A large share of office work is moving data from one place to another: invoice data from a PDF into accounting, order details from an email into the ERP, form contents into a spreadsheet. Nothing new is created along the way: it is retyping.
This transfer work can be automated. AI reads documents and emails, extracts the core data and transfers it into your systems. Your team reviews the results instead of typing them in.
Example: incoming invoices
The most common case is invoice intake: incoming invoices are read automatically, and supplier, invoice number and amounts land straight in the accounting software. How far this can go is shown by AI pre-assignment with a posting matrix: posting suggestions based on historical data, reviewed and approved by your accounting team. The bigger picture is on the E-Invoicing & DATEV overview.
Prerequisites and limits
Automated capture works when the rules are clear: which fields are needed, where the data flows, who decides ambiguous cases. A document the recognition cannot classify with confidence goes to a human for review, not into the system unchecked. And without a connection to ERP or accounting, every extraction remains piecework; the article without ERP integration, AI stays an expensive toy explains why.
How I work
The entry follows the AI automation approach: identify the time sinks, choose the solution by cost, integration, security and maintainability, build and test with real documents, then train and hand over. Before anything is built, we calculate whether the case pays off: sometimes the answer is no, and that is a result too. If your routine work is texts rather than documents, text-based routine work is the page you want.