The RevOps stuff* you've been missing

Every revenue outcome has two drivers, a person and an agent. The Context Line is the boundary between them: what agents own, and what rises to your sellers.

Enablement's new job is running outcome-based sprints: find the highest-value, lowest-execution gap in your GTM, practice on live deals, and measure against your won deals until the gap closes.

Stage-based forecasting treats every deal in a stage the same. Here's how to use a GTM context layer to setup deal-level forecasting for RevOps, and how deal reviews change as a sales leader when you run it.

A context graph tells you what happened in your deals, a context layer tells you what actually wins them, by training ML on your own closed-won and closed-lost outcomes. Here's the five-step build.

Gordon Ritter's Above the Model, applied to revenue: the durable edge is how your team sells, and RevOps is the function that captures it, trains models on your own deals, and compounds it every cycle.

What revenue leaders actually need to know about AI architecture, straight from a CTO.

RevOps agents waste money reprocessing the same deal on every call. Work it out once, route each step to the right model, and cut agent spend 30–70%.

GTM judgment is the tacit knowledge your best operators use to win: which play to run, for which stakeholder, at which moment. What it is, why snapshot AI can't capture it, and how a judgment layer compounds every cycle.