ACERCA DE ESTE EPISODIO
In this host-only episode of the RevOps Lab, Janis and Philipp take stock of what three years of building a forecasting tool — and hundreds of conversations with sales leaders, RevOps teams, and CROs — have taught them about getting to a reliable, repeatable forecast number. They unpack why forecasting is a process (not a number), why AI only works on top of a clean data foundation, and how the best companies combine roll-up, dynamically weighted, and AI-predicted forecasts into a single operating cadence.
We cover:
Why forecasting accuracy is the output of a well-run sales org, not the input
The operating cadence: weekly meetings, deal reviews, and stakeholder alignment that make forecasting work
Building the data foundation: activity capture, multi-threading signals, conversation intelligence, and CRM autofill
Why deal hygiene and shared qualification criteria (SPICED, MEDDIC) are non-negotiable before you forecast
Splitting the forecast into new logo, expansion, and renewal — and why bookings ≠ consumption
The three-pillar forecast: dynamically weighted + bottom-up roll-up + AI prediction (with corridors, not single numbers)
How to run a roll-up motion: baseline vs. best case, rep forecast vs. independent manager forecast
Why running roll-ups in spreadsheets breaks down at 50+ reps
Calculating dynamic stage probabilities by rep tenure or team — and when it's worth doing
Why AI predictions are only as good as the data foundation underneath them
Links:
Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/
Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/
WeFlow: https://www.getweflow.com
WeFlow RevOps resources: https://www.getweflow.com/revops
Join the RevOps Chat Community: https://www.getweflow.com/community
Subscribe to the RevOps Letter: https://www.getweflow.com/revops-letter
Operating Cadence master deck: ping Janis or Philipp on LinkedIn to request
Chapters:
(00:00) Intro: Has AI fundamentally changed forecasting?
(01:34) Why forecasting is a process, not a number
(02:25) What an operating cadence actually looks like week-to-week
(04:23) The data foundation: why CRM alone isn't the system of truth
(06:53) Anchoring deal conversations on a shared qualification methodology
(08:42) How AI adds an objective layer through automated capture and CRM autofill
(09:29) Stage entry/exit criteria and deal signals for deal health
(13:06) Why "comparable deals" matters as you scale past 50 reps
(14:23) Forecasting as the end result of a well-functioning sales org
(16:05) Splitting forecasts: new logo, expansion, renewal, bookings vs. consumption
(17:22) The three-pillar forecast: dynamically weighted, roll-up, AI prediction
(18:50) Roll-up forecasting: baseline vs. best case, rep vs. manager numbers
(24:07) Anatomy of a roll-up: hierarchy, gap-to-quota, pipeline coverage, deal-by-deal
(26:43) Why spreadsheets break down for roll-up forecasting at scale
(28:07) AI prediction models: aggregate vs. deal-by-deal scoring
(29:02) Dynamically weighted forecasts and rep-level stage probabilities
(32:44) Why AI predictions only work on top of clean data foundations
(34:27) Book recommendation & close
