Exploring the risks of integrating generative artificial intelligence into construction risk management : insights from a systematic literature review

Mohamed, Mohamed Abdelwahab Hassan and Al-Mhdawi, M.K.S. and Rahimian, Farzad Pour and Ojiako, Udi and O’Connor, Alan and Mahammedi, Charf (2025) Exploring the risks of integrating generative artificial intelligence into construction risk management : insights from a systematic literature review. In: 6th International Conference on Civil and Building Engineering Informatics, 2025-01-08 - 2025-01-11, Hong Kong University of Science and Technology.

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Abstract

The rapid advancements in Generative Artificial Intelligence (GenAI) have unlocked transformative potential across various industries, including construction. With its ability to generate content, automate processes, and enhance decision-making, GenAI offers significant opportunities to improve the efficiency and accuracy of Construction Risk Management (CRM). However, its integration into CRM also brings a new set of risks and uncertainties that are unprecedented in traditional risk management frameworks. To this end, the purpose of this research is to identify and classify the key risks associated with integrating GenAI into CRM. To achieve this, a three-step systematic literature review was conducted, analysing 48 scholarly articles on GenAI for CRM from Scopus-indexed academic journals published between 2014 and 2024. A total of 25 risk factors associated with GenAI integration in CRM were identified and classified under seven key categories: financial risks, technological adaptability risks, information integrity risks, input quality risks, and ethical and governance risks. This study enhances the understanding of risk factors in GenAI integration by presenting a structured framework that categorises the associated risks of GenAI integration into CRM while highlighting their interconnectedness. It also lays the foundation for interdisciplinary approaches and future empirical research to validate and expand these insights across diverse construction contexts.

ORCID iDs

Mohamed, Mohamed Abdelwahab Hassan, Al-Mhdawi, M.K.S., Rahimian, Farzad Pour, Ojiako, Udi ORCID logoORCID: https://orcid.org/0000-0003-0506-2115, O’Connor, Alan and Mahammedi, Charf;