How to build automations that pause and ask for help instead of quietly doing the wrong thing.
Key takeaways
Before launch, decide what situations the automation should not attempt to resolve on its own — low-confidence matches, unusual values, anything touching money or legal terms are common candidates.
Writing this list down forces a useful conversation about where your team actually wants control retained, rather than leaving it to be discovered by accident.
Exceptions sent to a shared inbox tend to get ignored; exceptions assigned to a named owner with a deadline get resolved. The routing logic matters as much as the detection logic.
This is a small design choice that has an outsized effect on whether the human-in-the-loop step actually functions in practice.
Every exception is also a data point. A cluster of similar exceptions usually points to a gap in the automation's design that's worth fixing at the source, rather than reviewing manually forever.
Teams that review this log monthly tend to shrink their exception rate steadily over time.
Confidence thresholds set at launch are rarely right forever. As the automation proves itself on more cases, some thresholds can loosen; as new edge cases appear, others may need to tighten.
A quarterly review of these settings keeps the balance between speed and safety from drifting in either direction.
A 30-minute call is enough to tell you whether AI pays for itself here.
