AI for Risk Reduction in Construction Projects: Examining Safety, Cost, and Schedule Impacts
DOI:
https://doi.org/10.54489/ijbas.v6i1.601Keywords:
Artificial Intelligence, Construction Risk Management, Predictive Analytics, Safety Performance, Cost Control, Schedule Management, Machine Learning, UAE ConstructionAbstract
AbstractThe construction sector has long grappled with recurring safety failures, chronic cost overruns, and persistent schedule slippages. Even with well-established project management frameworks in place, outcomes across the industry remain far below what they should be. A significant part of the problem lies in how risks are handled — specifically, the fact that most organisations continue to operate reactively, addressing problems only after they have already made their mark on project performance.
This paper investigates how Artificial Intelligence (AI) can be brought into construction risk management in a structured, systematic way — not as a standalone fix, but as a layer that fundamentally changes how teams detect, interpret, and respond to risk. The study draws on a qualitative research approach. Nine professionals — drawn from project management, safety engineering, and planning roles — were interviewed across live construction projects in the UAE. Their responses were examined through the thematic analysis process developed by Braun and Clarke (2006), organised around five key variables drawn from existing research.
What emerged from the interviews was consistent across participants: AI tools shift the entire frame of risk management from reaction to anticipation. Safety violations that previously went undetected for hours were flagged within minutes. Cost deviations that would have surfaced only in monthly reviews were visible three to four weeks earlier. These are not trivial differences — they represent the gap between managing a crisis and preventing one.
That said, the research makes clear that these benefits are not automatic. Three recurring obstacles cut across nearly every organisation studied: fragmented data environments, insufficient skill among staff, and deep-seated resistance to changing established working habits. Without confronting these directly, AI remains an expensive tool that underdelivers. The paper concludes with a five-pillar framework designed to guide organisations through the full implementation journey.
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