The urban water system is at the heart of urban cities, providing public health, economic productivity and environmental sustainability. Planning, building, running and managing these systems in the long term need to be governed in a coordinated way across the technical, financial and institutional fields. The research investigates the intersection of artificial intelligence (AI) and infrastructure asset management in the context of urban water systems and the implications for construction planning and management of infrastructure lifecycle over time. A multi-layered governance framework is suggested and explained as a conceptual approach to improve the governance of the infrastructure asset management of urban water systems with AI. The framework places asset data ecosystems at the base of the digital integration and the development of digital intelligence with AI. Predictive analytics and digital monitoring outputs feed into lifecycle governance systems that drive the renewal prioritisation, cost optimisation and capital planning decisions. This involved conducting a systematic literature review of 22 peer-reviewed publications spanning the years 2015-2025, to synthesize current knowledge on the applications of AI, the governance issues that arise and implications for the lifecycle of AI. The results show that the integration of AI applications is mostly limited to the operational and maintenance stages, and is relatively rare in the earlier stages of lifecycle like planning, design and construction. This research brings a coherent analytical approach for digital infrastructure governance for future water systems, enabling precise and forward-looking management for long-term water systems' resilience.
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