In its recently published position paper, “Data Spaces and AI: Trustworthy Agentic Participation in Data Spaces“, the International Data Spaces Association (IDSA) examines the close relationship between data spaces and artificial intelligence. The paper highlights that these two technologies are increasingly converging and becoming mutually dependent: while data spaces provide the trusted environment AI needs to access high-quality data, artificial intelligence is emerging as a key enabler for accelerating the deployment and scalability of data space ecosystems.

The paper starts from a clear premise: the most valuable data is typically held within organizations and cannot be shared without guarantees regarding its use, traceability, and the data sovereignty of its owners. In this context, data spaces provide a governance framework that enables organizations to define clear conditions for data sharing, specify who can access each dataset and under what circumstances, and ensure compliance with those conditions through enforceable usage policies.

This model is particularly relevant for the development of artificial intelligence. AI systems require not only large volumes of data but also data that is reliable, well described, and supported by verifiable provenance. By enabling organizations to share domain-specific information while retaining control over it, data spaces make it possible to train more accurate and trustworthy AI models without compromising data sovereignty.

At the same time, IDSA emphasizes that the relationship also works in the opposite direction. Artificial intelligence can play a decisive role in accelerating the adoption of data spaces by significantly reducing the effort required to onboard new participants. The paper highlights applications such as automated metadata generation, schema alignment across heterogeneous data models, the translation of legal terms into machine-readable usage policies, and automated monitoring of compliance with data-sharing rules. Tasks that would otherwise require substantial manual effort can be streamlined through AI, making data spaces easier and more cost-effective to scale.

For these reasons, the paper concludes that the future of artificial intelligence will not depend solely on more powerful models, but also on mechanisms that ensure trust, identity, governance, and the responsible use of data. In this context, data spaces are evolving beyond a purely technical infrastructure to become one of the key foundations for building trustworthy AI capable of operating securely across organizational boundaries.