Human Resource Strategy and Practice

AI-Driven Talent Management: Transforming Recruitment, Retention, and Workforce Analytics

DOI:

https://doi.org/10.63385/hrsp.v2i1.101072

Keywords:

Artificial Intelligence,Talent Management,Human Resource Analytics,Recruitment Technology,Employee Retention,Workforce Optimization,Algorithmic Decision-Making,HR Technology Adoption

Abstract

Contemporary organizations increasingly leverage artificial intelligence technologies to revolutionize human resource management practices, fundamentally altering how companies approach workforce optimization. This empirical investigation examines critical determinants and consequences of implementing AI-powered talent management systems across organizational contexts. The research specifically analyzes how three primary dimensions—technological capabilities within HR functions, data governance and ethical frameworks, and organizational preparedness coupled with cultural alignment—collectively determine the success of AI-integrated talent strategies. Additionally, this study explores the mechanisms through which these strategic implementations influence workforce perceptions and confidence in algorithmically-driven HR systems. Grounded in Strategic Human Resource Management (SHRM), Resource-Based View (RBV), Technology Acceptance Model (TAM), and socio-technical systems theory, the investigation employs qualitative methodology involving in-depth interviews with fifteen HR practitioners representing varied industry sectors across the United Arab Emirates. Drawing upon a conceptual model validated through four testable propositions, the theoretical framework underscores the complex, multifaceted character of AI integration in human capital management, encompassing technological infrastructure, organizational dynamics, and ethical imperatives. Empirical evidence demonstrates that effective deployment of AI in talent management necessitates comprehensive technological infrastructure alongside rigorous data stewardship, organizational change readiness, and transparent stakeholder communication to cultivate workforce confidence. The study also addresses critical risks including algorithmic bias, employee resistance, privacy concerns, and legal implications that organizations must navigate. This research contributes practical guidance for HR practitioners and organizational decision-makers pursuing implementation or enhancement of AI-driven talent management initiatives, while advancing scholarly discourse on the intersection of artificial intelligence and strategic human resource management.

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