A GenAI-driven risk management framework for sustainable development projects
A GenAI-driven risk management framework for sustainable development projects
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.
Mohamed, Mohamed Abdelwahab Hassan
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Al-Mhdawi, M.K.S.
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Ojiako, Udechukwu
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Qazi, Abroon
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Mahammedi, Charf
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Dacre, Nicholas
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5 March 2026
Mohamed, Mohamed Abdelwahab Hassan
e74fb8df-7966-479c-8607-282264ae64ca
Al-Mhdawi, M.K.S.
e23cdd27-fe4c-4aec-81b3-be2b2616bf6c
Ojiako, Udechukwu
ba4aa342-5408-48d7-b71d-8197388bbb80
Qazi, Abroon
a52beaff-aef6-4a48-8d11-5dcbe6dde2d7
Mahammedi, Charf
06646d31-597e-4059-9188-cb832267bbfc
Dacre, Nicholas
90ea8d3e-d0b1-4a5a-bead-f95ab32afbd1
Mohamed, Mohamed Abdelwahab Hassan, Al-Mhdawi, M.K.S., Ojiako, Udechukwu, Qazi, Abroon, Mahammedi, Charf and Dacre, Nicholas
(2026)
A GenAI-driven risk management framework for sustainable development projects.
Project Leadership and Society, 7, [100217].
(doi:10.1016/j.plas.2026.100217).
Abstract
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.
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Mohamed_Al-Mhdawi_Ojiako_Qazi_Mahammedi_Dacre_2026_GenAI_Risk
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More information
Accepted/In Press date: 25 February 2026
e-pub ahead of print date: 26 February 2026
Published date: 5 March 2026
Identifiers
Local EPrints ID: 510363
URI: http://eprints.soton.ac.uk/id/eprint/510363
ISSN: 2666-7215
PURE UUID: 5d62169f-02c7-43d9-9dc3-57e7dee70ae1
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Date deposited: 27 Mar 2026 17:38
Last modified: 28 Mar 2026 03:00
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Contributors
Author:
Mohamed Abdelwahab Hassan Mohamed
Author:
M.K.S. Al-Mhdawi
Author:
Udechukwu Ojiako
Author:
Abroon Qazi
Author:
Charf Mahammedi
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