The construction sector in Saudi Arabia is under increasing pressure to enhance productivity and technological capability in line with Vision 2030, yet the adoption of artificial intelligence (AI) remains uneven. This study investigates the multi-level barriers affecting AI adoption in the Saudi construction industry using a sequential explanatory design that combines large-scale survey analysis with Interpretive Structural Modeling (ISM) and MICMAC classification. Data were collected from 181 construction professionals through a structured questionnaire covering eight constructs and 50 measurement items. Descriptive statistics reveal moderate AI utilization with a clear preference for analytics-driven applications over physical automation technologies. Perceptual rankings identify trust deficits and workforce capability gaps as prominent concerns. However, the ISM hierarchy uncovers a different structural reality: limited government support emerges as the root driver, cascading through cost and leadership constraints into workforce deficiencies, attitudinal resistance, and ultimately data ecosystem challenges. This perception–structure divergence highlights the risk of prioritizing visible symptoms over foundational causes. The MICMAC analysis further confirms the dominance of policy and strategic drivers within the adoption system. The study contributes by providing one of the first hierarchical mappings of AI adoption barriers in the Saudi construction context and offers a phased intervention roadmap for policymakers and industry leaders. The findings emphasize that sustainable AI diffusion in government-influenced construction ecosystems requires coordinated action across regulatory, organizational, and human capital dimensions rather than isolated technical investments.
Tanoli et al. (Tue,) studied this question.