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 deployment

• SMS-first sequencing strategy that increased AI SDR pickup rates through A/B testing

• ICP based routing logic that books demos directly into the right rep's calendar

• Buy vs. build decision framework based on uptime requirements and maintenance cost

• Two-to-three month AI agent onboarding process before handoff to the business owner

• Slack-native AI chief of staff architecture that routes tasks across a team of specialized agents

• One-script Claude Code config deployment for consistent governance across all team members

• AI-first vs. AI-only operating model and why the 80/20 split on support tickets matters

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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 deployment

• The orchestration middleware layer and why it couldn't exist before modern AI

• How forward deployment engineering translates business logic into agent missions

• The build vs. buy inflection point: when to stop experimenting with Clay and Claude and standardize

• Confidence scoring and agent reasoning trails as a replacement for data trust

• Why structured CRM picklists are becoming a liability as AI-driven data search replaces manual filtering

• Automation vs. AI agents: the cost and scalability decision most teams are getting wrong

• Why the GTM engineer title is already passé, and what software development tells us about what comes next

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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 it

• The four readiness signals Regie uses to qualify or disqualify a prospect before the POC starts

• Workflow interviews with frontline reps vs. managers and why the gap between them matters

• Shifting from day-17 sequence dumps to signal-triggered task lists with built-in call context

• Why human-in-the-loop placement is a brand decision, not a product constraint

• Seat-based pricing with bundled AI credits and how the 80/20 on data consumption actually works

• The workflow complexity case against building agentic sales in-house

• Where the SDR and AE roles are heading as agents absorb more of the top-of-funnel motion

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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 sequencing

  
  

• Redirecting BDR headcount to fund GTM engineers and how to make that case

  
  

• Why GTM engineering embedded in RevOps eliminates an entire layer of alignment friction

  
  

• Building an attribution agent on Gong transcripts so attribution becomes a prompt, not a tool

  
  

• Research agent that cut AE pre-call prep from two hours to 15 minutes, driving 30% ARR lift

  
  

• Capturing 80% of MQLs through an AI inbound SDR and expanding from chat to multimodal

  
  

• Post-sales reachability agent orchestrating Zendesk, email, and Outreach to surface churn risk and cross-sell signals

  
  

• Evolving from a center-of-excellence model to specialized GTM engineers by segment

  
  

• Why shipping without a feedback loop kills adoption, and how to build the transition cycle

  
  

• What Shantanu actually tests for when hiring GTM engineers, and why technical skill is just the floor

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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 function

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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 instead

• Multi-factor customer qualification criteria that screens for GTM maturity, channel fit, and operational ownership before the sale

• Three internal questions every team must answer before any pilot has a chance of succeeding

• Outbound signal strategy: why commoditized signals fail and how to find ones that actually convert

• Using PLG and product usage data to trigger personalized cross-sell and upsell outreach at scale

• SMS and WhatsApp as pre-call warming channels to lift inbound connection rates

• The "17 problems" sheet Prabhav sends to companies considering building in-house

• How 11X runs a two-week deployment, including mailbox warming, CRM mapping, and why voice agents take slightly longer

• The case for collapsing the 50-tool GTM stack into a single agentic platform

• What rev ops looks like when sub-agents own execution and humans own optimization