Summary
Most revenue organizations are running playbooks that were built for a world that no longer exists. In this episode of the Growth Wizards Podcast, host Geoffrey Lugli sits down with Justin Shriber, CEO and co-founder of Terret, for a sharp conversation about what AI actually changes in GTM—and what it doesn't.
Justin's career spans McKinsey, Siebel Systems (the first CRM), Oracle, and LinkedIn, where he led revenue teams at scale. Now he's building what he calls a fleet of revenue agents at Terret, an AI revenue engine that turns insights into action.
Justin makes two bold claims early: the traditional CRO is a dinosaur, and MEDDIC is dead. Not because rigor doesn't matter, but because the market moves too fast for static frameworks. The companies winning today aren't the most disciplined—they're the most agile. They pick up signals faster, interpret them faster, and push changes to the front line faster.
Justin and Jeff get into what the future CRO looks like as an orchestrator rather than an executor, why most AI deployments fail because companies give LLMs half the data and wonder why they hallucinate, how dynamic playbooks are replacing the week-long conference room training that sales reps forgot by Friday, and why consumption-based pricing transfers risk onto customers in a way that smart vendors are starting to walk back. If you lead a revenue team or are thinking about where AI fits in your GTM motion, Justin's perspective cuts through the hype.
Timestamps
- [00:23] – From door-to-door window washing to Siebel, Oracle, and LinkedIn
- [01:26] – What AI actually changes for sellers—and why mediocre reps should find a new profession
- [02:42] – Why the traditional CRO is a dinosaur and what replaces them
- [04:21] – The future CRO as orchestrator: blending technology, human expertise, and process
- [06:02] – Why MEDDIC is dead and how AI builds custom playbooks from your best reps
- [08:06] – Dynamic playbooks: from a year-old static document to real-time intelligence
- [09:33] – AI as coach in your earpiece: prep sheets, post-call evaluations, manager summaries
- [11:13] – The biggest GTM AI mistake: giving LLMs half the data and expecting full answers
- [12:31] – Governance, token consumption, and why Terret built a revenue graph instead
- [14:39] – Fixed price vs. consumption vs. outcome-based pricing: who should carry the risk
- [16:19] – The easy button for AI: pre-built agents on a governed revenue graph, live in two days
Takeaways
- The CROs who survive won't be the most disciplined—they'll be the most agile, pushing signal-driven changes to the front line faster than anyone else
- MEDDIC was invented 30 years ago by one company with an inferior product—AI now lets every company build a playbook tuned to their own best reps
- Dynamic playbooks update in real time as competitors launch and objections shift—a year-old static document is already obsolete before reps finish training on it
- LLMs hallucinate because companies give them incomplete data—full context is the prerequisite for accurate AI reasoning
- Consumption-based pricing transfers risk onto customers—the competitive move is fixed pricing with the vendor carrying the efficiency risk
- Don't grope around with AI alone—pick one discrete project and find a competent partner to accelerate outcomes