The Center for Data Innovation appreciates the opportunity to submit this response to the European Commission’s consultation on its “Apply AI Strategy”—a blueprint for the adoption of artificial intelligence (AI) in EU strategic sectors to strengthen the Commission’s ambition to make Europe an “AI continent”.

The Center for Data Innovation studies the intersection of data, technology, and public policy. Its mission is to formulate and promote pragmatic public policies designed to maximise the benefits of data-driven innovation in the public and private sectors. It educates policymakers and the public about the opportunities and challenges associated with data, as well as technology trends such as open data, artificial intelligence, and the Internet of Things. The Center is part of the Information Technology and Innovation Foundation (ITIF), a nonprofit, nonpartisan think tank.

Executive Summary

The Center welcomes the European Commission’s ambition to accelerate the uptake of AI across the economy and public sector as part of its broader AI continent action plan. To make this ambition a reality, the EU should prioritise practical, near-term applications of AI that boost competitiveness, productivity, and innovation, while reducing ongoing barriers to adoption.

The European Commission should:

1. Establish a public sector AI Adoption fund to complement InvestAI;

2. Simplify AI regulation to support all business sizes and strengthen Europe’s strategic position;

3. Create flexible pathways to turn AI research into real-world impact; and

4. Align AI priorities to existing European common data spaces.

Establish a Public Sector AI Adoption Fund To Complement InvestAI

The EU launched its InvestAI initiative as part of the AI continent action plan, an initiative that aims to mobilise €200 billion in investment in AI to support the development of AI gigafactories.^[1] To ensure this investment translates into real-world impact, the Commission should establish a dedicated AdoptAI Fund focused on accelerating AI adoption across the public sector—complementing the AI factories initiative and addressing the urgent need for practical deployment.

Public institutions across Europe face many of the same barriers to AI adoption as the private sector: limited technical expertise, outdated digital infrastructure, budgetary constraints, and uncertainty about the operational and societal impact of AI tools. Yet, the potential benefits of AI in public service delivery—from improving healthcare systems and streamlining public administration to enhancing environmental monitoring and transport management—are substantial.

While funding has been made available for AI research and development through initiatives like Horizon Europe, there is a critical gap in targeted support for public sector actors seeking to implement and scale AI solutions. A dedicated public sector AI adoption fund would help bridge this gap by supporting public institutions with the resources needed to pilot, procure, and implement AI systems aligned with public value.

The fund should mobilise both EU-level and national public funding and be accessible to public bodies demonstrating high-impact use cases of AI—particularly in areas such as healthcare, education, urban planning, environmental protection, and justice. Support could include technical assistance, procurement support, skills development, and infrastructure upgrades, ensuring that public sector organisations have the tools and capacity to integrate AI responsibly and effectively.

The AdoptAI Fund would not only enhance public service delivery but also build institutional readiness and demand that supports the broader AI ecosystem. By investing in public sector adoption, the EU can demonstrate responsible leadership in AI deployment and ensure that the benefits of AI innovation are widely distributed across society.

Simplify AI Regulation To Support All Business Sizes and Strengthen Europe’s Strategic Position

The success of the Apply AI Strategy depends on the ability of businesses of all sizes to adopt and deploy AI technologies. The Commission is right to recognise the need for regulatory simplification, particularly for small and medium-sized enterprises (SMEs) and mid-caps, which often face steeper resource and expertise challenges.^[2] To maximise the impact of this strategy, the Commission should also ensure that simplification efforts support larger firms—including those developing foundational models—as these firms play a central role in driving innovation and building infrastructure on which smaller players rely. Doing so will strengthen Europe’s strategic position as a leading AI region—an effort the Commission should complement by reducing dependencies on non-EU technologies originating from strategic competitors.

While EU policy often focuses rightly on strengthening European industrial capacity and reducing strategic dependencies—especially in areas such as foundational AI, where non-EU actors currently dominate—the path to technological sovereignty cannot be protectionist in nature. Building European capacity requires enabling an innovation-friendly regulatory environment that supports responsible deployment across the entire business landscape. Larger AI developers, including many non-EU firms, face barriers such as legal uncertainty and fragmented compliance obligations across Member States. These barriers can delay the introduction of AI products in the EU market, slowing the diffusion of innovation.^[3]

These delays have broader consequences, particularly for SMEs that depend on the platforms, tools, and infrastructure developed by larger firms, many of which are global. If innovation at the top of the value chain slows, it drags down the broader ecosystem. The EU should affirm its legitimate interest in strengthening European capacity and reducing reliance on non-EU technologies—particularly from strategic competitors like China—while remaining open to cooperation with trusted partners, notably the United States, which will be essential to achieving scale and ensuring interoperability in global AI development.

Similarly, to ensure the AI Act accelerates rather than inhibits uptake, the Commission should pursue a regulatory simplification agenda that reduces friction for responsible AI deployments across the entire business landscape. This includes offering clear compliance guidance, streamlining documentation requirements, and providing broad access to regulatory sandboxes. By explicitly supporting responsible innovation from firms of all sizes, the EU can grow both its internal capabilities and its partnerships—strengthening resilience and competitiveness without resorting to blunt protectionist measures.

Create Flexible Pathways To Turn AI Research Into Real-World Impact

Europe has long maintained a competitive edge in AI research and development, particularly at the university level. However, it still struggles to consistently translate this intellectual leadership into widespread commercial and societal value. For example, Germany regularly leads in patent applications across the EU, and ranks among the top globally in AI publications, yet faces persistent gaps in turning these research outputs into deployable products or services.^[4] This shortfall is often attributed not just to a lack of capital, but to a lack of viable mechanisms for turning ideas into impact.

Some of the most valuable advances in AI—such as new algorithms to cut training costs, a better evaluation metric, or a more energy-efficient architecture—may lack a clear business model or commercial champion at the outset but still have the potential to significantly strengthen Europe's AI ecosystem if the right actors adopt them.

The Commission should therefore focus on creating more flexible and diverse pathways that support the transition of AI research into application. AI factories can play a key role in this process by embedding dedicated technology transfer teams, providing matchmaking services between researchers and industry actors, and offering subsidised access to infrastructure, intellectual property (IP) support, and proof-of-concept funding. AI factories should function as testbeds and translation hubs where stakeholders can develop, validate and connect with high-potential, scalable ideas—regardless of origin or immediate commercial viability.

Public funding should be designed with these flexible outcomes in mind. This includes prioritising research with plausible paths to uptake, incentivising collaboration between academia and industry, and ensuring that IP frameworks and technology transfer policies empower researchers to pass on ideas without needing to take on commercial risk themselves. By focusing on impact realisation, the EU can better ensure that its AI research excellence leads to measurable economic and societal value.

Align AI Priorities to Existing European Common Data Spaces

Access to quality data is essential for AI and is one of the key barriers firms cite in AI adoption.^[5] The Common European Data Spaces scheme helps to tackle data access issues by making sector data accessible to businesses, public administration, and individuals, in turn enhancing Europe’s data-driven economy. According to the latest staff working document, there are 14 common data spaces available, of which two are mature and well into deployment (research and innovation, and health), and seven are in an active phase of deployment (cultural heritage, manufacturing, language, media, mobility, public administration, and skills).^[6] These sectors represent high potential for AI development given the immediate access to a breadth of European-centric data.

To accelerate the data-driven and AI economy, the Commission should prioritise AI deployment in sectors with established data spaces, aligning these use cases to drive standards and interoperability requirements around this broad data collection, access and use. Furthermore, the Commission should accelerate the development of the other data spaces to support AI development in these areas.

Similarly, the Commission should reform the Data Governance Act (DGA) which currently siloes data for public good initiatives to non-profit entities. Substantial evidence exists to demonstrate the role of for-profit companies in innovating for the public good, including Meta’s No Languages Left Behind Initiative—a project that open-sources AI models that translate 200 languages, including low-resource languages such as Asturian and Urdu.^[7] Removing the DGA’s requirement, coupled with an effective and accessible European Common Data Space for languages, could drive a huge amount of AI adoption across member states newly represented in this AI ecosystem.

Endnotes