I keep seeing teams build a useful AI workflow, then get stuck on when to let it run without someone checking every step. My current bias is to keep human review until the team can explain what the workflow gets wrong, not just what it gets right. For anyone running AI in prospecting, RevOps, or sales enablement: what evidence made you comfortable moving a workflow from suggested actions to real automation? Was it a clean run count, a measurable error rate, or something else?
Iβd use a risk-weighted error threshold: automate low-impact, reversible actions first, then expand only when the workflowβs failure modes are predictable and contained, because 100 clean runs say little if run 101 damages a prospect relationship.
the error rate framing is spot on. we see the same pattern when we assess people's AI fluency β the verification dimension consistently scores lowest across the board (aisa.to/state-of-ai-fluency). teams that can define what "good enough" looks like for each step tend to automate way faster, because they've already built the acceptance criteria before the AI even runs
