The purpose of this study is to develop a framework that identifies the drivers, challenges, and benefits of integrating Generative AI (GenAI)–driven risk management into sustainable development projects. To achieve this aim, a systematic literature review was conducted, analysing 66 articles on GenAI applications in project risk management published in leading academic journals between 2014 and 2024. The findings indicate that integrating GenAI into risk management enhances sustainability performance by improving environmental, social, and economic outcomes. This contribution is reflected in mechanism-level improvements across the risk management process, including earlier risk identification and prediction, faster interpretation of unstructured project data, and enhanced decision support. These capabilities reduce rework and material waste, strengthen safety and quality management, and improve regulatory traceability and cost efficiency. GenAI also supports more accurate risk forecasting, resource optimisation, and compliance monitoring, enabling project teams to address sustainability challenges more proactively. Despite these benefits, several barriers limit widespread adoption, including technical constraints, legal and regulatory uncertainty, ethical concerns, organisational readiness issues, and resource limitations. The review further highlights that sustainability gains depend on data quality, system transparency, and effective human oversight, as weak governance may introduce bias and reduce decision reliability. The proposed framework provides a structured approach to overcoming these challenges, promoting effective and sustainable GenAI-driven risk management in sustainable development projects. The framework serves as a roadmap for organisations seeking to balance innovation with sustainability in project risk management practices during the era of digital transformation. • Conducts a systematic literature review (SLR) of 66 peer-reviewed articles on GenAI-driven risk management for sustainable development projects, published between 2014 and 2024. • Identifies key drivers of GenAI adoption in project risk management, including enhanced predictive accuracy, regulatory compliance, and sustainability objectives. • Examines major challenges and risks of integrating GenAI into project risk management, such as legal constraints, ethical concerns, resource limitations, skills gaps, and technical complexities. • Demonstrates GenAI’s impact on improving projects risk management across environmental, social, and economic sustainability in construction projects. • Develops a GenAI-driven risk management framework to support sustainable development projects.
Mohamed et al. (2026) studied this question.