Buyer's Guide

When safety teams evaluate AI monitoring platforms, the pitch they usually hear sounds something like this: "Our system detects unsafe behaviours in real time and sends alerts so you can act before an incident occurs." That is not a bad pitch. Real-time detection is genuinely useful, and most of the platforms that deliver it do so well. But there is a question that pitch leaves unanswered: what happens after the alert? Because alerts, on their own, do not change behaviour. Th

Getting AI safety monitoring working well at one site is a genuine achievement. Getting it working consistently across five, ten, or fifty sites is a different problem entirely. Most organisations that have tried to scale a safety technology deployment know the pattern: the pilot facility runs beautifully, results are strong, leadership approves the rollout — and then everything gets complicated. Different facilities have different camera setups. Site managers have different

One of the most common questions I hear from operations and EHS managers evaluating computer vision AI for safety is: "How long is this going to take?" It's a fair question. Most people's experience with enterprise technology deployments involves months of scoping, lengthy integration projects, infrastructure upgrades, and a go-live date that keeps moving to the right. When you've been through that cycle a few times, scepticism about timeline promises is healthy. So here's t

You've seen the demo. You're convinced the technology works. Now you need to convince everyone else. If you've ever tried to get budget approval for safety technology, you know the challenge. The EHS team is already on board. The CFO wants hard numbers. Operations wants to know it won't slow things down. IT wants to know it won't break things. And leadership wants to know why this investment, why now, and what happens if it doesn't work. Building a business case for computer

Every AI safety platform looks impressive in a demo. The footage is clear, the detections are sharp, the dashboard is clean. You walk out thinking, "That looks amazing." But demos are designed to look amazing. The vendor controls the environment, the camera angles, the lighting, and the scenarios they show you. The real question is whether the platform will perform like that in your facility, with your cameras, your layout, and the messy reality of a working warehouse or manu

As an engineer, I understand the appeal of building things yourself. There's a clarity to owning the entire stack, understanding every component, and having the flexibility to change anything at any time. When your organisation starts exploring computer vision AI for safety, the question "could we build this ourselves?" is a natural one. The honest answer is: yes, technically you could. But the more useful question is whether you should. And the data strongly suggests that fo

If you're reading this, you've probably been asked to evaluate, approve, or champion an AI pilot inside your organisation. Maybe it's a computer vision platform for safety. Maybe it's a predictive maintenance tool. Maybe it's one of six AI experiments your leadership team kicked off this quarter. Here's the part nobody wants to say in the steering committee meeting: most of those pilots are going to fail. Not because the technology doesn't work. Not because your team isn't ca