This paper explores the transition of operational human resource management (HRM) tasks to artificial intelligence (AI) and examines the resulting opportunities, challenges, and strategic pathways. We develop a theoretical analysis grounded in recent HRM literature and Tursunbayeva’s (2024) configurational framework, which views HRM practices as consisting of operational, relational, and transformational dimensions. The study is conceptual in nature, integrating insights from technology adoption (e.g., the technology-organization-people perspective) and responsible AI principles to extend and refine existing theory. We argue that delegating operational HRM activities to AI can significantly enhance efficiency and decision-making—such as through automation of repetitive administrative tasks and data-driven analytics—while freeing HR professionals to focus on higher-value relational and strategic roles. However, this shift also introduces notable challenges, including algorithmic bias, privacy and transparency concerns, workforce skill gaps, and employee resistance to AI-driven change. To address these issues, we propose strategic pathways for organizations, emphasizing the adoption of Responsible AI governance (ensuring fairness, accountability, and human oversight), proactive change management and upskilling of HR personnel, and alignment of AI initiatives with broader HR strategy (e.g., leveraging AI to support diversity and inclusion goals). In doing so, the paper contributes to HRM theory by extending Tursunbayeva’s framework with a more detailed examination of the operational HRM–AI interface and by incorporating multi-level and human-centric considerations. The resulting model offers a holistic view of AI integration in HRM, highlighting that AI’s benefits in operational HRM are maximized only when accompanied by ethical safeguards and strategic alignment. The paper concludes with theoretical implications and recommendations for HR practitioners seeking to responsibly navigate the AI transformation of HRM.