Guide

What do you need for AI automation? The requirements

Before AI automation even makes sense, a few things need to be in place in the business. What those are — and which supposed requirements actually aren't.

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Quote on request4 to 8 weeksAs of 2026

In short

For AI automation, a business needs three things: a concretely named process that currently costs time or orders, existing content or data to work with, and a person authorized to make decisions. Technical expertise is explicitly not required.

01 — Requirements

What you need for AI automation

A clearly defined trigger. Not “we should do something with AI,” but a process that demonstrably costs time or orders today.
Existing content or data. AI automation works with what's already there. The more complete the starting point, the shorter the setup.
Access to the systems involved. Whoever manages the credentials needs to be involved from the start — this is, in our experience, the most common cause of delays.
One decision-making contact person. One, not three.
Willingness to refine after launch. The first weeks in real operation show what was missing in the concept. That's normal and accounted for.

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 — 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.

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.

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.

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.

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 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.

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.

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