Researchers, technology company executives and innovation specialists analyse the main challenges that will shape the evolution of smart cities, from data interoperability to artificial intelligence governance and the role of digital twins.
Artificial intelligence and smart data management are becoming two of the main drivers of urban transformation. However, the real challenge no longer lies solely in developing more powerful algorithms, but in getting information to flow between organisations, infrastructure and services to improve citizens’ quality of life and move towards more sustainable urban models.
In this context, digital twins are emerging as one of the tools with the greatest capacity to optimise city management. These virtual models make it possible to simulate the behaviour of infrastructure and services in real time, facilitating data-driven decision-making. Several studies suggest that their deployment can improve the operational and capital efficiency of public infrastructure by between 20% and 30%, although their development still faces significant technological and organisational challenges.
Francisco Vea, Director of Organisation and Transformation at Simetría, believes the main obstacle is not technological but operational. “The real difficulty lies in how to move from a demo to an everyday tool that helps us manage services.” Added to this is a recurring problem in many organisations: “integration, since data from different departments is often not shared.” In his view, the transformation also requires a cultural change. “Instead of acting reactively, we have to act predictively.”
Patricia Tamarit, Director of Product Innovation at Nunsys, shares the same idea and argues that building truly smart cities requires integrating information from multiple disciplines and bodies to obtain a global view of urban reality. “Currently, digital twins are more geared towards prediction than towards directly operating the system,” she explains.
This lack of a cross-cutting view remains one of the main obstacles to unlocking the full potential of artificial intelligence applied to urban management. According to Francisco Vea, in most of the cities where his company provides services “that cross-cutting approach does not exist.” “At best there is a micro-twin for waste collection and another for street maintenance, without them communicating with each other.” The next step, he argues, is “to make those micro-twins interoperable, so that management is optimised not service by service, but as a whole.”
Governance, interoperability and trust
As artificial intelligence plays a more important role in decision-making, so does the need to define new data governance models.
Marcos Rozas, General Manager of Nealis Tech, raises an issue that goes beyond the purely technological to focus on responsibility. “When we get these twins to talk to each other, they will make mistakes. At what point in the chain do we need to act?” he asks, opening a debate that combines technology, ethics and governance.
Interoperability between platforms, data quality and the definition of responsibilities thus emerge as inseparable elements of the development of artificial intelligence in cities.
Sustainability starts with data
Growing European regulation is accelerating this transformation. The entry into force of the CSRD directive marks a paradigm shift by requiring companies to provide much more detailed information on their environmental, social and governance impact. In this new scenario, data is no longer just a management tool but becomes a strategic asset for demonstrating organisations’ commitment to sustainability.
Raúl Mir, CEO of Attrim Group, sums up this evolution with a clear idea: “Being sustainable is not just being green and social; it is committing to resources and well-being in a lasting way.” In his opinion, corporate reports “will no longer look only at financial results, but also at how they are obtained and whether they are sustainable.” This smart use of information, he adds, “will be key to deciding what to tackle, what to solve and where to direct innovation.”
Where can artificial intelligence have the greatest impact?
When analysing where artificial intelligence can add the most value in cities, specialists agree that technology must always respond to citizens’ real needs and not to the appeal of innovation itself.
Patricia Tamarit believes the first step is to correctly identify problems before designing solutions. “We need to investigate where the needs really are in order to focus and draw up transformation plans.” She also argues for building a European model based on shared data. “Working within an aggregated European system is essential, because only with information from citizens across Europe will solutions be scalable.”
Marcos Rozas shares this view. “Patricia has hit the nail on the head. It is important to have that cross-cutting approach and interoperability when managing data in an aggregated way.” In his view, an artificial intelligence strategy should prioritise areas such as “mobility, water management and care for vulnerable people.”
For Raúl Mir, real change must begin with data governance. “I would ask the City Council to require all the companies it works with to implement some kind of data governance support system, so that each one improves and makes progress on what data it needs to provide a better service.”
Francisco Vea draws attention to a very specific example: predictive maintenance of street conditions. In his view, smart management of urban pavements would increase road safety, reduce fuel consumption by between 8% and 12% and significantly improve air quality. The environmental impact would be equivalent to avoiding the emission of 4.5 million tonnes of CO₂, a figure comparable to taking around one million vehicles off the roads of a large city.
Far from simply automating processes, artificial intelligence is shaping a new model of urban management based on shared data, predictive decisions and collaboration between organisations.