AI Mythbusters: What Revenue Teams Get Wrong About Putting AI to Work

Revenue teams are under pressure to put AI to work.

So naturally, we’ve accumulated a few rules along the way:

Clean your data before you start. Give sellers better recommendations. Build agents for everything.

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But how much of that advice actually holds up in practice? 🤔

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In this live AI Mythbusters panel, RevOps leaders who are actively navigating AI inside their organizations will challenge some of the most common assumptions about putting AI to work across revenue teams.

Kevin Heraly (VP of Revenue Operations at Demand Science) and Sandy Robinson (Quavo Fraud & Disputes) join Camela Thompson (Head of Marketing at RevOps Co-op) and Sara Kinsey (VP or Marketing & Revenue at Von) for an operator-led conversation about what they've learned from actually implementing, testing, and governing AI, not just talking about it.

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We’ll dig into three big myths:

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Myth #1: “Our data isn’t clean enough for AI.”

Do you really need a pristine CRM before you start? We’ll explore where imperfect data is manageable, where missing context still creates real risk, and what “AI-ready enough” actually looks like.

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Myth #2: “If we tell sellers what to do, they’ll do it.”

A technically correct recommendation is useless if nobody trusts it. We’ll unpack why AI adoption is as much an operational and change-management problem as a technical one—and what actually gets users to act.

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Myth #3: “We need an agent for everything.”

More agents don’t automatically mean more value. We’ll talk about what happens when every team starts building its own agents, who owns them after launch, and when a use case would be better served by an automation, process change, or something much simpler.

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We’ll also tackle build vs. buy: what RevOps teams can realistically build themselves, what gets harder once a prototype reaches production, and how to weigh speed, maintenance, risk, and ownership before committing either way.

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🔑 Key Takeaways

• What actually needs to be true about your data and context before you put AI to work
• Why trust, workflow fit, and seller agency matter as much as the quality of an AI recommendation
• How to avoid an “agent graveyard” of disconnected tools, definitions, and owners
• How to decide when to build, buy, or simplify‍
• What separates an impressive AI prototype from a production workflow people actually use

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📣 Speakers

• Matt Volm, CEO and Founder at RevOps Co-op‍
• Kevin Heraly, VP of GTM Revenue Operations at Demand Science‍
• Sandy Robinson, VP of Revenue Operations & GTM Enablement at Quavo‍
• Sara Kinsey, VP of Marketing & Revenue at Von

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