Shaping Architecture with Generative Artificial Intelligence: Deep Learning Models in Architectural Design Workflow

Deep-learning generative AI promises to transform architectural design, yet its potential employment and ready-to-use capacity for professional workflows are unclear. This study presents a systematic review conducted in accordance with PRISMA 2020 guidelines, synthesizing peer-reviewed work from 2015 to 2025 to assess how GenAI methods align with architectural practice. A total of 1566 records were initially retrieved across databases, of which 42 studies met eligibility criteria after structured screening and selection. Each was evaluated using five indicators with a three-tier rubric: Output Representation Type, Pipeline Integration, Workflow Standardization, Tool Readiness, and Technical Skillset. Results show that most outputs are raster images or non-editable objects, with only a minority producing CAD/BIM-ready geometry. Workflow pipelines are often fragmented with manual hand-offs and most GenAI methods map only onto the early conceptual design stage. Prototypes frequently require bespoke coding and advanced expertise. These findings indicate a persistent gap between experimentation with ideation-oriented GenAI and the pragmatism of CAD/BIM-centered delivery. By framing the proposed rubric as a workflow maturity model, this review contributes a replicable benchmark for assessing practice readiness and identifying pathways toward mainstream adoption. For GenAI to move from prototypes to mainstream architectural design practice, it is essential to address not only technical barriers, but also cultural issues such as professional skepticism and reliability concerns, as well as ecosystem challenges of data sharing, authorship, and liability.