DEVELOPMENT OF AI-DRIVEN WORKFORCE ANALYTICS TOOLS TO IMPROVE TALENT MANAGEMENT, WORKFORCE PLANNING, AND PRODUCTIVITY

• Rahul Sharma

  Department of Computer Science, Indian Institute of Technology Bombay, India

Objective: Effective talent management and workforce planning are essential for organizational success in the knowledge economy. This research develops AI-driven workforce analytics tools that leverage machine learning to enhance talent acquisition, employee engagement, and productivity optimization. Method: Our integrated platform combines predictive models for employee turnover, performance forecasting algorithms, and skill gap analysis tools to support strategic workforce decisions. The system processes diverse HR data sources including performance reviews, engagement surveys, and productivity metrics to generate comprehensive workforce insights. Results: Deployment in multinational corporations demonstrates 22% reduction in employee attrition, 18% improvement in hiring quality, and 12% increase in overall workforce productivity. Novelty: The study advances HR analytics capabilities and provides evidence-based guidance for talent management practitioners.

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