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Artificial Intelligence Readiness and Productive Capacity in Africa: A Cross‐Country Empirical Analysis

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  • Siyuan Huang
  • Mukadaz Taofeeq Ishola
  • Kazeem Bello Ajide

Abstract

Artificial intelligence (AI) is widely viewed as an enabling technology for structural transformation, but national AI readiness does not automatically translate into productive capability. This study examines the conditional association between AI readiness and productive capacity in 45 African countries over 2018–2024, using the Oxford Insights Government AI Readiness Index. The Economic Complexity Index (ECI) is the principal outcome; the UNCTAD Productive Capacity Index (PCI) serves only as a robustness measure given conceptual overlap with the enabling conditions examined here. Dynamic System‐GMM addresses persistence and endogeneity, complemented by fixed‐effects, bias‐corrected LSDV, collapsed‐instrument, and restrictive‐lag checks. Results show a positive conditional association between AI readiness and economic complexity, strengthening where human capital and electricity access are more favourable; the institutional interaction is less robust once fully specified. Estimated turning points are sample‐specific, not universal thresholds. Because internal instruments cannot fully resolve reverse causality or omitted structural change, the study does not claim a causal effect. Instead, the evidence supports a conversion perspective: AI readiness is more likely to accompany productive sophistication where complementary capabilities are sufficiently developed. The paper contributes an empirically grounded country typology and policy implications for sequencing AI strategy alongside human‐capital, energy, and governance investment.

Suggested Citation

  • Siyuan Huang & Mukadaz Taofeeq Ishola & Kazeem Bello Ajide, 2026. "Artificial Intelligence Readiness and Productive Capacity in Africa: A Cross‐Country Empirical Analysis," African Development Review, African Development Bank, vol. 38(3), September.
  • Handle: RePEc:bla:afrdev:v:38:y:2026:i:3:n:e70074
    DOI: 10.1111/1467-8268.70074
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