AI Strategy–Practice Gaps in Government: Strategic Alignment and Public Data Ecosystem Readiness Signals

Artificial Intelligence (AI) adoption in the public sector increasingly depends on the readiness of Public Data Ecosystems (PDEs) and their governance. However, limited empirical evidence exists on how governments articulate AI-enabled activities in policy documents and whether these ambitions align with the data-related conditions required for implementation. This study presents a cross-national, data-centric assessment of AI activity patterns across eight European Union Member States. Using computational text analysis and expert assessments, we identify 210 AI-enabled activity patterns across seven categories and evaluate their perceived strategic importance and documented operational salience. The results reveal gaps between strategic priorities and operational articulation, particularly in PDE-dependent areas such as data interoperability, data quality, stakeholder engagement, and explainable and human-centric AI. A two-stage clustering approach combining hierarchical clustering and K-means identifies three data-centric profiles of policy-visible AI configurations: AI-first, data-first, and balanced. These profiles indicate that commitments to AI coexist with uneven policy articulation of the governance and data conditions associated with deployment. Rather than measuring implementation outcomes, the study provides a comparative framework for analysing how AI-enabled activities are framed, prioritized, and made visible in policy documents relating to PDEs, while informing future research on data-centric AI governance in the public sector.