The Strategic Challenges of Artificial Intelligence on Human Resource Management Practices

Abstract

Artificial intelligence is increasingly embedded in human resource management, shifting HR work from transaction-heavy administration toward data-driven decision support and workforce analytics. This transition creates measurable efficiency gains, but it also raises governance and legitimacy challenges in domains where decisions are high-stakes and socially sensitive. This paper examines the strategic challenges that shape responsible AI adoption in HRM and clarifies how these challenges influence decision quality, fairness, compliance, and trust. The study applies a qualitative approach based on secondary sources published between 2018 and 2024, including peer-reviewed articles, industry reports, and white papers. The evidence is synthesized through thematic analysis and complemented with comparative insights from organizational cases of AI use in recruitment, engagement, performance management, and training. Findings indicate uneven diffusion of AI across HR functions, with earlier uptake in recruitment and learning processes and comparatively lower automation in compensation and succession planning, reflecting higher governance sensitivity. Employee perception evidence shows strong support for efficiency benefits alongside contested views on fairness, with a pronounced preference for human review in consequential decisions. Organizational scale conditions adoption: larger firms exhibit higher budgets, stronger skills, and richer data environments, translating into higher functional uptake than small and medium enterprises. The risk assessment highlights hiring bias, data misuse, legal non-compliance, and employee distrust as priority risks that require proactive mitigation through auditing, privacy safeguards, and transparency measures. Future studies should test causal effects of AI on fairness and workforce outcomes using longitudinal and multi-method designs, including audit studies of recruitment systems. Additional work is needed on practical human-AI workflow design, explainability mechanisms, and scalable governance models suited to resource-constrained organizations.