Every AI project I've turned down started the same way: a client wanted to "add AI" to a process nobody had actually mapped yet. Process mining is the unglamorous step before that — and it's the difference between automating a real bottleneck and automating the wrong thing faster.
Process mining means using your own system logs — timestamps, handoffs, approvals — to see how work actually flows through your business, not how the org chart says it should. Most businesses are surprised by the gap between the two.
The three questions process mining answers
In practice, this looks like three questions, answered with data rather than opinion:
Where does work actually sit waiting, and for how long?
Which steps get redone or reversed most often?
Which handoffs between people or systems cause the most delay?
Only then does it make sense to ask where AI belongs
Only once those questions have real answers does it make sense to ask where AI belongs. An AI tool applied to a bottleneck you haven't actually found doesn't remove the bottleneck — it just does the wrong work faster, which is often worse than doing nothing, because now the wrong process is harder to see and even harder to unwind.
This is also why every AI Readiness Audit I run starts here, not with a tools discussion. You can't prioritise an AI investment against a bottleneck you haven't measured yet.
If you're not sure where your own bottleneck actually is, that's exactly what a Quick Scan is for.