Application of Artificial Intelligence in Construction Contract Management: A Systematic Review

Construction contract management (CCM) domains, such as risks, delays, budget overruns, disputes, claims, and litigation, are very common in construction projects. Compared with other industries, the construction sector is less digitalized, particularly in CCM. The application of artificial intelligence (AI) can be a perfect solution to enhance efficiency in CCM. However, despite growing interest, there is a lack of systematic analysis examining how AI is being used across the full spectrum of CCM activities. This gap limits both practitioners who need guidance on effective AI adoption and researchers who require a consolidated understanding of existing progress and challenges to direct future investigations. Given this, the goal of this study is to provide a detailed review of how AI is being used in various aspects of CCM processes, with a focus on identifying major barriers and future potential. A systematic literature review (SLR) was conducted to explore AI applications in various CCM domains. After reviewing the selected articles, the results were organized based on key CCM functions throughout the project life cycle. The review shows that most AI-based studies focus on contract document analysis, risk management, and claim and dispute management, while other areas, such as delay analysis and postcontract evaluation, receive less attention. This study found that the most commonly used AI applications are natural language processing, neural networks and deep learning, machine learning, fuzzy systems, and Bayesian methods. This study also discusses current challenges and future opportunities for AI integration. The value of this work lies in offering construction professionals useful insights into applying AI for smarter CCM. The review also highlights key barriers to AI adoption in CCM, including the lack of accessible and standardized datasets and the persistent implementation gap between academic AI solutions and real-world industry practices.