PIPELINE REVIEW / SURFACE EXCEPTIONS
A useful AI pipeline review should reduce a manager’s reading burden by surfacing evidence gaps and exceptions—not produce a prettier recap of every opportunity.
Start with explicit rules
Before involving AI, define what should trigger review: stale next steps, missing owners or dates, close-date slippage, stage evidence gaps, contradictory notes, absent recent activity, or other rules appropriate to the operating process.
Deterministic checks should be deterministic where possible. AI is then used to synthesize context and explain why an exception may matter.
Build an exception queue
The output should prioritize deals that need a conversation, not repeat healthy deals simply because they are in the CRM. A manager should be able to move from the queue into the underlying evidence quickly.
Do not delegate the forecast
A language model can organize evidence associated with a forecast call. It should not decide Commit, change a close date, or produce a probability that is presented as truth.
Forecast authority remains with the people accountable for the number.
Review false positives
Any exception system can over-flag. Review a sample of flagged deals and document false positives so the rules improve over time. A pipeline workflow that creates noise will quickly be ignored.
APPLIED SALES AI STANDARD
Treat AI output as a draft or analysis layer until the evidence has been checked and the appropriate human has approved the decision or action.
CONTINUE / RELATED METHOD
