Episode Details

What's up everyone, today we have the pleasure of sitting down with Lindsay Rothlisberger, Director of GTM Innovation at Zapier.

• (00:00) - Intro
• (01:23) - In This Episode
• (02:00) - Sponsor: Knak
• (03:08) - Sponsor: MoEngage
• (05:49) - How Zapier's RevOps Team Built Its AI Foundation
• (19:43) - Why Visibility Has to Come Before Governance in AI Adoption
• (24:58) - Sponsor: GrowthBench
• (25:58) - Sponsor: GrowthLoop
• (29:48) - How Zapier Fights Context Rot in Its AI Shared Brain
• (35:55) - How Zapier Governs Shared AI Skills from Review to Long-Term Ownership
• (39:27) - What Happens to RevOps When Everyone Around Them Can Build
• (45:05) - The Director of GTM Innovation Role and the Sharing Problem Nobody Has Solved
• (50:47) - What Keeps Lindsay Grounded in the Middle of All This Change
• (52:00) - Lindsay on Getting Buy-In and What She's Reading

Summary: When a startup claimed in April 2026 that it invented the marketing engineer role and that RevOps professionals "just do tool integrations," Lindsay Rothlisberger had heart palpitations. Her team at Zapier had been building AI into GTM workflows for years before the announcement. In this episode, she walks through the 6-component AI governance model she published publicly: a golden path to Cursor, a structured shared brain in Google Drive, data policies built with the security team, a visibility layer powered by a custom Zapier agent, a context engineering strategy that fights context rot, and a red-yellow-green skills review gate. She also names the part of the model that's still broken, and it's more honest than most AI governance conversations allow. If your team is figuring out how to govern AI at scale without killing the momentum, this is the inside view from someone who's done it.

About Lindsay Rothlisberger

Lindsay Rothlisberger is Director of GTM Innovation at Zapier, where she leads the company's AI-powered GTM transformation internally and works alongside customers navigating the same shift. She spent 4 years building Zapier's RevOps function from zero, scaling it into a cross-functional engine covering AI, systems, analytics, planning, and enablement, and growing ACV 10x in that time. Before moving into the innovation role, she led marketing operations and lifecycle programs at UNiDAYS across B2B and B2C markets. She writes on LinkedIn about what Zapier is actually shipping, what works, and what doesn't.

How Zapier's RevOps Team Built Its AI Foundation

Most RevOps teams doing serious AI work have been doing it longer than the current conversation suggests. The tools are newer and the terminology has changed, but building automated workflows that take unstructured data and produce structured, actionable outputs for salespeople and marketers? That's exactly what good RevOps teams were doing before anyone put a trending name on it.

Lindsay's team at Zapier started experimenting with AI several years ago, when it was first becoming accessible. Zapier gave its RevOps team the tools to experiment early, and rather than waiting for a strategy to materialize, they picked a specific, annoying problem: sales handoffs. Salespeople were going into first calls without enough context about the lead. The team pulled all the relevant unstructured data, engagement records, support tickets, email threads, and used AI to generate clean, contextualized briefing materials. The result was a measurable lift in lead-to-opportunity conversion rates, and a pattern the team has used ever since: find something specific that's visibly broken, prove AI fixes it, then apply that logic somewhere else.

That early foundation matters now because the landscape has shifted in a way that affects RevOps directly. Claude Code, Cursor, and similar tools have made it possible for people with no engineering bac