Governance and data readiness as prerequisites for digital twin adoption in small and midsize cities

Abstract

Digital Twin (DT) and artificial intelligence initiatives in local government often move faster than the governance and data conditions needed to support them. This Perspective proposes the Foundation First framework, a conceptual readiness pathway for small and midsize cities that treats data quality, governance capacity, cybersecurity, and public trust as prerequisites for responsible DT deployment, not afterthoughts. Developed through a structured narrative synthesis of literature on smart city maturity, digital transformation, data readiness, and urban governance, and illustrated through publicly documented practices in the City of Sugar Land, Texas, a midsize U.S. municipality as defined in this paper, with established data governance and asset management functions but no deployed DT platform, the framework outlines four sequential phases: foundational inventory and digitization, integration and open data, analytical and predictive pilots, and sustainable urban intelligence. Unlike maturity models that primarily assess technical sophistication, Foundation First emphasizes readiness sequencing by linking phase advancement to demonstrable data integrity, institutional alignment, audit practices, and governance controls. The framework also offers an illustrative alignment between phased municipal readiness and selected United Nations Sustainable Development Goals (SDGs), showing how incremental capability building can support broader public-value outcomes without proposing target-level SDG measurement or implying causal attribution between phase completion and specific SDG outcomes. By reframing DT readiness as a governance- and data-centered progression rather than a purely technical problem, this Perspective offers municipal leaders and researchers a practical, grounded approach for assessing whether cities are actually prepared to invest in advanced urban intelligence systems.