In the EU’s core policy agenda of strengthening regional industries,
the widespread adoption of AI and the enhancement of its utilization capabilities across all players have become urgent priorities.
AI adoption currently seems concentrated on execution efficiency — such as agent-based automation and process optimization — while its use in high-level strategic decision-making remains limited.
This suggests that the challenge is not the availability of AI, but how its outputs are actually used.
Today, AI systems can generate increasingly sophisticated outputs — analyses, predictions, and recommendations.
However, in many organizations, these outputs are not consistently translated into actionable decisions or economic value.
This issue is not confined to a specific domain or sector.
It applies across different organizational types.
Both corporate management and legislation can be seen as meta-level processes of judging and executing AI outputs.
The objectives differ — profit for enterprises, public value for governments — but the underlying decision architecture is fundamentally the same.
What is missing is not more advanced AI capability, but a shared mechanism to operationalize its outputs.
A shared operational layer for AI output utilization — agnostic to organizational type — can address this gap.
Such a layer would enable:
faster and more consistent decision-making
improved quality and accountability of judgments
accumulation of reusable organizational knowledge
For enterprises, this translates into higher productivity and competitiveness.
For public authorities, it enhances governance efficiency and policy consistency.
In this sense, the focus should shift:
from AI capability to AI utilization,
from output generation to outcome realization.
Strengthening this “utilization layer” can become a key enabler of regional economic development,
particularly by empowering SMEs and local industries to fully leverage AI in their daily operations.
Regulatory sandboxes offer a powerful and practical mechanism to accelerate this transition.
By expanding their use beyond compliance testing to include real-world experimentation of AI output utilization,
the EU can significantly increase the speed of adoption and capability-building across diverse organizations.
This would enable:
safe, iterative learning environments
cross-organizational knowledge sharing
rapid refinement of practical utilization models
AI access alone does not create value.
AI utilization does.
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Komentāri
Mototsugu, thank you for this insightful contribution. The distinction between AI capability and AI utilisation is highly relevant for Europe’s current competitiveness challenge.
The core issue increasingly seems to lie not in access to AI, but in the ability to translate outputs into consistent, repeatable decisions and measurable organisational value.
In that context, the idea of a shared utilisation layer is compelling. However, if it remains primarily conceptual, it risks becoming another coordination framework without sufficient operational traction.
The key gap is not only between output and decision, but also between:
- intended use and actual system behaviour,
- decision rationale and real-world outcomes,
- and more broadly, between ex-ante governance design and ex-post execution evidence.
This becomes particularly visible in cross-border and multilingual contexts such as public benefits, migration procedures, or allocation of public funds, where outputs are often reused beyond their initial framing.
From this perspective, a robust utilisation layer may need to be grounded in:
- behavioural validation,
- versioned decision boundaries,
- traceability with immutable records,
- reconstructable decision paths supported by execution-time evidence,
- and feedback loops capturing deviations between expected and observed outcomes.
These directions also align with the role of AI regulatory sandboxes under the AI Act, which provide controlled environments to develop, test and validate AI systems under supervision, including in real-world conditions, while identifying risks and supporting compliance .
In my own work on behavioural analysis and AI forensic investigation, I often observe that utilisation without execution evidence tends to amplify institutional friction rather than reduce it.
This suggests a growing convergence between AI utilisation, governance-by-design, and observability of systems in production while also raising practical challenges in terms of cost, standardisation, and cross-system integration.
How do you see this utilisation layer being concretely connected to execution-time evidence and post-deployment observability, so that it becomes not only usable, but also verifiable in practice?
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This is a very important distinction.
Almost every organisation today has unlimited access to AI capabilities, but unable to operationalise outputs into consistent, accountable decisions.
The gap is between decision and verifiable execution.
Utilisation only becomes meaningful when it's into a structured system that connects:
• AI outputs
• decision context
• human judgment
• execution trace
• and feedback over time
Without this, utilisation risks remaining ad hoc and non-reproducible.
This layer increasingly takes the form of orchestration frameworks, where AI outputs are not consumed directly, but routed, validated, and contextualised before becoming decisions - particulary in regulated environments.
Suggest is that the “utilisation layer” may not be a separate component, but rather a governance-native operational layer that integrates decision-making, traceability, and feedback into a continuous loop.
How do we design utilisation systems that are not only usable, but also auditable, reproducible, and aligned with evolving regulatory frameworks - that is the nowadays question!
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