International Journal of Science, Engineering and TechnologyAuthors: Assistant Professor Dr. C. Sharmila, Assistant Professor D. Mercy Smilin
Abstract: Artificial Intelligence (AI) revolution is completely transforming the landscape of labor markets, generating the problem of workforce overprovision in traditional jobs and severe deficits of skills that are essential in the age of AI. This research proposes a quantitative approach for SWP that would solve the stated paradoxes using analytical and collaborative modeling of multiple scenarios of human-AI collaboration. Analyzing the survey results among 1,010 C-suite executives and the experience of firms that implement AI technologies, it becomes possible to prove that the firms implementing the approach of AI-driven workforce planning along with appropriate reskilling initiatives enjoy much higher levels of efficiency and resilience of their workforce. The suggested approach includes the DWAM model consisting of four interconnected modules, namely predictive demand sensing, skills gap analysis, reskilling optimization, and human-AI collaboration design. Simulations show that proper SWP allows reducing displacement rates by 40% and increasing talent retention by 35%.
