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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 fails 05:31 - AI agents vs AI employees vs AI features 11:42 - Don't put AI in front of the king 12:57 - The neglected jobs AI can take first 15:41 - Why SaaS isn't seeing ROI on AI 22:32 - A dozen scars from relational outbound 23:50 - The after-hours test that changed the math 27:06 - AI books 28% of MQLs, humans book 22% 31:14 - Cold email with an AI employee on the replies 35:19 - Never sign a long-term AI contract 39:28 - The signal that lifted pipeline 27% in 60 days 42:16 - What your process needs before AI goes in 45:34 - Managing AI employees like human hires 51:51 - 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 together 05:08 - Sellers in front of customers 28% of the time 09:23 - Which SaaS players are actually in trouble 12:31 - Why seat based pricing stops making sense 13:58 - Lab work versus production 15:46 - Core versus chore 16:19 - What a hackathon day actually produces 21:09 - You're missing 70% of sales interactions 24:47 - The architecture cutoff for AI native 33:27 - What consolidation does to the software bill 46:37 - What to probe in a demo 49:31 - From 24 hours of forecast prep to two 52:01 - You don't have one CRM, you have three 55:24 - 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 weeks 02:45 - What Felix actually does 05:13 - Splitting customer success into three jobs 07:02 - The hours lost before a meeting even happens 09:11 - Why high quota attainment is a signal to hire 13:34 - There's no finish line on data 18:02 - Why they won't sell to a frustrated customer 24:09 - The blog that didn't exist ten minutes ago 28:36 - Go sit where the work is being done 30:40 - Pilot it with one rep and let them break it 34:23 - The tool they built that nobody used 35:56 - Why he's adding SDRs 39:35 - Who to hire for the build team 48:12 - Who's exposed in SaaS and who isn't 55:03 - 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 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

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 is 04:55 - An agent is only as good as its context 06:03 - An hour of Gong eats 50,000 tokens 10:35 - Software engineers are a year ahead of GTM 12:42 - Why territories are a thorny problem right now 13:29 - Catching Cursor at 30 people 14:58 - The progress you can't see in the data 17:34 - Momentum over headcount 20:35 - Letting the marketing engine find new personas 30:36 - Build the loop with you in the middle 32:32 - Where vibe coding belongs and where it doesn't 40:43 - Why the sales engineer falls before the SDR 46:35 - The list is the message 50:15 - 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't 03:18 - How bankers stage a business like a house 06:25 - Why pre-AI SaaS is reverting to 4 to 6 times revenue 08:37 - Why investors actually pay for predictability 11:49 - AI native reps closing $10M deals 14:30 - Product market fit is not a vibe 18:27 - How you fall out of product market fit 19:48 - The LTV to CAC and payback numbers he wants 25:36 - Treating your business like a code base 27:03 - The close rate a founder has to give up 29:59 - When the person with clout breaks the rules 32:30 - Strategy is deciding what money to turn away 38:10 - Where AI belongs: enrichment and list building 40:30 - Where AI does not belong 49:28 - 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 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

We get asked what's actually working in outbound constantly, and specifically what works for companies that don't have a great brand. So we read the industry reports, more than 100 million emails worth of data, and traced every stat we could back to its original source. The surprising finding was how unreliable the data is. In this episode, Eddie Reynolds and Rachael Bueckert go tactic by tactic through all six most common outbound strategies: targeted, warm signals, trigger based, third party intent, autonomous AI SDRs and spray and pray. You'll hear which one doubled reply rates, the templated LinkedIn message Eddie used to source millions in business, and the one signal he says he would spend his money on last. Resources Mentioned in This Episode: What's Working in Outbound in 2026 (Full Written Report) Free Outbound Pipeline Inspection 01:36 - 100 million emails and a fake return address 03:45 - Why we went digging for real data 08:53 - Targeted outbound: double the reply rate 10:02 - It's not six tactics, it's three 13:19 - Why Eddie won't use Outreach for this 15:56 - The segmentation step everyone skips 20:56 - Vendors claim 20%, we saw 2 to 5% 27:16 - The bar every hot list has to clear 31:37 - The LinkedIn message that sourced millions 36:49 - Who paid for the intent data studies 43:40 - 3% of AI SDR teams saw any revenue 51:09 - Does bad outbound burn your reputation? 54:17 - Spray and pray works, and that's the problem ___________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Adam Coffey priced out the sales tooling he was looking at and the quotes came back at several hundred thousand dollars each. His team built their own instead, and rolled it out globally to all sales reps and managers at Ivanti. It does the meeting prep, flags risks from call transcripts, emails and Salesforce fields, and gives a first pass at the MEDDPICC field updates that the rep can adjust. In this episode, Rachael Bueckert sits down with Adam, Director of Analytics and Reporting at Ivanti, to discuss what it takes to build your own GTM tools. They get into the real cost behind building your own sales tooling, why he has not seen AI work on a dirty CRM yet, and why he still calls dashboards mission critical with AI apps running on top of them. Resources Mentioned in This Episode: Sales Command Center Demo USC Frameworks Adam Coffey on LinkedIn 03:04 - What finance and rev ops get wrong in silos 09:52 - The sales command center he built 11:59 - The stack: Copilot, VS Code, Power Apps 15:43 - Build vs buy: the actual numbers 19:57 - Is SaaS in trouble? 24:33 - The security review a build has to pass 35:20 - You can't slap AI on a dirty CRM 39:53 - Data goes to IT to die 43:17 - One data model instead of hundreds 46:14 - Why dashboards still matter _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Most companies have a dashboard. Some of them even have accurate data. What wayy fewer of them have is someone who can look at that data on a Thursday morning and walk into a meeting and say: here's what I think is wrong, here's why, and here's what we should do about it. The gap between "workable data" and "actual decisions" is the problem we're talking about today. In this episode, Eddie Reynolds and Rachael Bueckert break down how to close the gap between having a dashboard and actually using it to move revenue. They cover why perfect data is the wrong goal and what to optimize for instead, how to structure a GTM Council meeting so it creates real accountability, and what the dashboard they built with Claude actually does (and how you can build your own). Resources Mentioned in This Episode: How to Build Your Own AI-Powered GTM Dashboard (Free PDF Guide) USC Demo Dashboard (Fictional $100M Company) GTM Ops Decision Tree GTM Ops Diagnostic Framework 01:40 - The gap between good data and actual decisions 06:12 - Why "perfect data" is the wrong goal 08:37 - Who should own dashboard insights (CRO vs. Rev Ops) 14:20 - What a GTM Council meeting actually looks like 17:33 - Why USC built their own AI-powered dashboard 22:09 - Ingesting data via MCP instead of building integrations 31:16 - Pipeline metrics: close rate, ASP, and sales cycle by segment 35:28 - Reading rep performance right to left 40:05 - AI email scoring that fixed a new rep's outbound in two weeks 43:52 - Where to start on Monday (the decision tree) 49:51 - $100M company with no lead definitions (and what happened next) _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Lou Shipley has sat in almost every seat that touches revenue. He's been the rep, the VP of sales, a three-time CEO, a board member, an investor, and now a senior lecturer at Harvard Business School where he teaches the most popular class on campus. When he walks into a company today and something feels off in go-to-market, he knows exactly where to look. In this episode, Rachael Bueckert sits down with Lou Shipley, author of Unlikely Entrepreneurs and senior lecturer at Harvard Business School, for a conversation on what actually breaks when growth stalls and how to fix it. The conversation covers how Lou rebuilt Black Duck's entire sales motion and took it from $20M to $90M in 4 years, the founder trap that catches most CEOs as complexity increases, and the Plane model that let him develop talent from within instead of chasing the free agent market. Resources Mentioned in This Episode: Unlikely Entrepreneurs (Lou Shipley) GTM Ops Frameworks 00:23 - Intro to Lou Shipley 01:11 - The youth hockey team test for your sales org 01:29 - What Lou looks for first when go-to-market feels off 03:28 - Product market fit vs. sales execution problem 05:07 - The founder trap: when your role changes and you don't 07:19 - What the board sees when numbers are being missed 08:28 - The role of an independent board member 12:32 - The sales math: velocity deals, ASP, cycle time, and forecasting a month ahead 15:00 - The single version of truth 18:34 - SQL definition, zombie pipeline, and why changing it kills your data 24:26 - Access to power and why it determines close rate 28:42 - The Black Duck turnaround: three things that changed 30:23 - The Jamie Dimon signal that triggered the product pivot 36:40 - Rebuilding the team: developing talent vs. chasing free agents 39:19 - The Plane model: gunner, navigator, copilot, pilot 42:30 - From $20M to $90M in four years 45:43 - The thinking machine: distributed leadership at scale 46:06 - Unlikely Entrepreneurs and the problem with the problem 54:31 - Where to start when growth has stalled _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Every sales org has one. The rep who ignores the playbook, barely touches the CRM until a deal closes, runs the lowest activity on the team, and still outsells everyone else combined. Most leaders explain it as raw talent. Eddie thinks that explanation is costing them far more than they realize. In this episode, Eddie Reynolds and Rachael Bueckert break down what that top performer is actually telling you about the business underneath them. The conversation covers why leaning on A players is a growth strategy with a ceiling, how to reverse engineer what your best rep is doing so your B players can replicate it, the territory problem that makes top performers look more elite than they are, and why the goal isn't to clone your all-star. Resources Mentioned in This Episode: GTM Efficiency Pyramid Framework GTM Ops Frameworks 01:10 - What that rep is really telling you 04:53 - The playbook they built that's better than yours 06:41 - What Salesforce's best reps were best at 09:08 - How to reverse engineer it into the org 09:46 - The territory problem hiding performance 10:45 - Account stages, account scores, and the neglected tier one 18:13 - Build for the average rep on an average day 21:29 - ICP, personas, and the process that makes B players succeed 24:51 - Reading metrics from right to left: close rate, ASP, sales cycle 31:19 - How common are shadow systems? 33:52 - C players hiding behind broken data 37:22 - What percentage of quota should come from A players? 40:15 - When star-led growth stops working 42:31 - The culture cost of giving A players a pass 47:58 - Where to start if you're recognizing your org in this _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok

Vercel is a $9.3 billion company powering much of the modern web (OpenAI, Nike, Netflix, Walmart) and 30% of the applications being deployed on their platform are now agent-built. David Totten is the VP of Field Engineering, which means he owns every technical touchpoint a Vercel customer has from the first pre-sales conversation through to post-sale optimization and support. In this episode, Rachael Bueckert sits down with David for a rare inside look at how a $9.3 billion company actually runs AI across its GTM org. The conversation covers Eve, the sentiment analysis agent that eliminated CRM data entry for an entire engineering team, how consumption telemetry across millions of deployments surfaces churn signals before they become churn, and the philosophy that changed everything: treating GTM like a product. Resources Mentioned in This Episode: GTM Ops Frameworks 00:22 - What is a VP of Field Engineering? 02:37 - Where field engineering sits in the revenue org 05:04 - What David changed when he joined Vercel 11:55 - Eve: the agent framework that lets anyone build agents 16:52 - Treating GTM like a product 31:34 - The sentiment analysis agent that eliminated CRM data entry 34:03 - Closing the gap between what reps do and what leaders see 39:43 - Automated call coaching and enablement 40:18 - Post-sale at a consumption-based company 42:31 - AI gateway: helping customers pick the right LLM 46:16 - Churn prevention via usage telemetry 49:21 - Visibility without accessing customer IP 57:22 - From 80% reactive to fully proactive: the 12-month roadmap _______________________________________________________________ GTM STRATEGY & AI ENGINEERING FOR B2B TECH ● Website ● LinkedIn ● TikTok