What "automating with AI" really means
Automating a process with AI means letting software handle the judgement-light, repetitive parts — reading a document, classifying a request, drafting a reply, flagging an exception — while your people handle the decisions that need context. Unlike rigid rule-based automation, AI copes with messy, unstructured inputs like emails, invoices and free text.
Which processes are worth automating
The best candidates are high-volume, repetitive and rules-based, with a clear input and output and a measurable cost today. Think invoice processing, order entry, customer-service triage, document extraction and report generation. Processes that are rare, highly variable, or that hinge on human relationships are usually a poor fit.
AI automation vs RPA
Traditional RPA follows fixed rules and breaks when the input varies; AI automation understands content, so it handles variation and unstructured data. In practice the strongest solutions combine the two: AI reads and decides, and automation moves the data between your systems.
The approach: from mapping to production
We map the process and its real cost, build a proof of concept on your own data, and only then move it to production with monitoring so accuracy holds over time. Every step is checked against the EU AI Act and GDPR. See our services for how the pieces fit together.
What it costs and returns
Start with a fixed-price audit at €2,500 to rank the opportunities by impact, effort and ROI, then a €20,000 proof of concept to prove it on your data. Measure the work before you change it, change one thing, and measure again — that is the only honest way to know the return.