<![CDATA[The rapid growth of smart city technologies and digital economy systems has significantly increased the complexity of urban governance, particularly in integrating heterogeneous data sources, supporting intelligent decision-making, and ensuring effective coordination across systems. However, existing approaches often remain fragmented, with limited integration between data infrastructures, artificial intelligence (AI), and governance mechanisms. This study addresses this gap by proposing and evaluating an AI-driven governance architecture designed to integrate smart city systems and digital economy ecosystems into a unified, data-driven framework. This research adopts the Design Science Research (DSR) methodology, encompassing problem identification, objective definition, architecture design, demonstration, evaluation, and communication. The proposed architecture is structured into five interconnected layers: data acquisition, data management, AI intelligence, governance, and service delivery. A demonstration scenario integrating smart mobility and digital economy systems illustrates the operational capabilities of the architecture. The evaluation is conducted using a multi-framework approach, incorporating COBIT, ISO 37120, TOGAF, NIST AI Risk Management Framework, ITIL, and GDPR, combined with expert-based assessment. The results indicate that the proposed architecture achieves a high level of effectiveness, with an overall evaluation score of 4.39, demonstrating strong alignment with governance, architectural, and service requirements. This study contributes by introducing an integrated AI-driven governance model that bridges smart city systems and digital economy ecosystems, enabling adaptive, predictive, and data-driven urban governance. The findings provide both theoretical insights and practical guidance for developing next-generation governance architectures in complex digital environments.]]>
