This study proposes a novel framework for developing tailored AI strategies at the national level. Employing the Oxford Insights AI Readiness Index, we clustered 193 countries based on similarities in their governmental policies, technological capabilities, data & infrastructure. Subsequently, we analyzed socio-economic factors characterizing these clusters. Clustering was performed using the index's 10 dimensions, while a decision tree analysis identified the relative importance of 6 key socio-economic variables (population, GDP per capita, employment rate, healthcare expenditure as a percentage of GDP, education expenditure as a percentage of GDP, and press freedom index) impacting AI development. Our results empirically validate existing theoretical frameworks by highlighting the significant influence of GDP per capita, population size, and government healthcare expenditure, while demonstrating the limited impact of other considered variables. This combined clustering and decision tree approach provides actionable insights for policymakers navigating the complexities of AI development. The findings offer valuable implications for the design and implementation of effective national AI strategies.
