AI and Data Engineering Frameworks for Smart Government Decision Making

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-ofthe-art AI developments and with the ML and AI strategies of important public and commercial institutions.The rapid development of analytics and AI technologies, combined with the capacity to harness the massive amount of data generated by public sector operations, unquestionably represents a significant opportunity for governments to transform their traditional ways of working.More than ever, there is a strong demand for real-time, objective, and evidence-based analysis to support the challenging decision-making environments created by the COVID-19 pandemic and other current global crises.However, in practice, only a limited number of governments have adopted a formal AI framework.Most AI innovations in the public sector remain isolated.Advanced ML models, especially deep-learning techniques, are mainly developed for specific applications, while broader ML initiatives are becoming more common, primarily driven by cloud-based platforms.At the same time, private organizations are increasingly offering prescriptive or predictive services to governments, filling in the gaps in their analytics capabilities.