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Skill Forecasting and Reskilling in the Age of Automation

Muhammad Shahzeb KhanVilla College, MaldivesSyeda Masooma ZehraBahria University, PakistanSyed Muhammad Noaman Ahmed ShahWestminster International University in Tashkent, UzbekistanSalman HameedBahria University, Pakistan
2025ng
ABI

Abstract

The accelerating integration of automation and artificial intelligence into the labor market necessitates a paradigm shift in workforce preparedness, emphasizing proactive skill forecasting and adaptive reskilling strategies. This chapter examines the critical role of AI in anticipating labor market evolution through real-time analysis of employment trends and emerging competency gaps. It further explores the application of AI in designing dynamic reskilling frameworks, incorporating personalized learning trajectories and immersive training methodologies to optimize skill acquisition. Performance metrics derived from AI-driven analytics enable continuous refinement of reskilling initiatives, ensuring alignment with evolving industry demands. Ultimately, the synthesis of predictive workforce analytics and AI-enhanced education models presents a scalable solution to mitigate technological displacement, fostering workforce resilience in an era of rapid automation. The findings underscore the imperative for policymakers, educators, and corporate entities.

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