Pipeline Visibility Cheat Sheet

What is pipeline visibility and how is it different from pipeline reporting?

Pipeline reporting tells you what the numbers are — deal count, total value, stage distribution. Pipeline visibility tells you why those numbers are moving, which deals are at risk, and what's actually happening inside each opportunity. The cheat sheet covers both, but the real leverage is in the deal health layer: activity velocity, multi-threading, access to power, and AI-summarized communication — things a standard Salesforce report won't surface on its own.

Do I need a tool like Weflow to apply this, or can I run it out of native Salesforce?

You can get partway there with native Salesforce — stage-based weighted forecasts, required fields, and basic pipeline views are all doable without additional tooling. Where native Salesforce falls short is automatic activity capture from Google Workspace or Microsoft 365, AI-generated meeting summaries synced to records, deal signals, and forecast roll-up with track change — those require a third-party tool or significant custom build work.

Which parts of the forecasting process should stay human-led versus handed off to AI?

The bottom-up forecast call — where reps and managers submit and adjust numbers based on deal context — should stay human-led because it builds accountability and surfaces qualitative judgment that AI can't replicate. Use the AI forecast as a sanity check after you've rolled up the bottom-up, not as a replacement for it. The cheat sheet is explicit on this: treat AI forecast output as an additional data point, not the primary number.

What CRM data do I need to have clean before any of this is worth running?

At minimum, you need four fields consistently populated on every open opportunity: stage, amount, close date, and forecast category. Without those, your weighted forecast math is broken and your pipeline waterfall analysis will be misleading. The cheat sheet recommends using stage exit criteria to enforce field completion — that's the most reliable way to get consistent data without relying on rep discipline alone.

How do I know if my deal health signals are actually flagging the right risks?

Cross-reference your warning flags — ghosted, single-threaded, no activity in X days — against deals that slipped or closed lost in the last two quarters and see if the signals fired before the outcome. If they didn't, your thresholds are wrong or the underlying activity data is incomplete, which usually means auto-capture isn't running correctly. Good signal configuration requires clean activity data first; the warnings are only as reliable as what's being logged.

How often should I run the forecast cadence described in this cheat sheet?

The cheat sheet lays out a weekly cadence: reps submit forecast calls on Thursday, managers and the CRO review on Wednesday of the following week, and RevOps spends Monday on analysis and chasing prior action items. That rhythm works well for most B2B sales orgs with deal cycles over 30 days — if your cycles are shorter, you may need to tighten it to twice weekly. The key is locking the forecast call and taking a pipeline snapshot at the same point each week so you have a clean baseline for tracking changes.