AI-Driven Talent Management: Transforming Recruitment, Retention, and Workforce Analytics
-
Shankar Subramanian IyerBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Rajesh AroraBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Divakar Gowda MurugendrappaBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Brinitha RajiSchool of Business, Global Business Studies (GBS) Dubai, Dubai 500651, United Arab EmiratesAuthor
-
Abhijit GangulyUniversidad Católica de Murcia Doctor of Business Administration (UCAM DBA), Westford University College, Dubai 50325, United Arab EmiratesAuthor
-
Soofi AnwarBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Ankithax MaheshBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Fernando Eraña Reyes JrBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
-
Raman SubramanianBusiness, Westford University College, Al Taawun Campus, Sharjah 61110, United Arab EmiratesAuthor
DOI:
https://doi.org/10.63385/hrsp.v2i1.101072Keywords:
Artificial Intelligence,Talent Management,Human Resource Analytics,Recruitment Technology,Employee Retention,Workforce Optimization,Algorithmic Decision-Making,HR Technology AdoptionAbstract
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.
References
License
Most Viewed
- AI-Driven Talent Management: Transforming Recruitment, Retention, and Workforce Analytics 842
- Influence of Human Capital Empowerment on Organizational Efficiency in Public Universities in Kenya 669
- Ethnic Discrimination and Workplace Productivity in Ghana’s Manufacturing Sector: The Mediating Role of Psychological Health and Moderating Role of Self-Esteem 892
- Architecting Creative Capacity: An HR Framework for Translating Team Motivation into Innovation through Emotional Intelligence and Knowledge Sharing 688
- Quiet Quitting among Tunisian Civil Servants: A Quantitative Analysis of Organizational Antecedents 320