Sorting the inbox, retyping receipts, transferring data from system A to system B, keeping master data up to date: a considerable share of qualified working time goes into tasks nobody would miss. This is exactly where AI automation comes in – not as yet another tool that needs attention, but as a workshop in the background that reliably handles recurring processes and frees your professionals for the work they were actually hired for.
What AI workflows can do today
Classic automation often fails when it meets reality: documents look different every time, e-mails are free text, exceptions are the rule. AI building blocks change that – workflows can now understand, not just execute:
- Document processing: Invoices, delivery notes, orders or contracts are read, classified, checked and assigned to the right case – regardless of layout.
- E-mail handling: Incoming messages are sorted by intent, enriched with case data, routed to the right team or provided with a draft reply.
- Order and case processing: Unstructured input becomes structured cases in the target system – including plausibility checks.
- Data maintenance: Master data is reconciled between systems, duplicates are detected, missing information is completed and changes are documented.
How it works technically
The basic pattern is always the same: a workflow engine orchestrates the process – it controls what happens, when and in which order. Wherever understanding is required, AI building blocks take over: they classify documents, extract data from free text, summarize or draft replies. Your existing systems – ERP, CRM, DMS, business applications – are connected through their interfaces.
One thing matters to us: we work tool-agnostic and build on what you have. AI automation is not a rip-and-replace project – your system landscape stays, the media disruptions in between disappear.
Control instead of black box: AI process governance
The most common – and justified – concern about AI in business processes: "What if the AI decides something wrong?" Our answer is a governance concept that is planned in from the start:
- Human-in-the-loop checkpoints: Critical steps – such as approvals or payments – stay with your employees. The AI prepares, the human decides.
- Escalation on uncertainty: If the system is not confident, the case is not guessed but handed over to the business department – together with everything already determined.
- Complete logging: Every automated decision is documented and traceable – anyone who wants to know why a case was processed a certain way gets an answer.
- Clear responsibility: The process still belongs to the business department. The automation assists – it does not rule.
Data protection and operation
Business processes contain personal and confidential data – and the operation of the automation is designed accordingly. We rely on GDPR-compliant processing, clear access concepts and, if required, operation on-premises or in your own cloud environment. Which data an AI building block sees is defined per process step – no more and no less than the step requires.
How to recognize good candidates
Not every process is worth automating. The best candidates share three characteristics: high volume, clear business rules and media disruptions – points where people transfer information from one format into another today. In our experience, such processes go productive in weeks, not months. And the first automated process creates the blueprint for all that follow.
The path to automation
- Potential analysis: Together we review your process landscape, evaluate candidates by effort and benefit and select the first process.
- Pilot process: One process is fully automated – including governance, logging and handover points to the business department.
- Expansion: With the experience from the pilot, further processes follow – on a platform that grows with you.
The fact that we model data cleanly, master interfaces and integrate systems reliably is no coincidence: data integration has been our core business since 2005.