Most teams hand out the AI first and align later, then wonder why results don't move. Wherever your team is with AI, we help you map how your work really runs, agree where AI should help, and leave with a plan you own, in a single day.
AI rollouts tend to follow the same script. Licences go out, a training follows, and individuals get quicker at their own tasks. Then the team, and the company, stay roughly where they were.
That's not a feeling. McKinsey calls it the gap between individual gains and enterprise impact: four in five people say AI makes them more productive, yet only about one in three companies sees any effect on profit, no better than a year before. Atlassian found the same pattern: people feel about a third more productive, while most companies don't see the benefit as a whole. The few who do get real value are nearly three times as likely to have redesigned how the work flows.
Source: McKinsey, The state of AI in 2026 (survey of 1,719 respondents).
The people are not the problem. Most have already built their AI skills, often on their own and ahead of their company. What's missing is the step after: the team looking at the work together, end to end, and the people who do it deciding where AI should help.
Human + Human + AI means people align with each other first, and only then with AI. AI doesn't fix a team that isn't aligned; it speeds up whatever is already there, including the confusion.
So we start with the humans: the team agrees on how the work really flows and where it snags, then decides together where AI earns a place. That's work people have to do in a room, with someone keeping it honest.
For whoever is responsible for AI adoption in the company, and the team that does the work. Before the day, interviews and a survey show us how the work runs today.
The team picks one of its core, value-creating workflows and maps it end to end, as it actually runs today: who does what, where it waits, where it gets redone.
We look for three kinds of moments along the map:
For every moment the team picks, they decide how the outcome is checked. Who reviews what AI produces, what "good enough" looks like, and when a human always decides. Control is designed in, not hoped for.
Nothing leaves the room without an owner. The team turns its choices into a short list of experiments, each with a validation criterion and a date.
One team of 5 to 20 people, one workflow, one day. In person or online.
When AI is part of a wider transformation, the same mapping and alignment runs as one track of our change enablement work.
The method isn't new: it's the same diagnose, align, equip and hold arc we've used at CME, Ipanema and Krealo, applied to AI. What we don't do is promise usage numbers. We measure the work instead: how long a task takes from start to finish. We help your team decide where AI belongs and how they'll check it. Whether it sticks is what the follow-up is for.
No. We're not a software vendor or integrator. We help teams decide where AI should help and how they'll control it, so whoever builds or buys the tools gets clear, owned requirements.
Whatever your organisation already has or is evaluating. The session is about the work, not the brand.
The people who do the work, plus whoever owns the outcome. If the people affected aren't there, the map will be wrong.
Then that's the first thing to talk about, openly. Starting with the tasks people dislike usually changes the conversation.
Adam and Larissa, the founders, or someone from our close circle of facilitators.
Each action carries its own validation criterion and date. At the follow-up we also look at how long the work takes from start to finish, compared with the map we made on the day.
Tell us which core workflow feels slowest. We'll tell you whether a single session or a wider programme is the right move.
or email hello@maketeamwork.com