Where AI Efficiency in GTM Actually Comes From with Jason Eubanks

Justin Gray says founders have come to expect their VC to go dark after the check clears and only show up for board meetings. His firm does the opposite: they invest, then spend the first few months inside the company building its go to market playbook, and hold off on new hires until they know what those hires will need. In this episode, Rachael Bueckert sits down with Justin, co-founder of InRevenue Capital and founder of LeadMD, to discuss what happens when your VC is also your GTM ops team. You'll hear when to hire after a raise, how he turns a founder's expertise into an ABM motion, what most partnerships get wrong, and why he wants the whole company speaking in the customer's KPIs. Resources Mentioned in This Episode: The First 90 Why Your Partner Channel Isn't Sending You Referrals USC Frameworks The Go-to-Market Cheat Code InRevenue Capital Justin Gray on LinkedIn 01:03 - Why he didn't build another consultancy06:16 - We pay to work really hard07:08 - The onsite workshop and the three pillars09:42 - Why not to hire right after you raise13:38 - Build a talent pipeline before the role opens15:01 - The playbook is always a pillar17:16 - Founders hate to kill deals17:35 - Bring value to the front of the sale21:22 - Making a portfolio company the Gartner of its space31:39 - Testing the buying cycle before you hire reps38:34 - Earning buy-in after the check clears45:02 - Why most partnerships run one way51:25 - How LeadMD won over Marketo's sellers56:08 - Speak in your customer's KPIs _______________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

Revenue leaders have to earn a board's trust before there are any results to show, then keep it when the results slip. Jason Baumgarten has sat in on a huge number of CEO and board transitions at Spencer Stuart. He walks through what wins that trust and what loses it, from the interview pitch that indicts the board to treating a revenue dip as a try harder problem. In this episode, Rachael Bueckert sits down with Jason, partner at Spencer Stuart, to discuss how revenue leaders earn and keep a board's trust. You'll hear what to ask before you take the job, what counts as progress before you have results to show, how a board decides whether a slump is a leadership problem, and what separates the revenue leaders who make it to CEO. Resources Mentioned in This Episode: Fit Happens Podcast What's Working in Outbound in 2026 USC Frameworks Jason Baumgarten on LinkedIn 01:52 - What boards look for beyond the resume03:30 - The interview pitch that indicts the board10:18 - Ask about the third rails before you take the job12:48 - Misaligned on mandate, not mission16:29 - What counts as progress before results18:45 - How a board reads bringing in outside help23:14 - It's rarely a try harder problem26:49 - Why boards bet on the incumbent30:40 - Missionary over mercenary31:05 - The sales leader who went back to IC37:07 - When you're not the right leader anymore47:02 - Blind spots moving into the CEO seat54:02 - Not innovating with AI will be a fireable offense1:01:12 - One thing to start doing this week _________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

Boards spent the last year telling revenue leaders to show ROI on AI, and in relational B2B sales it mostly hasn't shown up. Gabe Larsen sells AI employees for a living, and he's blunt about why: the returns come from volume and transactional work, and he has at least a dozen scars from trying it on relational outbound. In this episode, Rachael Bueckert sits down with Gabe, CRO at Atonom, to discuss where AI actually pays off in sales. You'll hear which kinds of leads AI handles well and which it doesn't, what AI can do for teams selling five and six figure deals, what happened when one company let AI take its after-hours leads, and how to pilot AI without locking into a contract you'll regret. Resources Mentioned in This Episode: What's Working in Outbound in 2026 USC Frameworks Atonom Gabe Larsen on LinkedIn 02:09 - Hot, warm and cold: where AI wins and fails05:31 - AI agents vs AI employees vs AI features11:42 - Don't put AI in front of the king12:57 - The neglected jobs AI can take first15:41 - Why SaaS isn't seeing ROI on AI22:32 - A dozen scars from relational outbound23:50 - The after-hours test that changed the math27:06 - AI books 28% of MQLs, humans book 22%31:14 - Cold email with an AI employee on the replies35:19 - Never sign a long-term AI contract39:28 - The signal that lifted pipeline 27% in 60 days42:16 - What your process needs before AI goes in45:34 - Managing AI employees like human hires51:51 - Where a revenue leader should start _________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

Every tool in your go to market stack carries its own database, and now every one of those vendors wants to sell you agents to run on top of it. Jason Eubanks' argument is that agents running against fragmented data sources will only ever produce unproductive, low quality automation. He ran into the problem himself as a CRO, with 22 products in his stack and sellers who were only in front of customers 28% of the time. In this episode, Rachael sits down with Jason, CEO and co-founder of Aurasell, to discuss where AI efficiency in go to market actually comes from. You'll hear what he means by core versus chore, why he thinks hackathon days produce token thrash instead of results, what he would probe in a demo to tell AI native from marketing, and how he thinks productivity per head should be measured now. Resources Mentioned in This Episode: USC Frameworks Aurasell Jason Eubanks on LinkedIn 04:10 - 22 products and 11 people stitching them together05:08 - Sellers in front of customers 28% of the time09:23 - Which SaaS players are actually in trouble12:31 - Why seat based pricing stops making sense13:58 - Lab work versus production15:46 - Core versus chore16:19 - What a hackathon day actually produces21:09 - You're missing 70% of sales interactions24:47 - The architecture cutoff for AI native33:27 - What consolidation does to the software bill46:37 - What to probe in a demo49:31 - From 24 hours of forecast prep to two52:01 - You don't have one CRM, you have three55:24 - Measuring revenue per human plus token spend ___________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

Fello grew 25% in about nine weeks, and Dustin Deno handled all of it without adding a single rep. His team built AI tools that took over the work around the meetings so his reps could spend their time in them. Now he's hiring again: more SDRs, reps out in territory taking customers to lunch, and a fully remote team getting together more often. In this episode, Rachael sits down with Dustin, CRO at Fello, to discuss using AI to buy his team more time with customers. You'll hear how he separates a real use case from a shiny one, what his team built that nobody used and why, why he's adding SDRs rather than cutting them, and which SaaS companies he thinks are actually at risk. Resources Mentioned in This Episode: USC Frameworks Fello Dustin Deno on LinkedIn 00:52 - 25% growth in nine weeks02:45 - What Felix actually does05:13 - Splitting customer success into three jobs07:02 - The hours lost before a meeting even happens09:11 - Why high quota attainment is a signal to hire13:34 - There's no finish line on data18:02 - Why they won't sell to a frustrated customer24:09 - The blog that didn't exist ten minutes ago28:36 - Go sit where the work is being done30:40 - Pilot it with one rep and let them break it34:23 - The tool they built that nobody used35:56 - Why he's adding SDRs39:35 - Who to hire for the build team48:12 - Who's exposed in SaaS and who isn't55:03 - Take your customers to lunch ___________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

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 like04:25 - Why not just buy a BI tool05:50 - What had to be settled before any code13:05 - What a good metric definition has in it16:32 - The no code version they built first20:29 - Claude Code as a junior dev you hold the hand of23:05 - The decisions a human had to approve24:24 - How you end up with vibe coded slop29:14 - One source per number, no exceptions32:27 - Why the AI never does the math38:17 - Pacing targets and half finished months45:00 - Why the assistant refuses some questions48:30 - Who gets to see the numbers1:01:27 - Where to start and what people get wrong ___________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

Everett Berry's view is that an agent is only as good as the context you hand it, and that managing that context matters more than the task you give it or how you prompt it. In this episode, Eddie Reynolds sits down with Everett, Head of Go to Market Engineering at Clay, who has also led their sales team, to discuss how Clay uses AI in their own GTM. Everett gets into why wiring an agent to all your systems over MCP doesn't work the way people expect, how Clay recut territories around account momentum rather than headcount, where he draws the line on vibe coding inside go to market, and which role he expects to be automated before the SDR. Resources Mentioned in This Episode: The AI-Driven Continuously Self-Improving ICP USC Frameworks Everett Berry on LinkedIn 01:42 - What an account agent actually is04:55 - An agent is only as good as its context06:03 - An hour of Gong eats 50,000 tokens10:35 - Software engineers are a year ahead of GTM12:42 - Why territories are a thorny problem right now13:29 - Catching Cursor at 30 people14:58 - The progress you can't see in the data17:34 - Momentum over headcount20:35 - Letting the marketing engine find new personas30:36 - Build the loop with you in the middle32:32 - Where vibe coding belongs and where it doesn't40:43 - Why the sales engineer falls before the SDR46:35 - The list is the message50:15 - Why Clay avoids cold outbound __________________________________________________________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 02:28 - The rule of 60 company that wasn't03:18 - How bankers stage a business like a house06:25 - Why pre-AI SaaS is reverting to 4 to 6 times revenue08:37 - Why investors actually pay for predictability11:49 - AI native reps closing $10M deals14:30 - Product market fit is not a vibe18:27 - How you fall out of product market fit19:48 - The LTV to CAC and payback numbers he wants25:36 - Treating your business like a code base27:03 - The close rate a founder has to give up29:59 - When the person with clout breaks the rules32:30 - Strategy is deciding what money to turn away38:10 - Where AI belongs: enrichment and list building40:30 - Where AI does not belong49:28 - Your cap table is your corporate strategy _________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok

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 less06:46 - Why LLMs are now a channel you have to plan for08:51 - $10K in seven days vs $500K in 19 months10:28 - What's working at the enterprise end12:08 - Buyers who know your competitive position better than you14:19 - The events he likes best15:25 - Three things that decide whether a booth pays off17:46 - Why execution beats channel choice25:25 - What has to be true before you deploy AI26:41 - What's working at the SMB end30:22 - Where ABM stops working32:30 - Why warm and cold leads need different reps37:57 - Why he's skeptical of intent platforms40:36 - 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 revenue08:18 - Why reps get 2,000 accounts and work 20010:44 - The targeting agent and the data source waterfall16:17 - Triggers and signals, and why one alone isn't enough20:19 - The tool stack and why Salesforce stays the source of truth23:33 - Why separate agents beat one big one26:00 - When to use an LLM and when to use n8n30:15 - The catch-22 of what to enrich first39:50 - Scoring: start with a hypothesis and iterate47:55 - What a good sales email actually looks like56:57 - Who gets a fully personalized message and who doesn't01:03:59 - The updates a machine should never make01:04:36 - The first question for your AI analyst: did we execute01:12:35 - The GTM council meeting that forces the loop _________________________________________________________GTM STRATEGY & AI ENGINEERING FOR B2B TECH● Website● LinkedIn● TikTok