Artificial intelligence promises to transform city services, from traffic management to energy efficiency, but its success hinges on a critical prerequisite: robust data groundwork. Without clean, interconnected, and well-governed data, AI models risk delivering inaccurate or biased outcomes. A recent virtual panel brought together experts to discuss how cities can build the necessary foundations, with Sunderland emerging as a prime example.
Why Data Groundwork Matters for AI
Katherine Flesh, a Microsoft expert in urban technology, emphasized that the greatest opportunities from AI in transport and other sectors depend on three pillars: strong data foundations, workforce readiness, and responsible governance. Cities often possess vast amounts of data scattered across siloed departments—public works, transportation, utilities, and more. For AI to work effectively, this data must be standardized, accessible, and secure. Flesh noted that transport agencies, for instance, are turning to AI to improve services, but without integrated data, they risk making decisions based on incomplete information.
Data groundwork involves more than just collecting numbers. Cities need to establish data governance frameworks that define ownership, privacy standards, and interoperability. They must invest in infrastructure that allows real-time data sharing between sensors, IoT devices, and central platforms. Furthermore, workforce training is essential so that city employees can interpret AI outputs and maintain systems responsibly. As Flesh pointed out, AI is only as good as the data it learns from, and the people who manage it.
Sunderland's Smart City Vision
Sunderland, a city in northern England, has been actively repositioning itself as a leading smart city through digital infrastructure and low-carbon innovation. The city’s approach provides a blueprint for others preparing for AI adoption. Sunderland has focused on creating a resilient, future-focused economy by deploying smart streetlights, sensors for air quality, and digital platforms for citizen engagement. These initiatives generate rich datasets that can feed AI applications, such as predictive maintenance for utilities or dynamic traffic routing.
The city’s leadership has also emphasized partnerships with universities and private sector firms to pilot AI projects. This collaboration ensures that data is collected ethically and that algorithms are tested before wide deployment. Sunderland’s experience shows that a step-by-step approach—starting with foundational data infrastructure and then layering AI—reduces risks and builds public trust.
Strategic Procurement as a Tool for Resilience
Beyond technical data groundwork, cities must rethink procurement strategies. Sam Markey, founder of Recurve, argued that strategic procurement is one of cities’ most underused tools for building resilience and long-term climate impact. When cities purchase technology, they often focus on upfront cost rather than long-term data compatibility. Markey advocates for procurement contracts that require vendors to provide open APIs, data portability, and regular updates to avoid vendor lock-in.
This is especially relevant for AI systems, which evolve rapidly. If a city locks itself into a proprietary data format, it may struggle to integrate new AI tools or switch providers. By embedding data standards into procurement, cities can ensure that the data gathered today remains usable tomorrow. Sunderland has adopted similar principles, preferring open-source platforms and modular systems that can scale as AI capabilities advance.
Learning from Energy and Lighting Systems
The energy sector offers valuable lessons. In a virtual panel on energy systems, experts discussed how local authorities can shape energy grids through renewables, flexibility, storage, and smarter networks. Data from smart meters and solar panels can be used to optimize energy distribution, reduce carbon emissions, and lower costs. AI can predict demand peaks and balance loads automatically, but this requires granular, real-time data from thousands of endpoints.
Similarly, smart lighting networks are becoming a foundation for broader IoT deployments. Cities like Los Angeles and Barcelona are turning streetlights into multi-sensor hubs that monitor traffic, noise, and weather. A series of episodes on thriving lighting cities explored how to build secure, interoperable infrastructure. Cybersecurity emerges as a critical concern: as lighting systems connect to the internet, they become potential entry points for attackers. City planners must incorporate security by design, ensuring data encryption and regular patch management.
Digital Twins and AI as Intelligent Operating Layers
Digital twins—virtual replicas of physical city assets—are increasingly seen as the intelligent operating layer for urban management. A panel discussion on digital twins and AI highlighted how these tools allow cities to simulate scenarios, such as traffic congestion or flood risks, before making real-world changes. Digital twins rely on continuous data streams from sensors, cameras, and citizen reports. AI then analyzes this data to generate insights and even automate responses.
For example, Dublin is innovating using digital twins to improve community services, reduce traffic, and drive economic growth. The city’s digital twin integrates data from multiple departments, enabling coordinated responses to issues like road maintenance or event planning. AI algorithms help prioritize repairs based on usage patterns and safety data. Dublin’s experience underscores that digital twins only deliver value if the underlying data is accurate, up-to-date, and shared across agencies.
Workforce and Governance: The Human Side of Data Groundwork
Preparing for AI is not just a technical challenge; it requires cultural change within city governments. Many public sector employees are wary of AI, fearing job displacement or opaque decision-making. To address this, cities must invest in training programs that demystify AI and emphasize its role as a tool to augment human judgment. Sunderland, for instance, has launched workshops for city staff on data ethics and algorithm transparency.
Governance frameworks are equally important. Cities need clear policies on data ownership, privacy, and accountability. Without them, AI projects can face public pushback or legal challenges. A responsible governance structure should include oversight committees with diverse stakeholders, regular audits of AI systems, and mechanisms for citizens to contest decisions made by algorithms. As Microsoft’s Flesh noted, responsible governance is the foundation upon which trust is built.
Toward Mainstream AI in Local Government
The path to mainstream AI in local government is not a sprint but a marathon. It begins with honest assessments of current data maturity, followed by targeted investments in infrastructure and skills. Sunderland’s example shows that a city can lead by focusing on fundamentals: digital infrastructure, low-carbon innovation, and community engagement. Other cities can replicate this model by starting with small-scale projects, like smart lighting or traffic sensors, and gradually expanding to more complex AI applications.
Sustainable, long-term impact requires collaboration across sectors. Public-private partnerships can bring in expertise and funding, while academic institutions can provide research support. Crucially, cities must ensure that the data groundwork supports not only AI but also broader goals like climate resilience and social equity. As the virtual panels made clear, data is the new currency of urban governance—and those who invest wisely today will be best positioned to thrive in the AI-powered cities of tomorrow.
Source: Smart Cities World News