If you work in safety or EHS, you probably have more information than you have time to review. Incident reports, near misses, training records, inspections, and employee concerns all deserve attention. Meanwhile, the work happening on the floor keeps moving.
Artificial intelligence is creating new ways to organize that information and identify potential problems. But can it actually help prevent injuries?
I think there is an opportunity here. The value depends on what we do with the information, how we involve employees, and whether it leads to better working conditions.
In our latest MuuvWell Podcast episode, we explored how AI could support safety leaders and where human judgment still matters.
What Does AI in Workplace Safety Actually Look Like?
You don’t have to be a technology expert to understand the basic applications. Three practical uses are helping draft documents, analyzing images or video, and finding patterns in safety data.
A safety manager could use AI to create a first draft of a toolbox talk or summarize a group of near-miss reports. Instead of reading every report individually to look for common themes, they could use a tool to help identify repeated locations, tasks, or concerns.
Other tools use computer vision to analyze movement and posture. NIOSH has explored how this technology can support ergonomic assessments by identifying body positions and movements from video. The potential is useful, but the findings still require careful interpretation.
These are different applications, and they need different levels of review. A tool that helps write a training outline is doing a very different job from one that flags a potential hazard around moving equipment.
The first question should be simple: What problem are we trying to solve?
Could Less Paperwork Mean More Time With Employees?
Documentation is part of the job. Safety leaders need records, training materials, and reports to communicate clearly and follow through on concerns.
But preparing those materials takes time.
AI can help produce a starting point. A knowledgeable person still needs to check the facts, make sure the content fits the workplace, and correct anything inaccurate or incomplete. A polished paragraph can still contain a mistake.
What interests me most is what safety leaders could do with the time they save.
Could they spend more time observing a demanding job? Talk with an employee who has mentioned discomfort? Follow up on a workstation change? Find out why a procedure is difficult to follow?
If AI helps free up time for those conversations, that could be a meaningful benefit.
Can AI Help Employers Prevent Injuries?
Some tools may help identify movement patterns that deserve a closer look, such as repeated bending, awkward reaching, or lifting from an uncomfortable position. Computer vision research shows promise for assessing posture and movement, although recognizing a movement does not establish someone’s full injury risk.
Think about an employee reaching across a workstation hundreds of times during a shift.
A video analysis tool might flag that pattern. The next step is to understand the task. Are materials too far away? Is the work surface at an appropriate height? Could the layout change so the employee can complete the same job with less reaching?
That is where the information becomes useful.
At MuuvWell, we look at workforce performance through the relationship between employees and the physical demands of their jobs. Understanding those demands helps employers identify opportunities to support their people and address strain.
An alert should prompt someone to look more closely at the work. The benefit comes when that assessment leads to a practical improvement.
Watch the Podcast
For more on how AI could support safety managers, injury prevention, and employee trust, watch our latest MuuvWell Podcast episode.
A Pattern Doesn’t Explain the Whole Problem
Suppose a review of incident reports shows that more incidents happen late in a shift.
It would be easy to conclude that fatigue is the cause. But several explanations could be involved: lighting, staffing, overtime, a different mix of tasks, or a production push near the end of the day.
The pattern gives you somewhere to investigate. It does not give you a complete answer.
Someone still needs to observe the work and ask employees what is happening. The person performing the job may explain something that never made it into a report.
The data itself also deserves scrutiny. Missing reports, inconsistent descriptions, and incorrect classifications can affect what a system finds. NIOSH has identified inaccurate outputs, bias, and privacy concerns among the risks employers should consider when using workplace algorithms.
Safety leaders need enough understanding of a tool to question its conclusions and recognize its limits.
Will Employees See Support or Surveillance?
If cameras or monitoring tools are introduced into a workplace, employees will understandably have questions.
What is being recorded? Who can access it? How long is it kept? Will the information be used to improve the job, evaluate performance, or assign blame?
Those questions deserve clear answers before implementation.
I believe employees will pay close attention to what leadership does with the findings. If repeated reaching leads to a better workstation, they can see a benefit. If every alert leads to criticism while the same difficult working conditions remain, trust will be harder to build.
Employees should have a role in identifying the problem, evaluating the tool, and reviewing proposed changes. OSHA’s safety program guidance emphasizes worker participation because employees bring knowledge and experience that help employers identify hazards and develop solutions.
Before adopting a monitoring tool, ask: What would our employees need to see to believe this is here to protect them?
Then make those expectations part of the plan.
Start With One Problem You Can Act On
You don’t need to introduce AI across your entire safety program at once.
Start with a recurring issue that deserves attention. It might be lifting strain in one department, repeated reaching at a workstation, or near misses in a busy traffic area.
A practical approach is to:
Define the problem. Be specific about the task, location, and concern.
Involve employees. Ask the people doing the work what they experience and what they would change.
Check the tool’s findings. Compare its output with direct observation and a knowledgeable review.
Assign follow-through. Decide who will investigate findings and make improvements.
Evaluate the result. Look for reduced exposure to the hazard and better working conditions, while tracking injuries and near misses over time.
Be careful about interpreting report counts alone. More near-miss reports could reflect employees becoming more comfortable speaking up. Fewer alerts could mean a useful change, or they could mean the tool is missing something.
The goal is to understand whether the work is becoming safer.
Better Information Should Lead to Better Working Conditions
AI may help safety leaders organize information, notice patterns, and decide where to focus their attention.
Leadership still owns the response.
Someone has to listen to employees, assess the work, make changes, and check whether those changes helped. A dashboard cannot do that on its own.
What is one problem in your workplace that better information could help you address?
At MuuvWell, we help employers turn concerns about physical job demands into practical injury prevention and workforce performance strategies. Our Onsite Performance Program brings support into the workplace so we can understand the work, engage employees, and help your team address opportunities to reduce strain and support employee performance.
If you’re looking for a proactive approach to preventing injuries, I would love to connect. Visit MuuvWell or reach out to me to discuss how our Onsite Performance Program could support your workforce.