The construction industry has experienced slow productivity growth over the past decade, and generative artificial intelligence (AI) has emerged as a potential solution. However, there is limited understanding of its adoption challenges, particularly from a decision-oriented prioritisation perspective to support effective implementation. This study aims to identify and prioritise the most significant challenges associated with the adoption of generative AI in construction projects. A systematic literature review (SLR) was conducted to identify the main challenges and sub-challenges discussed in the existing body of literature. Based on the SLR findings, an online questionnaire was developed to collect expert judgements from industry practitioners. The data were analysed using the Fuzzy Analytic Hierarchy Process (FAHP), and the robustness of the results was examined through sensitivity analysis by simulating variations in criteria importance. The results identify data privacy, security and legal liability; poor quality and availability of training data; and lack of construction-specific knowledge and language as the most critical challenges. These findings underscore the importance of data governance, data quality, and domain-specific capabilities for effective implementation. They also provide practical guidance for managerial decision-making by prioritising critical challenges for the effective adoption of generative AI in construction projects.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)