AI process discovery uses AI to work out how work actually gets done in a company, by reading the records, messages and documents that work leaves behind. The point is practical: find the slow, repeated or error-prone steps so they can be fixed.
The documented process is rarely the real one
On paper, an order is entered, checked and confirmed. In practice there is also the spreadsheet someone keeps because a report is wrong, the email where the delivery date changed, and the colleague who knows which customers need a call. Those workarounds are where the time goes, and no single system shows them.
How it differs from process mining
Process mining rebuilds a process from clean event logs, which works well inside one system such as an ERP. AI can also read unstructured sources (emails, chat, spreadsheets) and link them to structured records, so it can follow work that crosses tools.
What it can find
- Repeated manual checks, like comparing orders against a spreadsheet every morning.
- Data entered twice, in a system and again in a tracker.
- Handoffs that stall between people or teams.
- Records that disagree across systems.
What to look for
- Every finding traced back to its source records.
- Access scoped to the systems you choose.
- Plain-language results your team can check.
- Nothing changes without human approval.
Discovery can only see what is recorded, and links between records sometimes need a person to confirm them. Start with one workflow that touches a few tools. See how the Platypus platform works or start with one bottleneck.