From public innovation procurement requirements to AI-enabled socio-technical architecture: a longitudinal case study in industrialized housing.

Artificial intelligence (AI) is used in digital systems that combine data, computational methods, decision processes, and organizational arrangements. This paper examines how the requirements and institutional conditions of a public innovation procurement process are reflected in the development and specification of an AI-enabled socio-technical architecture for industrialized public housing delivery. The case concerns an AI-enabled system intended to support the design, assessment, and procurement preparation of industrialized housing within a public-sector delivery process. Using a longitudinal qualitative pre-deployment case-study design, we analyze a documentary corpus covering institutional procurement records, the initial technical proposal, its formal ex-ante assessment, and subsequent collaborative development. The architecture documented in the case comprises eight interconnected functional modules and three cross-cutting enablers: security, governance, and interoperability. It combines rule-based regulatory and contextual checking, generative and parametric design configuration, surrogate-model-supported performance estimation, cost and financial assessment, multi-objective optimization, multicriteria scenario comparison, and procurement-document generation. Across the corpus, we identify traceable correspondences between institutional requirements and functional, computational, governance, and interoperability arrangements, while keeping requirements, specifications, formal ex-ante assessment, subsequent development, and evidence requiring operational validation distinct. The contribution is an account of how AI-related and other computational capabilities are integrated with data, human decision points, governance arrangements, and procurement outputs within an evolving socio-technical architecture. We do not evaluate a completed system or establish operational effectiveness, scalability, or broader public outcomes. It provides a pre-deployment account of how procurement and institutional requirements correspond to the architecture of an AI-enabled decision-support system for industrialized housing delivery.