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Construction Readiness

How AI Advances Safety and Construction Readiness

Sandra MacGillivray
Sandra MacGillivray

In the Construction Readiness Assessment (CRA), Health, Safety & Security is a stand-alone category, but a strong safety culture spans the entire project lifecycle and is woven into workforce readiness, planning, execution, and even handover. This year’s Construction Industry Institute (CII) RT-422 research reinforces that reality, presenting 19 artificial intelligence (AI) use cases that shift safety from reactive response to proactive prevention. 

narrow conference room with about ten people in a HAZOP review. Mix of engineers in dress shirts and operations staff in safety vests, with hard hats on the table. One large screen at the front shows a process flow diagram with AI hazard detection overlays

One of my earliest conversations at a CII conference was with a now-retired professional who told me, “If you always make safety a priority, cost and schedule performance will follow.” That insight rings truer than ever. AI is now giving us tools to act on safety risks faster, anticipate hazards earlier, and embed prevention into every stage of project delivery.

While CII members continue to outperform industry averages for Total Recordable Incident Rate (TRIR) and Days Away, Restricted, or Transferred (DART), the persistence of fatal incidents underscores the continued need for innovative approaches to safety.

RT-422 identified 19 AI use cases that move safety from reactive response to proactive prevention. These span the project lifecycle and roles, from design engineers and field engineers to the craft workforce. The research covers innovations like AI-driven hazard detection, real-time environmental monitoring, adaptive safety training, and predictive analytics for risk mitigation.

By embedding these AI capabilities into CRA-aligned planning and execution, owners and contractors can elevate readiness from procedural compliance to actively engineering safety into every project start.

 

Why this matters for advancing Construction Readiness

The RT-422 findings have high applicability across several CRA categories:

  • Health, Safety & Security: AI-powered hazard identification and real-time monitoring systems strengthen safety programs before the first crew mobilizes. 
  • Human Resource Management: Adaptive, AI-driven safety training ensures the workforce is prepared for project-specific risks and evolving site conditions. 
  • Tools & Equipment: Wearables, sensors, and AI-enabled devices enhance personal protection and situational awareness. 
  • Planning: Predictive safety analytics allow risks to be identified and mitigated during the planning phase, not after incidents occur.

Integrating these AI solutions into CRA discussions helps both owners and contractors shift from asking “Do we have a safety plan?” to “Is our safety program built to prevent incidents under the conditions we will actually face?”   

 

Shout-Out to the RT-422 Research Team

The Artificial Intelligence for Construction Safety research was made possible through the collaboration of CII owners, contractors, and academics. Special thanks to the RT-422 research team members for their contributions: 
 
McDonough Bolyard Peck, Air Products, Fluor Corporation, ExxonMobil Corporation, Oracle USA, Texas A&M University, Kiewit, Worley, McDermott International, FTI Consulting, Ontario Power Generation, S&B Engineers, Cargill, Occidental Petroleum Corporation, Alvarez & Marsal, Deloitte, Bruce Power, The Procter & Gamble Company, Louisiana State University. 
 
Their combined expertise ensured this research is grounded in real-world safety challenges and delivers actionable AI solutions for serious incident and fatality prevention.

 

The Challenge

Present: Too often, safety decisions are made without a full, real-time picture of site conditions. Are you using live data and situational awareness tools to make faster, better safety decisions as work unfolds?

Future: AI can predict risks, adapt training instantly, and prevent incidents before they happen. The first step could be as simple as piloting one tool, applying it to a high-risk scope, or sharing early wins. How will you lead adoption, while also addressing worker trust, data quality, integration complexity, privacy, and cost, so AI-enabled safety becomes a reality on your projects?

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