Public sector leaders have long understood that artificial intelligence is not simply a technology project. It requires deliberate preparation: clean and accessible data, shared standards, secure infrastructure, and people with the right skills. In a new on-demand webinar, Sunderland shows how a city can build that foundation in practice. The session explores how Sunderland is repositioning itself as a leading smart city by aligning digital infrastructure and low-carbon innovation with long-term economic goals. Rather than treating AI as an isolated tool, the city is approaching it as part of a broader effort to improve services, strengthen resilience, and create a future-focused economy.
The webinar is designed for local authority leaders, urban planners, transport managers, and anyone responsible for digital strategy in the public sector. It draws on lessons from a 2026 international summit where cities shared practical experiences with AI, data, and infrastructure. Across those conversations, one message was repeated: AI will not transform cities until the data beneath it is ready.
Why data groundwork matters
AI systems can only be as effective as the data they learn from. If data is incomplete, poorly labelled, stored in silos, or vulnerable to security risks, even the most sophisticated algorithms will fail. Cities have learned this lesson in the early stages of their AI journeys. Many are now prioritising data governance, interoperability, and quality assurance as essential preconditions for AI adoption.
Data groundwork is not a one-off technical task. It is an ongoing practice. It includes understanding which datasets exist across departments, identifying gaps, creating metadata, setting common standards, and enabling secure data sharing with public and private partners. It also requires attention to legal and ethical responsibilities. Data must be handled in ways that protect privacy, support transparency, and build public trust.
Sunderland’s experience shows that some of the most important work never appears in a dashboard. It is the conversation between departments about what each team means by good data. It is the decision to stop maintaining duplicate systems that cannot talk to each other. It is the patient process of cleaning address records so that services can be linked across health, housing, transport, and energy.
Strengthening digital infrastructure
Another central theme is digital infrastructure. A city cannot deploy AI at scale if its underlying networks are weak. Sunderland’s approach includes investment in high-capacity connectivity, sensor networks, and intelligent systems that collect real-time information about how the city behaves. This infrastructure makes it possible to model scenarios, anticipate risks, and respond more quickly to changing conditions.
The webinar points to examples from cities around the world that are using digital twins – virtual replicas of physical places – to improve planning and operations. When combined with AI, digital twins can act as an intelligent operating layer for cities. Officials can simulate the effect of a new housing development on traffic, test flood defence measures, or see how an energy policy might affect vulnerable households. In each case, the value of the digital twin depends on the quality of the data powering it.
Transport is one of the most visible areas of opportunity. Public transport agencies are using AI to predict demand, optimise routes, manage congestion, and give passengers more reliable information. But these systems need clean, high-frequency data from vehicles, ticketing systems, traffic cameras, weather feeds, and citizen feedback. The greatest benefits are available to cities that have invested in data foundations, workforce readiness, and responsible governance before they buy algorithms.
Low-carbon innovation and energy resilience
Sunderland’s strategy also connects data and AI with its low-carbon transition.
Source: Smart Cities World News