Intro

Process automation with AI that solves the unstructured cases too.

Process automation with AI is the step that frees classic RPA from its tightest limit. Pure rule-based automation only works as long as every input looks the same. The moment an invoice has a new layout, a request arrives in free text or an edge case appears, it breaks or needs a human again. AI closes exactly this gap. It reads, classifies and understands the unstructured part, and the RPA layer then executes the clicks and postings. The result is a process that keeps running even when reality does not fit a fixed form.

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Where AI makes the difference

The value of AI shows wherever data does not arrive cleanly preformatted. A PDF invoice with a changing structure is read, line items and tax rates are recognized, the posting lands prepared in DATEV. An email request in free text is classified and routed to the right process. A contract or delivery note is checked, deviations are flagged. Everywhere someone reads, decides and retypes today, AI takes over the understanding part and automation the execution part. That is the difference between an automation that handles 60 percent of cases and one that carries the exceptions too.

RPA, AI and integration as one system

We do not treat the three layers separately. RPA clicks where no clean interface exists. AI supplies the understanding for unstructured inputs. Integration connects both to SAP, your CRM and DATEV, so results land in the system without retyping. Only this interplay makes process automation with AI robust. AI alone only produces suggestions, RPA alone breaks on every layout change. Together they create a workflow that runs reliably and can be maintained without every small change triggering a new project.

What you gain

You gain processes that no longer stop at every exception.
Your team reviews and approves instead of typing line by line.
Errors drop, because AI reads consistently and the automation skips no step.
And ROI stays provable, because we measure on the pilot how much a process costs today and what the automation saves.
No big-bang, but a clean start with one process that eats a lot of manual work and is clearly measurable.

Typical AI use cases

In accounting it is invoice processing with changing layouts.
In order handling it is extraction from orders that arrive by mail, PDF or form.
In customer service it is classifying and pre-qualifying requests.
In HR it is checking documents during onboarding.
We deliberately pick the case where a lot is read and decided today, because that is where AI has the biggest lever and ROI shows fast.

Process automation with AI vs. classic RPA

Process automation with AI vs. classic RPA
Criterion	Classic RPA	Process automation with AI
Process type	rule-based only	also unstructured
Inputs	fixed formats	PDFs, free text, edge cases
Setup	heavy	leaner with AI
Integration	point-to-point	SAP, CRM, DATEV connected
Maintenance	high on changes	more robust

The honest difference is coverage. Classic RPA handles the straight cases but fails on anything that deviates and sends it back to the human. With AI in front, deviations are processed cleanly too, and integration ensures nothing is maintained twice. More detail on RPA explained simply and in the comparison on Process automation with AI vs.

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Want to walk through it together? Book via our booking calendar, or write to info@prozessautomatisierung.co.

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FAQ

What is process automation with AI?

Process automation with AI combines RPA, AI and integration into one workflow. RPA handles clicks and postings, AI reads and understands unstructured inputs like PDFs and free text, integration connects everything to SAP, CRM and DATEV. So cases that pure rule-based automation fails run automatically.

When is AI worth it over pure RPA?

As soon as inputs do not always look the same. For fixed formats, RPA is enough. For changing invoice layouts, free text or frequent exceptions, AI carries the cases RPA hands back to the human. In the assessment we check whether AI justifies the effort or rules suffice.

Does the process stay traceable?

Yes. Every step is logged, and AI decisions get a human approval when uncertain. So the workflow stays auditable and you keep control over critical cases instead of trusting a black box.

How do you handle AI errors?

We define confidence thresholds. Clear cases run through automatically, uncertain ones land with a human for review. So manual work drops sharply without errors slipping through unnoticed. We tune the rate in real operation.

What data does the AI need?

Usually the documents and texts the process produces anyway, such as invoices, orders or requests. We work with servers and data in Germany and the EU and clarify early which data is processed and how it is protected.

How fast is an AI process in production?

Because we start with a clearly scoped pilot, the value is often visible within weeks. We bring one process live, measure the saving and only expand once it holds. That keeps risk small and ROI proven rather than claimed.