AI-Based Predictive Skill Gap Analysis for Workforce Planning
J.Noor Ahamed1, Jacob Tom2
1 Assistant Professor, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
ncmnoorahamed@nehrucolleges.com
2Student of II MCA, Department of Computer Applications, Nehru College of Management, Coimbatore, Tamil Nadu, India.
jacobtom1234@gmail.com
Abstract
Rapid advancements in automation, artificial intelligence, and large-scale digital transformation have fundamentally reshaped workforce requirements across industries and regions. Traditional workforce planning methods, which rely on historical employment trends and static skill classifications, are increasingly inadequate for predicting emerging skill demands in a dynamic economic environment. As a result, organizations, educational institutions, and policymakers face significant challenges in anticipating future talent needs and aligning skill development strategies with industry evolution.
This research presents a Predictive Skill Gap Intelligence Hub, an AI-driven analytical platform designed to proactively forecast labor demand–supply gaps, identify high-potential regional opportunity hubs, and evaluate workforce skill readiness. The proposed system integrates multiple macro- and micro-level indicators, including regional economic growth projections, automation velocity, policy intervention strength, investment intensity, and market volatility, into a unified decision-support framework. By combining probabilistic growth modeling with intelligent skill synthesis techniques, the platform enables accurate estimation of future workforce requirements under varying economic and policy scenarios.
Interactive visual analytics, including demand–supply trend analysis, geospatial hotspot mapping, skill gap radar assessment, and policy simulation dashboards, are employed to enhance interpretability and strategic decision-making. Experimental evaluation demonstrates that the system effectively identifies critical talent shortages, highlights regions requiring targeted intervention, and quantifies the impact of automation and policy measures on workforce sustainability. The results indicate that the proposed approach provides valuable insights for data-driven workforce planning, strategic governance, and long-term skill development initiatives in rapidly evolving digital economies.
Keywords: Skill Gap Analysis, Workforce Analytics, Artificial Intelligence, Predictive Modeling, Policy Simulation
