ORIGINAL RESEARCH article
Front. Built Environ.
Sec. Construction Management
This article is part of the Research TopicPeople, Process, Product, and Policy: Exploring the Nexus For The Sustainable Digital Transformation Of The Construction IndustryView all 7 articles
The Socio-Technical Gap: An AI Framework for Project Resilience in UK Construction
Provisionally accepted
Jawed Qureshi*
Kiran Rai- University of East London, London, United Kingdom
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The UK construction industry faces persistent productivity deficits, with performance 21% below the national economy average. This stems from fragmented Artificial Intelligence (AI) adoption, where dynamic scheduling and proactive risk management operate as isolated systems. Through a Systematic Literature Review following PRISMA 2020 guidelines, this study analysed 60 peer-reviewed papers (2009-2025) to investigate integration barriers and develop a conceptual solution. The review synthesised AI applications in scheduling optimisation and risk management, data integration enablers, and socio-technical adoption barriers. The primary contribution is an Integrated AI Project Control Framework featuring a Risk-to-Constraint Translation Engine that automatically converts heterogeneous risk signals into machine-readable scheduling constraints, establishing continuous feedback loops for adaptive project control. The framework addresses UK-specific challenges through modular design, BIM Framework alignment, and human-in-the-loop interfaces. A key limitation is that the framework remains conceptual, requiring empirical validation through prototype development and live deployment testing.
Keywords: artificial intelligence, construction management, digital transformation, Dynamic scheduling, ProactiveRisk Management
Received: 06 Nov 2025; Accepted: 23 Dec 2025.
Copyright: © 2025 Qureshi and Rai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jawed Qureshi
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