Local authorities across the globe are exploring how artificial intelligence can improve public services, manage infrastructure, and strengthen decision-making. Yet as the city of Sunderland in the north-east of England demonstrates, the ability to benefit from AI depends less on algorithms and more on the groundwork laid in data management, digital infrastructure, and workforce readiness.
A new on-demand webinar, titled “Preparing for AI: Understanding the Data Groundwork,” places Sunderland’s experience at the centre of a wider conversation about how cities can move from AI pilots to real-world impact. The session explores how local authorities can combine unified data platforms, agentic AI, and secure digital foundations to support workforce decisions and public service delivery. It also points to the broader technological and governance shifts that are transforming urban operations, from digital twins to intelligent transport and cyber-physical resilience.
Sunderland’s long-term connectivity investment and civic leadership
Sunderland has become a notable example of a city repositioning itself through digital innovation. New research highlights that its smart city programme is generating measurable economic, social, and public-service benefits. Key enablers include long-term investment in connectivity, sustained civic leadership, and trusted partnerships between the public and private sectors. These elements, the research suggests, are turning digital ambition into tangible outcomes, particularly in the areas of workforce transformation and data-driven governance.
The webinar stresses that technology alone is not enough. Understanding the data groundwork involves building a unified approach to information, ensuring data quality and interoperability across departments, and giving staff the skills and confidence to use AI tools. In Sunderland, that groundwork has involved creating secure digital foundations, establishing governance frameworks for emerging technologies, and fostering a culture where algorithms support rather than replace human decision-making.
Civic leadership has been instrumental in aligning different agencies around a common digital vision. Instead of isolated projects, Sunderland has pursued a programme-level approach where individual initiatives feed into a larger strategy. This helps avoid duplication, reduces risk, and ensures that investments in connectivity and data infrastructure produce long-term value for residents and businesses.
Connecting digital infrastructure to a resilient economy
Sunderland’s broader ambition is to use digital connectivity and low-carbon innovation to diversify and future-proof its economy. Once heavily reliant on shipbuilding and heavy manufacturing, the city now sees itself as a living lab for smart city technologies. Full-fibre broadband, 5G testbeds, and sensor networks are being used to tackle challenges in transport, energy, and urban planning. These infrastructure investments are expected to attract new industries and support existing businesses in areas such as advanced manufacturing, health tech, and the creative sector.
The city’s emphasis on low-carbon innovation also links digital transformation to sustainability targets. By integrating data on energy use, emissions, and infrastructure performance, Sunderland aims to become a model for environmentally responsible growth. This, in turn, underlines the need for robust data platforms that can handle diverse streams of information and provide actionable insights to decision-makers.
New approaches in emerging private city developments
The webinar’s themes also extend beyond established municipalities. Juan Carlos Lopez, chief technology officer and head of the value management office at Cayala, explains how agile transformation, digital infrastructure, and community-led services are supporting the expansion of Cayala, one of Central America’s largest private city projects located in Guatemala City. Cayala is being built as a mixed-use district with advanced connectivity and smart services from the ground up, offering a distinct contrast to retrofitting legacy systems in older cities.
Lopez describes how a private city developer can embed technology into the very fabric of urban development, creating a secure and manageable environment for residents, businesses, and visitors. By using digital infrastructure from day one, Cayala can offer services that are more responsive and personalized. Community-led services are particularly important in such projects, as they encourage social cohesion alongside technological progress. This private-sector approach offers lessons for municipal governments about procurement, speed of deployment, and customer-centric design.
Singapore’s continued pursuit of smart nation status
The island city-state of Singapore has long been seen as one of the world’s smartest nations, and it continues to enhance that reputation. With national programmes and a strong mandate for data-driven governance, Singapore is using AI, sensor networks, and integrated platforms to manage everything from mobility and utilities to public housing and healthcare. Key to its success is a centralised approach to digital identity, payments, and data sharing, which reduces friction for citizens while enabling policy teams to anticipate needs.
Singapore is also investing heavily in research and development around urban systems, including digital twins of the entire city. These tools allow planners to simulate the effects of policy decisions, traffic changes, or climate events before they are applied in the real world. But as in Sunderland, national leaders in Singapore frequently note that governance standards and data trust are foundational to AI adoption. Citizens need confidence that their data is being used responsibly, and public agencies need frameworks that bake in transparency, fairness, and accountability.
The emerging “AI super gap” between cities
Professor Jung Hoon Lee, a global expert on smart cities, recently joined a podcast conversation to discuss the latest Global Smart City Index. He warns of an emerging “AI super gap” between cities that are effectively integrating AI into their operations and those that are not. This gap, he says, is not simply a matter of financial resources or number of sensors. Rather, it stems from differences in data platforms, AI-ready infrastructure, and the quality of governance frameworks.
Professor Lee argues that cities must move away from isolated proof-of-concept projects and scale AI in ways that truly transform services. That requires integrating artificial intelligence into legacy systems, upskilling civil servants, and creating cross-departmental teams that understand both data science and public administration. Without these foundations, he suggests, the gulf between leading and lagging cities will widen, leaving many urban centres unable to keep pace with resident expectations or global economic shifts.
His observations point to a key theme: AI is not a technology project but an organisational change project. The cities that succeed will treat data as a strategic asset, appoint leaders with both technical and political skills, and work with research institutions to measure the social impact of AI. Professor Lee’s insights are drawn from comparisons between advanced Asian cities, European municipalities, and emerging hubs around the world, showing that the principles of good data governance apply across different cultural contexts.
Cybersecurity must be embedded in smart infrastructure
As cities extend their digital ecosystems, they must pay greater attention to cybersecurity. Fabio Mauri, head of technology operations and cybersecurity at Paradox Engineering, explains why security must be built into smart lighting infrastructure rather than added later. Smart street lighting is often the densest network of connected sensors in a city, and each fixture can serve as a point of entry for cyberattacks if not properly protected.
Mauri stresses that security should not compromise performance or ease of management. Instead, it requires a layered approach that includes secure-by-design hardware, encrypted communications, regular firmware updates, and network-level threat monitoring. Municipalities often underestimate how many devices are deployed and how challenging it is to keep them all patched. A single vulnerable light pole could potentially give attackers access to wider city networks, affecting transport systems, public safety, or eroding public trust in digital services.
His guidance aligns with the broader principle that digital foundations must be secure foundations. Even the most sophisticated AI analytics are worthless if the underlying infrastructure can be disrupted or manipulated. Smart cities therefore need to treat cybersecurity as an operational requirement, not merely a compliance exercise.
AI and data transformation in transport operations
Transport agencies are among the most eager adopters of AI, particularly for optimising signals, predicting maintenance, and improving passenger information. Yet as Microsoft’s Katherine Flesh explains, the greatest opportunities from AI in transport will depend on strong data foundations, workforce readiness, and responsible governance. Flesh notes that many agencies already gather vast amounts of data from ticketing systems, vehicle location sensors, cameras, and passenger feedback. The challenge is connecting those data sources so that AI models can provide reliable answers.
In an on-demand trend report webinar focused on transport operations and services, Flesh outlined how AI can move from descriptive dashboards to prescriptive actions. For example, predictive maintenance can keep rail fleets running on time, and dynamic scheduling can respond to disruptions before passengers feel them. But these outcomes require that staff understand how to work with algorithms, that data biases are addressed, and that there are clear rules for when AI makes decisions versus when it only suggests actions to human controllers.
The webinar also touched on the need to share data across agencies and even jurisdictions. A bus network that shares information with traffic signals and emergency services can deliver far greater benefits than each system operating in isolation. Flesh called on transport leaders to develop data standards and open interfaces, enabling both public agencies and private partners to contribute to a more resilient system. Workforce readiness, meanwhile, means helping existing employees acquire new digital skills and reassuring them that AI will augment their expertise rather than automate their jobs out of existence.
Digital twins and AI as an intelligent operating layer
Another on-demand panel discussion examined how digital twins and AI together form an intelligent operating layer for cities. This concept involves creating dynamic digital replicas of urban assets—such as roads, buildings, utilities, and public spaces—that can be updated in real time with sensor data. When AI models are applied to these digital twins, city managers can simulate various scenarios and choose the most effective response, whether that is adjusting traffic flows, closing a flood-prone area, or deploying maintenance crews.
Panel participants agreed that digital twins are most valuable when they are shared across municipal departments and incorporated into routine decision-making. If only a single planning team has access to the twin, its potential is limited. As with other AI applications, the underpinning data groundwork must be robust, with clear ownership, metadata standards, and quality control measures.
The discussion also highlighted the importance of scaling from asset-level twins to city-wide systems. Many cities have begun by building digital twins of beaches, tunnels, or central business districts. The next step is to connect these models so that they can represent the full complexity of a living city. This requires significant investment in cloud infrastructure, data integration, and organisational capability—reinforcing the message that AI’s success depends on foundational work rather than flashy applications.
Across all of these examples, a consistent theme emerges: cities flourish when they invest in data platforms, interoperable systems, secure connectivity, and people with the right skills. Sunderland’s experience, Singapore’s national strategy, Cayala’s private-sector innovation, and the expert views provided from transport, cybersecurity, and academic fields all point to the same core principles. Understanding what data is collected, ensuring it is trustworthy and accessible, and creating governance arrangements for the responsible use of AI are the first steps. Once those layers are in place, local authorities can unlock the potential of agentic AI, predictive algorithms, and real-time decision support to deliver better services and more inclusive communities.
The growing emphasis on data groundwork means cities will need to rethink procurement, consider new partnership models, and communicate openly with citizens about how data and AI are used. It also calls for a new kind of civic leader who is comfortable with technology, understands data policy, and can guide diverse stakeholders through complex transformations. While the technical tools will continue to evolve, the fundamentals of good data management and digital governance remain vital. The lessons from Sunderland and other cities make it clear that building these foundations today will determine which urban centres will lead in an increasingly AI-driven future.
Source:Smart Cities World News