Modeling Government AI Readiness Profiles Using Machine Learning: A Global Perspective

Andrés Navas Perrone, Ana Belén Tulcanaza-Prieto

Artificial Intelligence (AI) adoption has emerged as a critical priority for governments globally, driven by its transformative potential in improving public service delivery, governance efficiency, and innovation ecosystems. Despite this, substantial disparities exist in AI readiness and adoption levels across countries, necessitating an in-depth exploration of the factors influencing AI adoption. This study leverages data from the Oxford Insights Government AI Readiness Index to model cross-country patterns of government AI readiness through clustering, regression, classification, and explainable machine learning. A Random Forest regression model was first used to estimate the 2024 AI Government Readiness score using lagged 2023 indicators. However, because the dependent variable is a composite index constructed from conceptually related dimensions, this model is interpreted as a lagged score-approximation and benchmarking exercise rather than as an independent forecasting model. The main analytical contribution lies in the clustering-classification framework, which identifies four country-level AI readiness profiles and evaluates the indicators that most strongly distinguish countries across low, moderate-low, intermediate, and high readiness groups. SHAP and permutation-based interpretation methods are used to examine feature contributions, while recognizing that these results indicate model contribution rather than causal effects. The findings underscore the multifaceted nature of AI readiness, emphasizing the interplay between governance, digital infrastructure, and technological investment.