Municipal governments face mounting pressure to deliver infrastructure services with constrained resources, driving interest in large language models (LLMs) for planning and operational support. Yet empirical evidence reveals a persistent gap between analytical capability and governance readiness: while LLMs can replicate structured reasoning, they frequently produce unverifiable outputs, lack regulatory awareness, and misinterpret local operational constraints. This study presents a hybrid human-AI governance framework designed for decision-making in urban infrastructure and municipal governance settings. Building on a thematic analysis of 78 coded responses from 20 infrastructure professionals and six commercial LLMs across three infrastructure decision scenarios, the study identifies where human and machine reasoning converge and diverge, and translates those empirical patterns into an operational governance model. The framework defines a five-stage workflow supported by a RACI (Responsible, Accountable, Consulted, Informed) matrix that clarifies roles: AI systems perform data synthesis and option generation, while human professionals ensure contextual judgment, ethical oversight, and final authority. Each workflow stage and role assignment is explicitly linked to specific thematic findings from the comparative analysis, ensuring the framework follows from evidence rather than prescriptive assumption. The model addresses algorithmic accountability, data governance, and digital inclusion by embedding oversight, training, and documentation standards into municipal workflows. Grounded in international AI governance frameworks including the EU AI Act, the NIST AI Risk Management Framework, and the OECD AI Principles, the framework offers a replicable model for cities to adopt LLM-assisted decision support in ways that safeguard equity, transparency, and public trust. An 18-month phased implementation roadmap provides municipal agencies with a realistic adoption pathway from readiness assessment through evaluation and scaling.
Alence Poudel, Carla Barrios, Paola De La Torre et al.· Discover Cities· 0 citations
Water utilities struggle to manage aging infrastructure with constrained budgets. Traditional asset management depends on historical failure data, and hydraulic models analyze system performance, but these approaches often operate in isolation. This planning-oriented case study from Sugar Land, TX, presents the hydraulic impact factor (HIF), a composite planning index that integrates pressure, velocity, headloss, and water age into a single weighted score representing hydraulic stress on each pipe segment. HIF is integrated into the City of Sugar Land’s Integrated Asset Management System to refine water main replacement priorities. By adjusting a scoring-based likelihood of failure using hydraulic stress, the framework shifts capital programming from a reactive, break-driven pattern toward a proactive, risk-based approach. In the Sugar Land application, HIF adjusted benefit–cost prioritization (BCP) scores for 94.5% of the portfolio, increasing the mean BCP score by 24.4% and reshuffling 39 of the top 50 and 71 of the top 100 prioritized assets. The revised rankings advanced hydraulically stressed mains into earlier capital plan years while maintaining overall portfolio stability as confirmed by a Spearman rank correlation of 0.889 and sensitivity analysis across 200 Monte Carlo weight combinations. The results indicate that the HIF framework can help utilities direct limited capital toward mains with combined deterioration and hydraulic vulnerability, supporting asset life extension, regulatory compliance, and service reliability.
Alence Poudel, Carla Barrios, V. Mehta et al.· Journal of water resources p...· 0 citations
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.
