PIPELINEGuide
AI automation: comparing providers — what really matters
Offers for AI automation are hard to compare because everyone writes something different into them. The questions that make differences visible — and the warning signs.
When comparing providers for AI automation, the quoted price matters less than the question of what happens afterward: who maintains the system after go-live, and what does a later adjustment cost. That's the difference that only shows up after a few months.
01 — Criteria
How quotes differ
- What exactly is included. Two quotes with the same total can cover completely different deliverables. Ask for scope as a list, not as running text.
- Who delivers what. Content, images, data, access — if it's not specified, it becomes a point of conflict later.
- What operation costs. Calculated over three years, including licenses, hosting, and support.
- What happens if you want to switch. Do you get access, data, and documentation? The answer to this question says more about a provider than any reference.
- Who actually does the work. With small providers this matters: who sits in the meeting, and who then does it.
02 — 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?
03 — Warning signs
When to be skeptical
- Guaranteed results. Anyone promising top Google ranking or a specific revenue increase is selling something they can't deliver.
- No pushback. A provider who thinks every project is a good idea has either not listened or is only selling.
- Time pressure. Limited-time discounts on project prices are a sales tool, not an offer.
- Unclear ownership. Anyone who won't clearly say who owns data and access doesn't have your interests at heart.
- No contact with technical expertise. If only sales sits in the first meeting, that's where misunderstandings arise.
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
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.
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.
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 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.
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.
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.
06 — Next
Related pages
- AI automation — Overview
- AI automation — data protection
- AI automation — Cost-effectiveness
- AI automation — common mistakes
- AI automation — Alternatives
- AI automation — Implementation
- AI automation — How it works
- AI automation — checklist
- Process automation
- Document processing with AI
- AI knowledge base
- All guides
AI automation by region
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