Global AI Skills Index: Creating an Open Benchmark for Tracking National AI Upskilling Readiness

Abstract

Artificial intelligence continues to reshape global labor markets, amplifying the urgency for countries to develop scalable and equitable AI-related competencies. Yet, the absence of a unified global benchmark limits policymakers’ ability to compare national readiness or design targeted workforce development strategies. This study introduces the Global AI Skills Index (GASI), a comprehensive cross-national framework that evaluates AI upskilling readiness across four dimensions: AI Job Demand Density, Education-Industry Alignment, AI Skills Penetration Rate, and the Equity and Access Index. Drawing on standardized datasets from the OECD, UNESCO, ILO, World Bank, and LinkedIn covering 2018 to 2023, the index applies a weighted composite methodology to generate a harmonized 0-100 score for each country.
Methodological reliability was assessed through cross-validation with three established international benchmarks: the OECD Digital Skills Indicators, the Stanford AI Index, and World Bank–LinkedIn labor-market analytics. Strong alignment across these sources (R² = 0.76 to 0.84) demonstrates that GASI accurately captures systemic national differences in AI competence, digital readiness, and workforce capability. Quantitative findings highlight pronounced global disparities: advanced economies such as the United States, Singapore, and Finland achieve scores above 80, while countries across Sub-Saharan Africa and South Asia consistently fall below 55. A significant positive association between national education expenditure and AI job demand (R² = 0.68) further underscores the central role of sustained human-capital investment.
By integrating labor-market dynamics, education quality, skill prevalence, and equity considerations into a unified benchmark, the Global AI Skills Index offers an actionable, transparent framework for policymakers, educators, and international organizations. It enables evidence-based comparison, targeted intervention design, and long-term monitoring of national progress toward inclusive, AI-enabled workforce development.