Innovation needs investment. Yet money alone does not guarantee that promising ideas become useful solutions. Governments also need to bring people together, test new approaches and apply what they learn.
A European Commission Joint Research Centre study explores this connection. Published in September 2026, it examines experimental approaches to public administration and regional innovation performance.
The report encourages a broader view of innovation policy. The question is not just how much governments spend. It is also whether public institutions can turn funding, knowledge and ideas into lasting results.
Why funding is not enough
Governments can establish funds, launch support programmes and finance new technologies. Yet the harder work often begins afterwards.
Public agencies need to coordinate. Businesses and researchers need clear conditions. Regulators must assess potential risks. Successful solutions then need a place in budgets, rules and everyday procedures.
Without those connections, a promising project may remain an isolated experiment. The money is spent and a report is written. However, the way the system works stays the same.
The study identifies a connection between government quality and regional innovation performance. It also finds that regions with stronger governance attract more innovation funding.
That relationship does not prove that better governance automatically causes more innovation. Other factors matter too, including research capacity, the business environment and access to finance. The evidence should not be reduced to a single explanation.
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A pilot is not a policy
Public administrations have long used pilot projects. They allow governments to test a solution on a limited scale before broader use.
Yet running an experiment does not automatically mean an administration can learn from it. Officials must assess the results, discuss mistakes and adjust their next steps. Successful lessons must also become part of rules or working procedures.
This creates a cycle: identify a problem, bring participants together, test, collect feedback and implement. The solution can change at each stage. Sometimes the right outcome is to abandon the original idea. That can still be valuable if it prevents a larger mistake.
The researchers identify several functions of experimental governance. Administrations need to mobilise people and knowledge, create safe spaces for testing, and balance interests and risks. They must also turn lessons into lasting practice and coordinate the process.
Regulators need to learn too
This challenge is especially visible when technology develops faster than detailed regulation. Artificial intelligence is one example.
The source material describes an experiment at Barcelona’s Cruïlla festival. Developers, researchers, organisers and public authorities took part in testing AI-based solutions.
The data protection authority became involved early. As a result, participants could discuss potential privacy and rights issues before wider use. Some initial ideas were changed during preparation.
This approach does not remove oversight. Instead, it brings oversight into the development process. Regulators can see how a technology works, while developers learn about constraints earlier.
Early involvement does not guarantee success. However, it can help teams identify risks and revise a solution before scaling it up.
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From testing to permanent rules
Another example concerns digital public services. The source material describes the preparation of guidance for assessing the interoperability of trans-European digital services.
Representatives of national administrations helped develop the guidance. Drafts were then tested in practice. Feedback informed revisions before the final document was adopted.
The sequence matters. First comes a draft. Next, participants test it and share comments. The guidance is revised before it receives formal status.
This approach requires time, skills and a willingness to acknowledge weaknesses in an early version. Without those conditions, feedback can become a formality.
Lessons for Kazakhstan
European experience cannot simply be transferred to Kazakhstan. Administrative systems, resources and operating conditions differ. Still, the questions raised by the study can help assess local programmes.
For example, a new support instrument could begin with a clear problem and a plan for evaluating results. It is also useful to define who will implement a successful approach and how lessons will be preserved after the pilot ends.
Regulatory involvement matters as well. If legal or practical barriers emerge only after a product is developed, corrections may require extra resources. Earlier discussion can help reveal those barriers sooner.
This is not an argument against funding. Investment is needed throughout the process, from research to implementation. But its effectiveness also depends on how well the participants work together.
How to assess results
The number of programmes and the amount of funding show the scale of effort. They do not always show what changed in practice.
Other questions can help. How many pilots were evaluated? Which decisions changed after feedback? Which successful approaches were used more widely? Failed experiments can also be useful if their lessons prevent repeated mistakes.
That requires a system for recording and sharing experience. It can document the problem, participants, testing conditions and conclusions. As a result, knowledge is less likely to disappear when a project ends or a team changes.
Innovation policy is not only about money. It is also about how governments learn. Funding helps ideas get started. Governance affects what happens to them next.
Shyngys Yerbolat, EconomyKZ Research expert, exclusively for EconomyKZ.org
