From Traditional HRM to AI-Augmented HRM: A Conceptual Perspective
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Objective: This paper aims to develop a comprehensive conceptual framework for integrating artificial intelligence (AI) into human resource management (HRM). It seeks to capture both the opportunities and challenges of AI adoption, emphasizing performance management, employee engagement, workforce planning, and the evolving role of HR professionals. Ethical, organizational, and societal considerations, such as transparency, privacy, and algorithmic bias, are central to this framework.
Methodology: The study employs a conceptual approach based on an extensive review of interdisciplinary literature, including empirical research, theoretical models, and recent advances in AI technologies (e.g., predictive analytics, federated learning, generative AI). Key theoretical lenses—Resource-Based View, Sociotechnical Systems Theory, Algorithmic Management, Responsible AI, and Human-AI Collaboration—are integrated to analyze AI-HRM interactions and implications.
Results / Contributions: The chapter proposes a multidimensional framework highlighting four interconnected dimensions: (1) technological capabilities, (2) human and organizational factors, (3) ethical and regulatory environment, and (4) strategic alignment for value creation. This framework guides HR scholars and practitioners in responsibly adopting AI to enhance decision-making, efficiency, and employee experiences while mitigating risks such as bias, dehumanization, and resistance. It provides actionable insights for ethical governance, workforce transformation, and human-AI collaboration, and identifies future research avenues in contextual, industry-specific, and longitudinal studies of AI in HRM.
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