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Conceptual Review of Ai-Enabled Human Resource Management (Hrm) Systems in Strategic Contexts

Author

Listed:
  • Dr. Fabiam George

    (Subject Matter Expert, Kazian School of Management, Mumbai)

  • Ms. Helena Naitsuwe Amadhila

    (Subject Matter Expert, Kazian School of Management, Mumbai)

  • Dr. Tadios Mashoko Msipa

    (Subject Matter Expert, Kazian School of Management, Mumbai)

  • Mr. Precious Mhaka

    (Subject Matter Expert, Kazian School of Management, Mumbai)

Abstract

The integration of Artificial Intelligence (AI) into Human Resource Management (HRM) represents a paradigm shift in organizational strategic planning and workforce management. This conceptual review examines the transformative role of AI-enabled HRM systems in strategic contexts, synthesizing current literature on implementation frameworks, organizational performance impacts, and emerging challenges. Through systematic analysis of recent empirical studies and theoretical frameworks, this paper explores how AI technologies including machine learning, predictive analytics, and natural language processing are revolutionizing core HR functions such as recruitment, performance management, talent development, and workforce planning. The review identifies significant positive relationships between AI-driven HRM practices and strategic organizational outcomes, including enhanced decision-making efficiency, improved employee engagement, and sustainable competitive advantage. However, critical challenges persist, encompassing algorithmic bias, ethical considerations, organizational readiness deficits, and employee resistance to technological change.

Suggested Citation

  • Dr. Fabiam George & Ms. Helena Naitsuwe Amadhila & Dr. Tadios Mashoko Msipa & Mr. Precious Mhaka, 2026. "Conceptual Review of Ai-Enabled Human Resource Management (Hrm) Systems in Strategic Contexts," International Journal of Latest Technology in Engineering, Management & Applied Science, International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS), vol. 15(2), pages 1435-1453, February.
    • Handle: RePEc:bjb:journl:v:15:y:2026:i:2:p:1435-1453
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