In most mid-sized and large companies, learning to use AI follows the same script.
The board catches a healthy case of FOMO. The CEO walks in and asks: “Folks, is anything passing us by here?” There’s energy. There’s appetite to move. And then that energy hits a wall - the IT department.
Here’s the thing: the people who actually do the work know it better than their bosses. In five minutes they can produce a wave of arguments for why this won’t work. That’s not irrational resistance. It’s a rational concern about the quality of the work. IT knows the pattern: everyone is in a hurry today, but when it breaks, they’re the ones left holding the problem.
At Open Mercato we keep meeting companies that handle AI adoption better than the rest - especially at the level of whole teams. Here’s what they do differently.
1. Start with the list of things that could go wrong
Counterintuitive, we know. But when we sit down with IT and start writing out what might break - ourselves, first - we reach alignment faster.
People say: “finally, someone reasonable.” You move out of “who’s right” mode and into “shared exploration” mode. The list isn’t a blocker. It’s the on-ramp.
2. Don’t transform all of IT at once. Build a pirate ship.
The best rollouts we’ve seen all look the same: a small team running alongside the main org - maybe 10–20% the size of the “tanker.” Often reporting straight to the CEO. Volunteers only. The commandos who wanted to be there.
Classic IT stays the source of truth - your SAP, your data. The pirate ship builds new AI applications on top of it. It works surprisingly well, precisely because nobody is asking the tanker to turn on a dime.
3. POC mirroring. Nothing ships to production.
Our favorite trick. You build a bypass: take, say, 10% of the work - the same customer ticket - and route it in parallel to an AI agent and to a human. The human does their thing. The AI does its thing. Nothing goes live.
Then the human compares the output. You collect data for a month. And with that data, IT walks into the board for a concrete conversation - not a vibes-based one.
What usually happens: AI handles around 30% of cases. And there’s a subset of tickets, customers, or case types it solves brilliantly. That’s where you keep digging. The rest, you leave alone.
4. This is a game played on the relationship, not the license
We’ll say this even against our own short-term interest. In theory we should just sell you a license. But if we sell the license and the project falls apart a month later - you won’t renew it.
So: slower, on data, on trust. Long term, this is about the relationship.
A few years ago nobody bet that IT would be the first function under pressure from AI. Everyone said “we’ll be safe.” That’s probably part of why there’s so much emotion in these teams today.