As cities around the world accelerate their digital transformation journeys, the conversation has shifted from 'why AI' to 'how to prepare for AI'. A critical piece of that preparation is the data groundwork — the infrastructure, governance, and strategic alignment that turns raw information into actionable intelligence. In a recent SmartCitiesWorld webinar, Sunderland’s smart city initiative took center stage, illustrating how a mid-sized UK city is repositioning itself through digital infrastructure and low-carbon innovation.

Sunderland’s Smart City Vision

Once a powerhouse of shipbuilding and automotive manufacturing, Sunderland has reinvented itself as a testbed for smart city technologies. The city’s approach focuses on leveraging digital infrastructure — including IoT sensors, 5G connectivity, and open data platforms — to drive economic growth and improve quality of life. By integrating AI into its urban management systems, Sunderland aims to enhance public services, reduce carbon emissions, and attract investment in clean-tech industries.

The city’s latest profile highlights how it is using data from building management, transport networks, and energy grids to create a holistic view of urban operations. This data is then fed into AI models that predict maintenance needs, optimize traffic flow, and manage energy consumption in real time. For example, Sunderland has deployed intelligent street lighting that adjusts brightness based on pedestrian and vehicle presence, saving energy while improving safety.

The Data Foundation for AI in Transport

Transport is one of the most promising sectors for AI application, but success hinges on strong data foundations. Katherine Flesh from Microsoft explained that transport agencies often focus on the technology itself rather than the underlying data quality and governance. She emphasized that AI models are only as good as the data they are trained on, and that cities must invest in data curation, interoperability, and workforce readiness.

One of the key challenges is breaking down data silos between different transport modes — from buses and trains to ride-sharing services and bike rentals. When data is integrated and standardized, AI can deliver real-time route optimization, predictive maintenance for vehicles and infrastructure, and personalized travel recommendations. For instance, a digital twin of a city’s transport network can simulate the impact of new traffic policies or disruptions, allowing planners to test scenarios before implementing them.

Digital Twins: The Intelligent Operating Layer

Digital twins are emerging as a powerful tool for city management. A panel discussion at the SmartCitiesWorld Summit explored how digital twins combined with AI can serve as an 'intelligent operating layer' for cities. These virtual replicas of physical assets — from individual buildings to entire neighborhoods — allow decision-makers to monitor performance, predict failures, and optimize operations.

In Dublin, for example, the city is using a digital twin to reduce traffic congestion and improve pedestrian experiences. The twin integrates data from traffic cameras, air quality sensors, and social media feeds to provide a real-time view of street-level conditions. AI algorithms then suggest interventions such as adjusting traffic signal timings or rerouting vehicles to avoid pollution hotspots. Dublin’s approach also includes public engagement through interactive dashboards that show how city decisions impact sustainability goals.

Smart Lighting and Cybersecurity

One of the most visible smart city applications is intelligent street lighting. Cities Thriving on Lighting, a podcast series featured in the content, discussed how cities are turning existing streetlight networks into secure, interoperable, and future-proof infrastructure. Beyond energy savings, smart lighting can support environmental monitoring, public Wi-Fi, and even electric vehicle charging. However, with increased connectivity comes cybersecurity risk. The podcast highlighted the need for robust encryption, regular software updates, and strict access controls to protect these critical systems.

Sunderland’s lighting upgrade is a case in point: by replacing thousands of lamps with LED fixtures connected via a central management system, the city cut energy use by 70%. The system also collects data on traffic and pedestrian patterns, which later fed into AI models for urban planning. Yet, the city had to implement a security framework that isolated the lighting network from other municipal systems to prevent potential attacks.

Energy Systems and Local Authority Leadership

Energy infrastructure is another domain where data and AI are making an impact. A SmartCitiesWorld Summit 2026 virtual panel delved into how local authorities can shape energy systems through renewables, flexibility, storage, and smarter networks. By analyzing consumption data, cities can identify opportunities for energy efficiency, integrate solar panels and wind turbines, and incentivize demand response programs that shift usage to off-peak hours.

Sunderland has partnered with energy companies to develop a district heating network that uses waste heat from data centers. AI monitors the system to balance supply and demand, reducing costs by 15% and lowering carbon emissions. The city is also exploring vehicle-to-grid technology, where electric buses can discharge stored energy back into the grid during peak periods, acting as mobile batteries.

Strategic Procurement for Resilience

Sam Markey, founder of Recurve, argued that strategic procurement is one of cities’ most underused tools for building resilience and long-term climate impact. Instead of simply buying the cheapest products, cities can use procurement to support local businesses, drive innovation, and set sustainability standards. For example, a city might require that all new streetlights be compatible with open data standards, ensuring that future AI applications can integrate seamlessly.

This perspective aligns with Sunderland’s approach: the city’s procurement policies prioritize vendors that offer open APIs and data-sharing agreements. This has enabled the city to avoid vendor lock-in and maintain flexibility as AI technologies evolve. Markey’s insight highlights that data groundwork isn’t just about technology — it’s about the contracts and partnerships that determine who owns and controls data.

Lessons from Kansas City and Dublin

Kansas City’s streetcar authority offers a real-world example of how public transit can catalyze urban renewal. Executive director Tom Gerend explained how the return of rail reconnected downtown, unlocked riverfront development, and reshaped the city’s growth story. Data from fare collection, passenger counts, and mobile apps allowed the authority to optimize schedules and improve rider experience. Because the system was designed with data collection in mind, it became a rich source of insights for economic development decisions.

Similarly, Dublin’s smart city efforts include digital twin projects, traffic reduction initiatives, and programs to stimulate economic growth. By creating a single data hub that aggregates information from multiple agencies, Dublin has enabled AI-driven applications that reduce congestion by 20% and improve air quality monitoring. The city has also engaged citizens through participatory budgeting, allowing residents to vote on which data projects should receive funding.

Cybersecurity and Governance Across Sectors

As cities layer more AI applications on top of their digital infrastructure, cybersecurity becomes a cross-cutting concern. The second episode of Cities Thriving on Lighting discussed how cities can secure their smart lighting networks by using hardware-based encryption and zero-trust architectures. The same principles apply to other data-intensive systems: smart grids, water management, and emergency response. Governance frameworks must define data ownership, privacy protections, and accountability for AI decision-making.

Microsoft’s Katherine Flesh stressed that responsible AI governance includes transparency about how algorithms work, bias detection, and human oversight. For transport agencies, this means ensuring that route optimization does not disadvantage low-income neighborhoods, and that predictive maintenance does not overlook aging infrastructure in underserved areas. Data groundwork thus includes not only technical infrastructure but also ethical guidelines and community engagement.

The examples from Sunderland, Dublin, Kansas City, and others show that preparing for AI is not a one-size-fits-all process. Each city must assess its unique data assets, regulatory environment, and civic priorities. However, common threads include the need for open data standards, cross-departmental collaboration, and investment in human capital. Training data analysts, data scientists, and procurement officers to work with AI is as crucial as installing sensors or buying algorithms.

In conclusion, the journey toward AI-ready cities begins with understanding the data groundwork. As Sunderland demonstrates, even a city with limited resources can make significant strides by focusing on foundational elements: interoperable infrastructure, strategic partnerships, and a clear vision for data-driven governance. The upcoming SmartCitiesWorld Summit 2026 promises to showcase more such stories, but the lessons are already clear: AI transforms cities only when cities first transform their relationship with data.

Source:Smart Cities World News