Artificial intelligent in human resources management systems: opportunities, challenges, and implications for modern organizations

Artificial intelligence has moved from the margins of human resource management into its everyday machinery. Applicant tracking systems now rank candidates, chatbots answer policy questions, predictive models flag employees likely to resign, and large language models draft job descriptions and appraisal summaries. Research has grown quickly alongside this adoption, but it remains scattered across human resource management, information systems, operations research and computer science, and the regional scholarship published outside English-language journals is rarely integrated into it. This article reports a systematic review of 82 peer-reviewed studies published between January 2015 and March 2026, retrieved from Scopus, Web of Science, EBSCO Business Source Complete, ScienceDirect and the Russian scientific database eLIBRARY.RU, and screened following PRISMA 2020 guidance. Thematic synthesis produced six themes: recruitment and selection; learning, development and internal mobility; performance management and predictive analytics; employee experience and generative AI; determinants of adoption; and fairness, transparency and governance. Three findings stand out. First, the evidence base is heavily skewed towards recruitment, which accounts for roughly two in five studies, while compensation, industrial relations and occupational safety are barely represented. Second, reported benefits are concentrated in process efficiency, which is comparatively easy to measure, whereas claims about decision quality rest on weaker designs and shorter time horizons. Third, adoption failure is more often organisational than technical: the recurring constraints are data quality, scarce analytical skill inside the HR function, and the absence of an internal governance routine. We argue that value from AI in human resource management depends on treating it as an organisational capability rather than a procurement decision, and we set out a research agenda addressing longitudinal outcome measurement, employee justice perceptions, and evidence from emerging and transition economies. Keywords Artificial intelligence; human resource management; HR information systems; algorithmic decision-making; people analytics; systematic literature review; AI governance; algorithmic fairness