The article, authored by Alence Poudel and Emily Moore of the City of Sugar Land, Texas, introduces what the authors call the Foundation First framework, a four-phase readiness pathway that treats data quality, governance capacity, cybersecurity, and public trust as prerequisites for digital twin deployment rather than afterthoughts. Their central argument is blunt: procurement frequently moves ahead of the institutional conditions needed to support it, and the result is often visually impressive technology that is analytically unreliable and operationally insufficient.

The implementation gap the authors describe is well documented in the research literature. Technical capabilities for digital twins and AI-enabled analytics are advancing faster than actual operational deployment, and the reasons are rarely about computing power or software sophistication. Fragmented data environments, weak interoperability, and limited institutional capacity remain the norm in municipal governments. Core information is typically scattered across asset records, geographic information system platforms, permitting systems, work order platforms, financial systems, and department-specific spreadsheets that were never designed to work together. Even basic discrepancies between asset inventories, spatial records, and actual field conditions can compromise downstream modeling, maintenance planning, and simulation quality long before a city attempts anything resembling advanced simulation.

The problem is most acute in small and midsize cities, which the authors define not by fixed population thresholds but by operational reality: limited technical staff, constrained capital budgets, greater vendor dependence, and less in-house digital capacity than major metropolitan governments. Sugar Land, with roughly 110,000 residents and an annual operating budget of about $300 million, serves as the illustrative case throughout the paper. The authors note that the digital twin literature draws heavily from large, resource-rich cities with dedicated smart city offices and technology budgets that can exceed $100 million annually, and the lessons from those contexts do not always translate. A city of 100,000 residents managing aging infrastructure faces fundamentally different decisions than a metropolis of ten million.

The Foundation First framework reframes readiness as a gated progression in which advancement from one phase to the next depends not on acquiring new tools but on demonstrating that the underlying conditions for responsible and sustainable use have been established. The authors are explicit that the framework is conceptual rather than empirically validated, and that cities may progress at different rates across different domains, revisiting earlier phases when governance gaps or data quality problems surface.

Phase 1 addresses the minimum conditions for generating operational data that can be trusted. The first activity is a department-by-department audit of asset data covering water mains, valves, pumps, streetlights, drainage assets, and pavement segments. The purpose is not simply to compile an inventory but to identify inconsistencies between field conditions, GIS records, asset management systems, and legacy files, because those inconsistencies become sources of analytical error in later phases. The second activity is workflow digitization: converting paper-based or inconsistently recorded processes such as work orders, inspections, permitting, and field documentation into structured digital workflows. The third is establishing a policy foundation for data ownership, retention, stewardship, privacy, and internal sharing. The milestone for Phase 1 is a verified digital asset registry and baseline performance indicators that make service conditions measurable—what the authors describe as an organizational project rather than a technical one.

Phase 2 focuses on reducing internal fragmentation through system integration using application programming interfaces, middleware, shared identifiers, and metadata alignment, ideally aligned with recognized standards such as the Open Geospatial Consortium API for Features and ISO/IEC 30182. The authors illustrate the kind of lightweight semantic structure required with relationships such as a water main having a valve identifier, and a valve being located in a GIS polygon. Crucially, they argue that establishing API connections is not sufficient on its own. Discrepancies between platform-reported values and API outputs are common in practice, arising from data transformation logic, caching behavior, and differences in how vendor systems expose internal records. If undetected, these discrepancies propagate into analytics pipelines and eventually into digital twin inputs, becoming hidden sources of error. Phase 2 therefore requires explicit data fidelity validation: systematically comparing API-retrieved values against authoritative platform records before those data flows are used for decision support. This phase also includes building an open data capability for verified, non-sensitive datasets, which the authors describe as both a transparency mechanism and a governance discipline.

Phase 3 is where targeted analytics and limited-scope digital twin experimentation begin. The authors emphasize limited scope deliberately: rather than launching a citywide digital twin environment, cities should test whether improved data conditions can support a specific use case with operational value and manageable governance oversight. Activities include sensor deployment tied to clearly defined operational problems—pressure monitoring, traffic counts, pump performance, flooding indicators—rather than broad deployment in anticipation of future use cases, which the authors identify as a common contributor to implementation failure. Predictive maintenance, anomaly detection, and scenario analysis become viable at this stage, but only if data lineage, completeness, and consistency are sufficient to make outputs interpretable and defensible. Models built on poorly governed data risk producing outputs that appear credible but reflect data quality problems rather than genuine operational patterns. The phase also requires formalizing review mechanisms for AI use, algorithmic transparency, and ethical risk, and incorporating cybersecurity controls consistent with the NIST Cybersecurity Framework, since connecting sensors and APIs expands a city’s attack surface considerably.

Phase 4 describes a longer-term condition in which validated pilots, governance routines, and interoperability practices are mature enough to support broader urban intelligence capabilities across water, mobility, energy, land use, or emergency management. The objective is to scale without losing accountability or public legitimacy, with cyber-resilience embedded as ongoing institutional practice—continuous monitoring, periodic testing, auditable transactions—rather than a technical configuration addressed once at setup. The authors warn that without formalized recurring reviews, Phase 4 implementations risk fragmenting into loosely connected pilots.

A distinctive feature of the framework is its insistence that readiness be measured, not assumed. The authors propose a set of key performance indicators, including asset registry completeness, data conflict resolution time, workflow digitization coverage, API coverage, data fidelity validation rate, data lineage and quality scores, governance policy adoption, and pilot-specific value metrics. These feed into quarterly cross-departmental maturity reviews with go/no-go criteria for phase advancement, ideally supplemented by independent validation through external audit or peer review. Notably, the authors recommend that thresholds be established before reviews are conducted, so that advancement criteria are not adjusted in response to observed results.

The illustrative application to Sugar Land is based entirely on publicly documented practices and offers a sobering picture of where even a relatively advanced midsize city actually stands. Core infrastructure inventories have been documented and incorporated into asset management and GIS systems, operational workflows have been digitized through enterprise applications, and an internal data governance function with privacy policies has been established—all consistent with substantial progress through Phase 1. The city has integrated priority systems to some degree and launched an open data portal, but publicly available documentation does not indicate a fully standardized API interoperability framework across all core systems, nor a fully articulated ontology spanning utilities and transportation. Advanced metering infrastructure for the water utility has created near real-time consumption data, and the city has piloted AI-driven sentiment analysis of community perceptions, yet no fully developed predictive maintenance models or integrated digital twin pilots are publicly documented. Based on public evidence, the authors position Sugar Land at an early Phase 3—well along the readiness pathway but far from citywide digital twin deployment.

That pattern, the authors argue, is the norm rather than an indictment. Even relatively advanced small and midsize municipalities with genuine data governance investments typically sit at late Phase 2 or early Phase 3. The framework is designed to make that gap visible as a structured development pathway rather than a marker of inadequate ambition.

The paper also connects its phased logic to the United Nations Sustainable Development Goals in explicitly illustrative, non-causal terms. Phases 1 and 2, by prioritizing verified infrastructure data and accountable institutions, align thematically with SDG 6 on clean water, SDG 9 on industry and infrastructure, and SDG 16 on effective institutions. Phase 3’s predictive analytics connect to SDG 12 and SDG 13 on climate action, while Phase 4’s participatory governance aligns with SDG 11 and SDG 17. The authors stress that no formal scoring or weighting methodology was applied and that phase completion creates conditions for SDG-relevant action without by itself delivering specific outcomes.

The authors acknowledge four structural constraints the framework cannot eliminate: procurement and vendor dependency, interdepartmental coordination friction, genuine trade-offs between transparency and privacy, and the resource limitations that push cities toward hybrid strategies with pockets of sophistication alongside unresolved governance problems. The framework, they write, should be read as a structured guide rather than a deterministic checklist.

The broader message is a direct challenge to how municipal technology decisions are typically framed. The real question, the authors argue, is not whether a city can acquire a digital twin platform—it is whether the city is ready to use one responsibly, effectively, and at scale. Cities that establish governance and policy foundations before procurement, data integrity before analytics, and public trust before platform deployment are more likely to produce implementations that are operationally reliable and governable. A readiness-first approach, they conclude, does not constrain innovation; it aligns innovation with the institutional conditions required to sustain it. Future research directions include empirical testing of the framework across diverse municipal contexts, development of standardized foundational maturity indicators, and comparative studies across Latin American, Chinese, European, and U.S. governance systems.