Incidents are a regular occurrence in the construction industry: a worker slips on a wet surface, equipment gets operated incorrectly, or someone skips a necessary safety step that leads to an injury. These moments unfold quickly and require equally fast action from leadership. But no matter how often they occur, the question that follows every incident is the same. How can we prevent this from happening again?
Historically, corrective post-incident actions have relied heavily on experience or time-consuming analysis, where a safety manager reviews an incident report and spends time reviewing historical incidents and researching training options. Sometimes the corrective training or action is successful – but sometimes the same incident repeats itself again a short time later, suggesting that the corrective training missed the mark.
According to the Bureau of Labor Statistics, the construction industry recorded approximately 170,000 cases of injuries, illnesses and fatalities in 2022. With a number that high, and each of those incidents requires a corrective training response, there are high stakes around getting it right. And with nearly two in five industrial supervisors admitting to being unsure of how to act when they noticed a safety risk, it’s clear that there’s a disconnect between what needs to be done in terms of prevention, and what actually happens. For construction companies, effective corrective training is both a good practice and good for business – and that’s where AI tools are starting to have a big impact.
Going Instantly from Incident to Insight
AI tools can now help analyze workplace incident details and identify the most relevant corrective training from directly within the reporting process itself. Instead of a safety manager having to sort through dozens of potential courses or modules, AI reviews what happened and the circumstances around the incident to instantly suggest specific training that addresses the root cause.
However, this doesn’t replace human judgement. Construction safety professionals still need to make the final call. What AI does is accelerate the analysis and surface options that might not be immediately obvious. A fall from a ladder may point to refresher trainings on ladder safety, but depending on the incident details, it could also reveal training gaps around worksite organization, PPE usage or crew member communication protocols.
In construction, which data reveals is the most dangerous occupation in the U.S., AI tools continue to have immense potential to improve worker safety outcomes. Tools that can quickly match incident patterns with precise training interventions become particularly valuable for teams as they look to take more action around reducing repeat safety incidents. AI tools' analytical speed helps make them so effective – and when corrective action happens quickly and feels directly connected to what just occurred, organizations can reinforce the message that safety is a genuine priority and not just for show.
Getting Specific About Root Causes
Traditional corrective training often casts a wide net, meeting a requirement but not necessarily addressing why that specific incident happened. With the right AI tools, organizations can easily parse incident details more granularly. Was a tool malfunctioning? Was the worker fatigued? Was there confusion about the proper procedure? Each of these scenarios points to different training needs, and AI tools can help distinguish between them through analyzing the incident documentation.
Tools that use AI to process and analyze data can allow the identification of potential risks and hazards that are traditionally more difficult to pick up on. When applied to incident analysts, this function means connecting the dots between those seemingly unrelated factors – time of day, weather conditions, project phase, crew composition – that influence why an accident occurred and potentially what training or other proactive measures can prevent recurrence.
Similar tasks can be performed on different sites with different crews under different conditions, and because these environments are complex, these tasks can generate entirely different safety challenges. AI tools can recognize these patterns across many incidents and sites, and use their pattern recognition to help tailor action to the specific circumstances of each new incident.
Practical Implementation
The key to ensuring AI-driven recommendations is actually helpful to a construction organization is ensuring these models and tools have good data to back them up. Incident reports need to be detailed and specific, providing context that enables those smart training recommendations. For example, “worker injured using saw” offers far less insight for an AI model to work with than “worker sustained laceration on left hand while adjusting blade guard on table saw”.
Training teams to document incidents with thorough details pays dividends. Better data leads to better AI tool recommendations, and this leads to more effective corrective training. Taking the time to be thorough about incident details will help in the long run – not only because it helps these tools develop insights, but because it will allow for more accurate lookbacks and trend analysis over time, which ultimately helps teams better tackle repeat incidents.
Construction companies can also benefit from using AI tools to track training effectiveness over time. If a worker completes corrective training and then has a similar incident a few weeks later, that’s a red flag that the training didn’t stick, or didn’t effectively address the issue, which might require further action or amended training. If programmed and set up correctly, AI tools can help identify these cases and prompt a fresh approach based on what’s worked and what hasn’t.
AI’s Impact on the Future of Safety Culture
Construction work will always carry inherent risks. Industry leaders should be focused on learning from each incident in a way that reduces the likelihood of it happening again. AI tools can speed up that process by making preventive action recommendations more consistent and precise.
The average cost per medically consulted construction industry exceeds $40,000, not to mention lost productivity, recovery time and the human toll on the victim. When faster, more accurate training recommendations help prevent even a fraction of incidents, the return on investment becomes clear. AI tools woven into the incident process can help reduce risks, leading to workers achieving better outcomes – including less serious injuries and even fatalities.
