This study aims to bridge technology adoption and organizational theory. Despite sustained investment in Building Information Modeling (BIM) and, increasingly, Artificial Intelligence (AI), the Architecture, Engineering, Construction, and Facilities Management (AEC/FM) industry continues to underperform against its digitalization targets. Recent research indicates that up to 80 % of AI projects fail not due to technical shortcomings, but due to organizational challenges (Gartner, 2026). This paper argues that the dominant project-level, proof-of- concept approach, often grounded solely in the Innovation Diffusion Theory (IDT) / Rogers' innovation curve on the industry level and the Technology Acceptance Model (TAM) on the project/case level, is insufficient for sector-wide transformation. Drawing on a scoping literature review and abductive reasoning within a critical-realist philosophy, the study systematically examines the following theoretical frameworks: Innovation Diffusion Theory (IDT) / Rogers' innovation curve, ISO19650 Information management, Technology Acceptance Model (TAM), Actor-Network Theory (ANT), Socio-Technical Systems (STS), IDDS-CIB, Amara's Law and the EPIS Framework. Each is mapped against documented AEC/FM outcomes to reveal why technology alone cannot close the productivity gap. The paper concludes that successful BIM and AI implementation requires an integrated, theory-grounded approach that prioritizes organizational change alongside technological deployment.