Every RevOps leader has sat in a QBR defending a number that was already stale before the meeting started. Forecasting has become a trust exercise more than a math exercise, and the gap between what the model says and what the board believes is where careers get made or broken. This session moves past the mechanics of weighted pipeline and into the harder question: how do you build a forecasting process that survives contact with reality? We'll look at how leading RevOps teams are blending historical data, rep judgment, and macro signal into a forecast that's directionally trustworthy even when it's not perfectly precise, and how they communicate uncertainty to the board without losing credibility. Takeaways: - A framework for separating forecast confidence into commit, best case, and pipeline coverage, so leadership knows exactly what they're betting on - How to build feedback loops between forecasting accuracy and rep coaching, turning misses into a diagnostic tool rather than a blame exercise - Practical guidance on when to trust the model and when to override it, and how to document that judgment so it improves over time