AI adoption starts with a process, not with a model.
The most common question sounds like “we need AI, where do we start”. We answer from the other end: which process hurts, what it costs today and whether the improvement can be measured. Then we pick the minimal scope that tests it and take it to production.
Find where AI pays off
We look at processes with a lot of unstructured text, repeated judgement calls and manual sorting. That's where the effect is measurable. Elsewhere plain automation is usually cheaper.
A pilot with a measurable outcome
A small scope with a success criterion named up front. Not “let's try AI”, but “in N weeks we look at this specific number”.
Data and privacy
We work out which data can go to an external provider and which cannot, and what follows for the architecture: external model, self-hosted or a hybrid.
From pilot to production
The most common break point: the pilot worked and stayed a slide deck. We plan integrations, monitoring and running cost from the very start.
Where do we start with AI adoption?
With a process, not a technology. Take one task where people read, sort and make repetitive decisions a lot, and measure how much time it eats. That's your pilot candidate.
How long does a pilot take?
It depends on how accessible the data and systems are. We give concrete timelines after discovery, once the minimal scope is clear. Before that it's more honest not to promise.
Will our data go to an external model?
Only if you allow it. For sensitive data we use self-hosted models or a hybrid scheme where only an anonymized minimum leaves your perimeter.
What if the pilot doesn't confirm the effect?
That's a valid pilot outcome — it's what a pilot is for. The success criterion is named up front, so a negative result shows up quickly and cheaply, not a year into operation.
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PDF, 2 pages. No sign-up, no email — just the file.