PIPELINEGuide
Implementing AI automation: step by step
The path from decision to live operation — in the order that has proven effective in small businesses, including the points where projects typically get stuck.
AI automation is implemented by first naming the bottleneck, then narrowly defining the scope, and only then building. The implementation itself takes 4 to 8 weeks; delays in a project are almost never caused by technology, but by missing content, access, and decisions.
01 — Getting started
Where to begin
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.
Define a small scope
One process, one measurable goal. What works gets expanded — what doesn't costs little.
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.
Decide who is responsible
For the implementation and for operations afterward. Without this definition, every system becomes outdated.
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 — Process
The steps in detail
Sharpen the use case
Not “let's do something with AI,” but a concrete process with measurable results.
Gather test data
We need real examples from your business — the messier, the better for testing.
Build and measure
The process is built and measured against the test data. It only goes live when the accuracy rate is right.
Controlled operation
Initially, a human reviews every decision. Depending on results, the confidence threshold is gradually relaxed.
03 — 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.
04 — Bottlenecks
What typically causes delays
- Content and deliveries. By far the most common cause of delay. Text, images, price lists, access credentials — what's missing blocks progress.
- Decisions. When it's unclear who approves, the project waits. A named contact person with decision-making authority noticeably shortens the timeline.
- Access to third-party systems. Getting access to inventory management, phone systems, or CRM regularly takes longer than the integration itself.
- Endless review rounds. Two correction rounds are normal, five are a sign that the goal wasn't clear at the start.
05 — 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.
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.
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 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.
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 — Next
Related pages
- AI automation — Overview
- AI automation — team and acceptance
- AI automation — where to start
- AI automation — requirements
- AI automation — Case studies
- AI automation — Funding
- AI automation — Risks
- AI automation — Timeline
- Process automation
- Document processing with AI
- AI knowledge base
- All guides
AI automation by region
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