Using AI to reimagine infrastructure management and mobility planning

Cities today are operating in an era of mounting pressure. Roads, bridges, and utilities built decades ago are being stretched beyond their design limits – a situation exacerbated by continually growing urban populations and mobility demands. Budget constraints and siloed departmental structures often leave municipal leaders struggling to balance urgent repairs with long-term planning, while residents demand better services, greater transparency, and faster responses to everyday issues like potholes and water leaks.

Against this backdrop, cities are increasingly looking to AI-powered tools to not only see and understand the condition of their infrastructure in real time, but also to anticipate problems, coordinate more effectively across departments, and plan with greater foresight. By equipping planners with advanced analytics, real-time intelligence, and predictive capabilities, AI can help cities shift from reactive problem-solving to proactive capital planning.

Making asset management less daunting

For many cities, AI still feels like unfamiliar territory. Initial conversations often begin with municipal leaders admitting they feel overwhelmed. Their task is a challenging one – managing several different types of assets simultaneously and trying to prioritise infrastructure maintenance tasks when everything appears urgent.

AI helps simplify these questions. By automatically analysing imagery and sensor data, systems like Urban Hawk provide an immediate overview of infrastructure conditions. Tasks that once felt insurmountable become manageable.

The reality in cities today is that the demands on urban infrastructure are only growing. Citizens want smoother commutes, fewer disruptions, and more transparent governance

A major benefit is improved collaboration. Traditionally, utilities, public works, and local authorities have managed assets separately, often leading to duplication of effort and interdepartmental friction. AI closes this gap by making information accessible across departments, creating shared visibility and a common language for decision-making.

Perhaps the most transformative outcome is the shift in focus. Once cities are no longer consumed by daily emergencies, they can dedicate time and resources to long-term priorities – planning investments, modernising infrastructure, and rethinking mobility strategies.

Building a foundation – data strategies and incremental change

Before implementing AI, cities must consider their data strategies. Many municipalities have not yet developed clear approaches to collecting, managing, and sharing data across departments. Establishing this foundation is essential.

Standardising data remains a significant challenge, but a step-by-step approach helps. Starting by collecting data on a small scale allows cities to see value quickly, creating momentum for broader integration. Flexibility is also crucial. Cities must be willing to pivot if a solution is not working, avoiding the sunk cost trap of persisting with tools that do not deliver.

Technology providers play a key role in this process. Rather than forcing cities to adopt entirely new systems, the goal is to integrate AI with tools already in use, adapting to existing workflows while recommending improvements. Collaboration across departments – from public works to safety – ensures that AI does not just solve isolated problems, but supports a more connected urban future.

From visibility to insight

The impact of AI becomes visible to cities quickly. During pilots, leaders are often impressed by the ability to access detailed imagery and analytics at their fingertips. Instead of dispatching crews just to verify a problem, staff can confirm conditions instantly.

However, a second stage soon follows. Once the initial data has been ingested, cities begin to ask new questions. Can the system highlight different types of issues? Can datasets be combined to reveal patterns? How can the information be manipulated to uncover new insights?

Granular insights allow budgets to be allocated more effectively, ensuring scarce resources are directed to the areas of highest impact

This deeper understanding often develops over a year or more. It represents the point where cities move from simply seeing data to recognising its potential for strategic foresight. 

The power of AI lies not just in visualisation, but in prompting leaders to ask what can be done with the knowledge – how it can shape mobility planning, infrastructure investments, and citizen engagement.

Real-time data, digital twins, and proactive action

Closing communication gaps between departments and with citizens often depends on real-time intelligence. Urban Hawk deployments have demonstrated how transformative it is to have imagery and analytics immediately available. If a water utility reports an issue, staff can cross-check conditions without sending a survey crew. When a citizen calls about a pothole, the city may already be aware of it.

Pairing AI with real-time data and digital twins enhances this capability further. Digital twins provide a dynamic model of the city’s infrastructure, updated as new data flows in. This not only streamlines day-to-day response, but also improves the quality of long-term planning.

For municipal operations, the benefits are practical. Crews can be deployed directly to where they are needed rather than wasting time on inspections. Growing cities with flat budgets can use the same workforce to cover larger areas. On the citizen side, dashboards provide transparency – some cities even make them public, allowing residents to track progress on issues and see where resources are being allocated.

Redeploying staff and upskilling the workforce

Another longer-term benefit of AI-driven asset management is workforce transformation. Traditionally, maintenance crews spent much of their time identifying and reporting issues. With AI imagery and analytics, that task is largely automated. This allows staff to be redeployed towards more strategic roles.

Cities are increasingly thinking about how to upskill employees, equipping them to contribute to capital planning, budget optimisation, or predictive maintenance strategies. AI provides the data foundation to make this possible.

Predictive modelling is particularly valuable. By analysing patterns over time, AI can forecast when assets are likely to fail, allowing teams to address issues before they become critical. This reduces emergency repairs and improves the reliability of infrastructure.

On the financial side, better data has been described as the holy grail of capital planning. Granular insights allow budgets to be allocated more effectively, ensuring scarce resources are directed to the areas of highest impact.

On the citizen side, dashboards provide transparency – some cities even make them public, allowing residents to track progress on issues and see where resources are being allocated

The reality in cities today is that the demands on urban infrastructure are only growing. Citizens want smoother commutes, fewer disruptions, and more transparent governance. Municipal leaders must balance these expectations with limited budgets and increasingly complex challenges.

AI offers a pathway forward. By combining computer vision, predictive modelling, and digital twins, cities gain the intelligence and foresight needed to manage assets more effectively, optimise resources, and plan for long-term mobility needs. Just as importantly, AI fosters collaboration across departments, enhances workforce skills, and improves communication with citizens.

The journey is incremental – starting by capturing data, building data strategies, and scaling gradually. Yet the trajectory is clear. With AI, cities can move from reactive firefighting to proactive planning, transforming infrastructure management into a foundation for smarter, more resilient, and more connected communities.