Context
Interoperability for AI‑enabled public services refers to the sharing, reuse, and integration of interoperability solutions that support the development, deployment and operation of AI‑enabled systems across countries, sectors and administrative levels. As artificial intelligence becomes increasingly embedded in public sector activities, interoperability is essential to enable cooperation between systems, avoid fragmentation and support scalable, trustworthy public services.
In this context, interoperability solutions do not refer to AI models alone, but to the reusable components, specifications, and assets that allow AI‑enabled systems to work together. These may include shared data models, interfaces, metadata schemas, reference architectures, evaluation templates, or reusable processing pipelines. By focusing on such solutions, public organisations can enable AI systems to be deployed in different organisational and legal contexts while maintaining coherence and transparency.
By prioritising common data models, shared standards and open or reusable components, public organisations can support AI‑enabled interoperability solutions that are adaptable, auditable, and transferable across borders. This scenario also highlights the importance of secure data‑sharing frameworks and monitoring mechanisms to ensure that AI‑enabled services deliver public value while respecting applicable legal and ethical requirements.
This scenario illustrates how the mechanisms of the European Interoperability framework can be applied to AI‑enabled public services, drawing in particular on publication (Pathway 2), direct sharing (Pathway 3), adaptation (Pathway 4) and, where relevant, integration between portals or repositories (Pathway 6).
Applying this scenario in practice
The interoperability of AI‑enabled public services is achieved primarily through the reuse of supporting interoperability solutions, rather than through the exchange of complete AI systems. Public administrations may therefore focus on identifying, sharing and adapting the components that enable AI systems to interoperate across organisational and national boundaries.
To operationalise interoperability, public organisations may promote the adoption of common standards for data formats, interfaces and exchange mechanisms, where appropriate. Alignment at these levels enables AI‑enabled systems developed by different administrations or vendors to interoperate without requiring full harmonisation of internal systems.
Where full alignment is not feasible, structured mapping or translation mechanisms can be used to bridge differences, provided that the underlying interoperability solutions are clearly documented and reusable.
AI‑enabled public services often rely on access to sensitive or regulated data. Secure data‑sharing frameworks should therefore be used to enable controlled access to data while ensuring compliance with data protection, security and confidentiality requirements. Relevant interoperability solutions may include access‑control models, audit‑logging mechanisms or standardised data‑sharing interfaces that can be reused across services.
Interoperability in AI contexts is not static. Public organisations may benefit from putting in place processes that support continuous assessment, monitoring and improvement of shared interoperability solutions. This may include collecting feedback on reused components, monitoring performance or interoperability issues, and updating shared assets as technologies and use cases evolve.
Documenting lessons learned and publishing mature interoperability solutions on shared platforms can support wider reuse and help other public organisations build on proven approaches.
