• Radhika Thota
• Dr. Avula Sunitha

In an increasingly dynamic and competitive business environment, organizations are seeking data-driven methods in an attempt to enhance the human capital management. Predictive talent analysis and workforce execution have emerged as key technical tools to bridge by the workforce strategy and long-term business goals. Predictive talent analytics involves the application of historical and real-time HR data, statistical modelling, and machine learning algorithms to forecast future workforce trends like attrition, skill gaps, and high-potential talent. When accompanied by strategic workforce planning, covering aligning existing workforce ability with future organizational requirements, these analytics facilitate proactive decision-making, best-in-class utilization of resources, and enhanced talent development. This paper explains the principles, practices, applications, and benefits of bringing predictive analytics and workforce planning together. This paper also explains implementation challenges and the growing role of AI in shaping the future workforce strategy. Finally, the convergence of these practices allows organizations to create a dynamic, agile, and future-proof workforce.in Infosphere Technologies, TechNova Solutions Pvt. Ltd, Medivance Healthcare.

Predictive Talent Analytics, Workforce Planning, Human Capital Management, Forecasting, Strategic, Talent Development, AI&ML in Workforce Strategy, Attrition Prediction, Skill Gap Analysis.

"THE FUTURE OF TALENT: AI-DRIVEN PREDICTIVE ANALYTICS FOR STRATEGIC WORKFORCE PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 8, page no.387-391, August-2025, Available :http://www.jetir.org/papers/JETIRHC06059.pdf

"THE FUTURE OF TALENT: AI-DRIVEN PREDICTIVE ANALYTICS FOR STRATEGIC WORKFORCE PLANNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 8, page no. pp387-391, August-2025, Available at : http://www.jetir.org/papers/JETIRHC06059.pdf