Abstract
Purpose
This study assesses the significance of risks associated with integrating Generative Artificial Intelligence (GenAI) into risk management for sustainable construction projects (SCPs). Given the early stage of GenAI adoption in the construction sector, the study adopts an exploratory approach to evaluate how industry professionals perceive the efficacy and preparedness of existing risk management (RM) practices in addressing emerging GenAI-related risks. It also proposes response strategies to mitigate their potential impacts.
Methodology
The research followed a five-stage methodology. First, a systematic literature review (SLR) identified and classified GenAI-related risks. Second, a multi-criteria assessment model was developed to evaluate risk significance and the perceived efficacy of RM practices in addressing these emerging risks. Third, a structured survey involving 80 construction experts provided input data for the developed assessment model. Fourth, a fuzzy-based model was developed to quantify the perceived level of RM practice efficacy in relation to GenAI-related risks. Finally, a semi-structured expert survey identified best-practice response strategies for each risk category.
Findings
The study identified 30 risk factors grouped into five categories: input quality, technological adaptability, ethical and governance, information integrity, and financial risks. The results indicate a generally low-to-medium level of perceived preparedness of current RM practices in addressing GenAI-related risks. This finding suggests that existing RM structures may not yet be fully equipped to manage the complexities introduced by GenAI, particularly within the fragmented, multi-stakeholder, and sustainability-driven context of SCPs. Best-practice strategies were also identified for each risk category.
Implications
The study presents an integrated RM assessment model positioned as an early-warning tool for evaluating organisational preparedness for GenAI integration in SCPs. It identifies gaps in perceived RM efficacy, highlighting the need for targeted mitigation strategies. The proposed response strategies offer practical guidance for improving resilience and readiness in SCPs while considering sector-specific challenges such as temporary project organisations, regulatory demands, and lifecycle sustainability requirements.
Originality/Value
This study is among the first to assess GenAI-related risks in RM for SCPs using fuzzy logic. It adopts an anticipatory and perception-based approach suited to early-stage adoption rather than evaluating mature implementation. The findings highlight gaps in perceived RM preparedness and provide sector-specific insights that support more effective and responsible GenAI integration in sustainable construction.