Definition
AI automation introduces models where rules alone cannot handle language, documents, visual input, or changing context. It should be surrounded by deterministic steps and review where needed.
Why it matters
It expands what teams can automate, but creates a responsibility to measure confidence, quality, and exceptions.
Business example
An intake flow reads an emailed request, extracts the key fields, asks for what is missing, and creates the correct work item.
When to use it
Use it where unstructured input creates repeated delay but the downstream action can be constrained.
When not to use it
Do not automate a consequential decision when the error cost is not understood or cannot be caught.
How Automathing approaches it
We design for confidence thresholds, exceptions, and measured business outcomes rather than maximum automation.