Predictive Talent Management Leveraging AI for Workforce Optimization

Abstract

In the era of data driven decision-making, talent management has evolved beyond traditional intuition-based approaches into a strategic imperative powered by artificial intelligence. This study proposes a robust, predictive framework that leverages machine learning algorithms and behavioral analytics to proactively manage workforce dynamics. Drawing on comprehensive multi-source data from over 1,000 employees across diverse sectors including pharmaceuticals, IT, infrastructure, and education the model integrates key indicators such as Retention Risk Score (RRS), Career Progression Index (CPI), and Skill Gap Index (SGI) to uncover actionable insights into employee engagement, performance, and attrition risk. The framework demonstrates a predictive accuracy of 92% and reveals a statistically significant inverse correlation (r = –0.84) between engagement levels and attrition likelihood. Cluster based segmentation further enables organizations to classify employees into strategic categories such as high potential, at risk, and development needed facilitating targeted HR interventions. Enhanced with sentiment analysis and real-time dashboard visualizations, this AI driven system empowers organizations to transition from reactive HR operations to proactive, evidence-based talent strategies, thereby optimizing workforce stability, growth, and competitive advantage.