First, data sharing. Solving complex public sector challenges with AI depends on information flowing safely across organizational boundaries. In practice, this means making it easier for departments and agencies to reuse data that already exists. While most public sector organizations have initiatives underway, only 35% have rolled out or have fully deployed data-sharing methods. Programs like Europe’s Common European Data Spaces show what is possible: secure, trustworthy environments for collaboration that benefit both organizations and citizens.
Second, data control and sovereignty. Concerns about compliance and control are a daily reality for public sector leaders, and they are slowing AI adoption. More than half of public sector organizations are concerned about AI sovereignty, and these concerns are actively hindering wider adoption of generative AI. Compliance with data-localization laws and control over sensitive information become more complex when AI services are hosted in foreign jurisdictions. A 2024 European Commission report found that 80% of Europe’s digital technologies and infrastructure are imported. It is no surprise that sovereignty concerns are fuelling efforts to strengthen digital autonomy, from national cloud strategies to proposals such as the EuroStack initiative, which envisages €300bn of investment over a decade.
Third, a data-driven culture. This is a critical pillar of AI readiness. True data mastery requires more than tools – it demands leadership, collaboration, and trust in data-based decisions. Setting clear targets, aligning strategy with operational reality, and encouraging collaboration and shared behaviors across teams helps embed data use into everyday work, rather than treating it as an added burden.
Fourth, data infrastructure. Robust, cloud-based data infrastructure is essential for storing, processing and analyzing data at scale, while respecting sovereignty requirements. Today, the lack of such infrastructure is the primary obstacle to effective data use. Only 41% of public sector executives say they can access data at the speed required for decision-making. Budget constraints are a real barrier, but they need not be paralyzing. By focusing on gradual, outcome-driven improvements rather than costly overhauls, organizations can demonstrate value and secure further investment.
Public sector organizations such as the City of Tampere illustrate this four-pillar approach. By building data foundations gradually and strategically, while addressing data sharing, sovereignty, culture and infrastructure together, Tampere has shown how thoughtful investment can deliver tangible results without losing sight of long-term ambition.
From ambition to execution