Artificial Intelligence and Machine Learning Implementation Patterns in Architecture: A Cross-Sectional Analysis of Academic and Industry Sectors in Saudi Arabia

This study presents one of the first empirical assessments of artificial intelligence (AI) and machine learning (ML) adoption within architectural academia and the Architecture, Engineering, and Construction (AEC) industry in Saudi Arabia. Using a cross-sectional survey of 113 respondents—60 academics and 53 industry professionals—the research examines familiarity, current usage, perceived benefits, challenges, and future readiness for AI/ML integration. Results show high familiarity and strong perceived importance across both sectors, yet actual implementation remains uneven. Very large firms demonstrate the highest adoption capacity, while small and medium-sized firms face financial and organizational constraints. Academic institutions exhibit moderate familiarity but limited curricular and research integration due to faculty expertise gaps, restricted access to tools, and traditional pedagogical structures. Despite these barriers, both sectors consistently identify AI/ML as critical for enhancing creativity, efficiency, and industry preparedness. The study highlights organizational capacity as the primary determinant of adoption. It concludes with recommendations for curriculum reform, faculty training, industry–academia collaboration, and national policy frameworks to accelerate digital transformation aligned with Saudi Vision 2030. This research establishes a foundational baseline for future longitudinal and comparative studies on AI/ML integration in the regional architectural ecosystem.