Artificial Intelligence (AI) is steadily reshaping the way organisations recruit, train, evaluate, and retain their workforce. From resume screening chatbots to predictive analytics for attrition, AI-enabled tools promise faster decisions, lower administrative costs, and more objective people practices. Yet the adoption of AI within Human Resource Management (HRM) in India and elsewhere remains uneven, and many organisations struggle to move beyond pilot projects. This paper examines the principal barriers that restrict the effective integration of AI into HRM functions, drawing on secondary literature and conceptual analysis. Eight interlinked barrier categories are identified and discussed: technological and infrastructural constraints, financial limitations, organisational and structural rigidity, skill and competency gaps, employee resistance and psychological apprehension, ethical and algorithmic-bias concerns, data privacy and security risks, and cultural or change-management challenges. The paper argues that these barriers rarely act in isolation; rather, they reinforce one another, so that a purely technical fix (for example, better software) is unlikely to succeed unless matched by investment in skills, transparent governance, and change communication. The study concludes with practical recommendations for HR practitioners and policy makers, and outlines directions for future empirical research, including sector-wise and cross-cultural comparisons of AI adoption in HRM.