Artificial Intelligence and Workforce Analytics Transforming Strategic Human Resource Management Through Data-Driven Decision-Making

Abstract

Artificial intelligence and workforce analytics are changing strategic HRM by enabling business to turn workforce data into actionable, predictive and timely insights. This study examines the theoretical foundations, practical applications, practical implications, and governance implications of HRM and AI. It shows the applications of machine learning, NLP, predictive algorithms, generative AI, and people analytics in workforce planning, talent recruitment, performance management, learning and development, employee engagement, retention, compensation, and workforce optimization. Based on the synthesis, AI-driven analytics help in increasing decision-making, prediction of workforce, organizational flexibility, talent matching, and HR value added. The adoption of the model is successful, however, only if there is an integrated data infrastructure, quality information and interoperability, analytical competence, leadership commitment, participation of employees, and continuous validation of the model. Key issues are algorithmic bias, discriminatory outcomes, surveillance in the workplace, loss of privacy, lack of transparency for decision-making, cybersecurity risks, employee resistance to algorithms, and overreliance on quantitative measures. Explainable models, algorithmic auditing, data minimization, human-in-the-loop decision systems, involvement of stakeholders and accountable governance are thus the focus of the study. Further studies are needed to provide longitudinal and causal evidence, especially in SMEs and emerging economies, and examine the integration of technical, strategic, ethical, and social performance measures, as well as generative AI, digital organizational twin, and employee-centred analytics. Overall, good integration can help to enhance the performance of the company while maintaining employee fairness, autonomy, dignity and well-being.