Can You Actually Trust AI?

If AI feels like it’s moving faster than you can keep up with, you’re in good company. Even the people I know working at the frontier of this technology say the same thing: there’s a new model, a new tool or a new buzzword every week, and nobody can track all of it.

So let’s start with some reassurance. You’re not behind. Most people aren’t sure where AI fits in their work yet, and most employers haven’t helped them figure it out.

It isn’t too late to start, either. That’s one of the seven software myths we hear most often.

This is the first post in Human Out of the Loop, a series for people who don’t work in tech but are being asked to make decisions about it. Each short post takes one piece of AI, explains it in everyday language, and shows what Canadian workers are actually experiencing.

What does “human in the loop” mean?

People in the AI world use this phrase constantly. The fear most people have is the opposite: a human out of the loop. Software making decisions nobody reviewed, sending emails nobody read, or changing records nobody approved.

That fear is reasonable. And the good news is that it’s largely in your control. The rest of this series is about how to keep it that way.

Why does AI get things wrong?

Think of an AI model as a powerful engine. It’s very good at one thing: predicting the most likely next word, over and over, based on patterns it learned from an enormous amount of text.

That’s what makes it so fluent. It’s also why it can be confidently wrong. The engine isn’t looking facts up the way you would. It’s producing what sounds right. Most of the time that’s close to what is right. Sometimes it isn’t, and it sounds just as sure of itself either way.

An engine also doesn’t decide where to go. It needs a car built around it, someone at the wheel and a map. We’ll take that car apart piece by piece in this series, starting with why AI rarely gives the same answer twice (part 2).

What are Canadian workers seeing?

This fall we partnered with Angus Reid to survey 1,100 working Canadians about AI at work. One finding stood out more than any other.

For most people it’s a little. For a sizeable group, it’s a lot: about a third say more than half of what AI gives them needs fixing.

The mistakes don’t always get caught, either. 24% of AI users say they caught errors before they caused a problem. 18% say AI output did cause a problem at work: 15% a minor one, and 3% a serious one, such as a safety incident or a financial loss.

None of this means AI isn’t useful. 65% of AI users say it saves them time, about 6.1 hours a week on average for those who save time. It means the checking is part of the job.

When should you trust AI, and when shouldn’t you?

We asked workers in heavy industries such as construction, manufacturing and energy how much they trust AI to get different tasks right. Their answers line up with good common sense.

That matters, because AI is already arriving on job sites, from predicting schedule delays to spotting safety risks. The Construction Innovation Centre describes where it’s heading in From Tools to Intelligence.

For routine information, most people trust AI as long as they check it. For decisions that affect someone’s safety, trust falls sharply, and almost nobody trusts it completely. That instinct is exactly right.

The simple rule: the higher the stakes, the closer the check.

One habit that keeps you in the loop

It takes a minute. For a quick email, one glance might be enough. For a safety plan, a quote or a contract, it should take a lot longer, and probably involve a second person.

What this means for your business

If your team is already using AI, and the numbers say many of them are, the most useful thing you can do isn’t buying a new tool. It’s making the checking official.

• Name it. Decide who reviews AI work, and for which tasks.
• Budget for it. Review time is real work. Count it when you count the time AI saves.
• Support it. A short list of approved tools and simple rules on what information can go into them go a long way. Only 16% of workers say they have written rules on entering company or customer information into AI.
• Start with your people. Ask your teams where AI saves them time and where it makes them nervous, then build your approach around what they tell you.

Frequently asked questions

About the data. Survey conducted by Angus Reid Group for Punchcard, September 2–11, 2026, among 1,100 working Canadians, with additional recruitment in heavy industries. Results are self-reported. Questions on rework, errors and time savings were answered by 553 AI users. Full results are in The State of AI at Work in Canada.

Canadian attitudes to AI: KPMG and the University of Melbourne, Trust, attitudes and use of AI, June 2025 (1,025 Canadians).