Abstract
The construction industry is experiencing a rapid digital transformation with the integration of advanced technologies, including artificial intelligence (AI). However, the successful adoption of AI remains a challenge due to barriers in balancing technology with human and process factors. Under emerging Construction 5.0 principles, which emphasize human–machine collaboration and continuous learning, there is a critical need to better understand how AI-driven decision support can be effectively implemented in construction management. This study investigates key drivers, enablers, barriers, and opportunities in AI adoption within mass transportation logistics and pollution pattern analysis at a road construction site. The research builds on experience from this case to adapt a generic framework that supports a balanced integration of AI, organizational processes, and human insights. Using the integrated design and delivery solutions (IDDS) framework, this study employs an exploratory, abductive research approach, combining document reviews, stakeholder interviews, and operational observations to explore technological and organizational challenges. Key findings include: (1) AI adoption requires a balanced approach. Technological performance is crucial, but organizational learning, workforce engagement, and leadership support are equally critical; (2) lack of structured knowledge-sharing and training hinders AI adoption; further, some workers have limited awareness of AI’s potential, and leadership lacks clarity to communicate complex goals of digitalization efforts, thus hindering motivation and trust; and (3) adoption of AI in construction is fragmented. Without industry-wide collaboration and clear digitalization strategies, AI implementation is inconsistent, and interoperability challenges slow integration. This study contributes to the body of knowledge by providing a structured framework for AI operational deployment in construction that integrates organizational learning, human–machine collaboration, and process adaptation. The findings offer practical recommendations for improving AI-driven decision support through better leadership strategies, structured workforce training, and industry-wide collaboration to enhance interoperability and digital integration.