AI is everywhere. Most people are using it for something, and hardly anyone has stopped to work out where it should and shouldn’t sit in their business. That’s the part I care about, because it’s the part that goes wrong.
Why do we check AI work less than a person's work?
The better the output looks, the less anyone checks it.
If a new team member sent you a rough first draft, you’d go through it line by line. But give you a clean, confident AI output and you skim it and move on. Somewhere in that skim, you stop deciding whether it’s actually right.
Researchers call this automation bias. We tend to trust what a system hands us, and the more polished it looks, the more we trust it.
"The better the output looks, the less anyone checks it."
What's the AI risk that catches good businesses out?
It isn’t the dramatic one. Most people picture a data breach, a tool that goes rogue, or a job that disappears. Those risks are real.
The one that catches good businesses out is slower and harder to spot. A proposal goes out with a figure that’s slightly wrong. An email to a client doesn’t sound like you. A policy gets written that nobody really read. By the time you notice, it’s already happened.
Who should decide whether AI output is good enough?
You should, or someone you’ve clearly given that job to.
Knowing what good looks like, spotting the thing that’s slightly off, deciding what’s fit to send and what isn’t. That decision is the job. It’s what your name and your brand go on, and AI can’t make those decisions for you.
We put together the AI, Risk and Responsibility Guide to show exactly where that exposure sits in a business like yours. There are six risks worth knowing about, and a governance template you can adapt and put in place, so you stay the one deciding what’s good.
If you’ve been meaning to do something about AI but weren’t sure where to start, start there.