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From research to real-world impact: How EU projects can bridge user needs and innovation ecosystems
Europe has invested heavily in creating infrastructures that make artificial intelligence more accessible, transparent, and trustworthy. The development of the AI-on-Demand (AIoD) platform is in the center of this effort, bringing together researchers, companies, and public actors to develop a shared European AI ecosystem. But one question remains central: how can we make sure these projects really meet the needs of their users?
Listening to users: What we learned over the summer
This summer, our team engaged with potential users of the AIoD platform through insightful interviews. Detailed results will be shared a bit later but one thing is clear: users across sectors, from startups and SMEs to researchers and policy actors, are eager for an AI community and AI tools that are practical, useful, and trustworthy.
At the same time, we heard about obstacles:
- understanding the functionalities of complex platforms takes time and resources that many SMEs and individuals do not have,
- governance structures can feel distant from the daily challenges of businesses, and
- questions of trust, data use, and transparency remain top of mind
These insights stress the importance of bridging the gap between research-driven innovation and real-world applications.
Why EU projects matter
EU projects are not just technical ventures. They are part of a broader policy shift. Traditionally, innovation policy focused on economic growth and competitiveness. Today, as scholars such as Diercks, Larsen & Steward (2019) argue, we are entering a new era of transformative innovation policy, where innovation is guided by big societal challenges such as sustainability, inclusivity, and resilience.
Projects like AIoD live this shift. They are not only about advancing AI research, but also about creating infrastructures that:
- support SMEs and startups to scale responsibly,
- ensure trustworthiness and compliance with European values, and
- enable new forms of collaboration across sectors and countries.
Understanding ecosystems as complex systems
But translating this ambition into practice is not simple. AI infrastructures are ecosystems: networks of companies, researchers, policymakers, and users. As Phillips & Ritala (2019) remind us, ecosystems behave like complex adaptive systems. Their dynamics are non-linear, boundaries are fuzzy, and actors co-evolve in ways that cannot be fully predicted.
This means that building platforms like AIoD is not a one-off technical exercise. It is about creating adaptive structures that can evolve in response to user needs, market changes, and regulatory shifts.
Transformative governance: Five features for success
One promising approach comes from Könnölä et al. (2021), who developed a framework for transformative governance of innovation ecosystems. They identify five features that make ecosystems more resilient and adaptive:
- Diversity – involving a broad set of actors and perspectives.
- Connectivity – ensuring that knowledge, data, and ideas flow across boundaries.
- Polycentricity – enabling multiple centers of decision-making rather than one central authority.
- Redundancy – allowing overlapping functions to ensure resilience in times of uncertainty.
- Directionality – aligning activities toward shared societal goals.
For AIoD, these features are not abstract theory. They provide a practical guide for building on the platform so that it remains open, trustworthy, and impactful. For example, supporting diverse SMEs across Europe, connecting them with research institutions, and ensuring governance that reflects multiple voices can make the difference between a platform that is used widely and one that struggles to engage.
Bridging research to business impact
In our upcoming Impact Assessment Report (D2.2), we explore how AIoD can better connect research outputs to business cases. Many innovative tools and datasets exist in the research community, but businesses often lack the means or trust to adopt them. Bridging this gap requires both technical solutions (e.g., user-friendly interfaces, interoperability) and governance innovations (e.g., clearer rules for data use, incentives for co-creation).
The success of AI4Europe will not only be measured by the number of resources uploaded but by the extent to which research innovations find their way into practical business and policy solutions.
CKIR at Aalto: Contributing a governance and ecosystem lens
At the Center for Knowledge and Innovation Research (CKIR), Aalto University, our work focuses on these governance and ecosystem dynamics. We bring insights from innovation policy, ecosystem research, and organizational studies to understand how infrastructures like AIoD can scale responsibly and inclusively. By linking theory with practice, we aim to support the European AI ecosystem in becoming not only competitive but also resilient, trusted, and user-centered.
Looking ahead
The European AI landscape is still in the making. As AIoD evolves, it offers a testbed for learning how large-scale EU projects can balance research excellence with real-world impact. Listening to users, addressing obstacles, and applying frameworks of transformative governance can help ensure that platforms like AIoD become more than repositories—they can become living ecosystems that shape Europe’s digital future.
References
Diercks, G., Larsen, H., and Stewart, F. (2019). Transformative innovation policy: Addressing variety in an emerging policy paradigm. Research Policy, 48(4), 880-894. https://doi.org/10.1016/j.respol.2018.10.028.
Könnölä, T., Eloranta, V., Turunen, T., and Salo, A. (2021). Transformative governance of innovation ecosystems. Technological Forecasting & Social Change, 173, 121106. https://doi.org/10.1016/j.techfore.2021.121106.
Phillips, M. and Ritala, P. (2019). A complex adaptive systems agenda for ecosystem research methodology. Technological Forecasting & Social Change, 148, 119739. https://doi.org/10.1016/j.techfore.2019.119739.
