When teams can’t get reliable answers within the decision window, being “data-driven” quietly turns into a ticket-queue problem. TL;DR Decision-making slows to a crawl when every question has to go through a business analyst or RevOps request queue — and by the time the data shows up, the moment to act has already passed. The core challenge in data-informed decisions is getting answers fast without sacrificing trust in the numbers. “Self-service analytics” stalled out because the tools still demanded analyst-level thinking to use effectively. AI is what finally makes that original promise achievable. Introduction: the moment the analyst bottleneck becomes impossible to ignore The executive team kicks off the Monday operating review and notices gross margin is down 3.2 points week-over-week. They glance at the dashboard, then at the RevOps lead, and ask: “Is this real – and if it is, what’s going on and why?” The room reacts the way rooms always do when there’s no clear answer: people start filling the void with narratives. Someone brings up a discount. Someone else mentions a fulfillment hiccup. Another person blames “seasonality.” And then comes the familiar scene anyone who works on business performance reporting knows too well. A request gets filed. The analyst team is already overloaded. The earliest ETA is “later this week.” The call on whether to freeze spend, adjust pricing, or pause a campaign gets made without the real answer. Again. The answer is there. It lives somewhere in the data. But when the route from question to metric to explanation runs through tickets, backlogs, fragmented data, and slightly different definitions, the analyst bottleneck becomes the upper limit on how quickly the company can decide and act. It’s…