1. The Great Inversion
For more than fifty years the people who work on industrial safety have done their job in a certain way. They look at what goes wrong and try to figure out why it happened. This started to change a lot in the 1970s when the government starting adding rules and regulations. Since then, when something bad happens at work people come to investigate. They stop everything, put up tape around the area, and try to find out what caused the problem. Then they write a report about what they found out.
The industry learned the way by making mistakes and paying the price. Over time they built up a collection of safety rules that were written after something happened. This way of thinking, where we only make changes after someone gets hurt meant that safety improvements eventually leveled off. Even though the number of accidents at work went down a lot since the middle of the century, the number of really bad injuries and deaths did not go down as much as we would like. The industry saw a drop in total recordable incident rates, but the rate of serious injuries and fatalities stayed about the same. The reactive model has reached its limit. You just cannot look into something that has not happened yet. Human observation isn't the way to stay safe... It is not good enough to prevent every single mistake in a complicated and changing world. The reactive model is not working because people cannot stay alert all the time. The reactive model is limited by what people can do.
We have seen a change in the way we think about safety. Now we try to stop things from happening before they start instead of just looking at what went wrong after it happens. The safety model is really changing because of artificial intelligence, special computers that can work on their own and better sensors that can see and hear lots of things. All these new technologies are coming together to help safety people predict when something bad might happen and stop it before anyone even starts working. The safety professionals can now prevent safety incidents before the first tool is lifted, which is a big deal for the safety model.
2. The Rise of Computer Vision: The Digital Spotter
2.3 Verified Claim: Exclusion Zone Breaches and Projection Prediction
Machines like cranes and forklifts are always moving. A crane load is swinging around. A forklift is turning. New systems are really good at figuring out where the machine and the human worker are going. These systems do not just look at where the machine and the human worker are right now. They look at where the machine and the human worker will be in the next few seconds. The system checks the path of the machine and the path of the worker. If the machine and the human worker are going to crash into each other the system does something to stop it. The system triggers an intervention when it thinks a collision is going to happen.
In complicated systems this detection works with machine controls to make the machine stop right away in an emergency. This means that people do not have to react quickly to stay safe. The machine just stops by itself.
3. Predictive Analytics
"Predictive Analytics" is about stopping the accident before the accident happens. With predictive analytics we can look at things that have happened before and use that information to stop another incident before it happens.
The second part of the 2026 safety model is, about changing the way we look at things. We are moving from looking at what happened with descriptive analytics to trying to figure out what will happen with predictive analytics. The big change here is that orginizations are putting all the information together into one thing called a "Risk Forecast" for the 2026 safety model.
3.1 The "Risk Forecast" Verified
Nowadays modern environmental health and safety platforms look at a wide range of potential hazards to point out situations that are potentially dangerous. These platforms do this to help keep people safe. They look at variables to flag high-risk situations, like these.
Workforce Fatigue. The artificial intelligence looks at the schedule. How it compares to what happened in the past. If many workers are working too much and going over the safe limits for their shifts the system will increase the risk score for that particular site. This is because workforce fatigue is linked to more incidents happening. The artificial intelligence uses this information (along with other static and dynamic variables) to help figure out the risk score for the site.
Environmental risk. Systems use weather information from the internet to understand what is going on. If the temperature drops it might send a warning about Ice/Slip Risk for scaffolding. This takes weather information and turns it into something we can actually use to make decisions. Environmental risk is something we especially need to think about when we're working with scaffolding.
3.2 The Output: The Daily Risk Forecast
The best safety directors use a "Risk Forecast" for the day that is made by combining these variables. This risk forecast for the day is like a watchman that helps find risks before they happen. It lets the safety director send help like a safety officer.
4. The End of Paperwork: Generative AI and Voice Interfaces
The third pillar is about the people part of safety which's the hassle of paperwork. Now in the year 2026 Generative AI (or GenAI for short) has found a way to make things easier by using our voice as the way to get information in and out of the system. Generative AI has really changed things by signifiantally reducing the amount of time to get a proper digital safety system off the ground.
4.1 Verified Claim: Dictation and Structuring
Imagine a person in charge like a foreman, who does not have to stand around with a clipboard checking off boxes. This person can simply use their phone to record what people say during the safety talk. The safety briefing is recorded on the phone and the AI system listens to this audio. The AI system takes the audio. Finds the specific jobs that people need to do the dangers they might face and the safety steps they need to take. Then the AI system automatically fills out the document, with all the right information and puts it in the database. The safety briefing is now a document that is easy to find in the database.
4.2 Verified Claim: Suggesting Control Measures
When a worker finds something that's not safe the computer program looks at the companys safety rules and regulations to figure out what needs to be done to make it safe. This way the worker does not just have to remember what to do the companys safety manual and rules tell them what is required to make sure everything is safe. The safety manual and rules are like a guide for the worker to follow so they can make sure the safety controls they put in place are the accurate ones.
5. Conclusion
Computer vision is great because it gives safety managers the ability to see what is going on all the time twenty four hours a day, seven days a week.
Predictive Analytics has given them the ability to see what risks are coming. This means they can look at relevant incidents in the past, compare it to what is happening now and see any potential problems that could happen in the future.
Generative AI has given people a way to talk to things that makes following the rules a lot easier. With tools like Gong that transcribe + create task lists based off a meeting, staying organized has never been so easy.