Automation conversations almost always start from the tool and rarely from the process. The result is an integration that works flawlessly on a workflow nobody was using anyway.
What a good candidate looks like
- It repeats often and the same way — if every case is different, it is not a process, it is a decision
- The same data is entered twice, in two different places
- The rules can be written down. If someone says “it depends”, first find out on what
- Mistakes cost time to correct, not just to make
- Somebody can state precisely what a correct result looks like
What is not worth automating
A process that runs a few times a month and takes ten minutes does not justify an integration. Neither does one that changes every couple of months, because maintaining the automation will cost more than the manual work it replaces. And a process nobody can fully describe is not ready to automate — it is ready to be clarified.
Automating a poorly understood process does not fix it. It speeds it up.
Where AI belongs and where it does not
Deterministic rules remain the better fit for steps where a result is either right or wrong: calculations, validations, transfers between systems. A model earns its place where the input is unstructured — an email, a scanned document, free text — and where an approximate result checked by a person still beats nothing. The two combine: the model extracts, the rules decide.
