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Franco
Anzini
VP of Revenue Operations
Drata
Franco Anzini builds the revenue operating systems that carry B2B SaaS companies from early traction to market leadership. Over 20 years, Franco has taken GTM and revenue operations functions from zero to fully operational, twice guiding companies to IPO and contributing to three acquisitions along the way. Franco has scaled sales organisations from $10M to more than $250M in ARR by building the infrastructure, processes and teams that make hypergrowth repeatable. Franco has led teams through some of the messiest, highest-stakes moments in a company's life: building ops from scratch before Series A, re-platforming a CRM mid-hypergrowth, operationalising a new market segment ahead of an IPO, and rebuilding a fragmented revenue operations environment while the business kept moving. That experience spans the full GTM stack, from sales and marketing to customer success, RevOps and enablement, with a consistent record of 25 to 35% efficiency gains, successful M&A execution and board-level strategic influence at every stage. The lesson Franco takes from all of it is that the answer is never more complexity. The strongest GTM organisations are built on one discipline: relentlessly asking what problem they are actually trying to solve before building anything. It's why Franco doesn't arrive with a playbook, but writes one.
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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.