
GTM Engineering, RevOps, and the AI-Savvy Chief Revenue Officer with Jordan Crawford

Episode description
This episode is sponsored by TestBox. Please visit www.testbox.com to learn more!
If you think “go-to-market engineering” sounds like another buzzword, think again. In this episode, Jordan Crawford (the original go-to-market engineer and founder who’s been building in this space since 2016) breaks down what GTM engineering really is, how it’s fundamentally different from RevOps, and why it’s the secret weapon for building data moats in the age of AI.
Jordan explains how the best GTM strategies aren’t about buying the latest tool, but about connecting strategy to bespoke data—turning the messy, manual prospecting work of SDRs into repeatable, scalable intelligence. He shares why the future belongs to those who can operationalize insights from the ground up, not just bolt AI onto old playbooks.
We dig into:
What actually defines go-to-market engineering (and why most leaders still don’t get it)
The difference between GTM engineering and RevOps (hint: one builds strategy, the other maintains it)
How to build a data moat that your competitors can’t cross
The art of permissionless value propositions—delivering value before you even ask for a meeting
Why verticalizing your GTM motion (even in horizontal SaaS) is the path to compounding knowledge and results
The “system of intelligence” framework and how the CRO role is evolving into the Jedi master conductor of the revenue engine
If you’re a CRO, sales leader, or just tired of the “spray and pray” approach to market, this episode is your crash course in building a smarter, more defensible GTM machine for the AI era.
Key Takeaways
Data moats—unique, hard-to-replicate insights—are the new competitive edge.
Delivering “permissionless value” means offering prospects something useful before you ask for anything in return.
Verticalizing your motion helps you learn and compound insights, even in horizontal markets.
The CRO of the future is a conductor, not just a top seller—able to orchestrate data, systems, and people for maximum impact.
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