Artificial intelligence is poised to transform how cities operate, but its success hinges on a critical foundation: data. Without clean, interoperable, and well-governed data, even the most advanced AI algorithms will falter. This article examines what it takes to prepare urban systems for AI, drawing on real-world efforts by cities such as Sunderland, Dublin, and others that are strategically building their digital bedrock.
Why Data Readiness Matters for AI
AI systems are notoriously data hungry. Machine learning models require large volumes of high-quality, labeled data to train effectively. In a city context, this means sensor data from traffic lights, energy grids, water systems, public transport, and more must be collected, standardized, and made accessible. Yet many cities still operate with siloed datasets that cannot communicate with each other. Vendor lock-in, legacy systems, and inconsistent data formats create formidable barriers. As Cristina Bueti of the International Telecommunication Union (ITU) has stressed, cities must prioritise interoperability, inclusivity, and human oversight now—before fragmented systems become entrenched. Failing to act risks locking cities into unmanageable technological dependencies that undermine both equity and efficiency.
Digital Twins: The Intelligent Operating Layer
One of the most promising applications of AI in urban management is the digital twin—a virtual replica of a physical city system that allows real-time simulation, monitoring, and prediction. Digital twins enable city planners to test scenarios, optimise traffic flows, predict infrastructure failures, and improve emergency response times. However, building a digital twin requires an extensive network of sensors and a robust data integration platform. The data must be harmonised across different domains and updated continuously. Without this data groundwork, digital twins become static 3D models rather than dynamic decision-making tools. Panel discussions between experts from Woods Bagot and Impact Future have highlighted how cities can design for upstream resilience and downstream benefit, ensuring that digital twins serve both immediate operational needs and long-term strategic goals.
Case Study: Sunderland’s Smart City Transformation
Sunderland, a city in northeast England, offers a compelling example of how to reposition itself as a leading smart city. Once heavily dependent on traditional industries, Sunderland has invested heavily in digital infrastructure and low-carbon innovation to build a resilient, future-focused economy. Its strategy includes deploying IoT sensors across the urban landscape, from waste bins to streetlights, and creating an open data platform that encourages third-party innovation. The city has also launched pilot projects in intelligent transport and energy management, laying the groundwork for later AI integration. The key lesson from Sunderland is that AI readiness is not a one-time project but an ongoing process of data governance, partnership building, and community engagement. Without citizen trust and inclusive design, even the best data foundation will not produce equitable outcomes.
Dublin’s Data-Driven Urban Innovation
Dublin provides another instructive case. The city has undertaken multiple digital twin projects aimed at improving experiences and services for its communities. Dublin is using real-time data to reduce traffic congestion, promote economic growth, and test new mobility solutions. By integrating data from public transport, parking sensors, and air quality monitors, Dublin can make evidence-based decisions that enhance efficiency and quality of life. Importantly, the city has also focused on making data accessible to citizens through open data portals and dashboards, fostering transparency and civic participation. Dublin’s approach demonstrates that data groundwork is as much about governance and culture as it is about technology.
Smart Infrastructure: From Streetlights to Sensors
A common entry point for building a smart city data infrastructure is the streetlight network. Traditional streetlights are being replaced with LED fixtures that can carry sensors, cameras, and wireless communication modules. This transformation turns an ordinary asset into a backbone for a city’s IoT ecosystem. In a podcast mini-series on this topic, experts discussed how cities can turn existing streetlight networks into secure, interoperable, and future-proof infrastructure. The evolution from simple illumination to a connected, data-generating network involves careful planning around power supply, data transmission, and cybersecurity. It also requires standards to ensure that devices from different vendors can work together. This is a foundational step that enables AI applications such as adaptive lighting, environmental monitoring, and smart parking.
Interoperability and Standards: Avoiding Fragmentation
The greatest risk for cities today is that they will adopt AI solutions in a piecemeal fashion, leading to a patchwork of incompatible systems. Standards organisations like the ITU and the International Organization for Standardization (ISO) are developing frameworks to promote data sharing and interoperability. Cities must actively engage with these standards to ensure that their investments remain viable in the long term. For example, adopting common data models for urban sensors, transport, and energy can dramatically reduce the cost of integrating new AI tools. In addition, open APIs (application programming interfaces) allow different city departments and external innovators to access and use data without legal or technical barriers. Inclusivity is another crucial aspect: data systems must be designed to serve all residents, including those without digital access or skills.
Human Oversight and Ethical AI
As cities deploy AI for tasks like traffic enforcement, resource allocation, and predictive policing, ethical considerations become paramount. Biased data can lead to discriminatory outcomes, and opaque algorithms can erode public trust. Human oversight mechanisms—such as review boards, audit trails, and explainability requirements—are essential. The United Nations Virtual Worlds Day event, which explores turning AI, spatial intelligence, and the “Citiverse” ecosystem into trusted, people-centred outcomes, underscores the global consensus that technology must serve people, not the other way around. Data groundwork must include robust privacy protections, consent frameworks, and mechanisms for redress when AI systems cause harm.
Indoor Safety and Sensor Networks
Smart sensor networks are not limited to outdoor environments. Buildings can also benefit from AI-enabled monitoring to improve indoor safety. By detecting risks early—such as gas leaks, fire hazards, or structural weaknesses—and providing situational awareness, these systems contribute to healthier, more secure, and sustainable buildings. The data from indoor sensors must be integrated into broader city systems to enable coordinated responses, such as automated emergency alerts or dynamic evacuation routing. Again, this requires a common data language and secure transmission protocols.
Trends and Future Directions
Looking ahead, several trends are likely to shape the data groundwork for city AI. First, the growing availability of low-cost sensors and edge computing will make data collection more affordable and real-time. Second, federated learning techniques allow AI models to be trained on data from multiple sources without centralising the raw data, addressing privacy concerns. Third, new regulations such as the European Union’s AI Act and the Data Governance Act will impose requirements on data quality, transparency, and accountability. Cities that proactively align with these regulations will be better positioned to attract investment and talent.
Webinars and panel discussions continue to explore how AI and data are transforming transport operations and services, with a particular focus on urban mobility. Digital twins and AI as the intelligent operating layer for cities are recurring themes, and newsletters deliver the latest insights from around the world. For city leaders, the message is clear: the path to AI success begins with disciplined data management today. By investing in interoperability, inclusivity, and human oversight, cities can build the foundation that will allow them to harness the full potential of artificial intelligence for the benefit of all citizens.
Source: Smart Cities World News