Architecting a National AI Talent Ecosystem: A Systematic Scoping Review of Strategies for Education, Innovation, and Governance

Abstract

This paper addresses the critical need for a holistic, evidence-based national strategy to cultivate a world-class artificial intelligence (AI) talent pool. As AI reshapes global economies, labor markets, and the geopolitical landscape, national competitiveness hinges on the ability to develop, attract, and retain AI expertise. Employing a systematic scoping review methodology, this study synthesizes evidence from academic literature, government policy documents, and industry white papers to construct an integrated strategic blueprint. The analysis deconstructs the core components of a comprehensive talent development policy, proposing a multi-pillar framework that integrates a lifelong learning continuum, a differentiated talent pipeline architecture, synergistic public-private enablers, and modernized evaluation paradigms. Through a comparative analysis of divergent national strategies—including the market-driven model of the United States, the governance-first approach of the European Union, and the state-directed models of India, Singapore, the United Arab Emirates, and Canada—this paper illuminates the trade-offs between different philosophical and tactical choices. Key findings reveal the heterogeneous nature of AI's impact on labor, the centrality of public trust as a prerequisite for adoption, and a necessary paradigm shift from credential-based to competency-based talent evaluation. The proposed blueprint, which introduces a novel "Builder-Bridger" talent model, offers a comprehensive, actionable guide for policymakers and academic leaders aiming to build a sustainable and globally competitive national AI talent base capable of navigating the complexities of the AI era.