From Connected Infrastructure to Autonomous Governance: An AI-Driven Urban Decision Intelligence Framework for NEOM-Scale Smart Cities
TL;DR
The paper further outlines a four-stage implementation maturity path progressing from connected foundation and predictive intelligence to agentic coordination and controlled autonomy, and it defines governance controls, key performance indicators, and responsible AI safeguards covering data quality, model reliability, cybersecurity, privacy, explainability, and auditability.
Abstract
-Background: Smart cities are evolving from connected infrastructure models toward intelligent urban ecosystems capable of sensing, learning, predicting, simulating, and coordinating public-service decisions in near real time. NEOM-scale environments require more than 5G connectivity, IoT sensing, cloud platforms, and green data centers; they require a trusted decision intelligence layer that transforms continuous urban data into explainable governance actions. This paper proposes the NEOM Urban Decision Intelligence Framework (NUDIF), an AI-driven conceptual architecture that integrates connected infrastructure, interoperable data platforms, digital twins, multi-agent intelligence, governance dashboards, and sustainability optimization. The framework is organized into six interoperable layers spanning connected infrastructure, urban data management, analytics and digital twins, domain AI agents, decision intelligence, and autonomous governance execution. It explains how energy, mobility, water, environment, public safety, and public-service systems can be monitored and coordinated through specialized AI agents operating under transparent human oversight, where routine decisions may be automated, high-impact decisions require human approval, and strategic decisions remain under institutional control. The paper further outlines a four-stage implementation maturity path progressing from connected foundation and predictive intelligence to agentic coordination and controlled autonomy, and it defines governance controls, key performance indicators, and responsible AI safeguards covering data quality, model reliability, cybersecurity, privacy, explainability, and auditability. Representative indicators are mapped across energy, mobility, water, environment, public services, and governance domains to support measurable evaluation. The study contributes a practical model for future smart cities by linking digital infrastructure with accountable decision-making, urban resilience, sustainability alignment, and Vision 2030-oriented innovation, and it identifies validation directions for large-scale greenfield and brownfield urban environments.