PIPELINEGuide
Connect AI automation with existing systems
Does everything need to be replaced for this? Usually not. What can be connected to an existing system landscape, where the limits are, and which part typically takes the longest.
For AI automation, in most cases nothing existing needs to be replaced: inventory management, CRM, calendar, accounting, or phone systems are connected via interfaces. What matters is whether the respective system has a documented interface — and the time-critical part is almost never the technology, but obtaining the access credentials.
01 — Integration
Connection with existing systems
The most common misconception is that everything has to be replaced. In most cases, the existing system landscape remains in place, and AI automation builds on top of it — through interfaces, not through replacement.
What gets connected in practice: ERP, CRM, calendar, accounting, phone system, newsletter system, website. What matters is whether the respective system has a documented interface. Where none exists, there's usually a workaround — but that costs effort and needs to be clarified up front, not discovered later.
The time-critical part is almost never the technical integration, but obtaining access credentials. Starting early here noticeably shortens the project timeline.
02 — 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.
03 — 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.
04 — Questions
Frequently asked questions
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.
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 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'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 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 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.
05 — Next
Related pages
- AI automation — Overview
- AI automation — Timeline
- AI automation — requirements
- AI automation — costs
- AI automation — DIY
- AI automation — common mistakes
- AI automation — data protection
- AI automation — Funding
- Process automation
- Document processing with AI
- AI knowledge base
- All guides
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