Guide

What does AI automation cost? Factors, process, and funding options

There's no credible flat rate for AI automation — the projects behind them vary too widely. What actually drives the effort, how you get a concrete quote, and which subsidies are available in Austria.

Quote on request4 to 8 weeksAs of 2026
In short

A flat rate for AI automation cannot credibly be quoted — it depends on the scope of the project, not the provider. Depending on use case and data situation. The actual proposal comes only after a short, non-binding initial conversation.

01 — Price

What it costs

Price
Individual based on effort
Typical duration
4 to 8 weeks

Depends on the use case and data situation. A fixed price can only be quoted seriously after the initial consultation — send us a brief description of your project and we'll send a proposal that fits the scope and effort.

02 — Factors

What drives the price

  • Scope and number of integrations. Every additional system that needs to participate costs effort — regardless of how simple the core is.
  • State of your starting point. Clean data, existing content and documented processes significantly reduce effort. Chaos is the most expensive cost factor.
  • How much new material needs to be created. What you can contribute doesn't need to be invented by anyone. This is the biggest lever you have in your own hands.
  • Requirements for design and customization. A solid standard system costs a fraction of a custom solution — and is sufficient more often than vendors admit.
  • Operating model. One-time project costs or ongoing support: calculated over three years, this often gives a different picture than the quote comparison.

03 — Funding

Subsidies in Austria

For digitalization projects, there are several funding programs in Austria, such as KMU.DIGITAL or the aws digitalization funding. Whether and to what extent one of these is currently relevant for your project, we'll review with you individually. Current conditions: kmudigital.at, aws.at.

Which program is relevant, how high the subsidy currently is, and whether the budget for the current funding period is still open changes regularly — plus there are regional funding programs that vary by federal state. Only the information from the funding agencies themselves is reliable, not a fixed percentage stated here.

The most common mistake is always the same: commission first, then want funding. That doesn't work. Allow several weeks of lead time for application and approval.

04 — Scope

What's included

  • Classification of incoming messages by topic, urgency, and responsibility
  • Extraction of structured data from emails, PDFs, and forms
  • Pre-drafted responses that a person only needs to approve
  • Summaries of long processes for handoff
  • Clear threshold at which a human decides — nothing runs through unchecked
  • Log of every decision, so it remains traceable why something happened

05 — Questions

Frequently asked questions

Can we start small?

That's the only sensible approach. One use case, eight weeks, measurable outcome. What comes next, you decide based on what you've seen.

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 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.

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 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.

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.

06 — Error

The most common mistakes

  • Starting too big. Projects where nobody can use anything for months fail more often than average. A small, usable first step is almost always the better way.
  • Not involving the people who work with it. Requirements that only come from management rarely match the actual work.
  • Not defining a success measure. Without a metric defined in advance, you can't say afterwards whether it delivered anything — and the next decision will be made on gut feeling again.
  • Overlooking operating costs. The project price is only part of it. What operation costs over three years belongs in the same calculation.
  • Creating dependency. If you don't get access, documentation and source code, you're locked in. That needs to be clarified before commissioning, not after.

Next step

Let's talk briefly about whether this makes sense for you.

A twenty-minute conversation is usually enough to clarify whether a project makes sense. If not, we'll say so — that's cheaper for both sides than an offer nobody needs.