Industry Specific Framework for Artificial Intelligence Adoption in Project Risk, Cost and Schedule Management

Richard Bill Owusu Darko, Gershom Randy Mensah and Yetunde Omoyiwola Fawehinmi
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Richard Bill Owusu Darko: College of Professional Studies, Northeastern University, Portland, Maine, USA.
Gershom Randy Mensah: College of Professional Studies, Northeastern University, Portland, Maine, USA.
Yetunde Omoyiwola Fawehinmi: Department of Educational Administration and Human Resources, Texas A&M University, College Station, Texas, USA.

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Abstract: Artificial intelligence (AI) is increasingly influencing project management practice, particularly in relation to project risk, cost and schedule management. However, the reviewed literature indicates that existing applications and adoption approaches remain fragmented across sectors and project functions. This study examines the use of AI in project risk, cost and schedule management and develops an industry-sensitive conceptual framework for structured AI adoption. A scoping review approach was used to map relevant literature published between 2015 and 2026 across construction, infrastructure, engineering, information technology, energy, insurance and public sector project contexts. The review identifies key AI functionalities, including predictive analytics, machine learning-based forecasting, natural language processing, optimisation algorithms, explainable AI and intelligent decision-support systems. These functionalities are aligned with project management tasks such as risk identification and mitigation, cost estimation and control, financial forecasting, schedule planning, delay prediction and resource allocation. The findings suggest that AI can support improved forecasting, earlier risk detection, enhanced cost control and more adaptive schedule management. However, successful implementation depends on data quality, digital infrastructure, organisational readiness, workforce capability, governance arrangements, transparency, cybersecurity and human oversight. The proposed framework integrates these factors across readiness, data and technology infrastructure, AI functional modules, governance and outcomes. The study contributes to project management scholarship by linking AI capabilities with core project management functions while recognising the need for sector-specific adaptation.

Date: 2026-07-10
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Published in Journal of Economics, Management and Trade, 2026, 32 (7), pp.51-77

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