How Clay Uses AI in Their Own GTM with Everett Berry

USC's revenue and marketing dashboard used to run on a hand assembled Google Sheet of about 50 metrics, revised by hand roughly twice a month for two years, with comments on cells, notes off to the side, and conflicting numbers the team had to stop and trace in meetings. Gerry Marletta rebuilt it as a dashboard using Claude Code, and the surprising part is that 98% of it is programmatic, with no model involved anywhere in the math. In this episode, Rachael sits down with Gerry, SVP of GTM Architecture at USC, to discuss how to build an AI powered GTM dashboard the way he did it. You'll hear what had to be settled before a single line of code got written, who decides when two teams disagree on what a number means, which decisions they forced a human to approve mid build, why the chatbot is gated so it can never do the math, and what people are most likely to get wrong on their first attempt. Resources Mentioned in This Episode: Build Your Own AI-Powered GTM Dashboard (Guide) USC Frameworks Gerry Marletta on LinkedIn 02:17 - What Monday mornings used to look like 04:25 - Why not just buy a BI tool 05:50 - What had to be settled before any code 13:05 - What a good metric definition has in it 16:32 - The no code version they built first 20:29 - Claude Code as a junior dev you hold the hand of 23:05 - The decisions a human had to approve 24:24 - How you end up with vibe coded slop 29:14 - One source per number, no exceptions 32:27 - Why the AI never does the math 38:17 - Pacing targets and half finished months 45:00 - Why the assistant refuses some questions 48:30 - Who gets to see the numbers 1:01:27 - Where to start and what people get wrong ___________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Sam Jacobs teaches that product market fit isn't a vibe and it's not a feeling. It's a function of your unit economics, and it isn't permanent either. You can fall out of it when your acquisition costs climb or your retention slips, and that is the signal to pull back on growth spending rather than push harder. In this episode, Eddie Reynolds sits down with Sam Jacobs, founder and CEO of Pavilion, to discuss what it takes to make revenue predictable and why predictability is what a buyer is actually paying for. Sam walks through the LTV to CAC range he wants a company sitting in and how fast he expects acquisition costs to be paid back, why one private equity backed business sold at roughly half the multiple its owners had expected, and the trade a founder has to accept to build a machine that runs without them. They also get into where AI genuinely helps a sales org and where Sam thinks it shouldn't be, and he closes on why your ownership structure is the most consequential long term decision you will make. Resources Mentioned in This Episode: Sundays with Sam #21: Predictable Revenue Pavilion University (Intro to P&L Fluency, CRO School, AI in Go to Market School) USC Frameworks

Richard Perez leads the sales and go to market practice at Apax Partners, a private equity firm, where he works with their portfolio companies on how they sell. We asked which motions he'd back at the enterprise end, which at the SMB end, and what he wouldn't spend his own money on. Almost every answer came back with conditions attached. In this episode, Eddie Reynolds sits down with Richard to discuss the GTM motions and channels working right now. You'll hear where he thinks ABM stops working, the three things that decide whether a conference booth pays off, why he's skeptical of most intent platforms, and the number that tells him a sales organization hasn't been engineered properly. Resources Mentioned in This Episode: USC Frameworks 01:14 - Motions vs channels, and why the label matters less 06:46 - Why LLMs are now a channel you have to plan for 08:51 - $10K in seven days vs $500K in 19 months 10:28 - What's working at the enterprise end 12:08 - Buyers who know your competitive position better than you 14:19 - The events he likes best 15:25 - Three things that decide whether a booth pays off 17:46 - Why execution beats channel choice 25:25 - What has to be true before you deploy AI 26:41 - What's working at the SMB end 30:22 - Where ABM stops working 32:30 - Why warm and cold leads need different reps 37:57 - Why he's skeptical of intent platforms 40:36 - The cost of a dollar of new logo revenue ___________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

SaaStr surveyed companies that had implemented AI SDRs and found only 3% had any revenue to show for it. Eddie's read is that this is the same pattern as Outreach and Salesloft a few years ago, and Gmail and Salesforce before that. So we mapped the system that has to be built around an ai-powered outbound motion before it will actually work. In this episode, Eddie Reynolds and Gerry Marletta, SVP of Revenue Operations Systems at USC, walk through how to build an AI-native outbound engine, agent by agent. You'll hear how they got around the catch-22 of deciding which accounts to enrich before you've enriched them, when to hand a step to an LLM and when to use an orchestration tool instead, what the agent that grades every rep email actually checks for, and the updates Gerry says a machine should never be allowed to make. Resources Mentioned in This Episode: Building an AI-Native Outbound Engine (Full Written Article) The AI-Driven Continuously Self-Improving ICP Free Outbound Pipeline Inspection USC Frameworks 04:18 - Only 3% of AI SDR buyers saw any revenue 08:18 - Why reps get 2,000 accounts and work 200 10:44 - The targeting agent and the data source waterfall 16:17 - Triggers and signals, and why one alone isn't enough 20:19 - The tool stack and why Salesforce stays the source of truth 23:33 - Why separate agents beat one big one 26:00 - When to use an LLM and when to use n8n 30:15 - The catch-22 of what to enrich first 39:50 - Scoring: start with a hypothesis and iterate 47:55 - What a good sales email actually looks like 56:57 - Who gets a fully personalized message and who doesn't 01:03:59 - The updates a machine should never make 01:04:36 - The first question for your AI analyst: did we execute 01:12:35 - The GTM council meeting that forces the loop _________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

New logos get most of the attention in a revenue org. Robert Clarkson calls that the most obvious of three growth levers, and it's the one he spends the least time on. He has run revenue at PayPal, Stripe, Payoneer and now Fiserv, which moves $4.6 trillion in payment volume a year. At one company he joined that had grown 16% to 19% a year for three straight years, he stopped measuring the sales team on what it sold and started measuring it on what customers actually got out of it. It grew 26% the next year and never had a year under 30% after that. In this episode, Rachael Bueckert sits down with Robert, Chief Revenue Officer for SMB and Clover at Fiserv, to walk through the model he has carried from company to company. They cover how he writes a customer success measure into the sales compensation plan, how he splits his team into hunters and growers, and why he will accept almost any CAC as long as the long term value holds up. Resources Mentioned in This Episode: USC Frameworks 01:15 - The three growth levers every CRO has 04:53 - Inheriting a quilt when you need a mosaic 13:21 - Giving SMBs the analytics only enterprises had 23:27 - The model he runs at every company 27:36 - Putting customer success in the sales comp plan 32:23 - Why net dollar retention has to clear 100% 40:19 - Hunters and growers 41:59 - Six features sold, five in use 46:38 - From 19% to 26% to never under 30% 49:28 - Uber didn't build a better taxi _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok