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Guide

AI automation: where to start without getting scattered

The most common mistake when starting is not the wrong technology, but the wrong first step. A sequence that has proven itself in small businesses.

Request assessment ↗AI automation
Quote on request4 to 8 weeksAs of 2026
In short

Getting started with AI automation doesn't begin with technology, but with a week of tracking: what repeats, what costs the most time. Then a single, clearly defined process with a measurable goal — only with clear scope do quotes become comparable at all.

01 — Getting started

Where to begin

  1. Name the bottleneck

    Not the most interesting thing, but what takes the most time. A week of tracking what repeats delivers a more realistic list than any workshop.

  2. Define a small scope

    One process, one measurable goal. What works gets expanded — what doesn't costs little.

  3. Survey the starting situation

    What content, data, and access is available. For AI automation, this is the factor that most strongly influences effort and timeline.

  4. Decide who is responsible

    For the implementation and for operations afterward. Without this definition, every system becomes outdated.

  5. Only then request proposals

    With a clear scope, proposals become comparable. Without it, you get numbers that have nothing to do with each other.

02 — Fit

When it's worth it

  • The process in question occurs at least weekly.
  • It follows rules you can write down — even if there are exceptions.
  • Today it's manual work that no one enjoys doing.
  • There's one person who decides, not three who need to coordinate.
  • You can name what would tell you it worked.

03 — Checklist

Clarify before the first quote

  • What should improve? In one sentence, no jargon.
  • How will you know? One number that should change.
  • Who decides? One person, not a committee.
  • What budget is available? A range is enough — it makes quotes comparable instead of arbitrary.
  • By when? And what the deadline depends on.
  • Which systems are involved? And who has access to them.
  • What do you already have? Content, data, preliminary work — this reduces the cost.

04 — Questions

Frequently asked questions

Does our data end up in a language model's training?

No. We exclusively use access where training with customer data is contractually excluded, and we process in the EU. For particularly sensitive data, we use models that run on your own infrastructure.

What does the EU AI Act say about this?

Most business use cases fall into the low-risk category and primarily trigger transparency obligations. Where customers interact directly with the system or AI-generated content is published, we label it. For applications in HR, we look more closely — stricter rules apply there.

What's the difference from regular automation?

Regular automation follows fixed rules: if field A is filled, do B. AI automation comes into play where there are no fixed fields — free text, images, changing document formats. The right answer is usually both together, not one or the other.

How does this differ from a chatbot?

A chatbot answers questions in dialogue. AI automation runs in the background without conversation — it reads, categorizes and pre-decides while someone else is focused on the actual work.

How do we keep track of automatic decisions?

Every decision is logged: which input, which result, which confidence level. You can spot-check this anytime, just like with a human deputy.

Do we need large amounts of data for this?

No. Unlike traditional machine learning, a few dozen good examples are enough for most use cases, because the language model already brings language competence.

What if the documents or formats change?

As long as the content meaning stays the same, the system handles it well — unlike rigid, field-based recognition. Fundamentally new document types need brief adjustment, not a rebuild.

05 — Next

Related pages

AI automation by region

Next step

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