A lot of AI spend in RevOps gets justified after the fact rather than before it. This session starts from the opposite direction: before you buy, build, or roll out anything, what specific problem is this actually meant to solve, and is solving it worth the time, energy, and money it's going to take? We'll work through a practical way of separating AI use cases that pay for themselves, in hours saved, forecast accuracy, or deals that didn't slip, from the ones that sound impressive in a demo but quietly drain a team's time without ever showing up on a scorecard. That includes being honest about the hidden costs that rarely make it into the business case, the ongoing babysitting a workflow needs, the data clean up it depends on, and the change management required to get a team to actually trust and use it. The goal isn't to talk anyone out of AI, it's to leave with a straightforward way of testing any proposed use case against a simple question: if this works exactly as promised, does the payoff genuinely outweigh what it costs us to get there? That's the filter worth applying before the budget gets signed off, not after.