A European Commission-backed e-government benchmark report warns that EU member states need to pick up the pace on digital transformation.
The annual report, released last week, identified AI sovereignty as one of Europe’s defining technology priorities.
Included in the benchmark system for the first time, AI sovereignty reflects a shift in how governments think about technological control. Governments are looking beyond where data is held to how models and infrastructure are actually built, and how they can maintain oversight of their use.
In this Q&A, Marc Reinhardt, executive vice president and public sector leader at France-based multinational IT consulting firm Capgemini, and a contributor to the report, discusses the growing importance of AI sovereignty and why governments may need to rethink traditional approaches to technology procurement and deployment in the age of AI.
Why do you think sovereignty was included in this year’s report?
Marc Reinhardt: It’s definitely a new phase for the report. The fact that sovereignty appears in the benchmark at all is significant. Historically, discussions around sovereignty were focused on infrastructure; cloud providers, data hosting and where critical systems were located. Now the debate is broadening as the EU moves to define what digital sovereignty means in practice through new policy initiatives and requirements.
In this context, it’s natural that governments are beginning to assess where they stand. They’re asking what sovereignty means across the entire digital stack, and what level of control they need over the technologies that underpin public services.
Where does AI fit within the broader digital sovereignty discussion?
Reinhardt: AI adds a completely new dimension to the debate. AI systems don’t exist in isolation; they run on infrastructure and models, and they embed assumptions, values and ways of reasoning.
Countries are increasingly asking not only where their data is hosted, but also what models they are using, who developed them and whether they understand how those systems make decisions, based on the data they’ve been trained with. For some use cases, those questions may not matter very much. For others, such as welfare decisions or highly sensitive government services, they become much more important.
Compared with cloud sovereignty, which is relatively mature, AI sovereignty is still evolving, and its definition remains more fluid. But it’s rapidly moving to the forefront of policy discussions.
What does AI sovereignty look like in practice?
Reinhardt: Debates around sovereignty were typically politically motivated, and demands were sometimes very abstract. Now, we’re doing more to clarify and demystify the concept of AI sovereignty.
The starting point is understanding what you’re trying to protect and why. Are you concerned about data access? Resilience? Regulatory compliance? National security? Once you understand the objective, it becomes much easier to identify the right solution.
The reality is that sovereignty isn’t a single solution. There’s a broad range of options available, from different cloud environments to sovereign AI platforms and locally controlled deployments. The challenge today is no longer a lack of options, but about understanding which option makes sense for which use case.
What is appropriate for publicly available business registration data may be very different from what is required for health records or other sensitive information. The level of sovereignty should reflect the sensitivity of the workload and the risks involved.
Is there a risk that pursuing sovereignty could come at the expense of AI innovation?
Reinhardt: There are certainly trade-offs, but the emerging consensus is that this isn’t a binary choice.
Just as organizations increasingly operate in a multi-cloud environment, we’re likely moving toward a multi-AI world where different models are used for different purposes. Some applications may use leading commercial models, while others may rely on open source or locally hosted systems where greater control is required.
The goal is really to find a balance between sovereignty and solving the problem at hand. At the end of the day, we have to solve problems. We have to find solutions to improve society and civilians’ lives. If the solutions currently available in Europe do not do that, European organizations will need to be pragmatic and use what is available and affordable in the market.
You’ve worked on a sovereign AI platform with the German Federal Ministry of Digitalization and State Modernization. What lessons does that project offer?
Reinhardt: One important lesson is that the problem you’re trying to solve should be the starting point, not sovereignty for the sake of sovereignty.
In this case, the focus was on improving planning and approval processes for major infrastructure projects. We were able to build a solution that met those requirements while also using highly sovereign components, including European AI alternatives.
That demonstrates an important point: European technologies are already capable of delivering meaningful results in many scenarios; we just need to understand where they fit best and where they can create value.
What should governments be doing differently as they develop AI strategies?
Reinhardt: Governments need to rethink how they procure and deploy technology.
Historically, public-sector technology projects were designed around long planning cycles, detailed specifications and systems intended to last decades. That approach doesn’t work well for AI because the technology evolves so quickly.
Instead, governments need shorter development cycles, smaller projects and a willingness to continuously reassess their options by decoupling AI models from the systems they’ve deployed in. AI models, sovereignty requirements and available technologies are changing far too rapidly for traditional procurement approaches.
That requires a cultural shift as well. Public institutions are designed to minimize risk, but AI demands more experimentation. You have to sometimes, frankly, take a bit more risk. Sometimes playing it safe doesn’t translate into tangible benefits. We need to start thinking differently as a society and reward people for being innovative.
Editor’s note: This interview has been edited for clarity and conciseness.
