From typology to governance architecture: interdependent AI roles in open government data ecosystems

Purpose While Artificial Intelligence (AI) is increasingly embedded in Open Government Data (OGD) ecosystems, the mechanisms through which it restructures information practices and governance remain insufficiently theorized. This study empirically evaluates and refines an AI–OGD typology, explaining how AI roles are structured, sequenced and shaped by contextual conditions. Design/methodology/approach A mixed-methods expert panel evaluation (N = 9) was conducted with specialists in information systems and public administration. Five structured tasks were used to assess the conceptual clarity, role distinctiveness, sequencing and strategic positioning of the proposed AI roles, combining qualitative assessment with quantitative measures of expert agreement and perceived influence and impact. Findings Results support the typology while extending its theoretical interpretation. Rather than a flat classification, AI roles are perceived as forming a hierarchical and interdependent system, progressing from foundational portal-oriented functions to high-impact ecosystem-level roles. The findings further reveal low classification accuracy as an indicator of functional overlap, suggesting that AI roles operate along a continuum rather than as discrete categories. In addition, implementation patterns are shown to be cyclical and contingent on socio-technical factors such as organizational readiness, data infrastructure maturity and governance capacity. Practical implications The study provides a structured roadmap for policymakers and public managers to sequence AI adoption, balancing foundational data capabilities with ecosystem-level value creation. Originality/value This study contributes beyond validation by reconceptualizing AI roles in OGD ecosystems as a dynamic governance architecture. It advances research by integrating empirical validation with explanatory insight, offering a more practice-sensitive and theoretically grounded understanding of AI-enabled public data ecosystems.