistance, and weak governance. Reported benefits include better recruitment, operational efficiency, analytical decision support, employee-related outcomes, innovation, and organizational performance, although risks of unfairness and dehumanization persist. Sustainable AI-HRM therefore depends on technological capacity combined with organizational readiness, workforce competence, and responsible governance.