Machine Learning Applications for Talent Management Decisions

Abstract

Machine Learning (ML) has emerged as a transformative technology in Human Resource Management by enabling organizations to make accurate, strategic, and data-driven talent management decisions. Traditional talent management approaches often depend on subjective judgments and historical practices; however, machine learning algorithms provide predictive insights by analyzing large volumes of employee-related data. This article explores the applications of machine learning in talent acquisition, employee performance prediction, career development, workforce planning, and retention management. ML-based systems help organizations identify high-potential employees, predict turnover risks, optimize recruitment decisions, and enhance workforce productivity. The integration of machine learning with talent management practices allows organizations to move from reactive decision-making toward proactive human capital strategies. However, ethical concerns related to data privacy, algorithmic bias, transparency, and human oversight remain important challenges. The study highlights the importance of combining technological capabilities with managerial expertise to ensure responsible and effective implementation of ML-driven talent management systems.