How Is the Architecture, Engineering and Construction (AEC) Industry Adopting Artificial Intelligence? A Cross-Sectional Survey Study

Artificial intelligence (AI) is increasingly integrated into tools and workflows across the architecture, engineering, and construction (AEC) industry. While prior research has largely focused on technical capabilities and application-specific implementations, limited empirical evidence exists on how AI is currently adopted within professional practice. This study presents a comprehensive cross-sectional analysis of AI adoption based on a large-scale survey of 501 AEC professionals spanning multiple disciplines, organization types, and geographic regions. The analysis examines individual-level familiarity, usage patterns, and training pathways, alongside organizational characteristics associated with AI exposure and integration. Results indicate that AI adoption is widespread but heterogeneous. Generative AI tools have emerged as the primary entry point into AI-enabled workflows, while more specialized systems such as computer vision and robotics remain less prevalent. Statistical modeling reveals that organizational context plays a dominant role in shaping adoption. Professionals in large organizations and hybrid work environments exhibit significantly higher levels of AI familiarity and usage, while extensive professional experience is associated with lower engagement. Regional differences further highlight disparities in exposure and training opportunities, with respondents in the United States reporting higher familiarity, usage intensity, and access to formal training compared with their global counterparts. Across the sample, AI is primarily perceived as a tool for automating repetitive tasks and augmenting data analysis, reflecting its role in enhancing efficiency rather than replacing professional judgment. Our findings provide one of the first large-scale empirical characterizations of AI adoption in the AEC industry. The results highlight the central role of organizational readiness, workforce development, and workflow integration in shaping AI uptake, and offer actionable insights for industry strategy, education, and future research on AI-enabled engineering systems within the AEC industry.