Exploring the potential of artificial intelligence for improving on-site construction logistics; Opportunities and challenges

Abstract

The construction industry continues to face multiple challenges related to on-site logistics, including fragmented information flows, inefficient material handling, limited space availability, and reactive planning practices. These challenges are particularly significant in urban construction projects, where restricted access and dynamic site conditions increase the complexity of logistics operations. At the same time, recent advances in Artificial Intelligence (AI) have created new opportunities to support decision-making, optimize resource utilization, and improve coordination across construction processes. The aim of this thesis is to explore how AI can support on-site construction logistics and to identify both the opportunities and barriers associated with its implementation. The study was conducted through a qualitative research approach combining a literature review with semi-structured interviews involving logistics managers, consultants, coordinators, and other industry practitioners. The findings indicate that many logistics-related problems originate from insufficient early-stage planning, fragmented communication between stakeholders, and a predominantly reactive approach to logistics management. Interviewees generally expressed positive attitudes toward AI and multiple areas were identified that have significant potential for AI implementation such as decision support, planning optimization, data integration, clash detection, traffic routing, and automation of administrative tasks. However, several barriers were also identified, including low digital maturity, poor data quality, organizational resistance to change, and the highly contextual nature of construction projects. The study concludes that AI has considerable potential to improve on-site construction logistics, primarily as a decision-support tool rather than a replacement for human expertise. Successful implementation depends not only on technological capabilities but also on improvements in data management, organizational readiness, and the integration of logistics considerations into early project planning. The results suggest that gradual adoption of AI-supported processes may provide the most realistic pathway toward more efficient and proactive construction logistics management.