Prioritizing artificial intelligence adoption challenges in construction supply chains: evidence from the UAE

Artificial Intelligence (AI) has the potential to enhance efficiency, transparency, and sustainability in construction supply chains (CSCs); however, its adoption in the United Arab Emirates (UAE) remains limited due to interrelated technological, organizational, and governance challenges. Existing studies often examine AI adoption barriers in isolation, offering limited insight into their systemic interactions within construction contexts. This study aims to identify and prioritize the key challenges constraining AI adoption in UAE CSCs through an integrated decision-support framework. A hybrid approach was employed in which the Fuzzy Delphi Method FDM was used to validate twenty AI adoption challenges, Interpretive Structural Modeling ISM combined with MICMAC analysis was applied to examine their interdependencies, and machine learning ML techniques were used to validate the relative influence of the identified challenges. The results indicate that data security and authenticity concerns, legacy system integration, ethical and governance issues, and vulnerability to cyber threats constitute foundational barriers that shape downstream technological and organizational efforts. These findings highlight the importance of addressing governance and infrastructure readiness before large-scale AI deployment in CSCs. The study provides construction managers and policymakers with a structured basis for prioritizing AI adoption challenges and supports more informed decision-making toward sustainable digital transformation in the construction sector.