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.
Qureshi et al. (Mon,) studied this question.
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