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<p>Artificial intelligence has become a central component of national competitiveness, yet most global assessments of AI readiness rely on static, cross-sectional benchmarks that obscure the dynamic and path-dependent processes through which capability develops, diverges, and stabilizes over time. This study reconceptualizes AI readiness as an evolving system and addresses three questions: the trajectories countries follow as readiness develops (RQ1), the structural foundations shaping these pathways (RQ2), and the role of policy–capability alignment in explaining outcomes (RQ3). A multi-method design is applied to panel data covering 137 countries from 2021 to 2023, integrating Oxford Insights AI readiness scores with World Bank indicators. Gaussian Mixture Models identify trajectory archetypes, Principal Component Analysis uncovers latent structural architecture, and a policy–capability alignment regression model evaluates how governance, digital infrastructure, and economic structure jointly explain readiness outcomes. Five trajectory types emerge—Stable Performers, Emerging Climbers, Declining Strugglers, Fast Leaders, and Steady Climbers—exhibiting clear divergence in both level and stability. Structural analysis reveals a layered architecture in which governance forms the core dimension, digital infrastructure drives readiness outcomes, and economic structure acts as a conditional enabler. The alignment model shows strong explanatory power (R<sup>2</sup> = 0.752), with residual analysis identifying countries that exceed predicted performance, indicating structural overperformance driven by coordinated policy and strategic agency. The study contributes by introducing an empirical typology of AI readiness trajectories and formalizing the Level–Momentum–Stability framework as a diagnostic approach to capability development. The findings show that AI readiness is a system-building process shaped by the interaction of governance, digital infrastructure, and policy alignment over time, rather than an income-driven progression.</p>

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A dynamic empirical typology of national AI readiness trajectories structural architecture and policy capability alignment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月19日

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  • Abedallah Zaid Abualkishik,
  • Nazar Zaki,
  • Mohamed Elhoseny,
  • Reem Atassi,
  • Zahid Halim

摘要

Artificial intelligence has become a central component of national competitiveness, yet most global assessments of AI readiness rely on static, cross-sectional benchmarks that obscure the dynamic and path-dependent processes through which capability develops, diverges, and stabilizes over time. This study reconceptualizes AI readiness as an evolving system and addresses three questions: the trajectories countries follow as readiness develops (RQ1), the structural foundations shaping these pathways (RQ2), and the role of policy–capability alignment in explaining outcomes (RQ3). A multi-method design is applied to panel data covering 137 countries from 2021 to 2023, integrating Oxford Insights AI readiness scores with World Bank indicators. Gaussian Mixture Models identify trajectory archetypes, Principal Component Analysis uncovers latent structural architecture, and a policy–capability alignment regression model evaluates how governance, digital infrastructure, and economic structure jointly explain readiness outcomes. Five trajectory types emerge—Stable Performers, Emerging Climbers, Declining Strugglers, Fast Leaders, and Steady Climbers—exhibiting clear divergence in both level and stability. Structural analysis reveals a layered architecture in which governance forms the core dimension, digital infrastructure drives readiness outcomes, and economic structure acts as a conditional enabler. The alignment model shows strong explanatory power (R2 = 0.752), with residual analysis identifying countries that exceed predicted performance, indicating structural overperformance driven by coordinated policy and strategic agency. The study contributes by introducing an empirical typology of AI readiness trajectories and formalizing the Level–Momentum–Stability framework as a diagnostic approach to capability development. The findings show that AI readiness is a system-building process shaped by the interaction of governance, digital infrastructure, and policy alignment over time, rather than an income-driven progression.