Adapting to the AI revolution: comparative analysis of national workforce strategies

This paper assesses national workforce strategies for adapting to artificial intelligence (AI) across 55 countries. The background of the research is defined by the rapid diffusion of AI technologies, which creates new challenges and opportunities for labour markets and policy-makers worldwide. Objectives: The primary objective is to identify the key determinants that drive the adoption of both skills-oriented and transformation-oriented strategies in national AI workforce development. Methods/Approach: The study constructs two composite indices—AI Skills Focus and AI Transformation Focus—based on employer survey data. These indices capture, respectively, the prioritization of hiring and training for AI-related skills, and the implementation of broader organizational strategies such as business model reorientation and workforce transitions driven by AI. Five explanatory variables are analyzed: funding for reskilling and upskilling, skills gaps in the labour market, the proportion of tasks performed by technology (human-machine frontier), labour-market churn, and the degree of organizational AI exposure. Results: The findings demonstrate that organizational AI exposure is the strongest and most consistent predictor of both indices, indicating that widespread AI adoption at the firm level is closely linked to national workforce adaptation. In contrast, larger skills gaps are associated with lower adoption of AI workforce strategies, suggesting that talent shortages impede rather than accelerate adaptation. Conclusions: The results highlight the importance of ecosystem-based approaches in public governance, where collaboration among government, employers, and education providers fosters readiness and adaptability. The study recommends policy measures focused on building supportive institutional environments and addressing skills shortages through integrated, long-term reforms.