From Smart Cities to Intelligent Cities: Laura Reupke on AI, Trust, and Europe’s Next Urban Chapter

Cities are under pressure to adopt AI responsibly, balancing efficiency, ethics, and inclusion. How do you see BABLE supporting local leaders in navigating that balance?

What we really help cities with is creating the foundation first, because that is what you need at the beginning. AI only works when the data behind it is reliable.

The challenge is that many cities simply do not have direct access to all their data. Data often sits with different institutions or private companies. And even when cities have internal data, the question becomes how to use it, how to interpret it, and how to make sure it is not biased. This is a major topic when it comes to AI.

So in the end, the key question is how to manage data responsibly. Data is the very first part. What we do is work with cities on data readiness, creating governance structures and transparent processes around data.

When you look at cities like Helsinki or Amsterdam, I really appreciate their leadership. They have AI registries where they show every system they use in the city and how it works. Even though this is not a legal requirement, they do it because they are committed to trust. Citizens know what systems are being used and why.

We bring these kinds of examples directly into our work with cities, and we also support them through training via our academy. We help city teams build a better understanding of AI and continuously highlight role models they can learn from. At the core of our work is the conviction that ethics and efficiency can grow together and that they are not in conflict.

From your perspective, what will distinguish the most forward-looking European cities five years from now? The ones that truly manage to integrate AI not just as a tool, but as a trusted civic partner?

For me, the cities that will really stand out are the ones that understand they cannot approach AI alone. Cities need more than just technology; they need guidance and partners to work on these topics.

The most forward-looking cities are those that build strong ecosystems around them, bringing together universities, research institutions, innovators, and public authorities. AI becomes manageable and responsible when cities are connected to the right partners and can work together on real-life challenges.

This is also why testing environments are so important. When innovators and public institutions can work together in a structured way, AI becomes practical, understandable, and aligned with public value.

In that sense, leadership in AI is not about having the most advanced tools, but about building partnerships that create trust, clarity, and shared learning.

We often hear that cities don’t need more pilots, they need scalable impact. How will BABLE help cities move from isolated AI experiments to ecosystem-wide deployment?

Scaling is a huge topic, not only in AI, but in innovation in general. When it comes to AI, scale really requires common standards, because only then can solutions be replicated. It has to be a repeatable process. That is the foundation.

What we do through CitCom TEF is structured around three themes – energy, mobility, and connectivity. The important part is that when one city validates an AI solution and shows how it solves a specific challenge, other cities can replicate it.

Not every city has to start with a pilot, and not every city has to start from scratch. What matters is that someone starts and builds in a way that creates a blueprint others can use. That is exactly the core idea of CitCom TEF.

At BABLE, we make sure that these insights travel, through our trainings and through our collaboration with cities, so that the work is shared and replicable. This is crucial, especially when it comes to time. We don’t have unlimited time, particularly with climate challenges. Every city running its own tests from scratch is also a waste of resources.

AI can help cities anticipate, not just react, from traffic flows to energy demand. What are the most promising city use-cases you see emerging right now that could define Europe’s leadership in this field?

When it comes to emerging AI use cases for cities, what I see is quite broad.

There are, for example, applications around emergency planning – flooding, firefighting, depending on the city. In this area, we also see many companies emerging around sensors, which are becoming more and more relevant.

Another strong area is mobility. My background is very much in mobility, and here we see a lot of potential in predictive intelligence: understanding how people move, how congestion is created, and where different solutions are needed for public transport and street infrastructure. And street infrastructure does not only mean cars, it also includes bicycles, walking, and all forms of movement in the city.

Energy is another important area. Forecasting heating systems, especially in the context of climate change, becomes increasingly relevant. Cities can work with local demand modelling and battery storage optimisation. In every one of these areas, AI already offers many possibilities, but again, it depends on having the right data.

And alongside data, trust is essential. I can see this even in my own neighbourhood, where we are creating a superblock and looking at predictive models. The first step is always data collection, and people immediately ask what sensors are doing, whether they are recording them or not. When you explain clearly what the sensors do, people feel more comfortable. It shows that trust-building is crucial.

In the end, it always starts with data and continues with governance and trust-building, regardless of the specific use case. And the range of possible AI applications for cities is huge.

Many AI success stories are coming from cities outside the traditional “tech capitals.” Which emerging urban innovators have impressed you most recently and why?

One example that has inspired me a lot is Oslo. What they want to achieve there is impressive.

Their goal is that by 2030, the individual cars in the city centre around 400,000 will be replaced by about 30,000 autonomous, on-demand vehicles. That is the ambition, and they have already started working towards it.

What impresses me is how many AI use cases come together in this vision. It includes predicting congestion, but also the autonomous vehicles themselves, which rely heavily on AI to function.

For me, Oslo is one of my favorite examples, because I am really impressed by what they are aiming to do.

If you imagine the European city ecosystem five years from now, what would success look like for you? Not only for BABLE, but for cities and citizens as a whole.

For me, success means that cities feel confident shaping AI according to their own values and their own needs. This is one important part. It also means that they have a strong foundation: transparent governance around data, clear rules on how AI is used, and trusted partnerships whether with universities or with other cities.

It is equally important that citizens understand how AI is being used and that they approve of it, because they see the value in it. The city itself needs to drive this with confidence, but citizens also need to feel convinced and able to trust it.

Will we fully manage this in five years? I hope that we will at least be a step further that we will have taken the right actions and are clearly moving in the right direction.

This is how I see success for cities. And when it comes to BABLE, success for me means that we build this together with the ecosystem that makes it possible. Through CitCom TEF, which for me is a very strong example, and through our other work, we want to ensure that every city, regardless of size, small, mid-sized, or large, can innovate with responsibility and with ambition.

If, in five years, European cities are using AI to improve everyday life at scale, then we will have delivered on our mission.