Architectural Prerequisites for AI Adoption in Institutions and Enterprises

This paper examines the architectural prerequisites for AI adoption, distinguishing visible AI discourses around efficiency, workflows, labour-market effects, fairness, skills and implementation from deeper infrastructure-level consequences for institutional and enterprise systems. As a basis for understanding and developing a pre-mapping layer, it identifies infrastructural anchor points that enter view during preparation or re-evaluation, within which decision authority and data sovereignty are positioned as the highest-order neuralgic point. Subsequent layers of mechanisms, including data quality and access, guardrail and output formation, responsibility allocation and related structural variables, are assessed from an architectural point of view. The paper further situates AI within a broader convergence of emerging technologies, particularly DLT and blockchain, within the same logic of architectural prerequisites and infrastructure-level consequences, and concludes by delineating a prior structural reading through which institutions and enterprises can situate their decision space within AI-related infrastructural, organisational and technological convergence.