AI adoption in your company — where to start

Start with a process that meets three conditions at once: it's repetitive, has a countable time cost, and tolerates a mistake. Classifying incoming documents usually meets all three. "An assistant that knows the whole company" meets none of them — which is why so many rollouts that start there never finish.

Why pilots don't reach production

A demo shows a model handling a dozen or so examples chosen by the person who prepared them. Production is tens of thousands of examples nobody chose: scans crooked on the glass, documents in a format outside the test set, invoices with a handwritten note in the margin.

The difference between the two isn't model quality. It's handling the cases where the model isn't sure — and that's exactly what a rollout is actually made of. A pilot that skips it isn't an earlier version of the rollout. It's a different thing entirely.

How to choose your first use case

Make a list of candidates and score each on three dimensions.

Repeatability. How many times a month does someone do this? Below a hundred, the savings rarely justify a rollout.

Time cost. How many minutes does one instance take? Multiply by repeatability. That number sets the upper bound on a sensible budget.

Tolerance for error. What happens if the model gets it wrong? If the answer is "someone catches it at review" — good. If it's "money leaves an account" — you need a human approval step, which changes the maths.

The best candidates in a typical company: classifying and routing incoming documents, extracting data from invoices and orders, drafting first responses to repetitive support questions, searching internal documentation.

Set a success metric before you start

Before a prototype exists, write down one sentence: "This rollout makes sense if the model handles at least X% of cases on its own, at accuracy of at least Y%."

Without that number, there's no way to later decide whether the project succeeded. With it, the prototype ends in a clear decision: proceed, or stop. Stopping is also a good outcome — just a cheaper one than finding out a year in.

An architecture that survives production

A rollout that lasts has four elements a demo doesn't.

A confidence threshold. The model returns a result along with its own confidence score. Above the threshold, automation runs; below it, the case goes to a human. Setting that threshold is a business decision, not a technical one.

A queue for uncertain cases. The place where a human resolves cases below the threshold — and its size is the real measure of how well the system is working.

A decision log. A record of what the model received, what it returned, and with what confidence. Without it you can't diagnose degradation or answer an auditor's question.

Continuous measurement. Data changes. A model that was 94% accurate in March can be at 80% by November — not because it broke, but because the input stream changed.

Where your data goes

The question that comes up in every first meeting, rightly so. You have three options:

• A hosted model, no-training agreement. Data leaves the company, but the provider commits not to use it for training. Cheapest and fastest, sufficient for most use cases.
• A hosted model in your own private cloud. Data never leaves your infrastructure. More expensive, justified for sensitive data.
• A model on your own hardware. Full control, the highest entry and running cost. Makes sense under regulatory requirements that rule out the other options.

The choice is a decision about risk, not technology. You make it once, and it affects cost for the entire lifetime of the solution.

What it costs

• Prototype on your data: PLN 15–30k net, 2–3 weeks. Ends with a number you can base a decision on.
• Production rollout: PLN 60–200k net, depending on the number of integrations and approval requirements.
• Running the models: from a few hundred to a few thousand zloty a month, roughly proportional to volume.
• Maintenance and tuning: usually 15% of the rollout's value per year.

Four mistakes that come up most often

Starting with the most impressive use case. A conversational assistant impresses the board and is the hardest possible starting point — unbounded scope, no good success metric, high expectations.

No human in the loop. A system with no path for uncertain cases either performs badly or needs such a high confidence threshold that it only automates a small share of cases.

Measuring accuracy alone. 95% accuracy sounds great until you work out that at a thousand documents a day, that's fifty errors someone has to catch.

Treating the rollout as a closed project. A model needs watching, like any other piece of production infrastructure. A rollout with no measurement plan ages quietly.

The first step you can take this week

Pick one process. Count how many times a month it happens and how many minutes it takes. Write down what happens when someone gets it wrong.

Those three numbers are enough to judge whether a prototype is worth building — and they're worth more than a month of conversations about AI's possibilities.