Machine Learning and Human Resource Management: A Path to Efficient Workforce Management

In order to achieve effective workforce management, this empirical study investigates the incorporation of machine learning into human resource management (HRM). HRM is a fundamental function that oversees talent acquisition, employee welfare, and performance optimization in organizations. The dynamic nature of today's workplace presents special opportunities as well as challenges for HRM. Machine learning, a branch of artificial intelligence, has the potential to completely transform human resource management (HRM) by means of the use of data-driven decision-making, bias mitigation, employee experience personalization, as well as procedure optimization. The first section of the paper provides an overview of machine learning's application to HRM, with a particular focus on forward-thinking employee turnover prediction, personalized onboarding and training, recruitment automation, in addition to predictive analytics for employee success. Machine learning promotes fairness and equal opportunities by utilizing objective data to address bias in HR procedures. There are numerous advantages to incorporating machine learning into HRM, such as objectivity, personalization, automation that reduces costs, and decision-making based on information. The practical advantages of integrating machine learning in HRM are demonstrated by real-world case studies from businesses like Hilton, Xerox, and IBM. The resulting advantages include improved productivity, lower attrition, and higher employee engagement.