Should GTM teams build AI agents?

Build or buy? I asked while knee-deep in Claude Code myself.

What operators are learning about AI agents the hard way: the answer isn't build or use. It depends on who's asking.

The most honest answer in the thread.

Nick Ashwin ran a sales team tasked with demonstrating AI utilization every month. His read: asking GTM reps to build agents is an opportunity cost question, not a skills question. The realistic path is one dedicated person with both GTM experience and agent-building chops.

But the kicker: teams that understand even the basics make dramatically better decisions about which tools to buy, which demos are BS, and which vendors are overselling.

The framework that stuck.

20% builds. 80% consumes. Maurice restructured his team around exactly this; one Agentic Engineer, everyone else as Revenue Operators. Simple. Already running.

The counterintuitive insight..

Designated AI-building talent; IT, RevOps, tiger teams, consistently make substandard agents because they aren't subject matter experts. But when SMEs are trained to build their own workflows? They make the best ones. They know exactly what will actually work.

Proof it's already happening!

Pat Hopkins built a PLG deal bot that cleans missing data, associates it to the right objects, and verifies line items against Stripe invoices. Hands-on keyboard time: 1-2 hours. Hours saved per week: 6-10.

The question before the question.

Before asking whether to build or use, ask whether you need an agent at all. Most teams don't. They need a simple automation or a small script. The agent obsession is producing complexity that quietly gets abandoned for easier tooling.

The line that reframed everything.

GTM Engineers who can build and use. Revenue Operators who set the strategy. And on SDRs: "SDR would be gone. Just the title, not the person."

The role survives. The label doesn't.

Where do you land - build, use, or both?

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The SaaSpocalypse Debate: AI and the Future of RevOps

Dive into the "SaaSpocalypse" and the shifting landscape of RevOps in the age of AI. Rather than just managing tool sprawl, RevOps leaders are now architecting the data models and automated workflows that define modern revenue systems. Join us to discuss whether the SaaS stack is truly shrinking or simply evolving, and learn what it takes to design systems that AI can reliably navigate.

5 AI Myths That Are Holding Your Revenue Team Back

Von CEO Sahil Aggarwal recently challenged five widely held beliefs about AI and revenue teams at a conference and the audience reaction proved he hit a nerve. In this 20-minute talk and live discussion, he unpacks the assumptions keeping RevOps teams stuck: data readiness, headcount planning, and AI strategy that's already a year behind. Stick around after for a live conversation with RevOps leaders navigating these exact challenges in real time.

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Most QBRs never happen and when they do, they follow the same tired formula: dense slides, scattered context, and a meeting your customer didn't want to attend. The real cost is value blindness. In this webinar, you'll learn how to rethink the business review entirely, structuring every conversation around three questions: what outcome did we deliver, what should you do next, and how can we help you get there. Plus, how AI can cut prep time by 80% and make high-quality reviews possible at scale whether you manage ten accounts or thousands.

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