Across Latin America, the accelerating transformation of work is outpacing the capacity of education and training systems to respond. This study argues that the region¿s policy frameworks must evolve from reactive labour-market monitoring toward proactive skills intelligence and forecasting. We begin by outlining the limitations of traditional forecasting models that rely on lagging economic indicators and static occupational classifications. Drawing on an operational deployment in Mexico's automotive and infocomm sector, we present a data-driven approach that integrates real-time labour-market data, AI-enabled skills taxonomies, and scenario modelling to anticipate future demand. Drawing on recent initiatives in the region, we explore how such methods can inform curriculum design, vocational education and training, and workforce development policies. The study concludes by discussing the institutional and governance reforms required to embed skills forecasting into national education and innovation agendas, positioning Latin America as a testbed for global policy learning on the future of skills.
