How AI and digital twins empower cities to prepare for crises before they happen

The cities of the future will not only be strong but also resilient, anticipating disruptions well before they happen and maintaining service continuity even under dire circumstances. As infrastructure becomes increasingly interconnected, the risks also increase. From extreme weather and ageing assets to cyberthreats, supply chain disruptions and geopolitical uncertainty, these challenges are essentially reshaping how infrastructure projects are planned, designed and operated. 

These shifts have prompted developers to rethink the true definition of resilience across infrastructure assets.

The focus therefore needs to shift from building stronger infrastructure to building smarter, more predictive infrastructure, leveraging advanced technologies such as AI and digital twins to prepare cities for crises even before they occur.

As cities grow complex, traditional approaches to infrastructure management become less effective. However, many infrastructure operators still follow scheduled inspections and respond only after a fault has already occurred. This ultimately leads to costly repairs, service disruptions and increased risks to public safety. AI empowers property owners to prevent this by identifying potential issues well before they escalate into major failures.

Recognising this, the UAE is expanding its transport networks and scaling smart cities, while investing in clean energy, utilities and digital infrastructure. At the heart of this vision are ambitious national initiatives such as the Dubai Economic Agenda (D33) and UAE Net Zero 2050, guided by which the nation is also laying the foundation for long-term economic development.

However, the true value of AI extends beyond this. It empowers city authorities to move from reactive maintenance to proactively preparing for disruptions. When combined with predictive analytics, AI enables decision makers to analyse how infrastructure is likely to perform under different scenarios, allowing them to make faster, more informed decisions before a crisis unfolds.

Digital twins support this by providing a conducive environment for the practical application of AI. A digital twin is much more than just a 3D model – it is a dynamic, data-driven representation of a physical asset that continuously reflects its real-world conditions throughout its lifecycle.

By integrating engineering, operational and sensor data, digital twins allow infrastructure owners to simulate how assets will perform under diverse scenarios. Whether preparing for floods, extreme heat, transport disruptions or utility failures, city authorities can test response strategies in a virtual environment, understand how failures may cascade across interconnected systems and make informed decisions without disrupting real-world operations.

The shift from reactive crisis management to predictive scenario planning enables infrastructure operators to respond swiftly with confidence and seamless coordination in the face of disruptions. As cities continue to expand and evolve, engineers should leverage AI and similar digital technologies to evaluate how infrastructure will perform over its entire lifecycle. This is key to building resilient buildings of the future rather than simply designing for today's requirements.

Predictive modelling and simulation empower project teams to assess how assets will respond to changing environmental conditions, increasing urban populations, evolving mobility patterns and unexpected disruptions. It further enables designers to optimise infrastructure before construction begins, reducing carbon footprints, future operational risks and improving long-term resilience. The focus should shift from merely developing an asset to determining whether it can continue performing reliably over the next several decades despite an increasingly uncertain operating environment.

Resilient infrastructure depends on both strong physical assets and reliable information flow. During an emergency, multiple stakeholders, from infrastructure operators and government agencies to emergency responders and utility providers, need to coordinate and make quick decisions. This becomes difficult when there is a lack of access to accurate and connected data.

Within a connected data environment, all stakeholders are provided with a single, reliable source of truth across the infrastructure life cycle. Moreover, by integrating engineering, construction and operational information in one place, decision makers can gain greater visibility into asset conditions, ongoing risks and system-wide impacts, enabling faster and more coordinated responses during periods of disruption.

In such a scenario, AI and digital twins empower infrastructure operators to continuously monitor these assets, predict maintenance requirements, optimise operations and identify vulnerabilities, to prevent possible service disruptions. This shift towards predictive operations also helps reduce downtime, improve public safety and ensure that essential services continue functioning even during periods of uncertainty.

It is evident that the objective isn’t to simply prevent infrastructure failures anymore – it is to maintain the continuity of critical services when communities need it the most. Furthermore, as the frequency and complexity of disruptions continue to increase, cities can no longer choose to respond only once the crisis has already unfolded. AI, digital twins and predictive analytics support this by providing governments with the tools to anticipate risks, evaluate different scenarios and make better decisions before problems escalate.

In particular, as nations like the UAE heavily invest in smart cities and next-generation infrastructure, it is critical to embed AI into physical assets as a means to ensure long-term resilience, sustainability and economic growth. Ultimately, the cities that thrive in the future will not simply be those with the newest infrastructure, but those that are equipped to continuously adapt and respond to changes through data-driven decision-making.

- In association with Bentley Systems