I've worked with teams across fintech, e-commerce, health tech, and logistics on AI implementation. Different industries, same mistakes. Every time.
Here are the five I see most often.
1. Starting with the hardest problem
Every time. The team sits down, identifies 10 potential AI use cases, and immediately gravitates toward the one with the biggest potential impact. Which also happens to be the most complex, the most politically sensitive, and the one most likely to fail.
I worked with a logistics company last autumn who wanted their first AI project to be demand forecasting. Noble goal. Massive potential value. Also requires clean historical data going back years, integration with multiple warehouse systems, and buy-in from a supply chain team who'd been doing it manually for a decade and didn't love the idea of a machine replacing their judgement.
We shelved it. Built an agent that automated their daily inventory reconciliation instead. Took two weeks. Saved someone 90 minutes a day. Nobody felt threatened by it because nobody wanted to do it in the first place.
Six months later, the team has built four AI tools and the supply chain team is now asking when they can get the demand forecasting agent. The resistance disappeared because they'd seen the other tools work.
Start easy. Build trust. Then tackle the hard stuff.
2. Not measuring the baseline
You can't prove AI is working if you don't know what "before" looked like.
I'm amazed how often I ask "how long does this task take currently?" and the answer is a shrug. "A few hours?" "Most of Monday morning?" These are guesses. And when you deploy an AI tool and someone asks what the ROI is, "it used to take a few hours and now it takes less" isn't going to cut it.
Before you build anything, time the damn process. Literally sit there with a stopwatch (or ask the person doing it to track their time for a week). Get a real number. Then you've got something to compare against.
One of my clients timed their invoice processing workflow before we automated part of it. The average was 47 minutes per invoice, 25 invoices per week. That's 19.5 hours per week. After the AI took over the data extraction and categorisation steps, it dropped to 12 minutes per invoice for the human review part. That's a specific, defensible number. It went in the board pack. It justified the next AI investment.
Without the baseline, it's just a story. With the baseline, it's a business case.
3. Treating AI like magic
This one's more subtle. Teams deploy an AI tool and expect it to just work, forever, without maintenance.
It won't.
Models get updated. Your data changes shape over time. The business process evolves. Edge cases accumulate. An agent that was 95% accurate on day one might be 85% accurate three months later, not because it broke but because the world around it shifted.
I set a monthly review for every agent I deploy. 20 minutes, max. Check the outputs, check the error rate, check the cost. If something's drifting, catch it early.
The teams that treat AI like a living thing (feed it, check on it, adjust it when needed) are the ones getting sustained value. The ones that treat it like a microwave (put stuff in, press a button, walk away) are the ones calling me six months later saying "our AI doesn't work anymore."
4. Building in isolation
The engineering team builds an AI tool. They're proud of it. It works beautifully. They show it to the people who are supposed to use it.
"That's not how we do it."
I've watched this happen more times than I can count. Engineers build something technically excellent that doesn't match how the actual process works. Not because they're bad engineers. Because they didn't spend enough time with the end users before building.
The fix is stupid simple. Spend a day with the people who'll use the tool. Watch them work. Ask them to walk you through the process, step by step, including the weird exceptions they handle without thinking about it. Those exceptions are where your agent will break.
At one company, the support team had an informal rule that any ticket from a customer with "enterprise" in their plan name got priority handling, regardless of the ticket category. It wasn't documented anywhere. It was just something everyone on the team knew. The AI tool routed those tickets like any other. Took two weeks for someone to notice and flag it.
Build with the users, not for them. The difference in adoption is night and day.
5. No feedback loop
You ship the AI. It runs. People use it (or don't). And you have no mechanism for finding out whether the outputs are actually good.
This links back to the deployment problems I wrote about in my post on deploying AI agents. If nobody tells you when the AI gets something wrong, you'll only find out when something goes properly sideways.
The minimum viable feedback loop is embarrassingly simple. A button. Thumbs up, thumbs down. On every output. That's it.
I added this to an agent that drafts internal comms for a client. First week, 70% thumbs up. I looked at the thumbs down cases, found two patterns (it was getting the tone wrong on urgent messages and miscategorising one specific type of update), fixed both, and got to 92% thumbs up within two weeks.
Without the feedback mechanism, I'd have had no idea. The team would've silently lost trust in the tool, used it less and less, and eventually someone would've said "that AI thing didn't really work out."
Every single one of these mistakes is avoidable. None of them are about technology. They're about process, communication, and discipline. The AI is usually the easy part.
If you're about to start your first AI project and want to dodge these from the start, that's a big part of what we cover in our AI readiness assessment. It's cheaper to avoid the mistakes than to fix them after.