Exploring Machine Learning Applications in Human Resources Management: A Comprehensive Review

In the modern business landscape, effective human resources management (HRM) stands as a linchpin for organizational success, with machine learning (ML) emerging as a transformative force in revolutionizing HR practices (Kess-Momoh et al. in World J Adv Res Rev 21(1):746–757, 2024). This review paper delves into the multifaceted applications, challenges, and prospects of ML within HRM. ML integration has fundamentally reshaped recruitment processes, facilitating automated resume screening, candidate selection, and predictive analytics for talent acquisition (Paramita in digitalization in talent acquisition: a case study of AI in recruitment, 2020). By leveraging ML algorithms, organizations can efficiently sift through vast candidate pools while mitigating biases inherent in traditional recruitment methods (Vishwanath and Vaddepalli in Tuijin Jishu/J Propul Technol 44(3):2023, 2023). Beyond recruitment, ML holds promise in talent management, enabling predictive modeling to identify high-potential employees and tailor personalized training and development plans (Sheshadri in Glob J Bus Integral Secur, 2016). Succession planning is likewise enhanced through ML-driven strategies, ensuring organizational continuity by grooming future leaders. Employee engagement and retention, crucial factors for organizational sustainability, are also revolutionized by ML. Sentiment analysis of employee feedback provides valuable insights into workforce morale, while predictive modeling helps identify turnover factors, allowing for targeted retention strategies (Garg et al. in Int J Prod Perform Manag 71(5):1590–1610, 2022). Nonetheless, the widespread adoption of ML in HRM is accompanied by ethical and technical challenges. Concerns regarding algorithmic biases and data privacy necessitate initiative-taking measures to ensure fairness and transparency. Moreover, integrating ML with existing HRM systems presents technical hurdles, including data integration and system compatibility issues. Despite these challenges, the future of ML in HRM appears promising, with opportunities for improved efficiency, effectiveness, and employee satisfaction. Through case studies and real-world examples, this review paper underscores the practical implications of ML in HRM, offering insights to practitioners, researchers, and policymakers seeking to harness ML’s transformative potential to drive organizational success and foster a thriving workforce.