Beyond the AI divide: A straightforward approach to identifying global and local overperformers in AI preparedness

Abstract

This study introduces a straightforward data-driven framework to identify countries that outperform in artificial intelligence (AI) preparedness relative to their economic complexity, utilizing the IMF's Artificial Intelligence Preparedness Index and a multidimensional Economic Complexity Index derived from trade and research data. Employing weighted least squares, the study estimates expected AIPI scores and classifies countries as global or local overperformers if their observed scores exceed predictions and surpass income-group medians. The analysis identifies 10 high-income global overperformers and 14 local overperformers across middle- and low-income groups, revealing regulation and ethics as universal drivers of overperformance, with digital infrastructure and human capital varying by economic context. Case studies elucidate diverse coordination models—state-led, market-responsive, and distributed innovation—while highlighting transferability constraints due to institutional and historical factors, among others. The replicable methodology provides policymakers and other key actors a robust tool to benchmark AI readiness and design context-specific strategies, addressing the global AI divide. The study opens avenues for future research into refined AI preparedness metrics, alternative identification techniques, and comparative analyses of national innovation systems.