This is like a detailed scoreboard for how different countries and sectors are doing in AI skills and capabilities. Instead of guessing where AI talent and investment are, it gives a structured set of indicators so leaders can see who is ahead, who is catching up, and where the biggest gaps in skills and workforce readiness are.
Policy makers, HR leaders, and large employers lack a consistent, data-backed way to understand AI capabilities across countries and labor markets. This framework standardises how AI skills, talent, R&D, and adoption are measured so they can plan workforce development, education, and talent strategies instead of flying blind.
Methodological depth and institutional credibility in how indicators are defined, validated, and maintained over time, plus access to multi-country statistical data that is hard for private firms to replicate.
Classical-ML (Scikit/XGBoost)
Structured SQL
High (Custom Models/Infra)
Integrating heterogeneous national and sectoral datasets with consistent definitions and quality at global scale.
Early Majority
Unlike ad-hoc AI ‘readiness’ scores from private indexes, this is positioned as an official, statistically grounded indicator system aimed at government and institutional decision-makers, with a strong focus on labour markets, skills, and capability measurement rather than vendor benchmarking.