Europe’s network of publicly backed digital innovation centres is entering a more demanding phase as Brussels connects local adoption support with its expanding AI infrastructure, shifting attention from how many organisations can experiment with artificial intelligence towards whether those experiments become useful enough to survive inside ordinary budgets and workflows.

The European Digital Innovation Hubs network now spans 40 countries, while the latest Digital Europe call is backed by €79.2 million. More than €1.2 billion has been co-invested in the programme by the European Commission, national and regional authorities, and participating organisations since 2023.

Following the latest assessment, 62 hubs received the EU’s STEP Seal for projects supporting strategic technologies, particularly AI, and 45 secured direct Digital Europe funding. The remaining projects can seek support from national or regional sources without repeating the full assessment process.

The network offers technical advice, training, maturity assessments, finance guidance, and “Test Before Invest” services that allow companies and public bodies to experiment before committing to larger projects. Its new phase puts AI more firmly at the centre while tying the hubs to AI Factories, testing facilities, and regulatory sandboxes elsewhere in Europe’s increasingly complicated technology-support system.

Infrastructure is only the first layer

European AI policy has devoted large sums to computing capacity, including AI Factories built around the EuroHPC supercomputing network. Those facilities can provide access to compute, data, and expertise, yet a manufacturer, council, hospital, or smaller business still has to work out whether a particular use case is worth deploying and how it fits existing systems.

Production adoption requires more than model access. Organisations need usable data, integrations, governance, staff who understand the new workflow, procurement arrangements, security controls, and some credible measure of whether the system saves time, reduces cost, improves service, or creates another operational benefit.

Those requirements are particularly demanding for smaller companies without specialist AI teams. Public-sector bodies face additional constraints around legacy technology, procurement, accountability, and data use, while industrial businesses may need physical testing environments before software can be introduced into production systems.

EDIHs are intended to bridge that gap by giving organisations local access to expertise and then connecting them to deeper infrastructure where necessary. The value of the model depends on whether those connections work in practice rather than leaving users to navigate several overlapping EU programmes.

Early evidence needs a harder benchmark

Previous Commission analysis suggests that the first generation of hubs improved organisations’ digital maturity. A 2025 Joint Research Centre assessment drawing on more than 10,000 SMEs and 800 public-sector organisations reported improvement among most participating businesses, with comparatively strong gains around AI and automation.

Those results provide evidence that technical assistance can move organisations forward, but maturity scores do not automatically translate into higher productivity, lower operating costs, or successful long-term deployment. A company can become more capable of experimenting with AI without finding a use case that deserves permanent investment.

The next phase therefore needs more demanding evidence about what happens after organisations leave a funded trial or training programme. Continued use, private spending, measurable operational gains, and systems that remain in production after subsidy ends provide a stronger test than participation counts alone.

The scale of the network gives Brussels an opportunity to collect that evidence across sectors and regions. Europe’s technology economy has long combined advanced industrial companies and strong research institutions with a large group of smaller organisations that adopt new software, automation, and data systems more slowly.

Local help must connect to the wider stack

A distributed hub model is meant to reduce that gap by putting expertise closer to organisations outside the largest technology centres. Local staff can understand regional industries, public services, and skills shortages better than a central programme, although decentralisation also creates the risk that many centres rebuild similar capabilities in parallel.

The network now sits inside a broader European AI stack that includes AI Factories, testing and experimentation facilities, regulatory sandboxes, research programmes, and national support schemes. Organisations approaching that system may not know whether they need compute, compliance advice, technical testing, finance, training, or simply a better-defined business problem.

Navigation consequently becomes part of the service. A useful hub should be able to tell an organisation that it does not yet need an AI model just as readily as it directs another towards specialist infrastructure, particularly as political and commercial pressure encourages companies to adopt tools before they have identified a worthwhile process to change.

The new funding round gives EDIHs a larger role in preventing Europe’s investment in AI infrastructure from becoming detached from the organisations expected to use it. Their success will be easier to judge when the network can show not only that more companies tried AI, but that a meaningful share moved from public-supported experimentation into systems that remained useful after the programme stepped away.