Generative AI empowering the management of major complex engineering projects: Complexity, mechanisms, and future directions

Abstract

Major complex engineering projects encompass infrastructure, complex software and product development, temporary responses to public-health emergencies and natural disasters, and large organized activities such as the Olympic Games. Across these settings, projects combine systems integration, lifecycle uncertainty, fragmented governance, and intensive data demands. This Comment uses selective evidence synthesis to examine how generative artificial intelligence (GenAI) can address these challenges through knowledge integration, scenario generation, contract and risk analysis, coordination, and organizational learning. Task-specific findings indicate potential benefits, but do not establish autonomous project-management capability. Multimodal integration requires aligned engineering data, tool-mediated reasoning, and verification. GenAI should therefore operate within a hybrid architecture: it expands and explains alternatives, validated engineering models test feasibility and trade-offs, and accountable professionals authorize decisions. China-specific digital supervision and data-security requirements further condition deployment. Future research should assess real lifecycle outcomes through field trials and cross-project benchmarks.

Keywords

generative AI / major complex engineering / systems integration / engineering governance / major events

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Higher Education Press 2026

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