Stacked GTM

Ryan Milligan started at Quotapath as Director of RevOps, spent four and a half years building the data and systems foundation, and is now CRO. His team has run at 100% blended quota attainment in 8 of the past 10 quarters, never below 90%, and has grown closed ARR per rep 1.7x in 18 months. The GTM engineering team doing most of the building is two people.In this episode, Ryan gets specific about how the whole system works: the data architecture decision he made on day one that still underpins everything, how he splits Dust and Claude into distinct roles across the sales cycle, and why he thinks the current wave of everyone building their own tools is a bubble with a painful correction coming. He also makes a sharp case that comp plan design is one of the highest-leverage tools a CRO has for changing the mix of revenue being closed, not just paying people.Topics discussed:Processing data in the warehouse nightly and reverse ETL-ing into CRM so both always speak the same languageThe build vs. buy litmus test: uniquely bespoke and relatively fixed vs. everything elseRep-built V1 prototypes handed to RevOps for productionizing and org-wide rolloutDust as system of record, Claude as system of action, and how they split across the sales cycleThursday multi-thread standup: every rep required to arrive with all multi-threads queued for active oppsThe "what would it take to close twice as many deals" framework for identifying which agents to build nextWarm outbound architecture using Clay, Unify, and product interaction data as intent signalsComp plan design as a lever for changing the shape of revenue reps close, not just incentivizing volumeWhy data architecture is the only real defense against confident AI hallucinationGTM engineer defined as the owner of the full prospect-to-renewal lifecycleListen to more episodes: Apple Spotify YouTube

Mark Deacon, CRO of CaniBuild, has gone further than most leaders talking about agentic GTM. He's actually built it, measured it, and has the numbers to back it up: a 400% improvement in revenue per human headcount and a demo-to-close rate now sitting above 60%, more than double what it was before AI.What separates this conversation is the operational depth. Mark walks through the exact sequencing logic behind their AI SDR workflow, the buy vs. build decision criteria he applies to every tool, how he onboards and governs AI agents the same way you would a new hire, and the centralized AI operating system he built from scratch to keep an 80-person company running with consistent governance across the stack.Topics Discussed:400% revenue per headcount improvement and 60%+ demo-to-close rate after AI deploymentSMS-first sequencing strategy that increased AI SDR pickup rates through A/B testingICP based routing logic that books demos directly into the right rep's calendarBuy vs. build decision framework based on uptime requirements and maintenance costTwo-to-three month AI agent onboarding process before handoff to the business ownerSlack-native AI chief of staff architecture that routes tasks across a team of specialized agentsOne-script Claude Code config deployment for consistent governance across all team membersAI-first vs. AI-only operating model and why the 80/20 split on support tickets mattersListen to more episodes: Apple Spotify YouTube

When Scalestack audits a new enterprise prospect, CRM data quality typically comes back at 30-40%. That's the starting point for most companies trying to run AI agents across their GTM motion, and it's why most of those initiatives quietly fail. Elio Narciso, who left AWS to build Scalestack, makes the case that the missing piece isn't the AI layer, it's the orchestration middleware that sits between your data sources and your activation layer, and that without it, AI doesn't produce bad outputs, it weaponizes your existing bad data at scale.What makes this conversation worth your time is that Elio goes well beyond "clean your data." He gets into the mechanics: why deciding when NOT to use AI and to use simple automation instead is one of the most important cost and scale decisions a GTM team can make, why dropping structured CRM picklists in favor of unstructured data may be one of the most underappreciated shifts happening right now, and why the GTM engineer role as it's currently defined is already becoming outdated, with software development as the more honest blueprint for where revenue teams are headed.Topics Discussed:Enterprise CRM data quality averaging 30-40% at the point of AI deploymentThe orchestration middleware layer and why it couldn't exist before modern AIHow forward deployment engineering translates business logic into agent missionsThe build vs. buy inflection point: when to stop experimenting with Clay and Claude and standardizeConfidence scoring and agent reasoning trails as a replacement for data trustWhy structured CRM picklists are becoming a liability as AI-driven data search replaces manual filteringAutomation vs. AI agents: the cost and scalability decision most teams are getting wrongWhy the GTM engineer title is already passé, and what software development tells us about what comes nextListen to more episodes: Apple Spotify YouTube

Matt Millen spent years running revenue at Outreach, watching companies stall out post-onboarding, and built Regie.ai to fix the problems that sales engagement itself created. He was attaching generative AI to tools like Outreach and SalesLoft years before ChatGPT, which gives him a rare vantage point on what actually works vs. what's still hype.In this episode, Noah and Andy get into why half of all AI POCs were failing (hint: it was both sides' fault), how Regie thinks about workflow before product, and why Matt believes "where humans enter the loop" is a brand decision, not a platform limitation. He also shares a blunt breakdown of which companies are good fits and which ones waste everyone's time, and makes the case for why the buy vs. build debate is being driven by CEOs who haven't thought through the workflow complexity underneath.Topics Discussed:Why 50% of AI POCs were failing and the two-sided imbalance that caused itThe four readiness signals Regie uses to qualify or disqualify a prospect before the POC startsWorkflow interviews with frontline reps vs. managers and why the gap between them mattersShifting from day-17 sequence dumps to signal-triggered task lists with built-in call contextWhy human-in-the-loop placement is a brand decision, not a product constraintSeat-based pricing with bundled AI credits and how the 80/20 on data consumption actually worksThe workflow complexity case against building agentic sales in-houseWhere the SDR and AE roles are heading as agents absorb more of the top-of-funnel motionListen to more episodes: Apple Spotify YouTube

Shantanu Shekhar, VP of Revenue Operations at Personio, funded his GTM engineering team by cutting two BDR heads and redirecting that budget into builders. Twelve months later, AE productivity is up 30%, pre-call research time dropped from two hours to 15 minutes, and 80% of MQLs run through an AI inbound SDR. He tells Noah and Andy exactly how he got there.What makes this episode worth your time is the operational specificity. Shantanu doesn't talk about AI strategy in the abstract. He walks through the four-pillar charter he built, the agents his team shipped, the ones that flopped on adoption, and the build-versus-buy calls that didn't go as planned. If you're trying to stand up a GTM engineering function or make the case for one, this is the closest thing to a playbook you'll find.Topics discussed:Four-pillar GTM engineering charter: culture, process, data, and systems sequencingRedirecting BDR headcount to fund GTM engineers and how to make that caseWhy GTM engineering embedded in RevOps eliminates an entire layer of alignment frictionBuilding an attribution agent on Gong transcripts so attribution becomes a prompt, not a toolResearch agent that cut AE pre-call prep from two hours to 15 minutes, driving 30% ARR liftCapturing 80% of MQLs through an AI inbound SDR and expanding from chat to multimodalPost-sales reachability agent orchestrating Zendesk, email, and Outreach to surface churn risk and cross-sell signalsEvolving from a center-of-excellence model to specialized GTM engineers by segmentWhy shipping without a feedback loop kills adoption, and how to build the transition cycleWhat Shantanu actually tests for when hiring GTM engineers, and why technical skill is just the floorListen to more episodes: Apple Spotify YouTube

Everett Berry's definition of GTM engineering is deceptively simple: remove the technical constraints that stop companies from growing as fast as possible. But in this episode, he unpacks what that actually requires in practice, and most senior GTM leaders will recognize immediately that the bottleneck almost never comes from a lack of ideas.From how Canva monitors customer social feeds at scale to detect poor graphic design and route it into outbound plays, to how Clay itself rebuilt its entire events invite system from scratch over two to three months of painful iteration before it worked, this conversation goes deep on tactics, org design, and where the role is headed.Topics discussed:·       GTM engineering as a builder discipline, not an evolution of marketing ops·       The three-layer implementation hierarchy: data quality, process automation, net new plays·       Why centralization matters even when GTM engineers are embedded across functions·       Rep ride-alongs as the primary method for finding plays worth scaling·       Clay's infrastructure stack: Audiences on ClickHouse, Sequencer, Agents, and MCP connectors to ChatGPT and Claude·       How PLG companies run self-serve to sales-led conversion plays, and why enterprise expansion still requires humans·       The failure culture leadership must create before the first GTM engineer can succeed·       Why vibe coders are often the wrong hire, and Clay's interview process for testing process thinking·       GTM engineering as a small, permanent tiger team rather than a scaling headcount functionListen to more episodes: Apple Spotify YouTube

Prabhav Jain, CEO of 11X, opens with something you rarely hear from a founder in this category: AI SDRs, as the industry has framed them, don't actually work. The problem isn't the technology. It's that everyone is pointed at the wrong question. This conversation gets into what the right questions are, and how 11X has built its entire go-to-market, product, and customer qualification process around answering them.From a multi-factor customer qualification model that disqualifies CEOs and CROs as sponsors by design, to a two-week deployment process and a living "17 problems" document they hand to DIY skeptics, Prabhav shares the operational specifics that most founders keep internal.Topics Discussed:Why "AI SDR" is the wrong frame, and what actually works insteadMulti-factor customer qualification criteria that screens for GTM maturity, channel fit, and operational ownership before the saleThree internal questions every team must answer before any pilot has a chance of succeedingOutbound signal strategy: why commoditized signals fail and how to find ones that actually convertUsing PLG and product usage data to trigger personalized cross-sell and upsell outreach at scaleSMS and WhatsApp as pre-call warming channels to lift inbound connection ratesThe "17 problems" sheet Prabhav sends to companies considering building in-houseHow 11X runs a two-week deployment, including mailbox warming, CRM mapping, and why voice agents take slightly longerThe case for collapsing the 50-tool GTM stack into a single agentic platformWhat rev ops looks like when sub-agents own execution and humans own optimization