AI-Driven and Data Engineering Frameworks Supporting Smart Public Sector Decision Processes
The demand for real-time evidence-based policy decision-making and management is driving governments to enhance their analytical capabilities through AI. However, actual AI uptake in the public sector remains limited. Current government ML models and innovations rely primarily on internal IT infrastructure and cloud-based platforms. This research outlines the AI and data engineering frameworks required to execute a future-ready government analytical agenda for smart decision-making. The analysis identifies three types of data pipeline architectures and the foundations of an integrated data ecosystem tailored to the specific characteristics of public sector data. These are combined with the essential requirements for data quality and governance, and different AI deployment models for PLG predictive and prescriptive analytics applications. Finally, seven use areas for healthcare, social services, urban planning, transport, crime and disaster response are examined. The resulting design delivers a comprehensive, objective, and evidence-based perspective on AI frameworks for real-time smart government. Despite practical implementation challenges, the recommendations align both with state-of-the-art AI developments and with the ML and AI strategies of important public and commercial institutions.