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Beyond the AI Divide : A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness

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  • Pierre Jean-Claude Mandon

Abstract

This paper examines global disparities in artificial intelligence preparedness, using the 2023 Artificial Intelligence Preparedness Index developed by the International Monetary Fund alongside the multidimensional Economic Complexity Index. The proposed methodology identifies both global and local overperformers by comparing actual artificial intelligence readiness scores to predictions based on economic complexity, offering a comprehensive assessment of national artificial intelligence capabilities. The findings highlight the varying significance of regulation and ethics frameworks, digital infrastructure, as well as human capital and labor market development in driving artificial intelligence overperformance across different income levels. Through case studies, including Singapore, Northern Europe, Malaysia, Kazakhstan, Ghana, Rwanda, and emerging demographic giants like China and India, the analysis illustrates how even resource-constrained nations can achieve substantial artificial intelligence advancements through strategic investments and coherent policies. The study underscores the need for offering actionable insights to foster peer learning and knowledge-sharing among countries. It concludes with recommendations for improving artificial intelligence preparedness metrics and calls for future research to incorporate cognitive and cultural dimensions into readiness frameworks.

Suggested Citation

  • Pierre Jean-Claude Mandon, 2025. "Beyond the AI Divide : A Simple Approach to Identifying Global and Local Overperformers in AI Preparedness," Policy Research Working Paper Series 11073, The World Bank.
    • Handle: RePEc:wbk:wbrwps:11073
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    1. Philip Trammell & Anton Korinek, 2023. "Economic Growth under Transformative AI," NBER Working Papers 31815, National Bureau of Economic Research, Inc.
    2. Hunt, Jennifer & Cockburn, Iain & Bessen, James, 2024. "Is distance from innovation a barrier to the adoption of artificial intelligence," LSE Research Online Documents on Economics 126840, London School of Economics and Political Science, LSE Library.
    3. Madsen, Jakob B., 2016. "Barriers to Prosperity: Parasitic and Infectious Diseases, IQ, and Economic Development," World Development, Elsevier, vol. 78(C), pages 172-187.
    4. Garett Jones, 2011. "National IQ and National Productivity: The Hive Mind Across Asia," Asian Development Review (ADR), World Scientific Publishing Co. Pte. Ltd., vol. 28(01), pages 51-71, June.
    5. Zeugner, Stefan & Feldkircher, Martin, 2015. "Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 68(i04).
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    Cited by:

    1. Mahajan, Prashant Dr., 2025. "AI–Family Integration Index (AFII): Benchmarking a New Global Readiness for AI as Family," OSF Preprints wgm2q_v1, Center for Open Science.

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