Practice readiness in generative and AI-driven architectural design: A systematic assessment

Generative and AI-driven design methods are proliferating in architectural research. Yet, their potential impact on day-to-day practice remains unclear. This review maps 67 case studies drawn from 65 peer-reviewed publications, across nine generative categories organised under four computational approaches; rule-driven, search-based, data-driven and hybrid. Each case is assessed on a three-tier scale through a Practice Engagement Index (PEI) comprising six indicators: workflow accessibility, real-world constraints alignment, common building type, geometric viability, funding, and physical construction application. Results show that most case studies engage convincingly with realistic constraints (62.6%) and common building types (58.2%), yielding potentially buildable geometry (47.7%). Far fewer achieve sufficient workflow accessibility (17.9% high tier, with 50.7% scoring low), secure industry funding or reach physical construction. Although publication volume has surged since 2020, two-thirds of studies cluster at moderate readiness (PEI ≥ 1.0) and very few approach high engagement (PEI > 1.5). These results suggest that researchers increasingly acknowledge professional realities yet still struggle to translate technical novelty into routinised tools and construction-phase implementation. By pairing a taxonomy of generative categories with a replicable, indicator-based assessment index, this study offers a transparent instrument for comparing practice-readiness across methods and for tracking progress over time. Further progress will depend less on algorithmic sophistication than on containerized plug-ins for mainstream CAD/BIM packages and IFC pipelines, industry partnership, and pathways to construction.