Human-Level Barriers to Generative AI Adoption in Architectural Practice

Abstract

Generative artificial intelligence is entering architectural practice rapidly, although it is not yet possible to speak of full adoption, since it is used for informal experimentation such as ideation, visualisation, drafting, text production, and workflow acceleration. This narrow interpretation overlooks a deeper human-level adoption gap. The central challenge is not only whether AI systems can produce useful design-related outputs, but whether architectural practices can integrate them responsibly within workflows shaped by key professional and procedural constraints. This paper addresses this gap by examining the barriers, readiness conditions, and governance requirements for responsible generative AI adoption in architectural practice. The study is a PRISMA‑guided systematic review and thematic synthesis of peer‑reviewed articles, proceedings, book chapters, and selected governance documents (2014–2026) from architecture, design research, HCI, human factors, automation, AI ethics, and governance. Instead of evaluating individual AI tools or collaboration models, it analyses reported human‑level barriers and enablers across five interrelated dimensions: trust and reliance, accountability and provenance, skills and role transformation, organisational readiness, and professional governance. The synthesis is then mapped to RIBA project stages to identify where risks, responsibilities, and verification thresholds change across the project lifecycle. The paper makes three contributions: a taxonomy of human‑level barriers to generative AI adoption, a stage‑specific risk and responsibility map, and a practical adoption‑readiness checklist for architectural firms. The paper argues that responsible AI adoption in architecture is a human and organisational challenge: AI can support professional work only when embedded within transparent, stage‑gated, and accountable systems of judgement.

Keywords: Generative AI, human-AI Interaction, Architectural Practice, Professional Judgement, Responsible AI.

DOI: 10.54941/ahfe1007970