#116 Sales Forecasting in the Age of AI – with Janis Zech & Philipp Stelzer (WeFlow)

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 inputThe operating cadence: weekly meetings, deal reviews, and stakeholder alignment that make forecasting workBuilding the data foundation: activity capture, multi-threading signals, conversation intelligence, and CRM autofillWhy deal hygiene and shared qualification criteria (SPICED, MEDDIC) are non-negotiable before you forecastSplitting the forecast into new logo, expansion, and renewal — and why bookings ≠ consumptionThe 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 forecastWhy running roll-ups in spreadsheets breaks down at 50+ repsCalculating dynamic stage probabilities by rep tenure or team — and when it's worth doingWhy AI predictions are only as good as the data foundation underneath themLinks:Janis Zech on LinkedIn: https://www.linkedin.com/in/janiszech/Philipp Stelzer on LinkedIn: https://www.linkedin.com/in/philippstelzer/WeFlow: https://www.getweflow.comWeFlow RevOps resources: https://www.getweflow.com/revopsJoin the RevOps Chat Community: https://www.getweflow.com/communitySubscribe to the RevOps Letter: https://www.getweflow.com/revops-letterOperating Cadence master deck: ping Janis or Philipp on LinkedIn to requestChapters:(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