From a promising pilot to AI your team uses every day.
Most AI does not fail in the demo. It fails on the way to the daily work.
A pilot proves an idea. AI implementation makes it part of how your company works: connected to the systems that hold the data, built into the process, owned by someone and used by the team. That step is where most AI projects stall, and it is the step we take with you.
We work with the tools you have chosen, from Microsoft Copilot to an existing platform or a build on our own toolbox Forge. What matters is that it runs, keeps running and that your team can carry it on without us.
What you get
A plan to production
What it takes to get from pilot to daily use: the data, the connections, the people involved and the decisions that still need making.
Connected to your systems
The AI reads from and writes to the systems where the work happens, so nobody copies results from one screen to another.
Built into the process
The steps around the AI change too: who checks what, where the exceptions go and what the team no longer has to do.
Ownership and rules
An owner per application, an entry in the AI register, a short AI policy and a check before changes go live.
A team that carries on
The people who use it know how it works, where it goes wrong and when to step in, with documentation for whoever comes next.
How we work
Take stock
Where the pilot or idea stands, what it needs to run in production and what success looks like in numbers. The first conversation is free.
Connect and adapt
The links to your systems, the changes to the process and the tests on real cases, in small steps.
Roll out
First with one team, then wider, with the people who do the work involved from the start.
Hand over
Your team owns it. We agree how it is maintained, measured and improved after we leave.
When it fits
A pilot that never went live
It worked in the test and has been waiting for months to become part of the work.
Tools bought, little use
The licences for Copilot or ChatGPT are paid for, but the way people work has hardly changed.
IT and the business talk past each other
IT keeps the systems running, the business wants results, and nobody connects the two.
You want to get it right first time
You are about to start and want the data, the rules and the people in place from day one.
Questions and answers
What does AI implementation involve?
Everything between a working idea and daily use: connecting the AI to your systems, changing the process around it, bringing the team along, setting up ownership and rules, and checking that it delivers what you expected.
Why do so many AI pilots stall?
Usually not because of the AI. The data sits in another system, nobody owns the result, the process around it never changed or the team was never involved. A good AI implementation deals with exactly those points.
Do you only implement your own software?
No. We work with the tools you have, from Microsoft Copilot to an existing platform. Where something needs to be built, we can do that on our own toolbox Forge, but that choice is yours.
How do you get the team on board?
By involving the people who do the work from the start, testing on their own cases and letting them see the results before anything changes. The training follows the way they actually work.
How long does an AI implementation take?
For one bounded process, think in weeks, not months. A wider rollout across several teams takes longer. We agree the steps and the measures of success before we start.