AI Spaces: From Silos to Sovereign, Cross-Organizational Intelligence

Abstract

This paper proposes AI Spaces as a framework for enabling AI systems to collabo- rate across organizational boundaries under real-world constraints of data sover- eignty, confidentiality, regulation, and misaligned incentives. Many of today’s industrial and societal challenges cannot be resolved within the information held by any single organization. Responding to demand fluctuations in supply chains, acceler- ating product development in manufacturing, enhancing diagnostic support in healthcare, and optimizing operations in next-generation smart buildings — all of these require the secure, sov- ereignty-preserving utilization of data and knowledge distributed across multiple organizations. AI Spaces address these challenges by integrating Data Spaces infrastructure with AI, enabling cross-organizational intelligence without the need for centralized data aggregation. Three fundamental patterns of cross-organizational AI collaboration underpin AI Spaces. The first is collaborative model development, where organizations jointly improve models through federated learning while retaining full control over their data. The second is inter-organizational model inference, enabling AI systems to perform reasoning by selectively accessing distributed, sovereign data at inference time. The third is autonomous agent collaboration, where multi- agent systems coordinate decisions and actions across organizational boundaries. These patterns are applicable across domains, and real-world systems frequently combine more than one. The sustainable operation of AI Spaces cannot be guaranteed by technical feasibility alone. This paper identifies three mutually reinforcing institutional conditions. The first is incentive design: organizations sustain participation not only through financial reward, but through non-mon- etary value such as access to shared data, participation in governance, and reputation derived from visible contributions. The second is technology standardization: without interoperability at the interaction layer — spanning agent communication protocols, semantic frameworks, policy languages, and trust and identity infrastructure — the network effects essential to AI Spaces cannot be realized. The third is quality management, centered on the AI Bill of Materials (AI BOM), which ensures the transparency, traceability, and auditability of composite AI systems. At the technology layer, concrete approaches including Federated RAG (F-RAG), the Secure Inter- Agent Gateway, Digital Rehearsal, and Optimization under Incomplete Information demonstrate practical feasibility across diverse domains, from pharmaceutical logistics to healthcare and smart buildings. AI Spaces represent essential infrastructure for overcoming data silos and harnessing collective intelligence across organizations in a secure, trustworthy, and scalable manner. Realizing this vision requires coherent effort across both technical architecture and institutional design.