PREDICTIVE ANALYTICS & GENERATIVE AI FOR WORKFORCE PERFORMANCE: ENHANCING PRODUCTIVITY IN THE POST-DIGITAL ERA

Abstract

In the post-digital era, organizations are reimagining workforce performance through the integration of predictive analytics and generative artificial intelligence (AI). This study explores how the convergence of data-driven forecasting models and generative AI tools can optimize employee productivity, enhance decision-making, and reshape organizational efficiency. Predictive analytics enables enterprises to anticipate workforce trends, identify performance gaps, and strategically allocate resources using models such as regression, random forests, and neural networks. Complementarily, generative AI tools    such as ChatGPT, Copilot, and Gemini    extend human capability by automating creative tasks, personalizing learning pathways, and facilitating knowledge synthesis. The research adopts a mixed-method approach that integrates predictive modelling with empirical data from hybrid work environments to evaluate the tangible improvements in productivity, engagement, and innovation. Results indicate a significant performance uplift when predictive analytics is paired with generative systems, with measurable gains in task efficiency, collaborative output, and strategic adaptability. The study underscores the growing necessity of human–AI synergy and presents a framework for leveraging intelligent systems as a catalyst for sustainable workforce transformation in the post-digital landscape.