The Safety AI Maturity Model: Where Does Your Business Stand?

Paper logs, clipboards, and walkthroughs. You’ve probably used them all. They were fine, for a while, but as operations scale and risks multiply, “fine” stops being good enough. You can’t prevent what you don’t see, and that’s the problem: manual safety processes miss too much, too often. Incidents keep slipping through the cracks. Compliance reviews turn into fire drills, and safety teams, no matter how committed, are stretched thin.

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There’s a better way. EHS management systems and AI-powered safety tools are now giving teams 24/7 visibility into workplace risks and the ability to act before things go wrong. But not all companies are ready to jump into full automation overnight. That’s where the Incident Reporting Tech Maturity Model comes in. This article will help you figure out where your business stands today and what it takes to move forward.

The Incident Reporting Maturity Model maps out five stages of technology adoption that lead to full AI optimization. While AI doesn’t play a role in the earliest stages, they’re essential for laying the digital groundwork AI needs to perform effectively.

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Here’s a quick look at the five stages:

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• Traditional: Manual checklists and reports captured via paper or spreadsheets. Reactive by nature.
  
  
• Digital: Some automation is in place, such as digital incident reporting through EHS management platforms or data collection via IoT sensors, but AI hasn’t yet been introduced to analyze or act on that data.
  
  
• AI-Assisted: Basic AI in use, often for PPE detection or flagging hazards.
  
  
• AI-Driven: Predictive tools, such as LLMs, are increasingly guiding safety actions through AI-assisted hazard detection, moving beyond manual action creation alone.
  
  
• AI-Optimized: Incident reporting and corresponding actions are fully automated through AI tools, making them adaptive and deeply integrated into day-to-day operations.
  
  

If you’re in stages one or two, you’re far from alone. A global survey by Verdantix found that 29% of firms plan to increase their investment in computer vision or launch pilot programs this year. This reflects a broader trend, most companies are still early in their journey, working to understand the value for their organisation and how to integrate it into existing safety programs. What matters most is continuing to move up the curve.

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Making the Climb: From Manual to AI-Optimized Safety

So how do you actually move forward? Here are five practical ways to progress your AI maturity without disrupting daily operations.

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1. Audit Your Current State Honestly

Before jumping into AI, take stock. What systems are you already using? Do you have consistent incident data? Is your CCTV network active but underused? This isn’t about getting everything perfect first, it’s about knowing what gaps exist. If you're still using whiteboards to track near-misses, that’s a sign you’re ready for more. Start by mapping your current safety workflow, from hazard identification to follow-up.

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2. Get Wins Early by Automating One Risk

You don’t need to digitize everything on day one. Start with one critical area where risk is high or compliance pressure is mounting. Let’s say forklift speed violations are common in your warehouse. AI can flag those in real time and send alerts to your supervisors instantly. That’s a simple, high-impact use case. It also builds internal support for further rollout.

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3. Integrate Safety with Your Existing Tech Stack

Many safety tools struggle to take off because they live in a silo. Look for solutions that work with the infrastructure you already have, like your cameras, your cloud, and your workflows. The best safety AI platforms plug into your existing CCTV, log events automatically, and feed data into your reporting tools. That means less lift from your IT team and faster ROI.

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4. Use AI to Inform, Not Replace, Your Team

AI doesn't replace safety teams, it supports them. But to get the most out of it, safety leaders need to trust what the data is telling them. That means using AI not just for alerts, but for trend analysis. Are most violations happening during the night shift? Is one zone seeing repeated issues? This kind of pattern spotting turns your monthly safety review from guesswork into action.

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5. Build a Culture That Embraces Data

Technology only works when people use it. That’s why culture matters. Teams need to know that AI isn't there to “watch” them, it's there to protect them. Share wins with staff. Show them the reduction in incidents. Use video footage in toolbox talks. Let them see how the system helps, not punishes. Companies that connect AI to culture see faster adoption and better results. At Marks & Spencer, for example, incident rates dropped 80% after AI deployment​. (2025 Safety Computer Vision Market Report)

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Final Thoughts

AI in safety isn’t some far-off idea. It’s already happening, and not just at tech giants or deep-pocketed multinationals. Forward-thinking manufacturers, logistics firms, and warehouse operators are using AI right now to make smarter, faster safety decisions.

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So, where are you on the maturity model? More importantly, where do you want to be? With the right steps, the move from reactive to proactive doesn’t have to be overwhelming. Start small, build momentum, and let the data show the way.

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