Guide

Checklist: preparing for AI automation

What you should clarify before requesting offers for AI automation. The more of this is in place beforehand, the more reliable the offers will be.

Request assessment ↗AI automation

Quote on request4 to 8 weeksAs of 2026

In short

Before deciding on AI automation, a quick stocktake is worthwhile: which workflow costs the most time, who will operate the system later, and whether the necessary content even exists. The complete checklist is further below.

01 — 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.

02 — Participation

What we need from you

A contact person who can make decisions — not just forward them.
Access to the affected systems, in time and not on launch day.
Real examples from daily business — the messier, the more useful for testing.
Two to four hours for coordination, distributed over the project duration.
An answer to the question of what success means. Without it, nothing can be assessed afterward.

03 — Questions

Questions you should ask

What happens if the project becomes more expensive than estimated — who bears that?
Who owns data, access, and, if developed, the source code?
What does operation cost in the second and third year?
What does the documentation we receive at the end look like?
How would you measure whether the project was successful?
Is there a case in which you would advise us against this?

04 — Questions

Frequently asked questions

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 reliable is it?

That depends on the use case, and we measure it before anything goes live. For classifying incoming messages, well-built systems regularly exceed ninety percent. What matters isn't the rate alone, but what happens when there's an error — that's why we always build in a confidence threshold.

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.

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.

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.

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.

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.

05 — Next

AI automation by region

Next step

Initial assessment, no obligation.

Briefly describe what you need. You'll get an honest answer about what's realistic and what it will roughly cost.

Send inquiry ↗ office@pipeline-solutions.at OR +43 681 207 64 203