THE ROLE OF MACHINE LEARNING IN ENHANCING TALENT ACQUISITION AND WORKFORCE PLANNING
DOI:
10.54443/morfai.v5i6.4455Published:
2025-11-24Downloads
Abstract
This study investigates the transformative potential of machine learning (ML) in modern human resource management, addressing industry-wide challenges in talent acquisition, retention, and strategic planning. Through a mixed-methods approach analyzing 45,000 employee records across multiple sectors and employing algorithms including NLP and predictive modeling, the research evaluates ML's efficacy against traditional HR processes. Results demonstrate that ML-driven systems significantly enhance operational efficiency, improving screening speed by 95% and hiring accuracy by 50%, while reducing bias by 60%. Furthermore, ML enables proactive talent management through precise attrition prediction and data-driven succession planning. The discussion concludes that ML integration is pivotal for evolving HR from an administrative function to a strategic asset, fundamentally enhancing organizational agility and human capital optimization, though its success is contingent on ethical implementation and robust data governance.
Keywords:
Predictive Analytics Machine Learning Talent Management Employee Attrition HR OptimizationReferences
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Copyright (c) 2025 Verbian Hidayat Syam, Oktavianti Oktavianti, Rizki Eka Putra Eka Putra
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