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Alfie
Gomez
Director, Revenue Operations
ServiceRocket
Alfie Gomez is Head of Revenue Operations at ServiceRocket, the leading Atlassian Solution Partner. He runs the operational backbone from lead to recognised revenue: the seams between sales, delivery and finance where margin leaks and forecasts drift. His brief is to make operating teams aggressively effective, and he treats strategy and development as one job, designing the operating model and writing the systems that enforce it, so the controls hold in practice and not only on a slide. His method is finance-grade operations: one reconciled view of bookings, backlog, margin and revenue, data fixed at the source rather than at the reporting deadline, and a number that does not ship until it ties to the ledger. AI runs inside the same discipline. Agents are cheap, the context they read from is the expensive part, so he invests in the records and harness, letting the agents do the rest, with a permission check on every write and a person accountable for every call. A Chilean in the Bay Area and a Universidad de Chile graduate, he started in sales and moved through marketing and customer success before taking on the back end of the business, where everything learned at the front gets tested.
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21 January 2027 09:30 - 10:00
Panel | What problem AI is actually solving in RevOps?
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.