Stacked GTM – Details, episodes & analysis

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Stacked GTM

GTM Council and Frontlines.io

Business

Frequency: 1 episode/7d. Total Eps: 9

Deep-dive into how AI is impacting GTM - Each series of 5-7 episodes explores one area from the perspective of top practitioners and vendors. Presented by the GTM Council - the exclusive community for operational GTM leaders.

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Apple Podcasts

🇫🇷 France - management
04/07/2026
#92
🇫🇷 France - management
03/07/2026
#82
🇫🇷 France - management
02/07/2026
#73
🇫🇷 France - management
01/07/2026
#51
🇫🇷 France - management
30/06/2026
#30
🇬🇧 Great Britain - management
20/05/2026
#96

Spotify

No recent rankings available

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https://www.youtube.com/@TheFrontLinesGTM
6 shares

https://open.spotify.com/show/6CR6uDvyVyS2x3BK0vNOqd
6 shares

https://www.linkedin.com/in/ryanemilligan/
1 share
https://www.linkedin.com/company/tryprofound/
1 share
https://www.linkedin.com/company/quotapath/
1 share

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Score global : 38%

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GTM Engineer: Shantanu @ Personio

Tuesday, May 19, 2026 • Duration 49:57

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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GTM Engineer: James @ Profound

Tuesday, June 30, 2026 • Duration 45:55

James Underhill, Head of GTM Ops at Profound, runs the ops and systems function through one of the more extreme growth curves you'll hear about: five times ARR in nine months, headcount from sixty to two hundred, and an AE team scaling from thirty toward a hundred by year end. His operating principle is simple: buy infrastructure, build applications. That single bias shapes how his team is staffed, what they buy, what they build, and where they draw the line on both.

He built a deal desk bot in under twenty minutes on Dust without writing a line of code. His team deflected 70% of support inquiries with an internal triage agent. And he replaced the traditional business partner role entirely by giving the field direct, semantic access to their own data.

Topics Discussed

Why calibrating to the first derivative of growth matters more than current headcount
Using Dust as a low-code agent platform across GTM, CS, and internal ops
Deflecting 70% of support inquiries with a confidence-gated triage agent
Buy infrastructure, build application as a disciplined team operating principle
Why GTM engineering at this level requires actual software engineers, not just technical curiosity
GitHub fluency as the new hiring proxy for this function
Snowflake plus a semantic layer as the foundation for real-time, conversational data querying
Centralizing Claude Code skills in Notion for rep-accessible, field-ready workflows
The hidden maintenance cost of vibe-coded tools and why it compounds fast
Replacing business partner roles with self-serve data access
Where Gong's call intelligence falls short and why that becomes a build opportunity
How to decide what to build versus buy when your team could technically do both

Agentic Sales: Seth @ Sandler

Tuesday, June 23, 2026 • Duration 52:25

Seth Marrs spent six years at Forrester as their lead sales AI and technology analyst, which means he evaluated every major wave of GTM tech and has the receipts to back his calls. Now at Sandler as Chief Strategy Officer, he's seeing the practitioner side up close. His read on the agentic SDR market is blunt and sits well outside the vendor narrative, and he supports it with something most guests can't: actual data on what happens to sellers when you train them, test them, then watch them on live calls the next day.

He also gets specific on the parts most people gloss over. Why removing the seller from data capture is the first move that makes everything downstream possible. Why "next best action" is a flawed idea that quietly argues against needing sellers at all. And why the agentic opportunity in sales may sit with the SE and the human selling skill, not the SDR everyone is trying to automate away.

Topics discussed:

Why "fire your BDRs and automate with AI" is the vendor red flag to watch for
The recording-permission gap that breaks the data flywheel on outbound
Keeping BDR research depth while moving from 10 calls to 50
Sales motion as the deciding factor: high-velocity vs. long-cycle BDR economics
Removing the seller from data capture entirely to build a reliable dataset
Compete score: revenue per rep normalized across net-new, farmer, and CSM roles
The 40 to 80 to 40 adoption collapse measured on live calls, not surveys
Certifying sellers on what they actually say to customers, not test completion
Why top-three suggested actions beat a single prescribed next best action
The agentic SE: training a bot on the technical layer while humans own the conversation
Extracting intelligence from the bottom of the funnel where capture doesn't exist
Buy the infrastructure, build the AI layer on top: the maintenance trap with vibe-coded tools

GTM Engineer: Ryan CRO @ Quotapath

Tuesday, June 16, 2026 • Duration 49:24

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 language
The build vs. buy litmus test: uniquely bespoke and relatively fixed vs. everything else
Rep-built V1 prototypes handed to RevOps for productionizing and org-wide rollout
Dust as system of record, Claude as system of action, and how they split across the sales cycle
Thursday multi-thread standup: every rep required to arrive with all multi-threads queued for active opps
The "what would it take to close twice as many deals" framework for identifying which agents to build next
Warm outbound architecture using Clay, Unify, and product interaction data as intent signals
Comp plan design as a lever for changing the shape of revenue reps close, not just incentivizing volume
Why data architecture is the only real defense against confident AI hallucination
GTM engineer defined as the owner of the full prospect-to-renewal lifecycle

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Agentic Sales: Mark @ Canibuild

Tuesday, June 9, 2026 • Duration 48:28

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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GTM Engineer: Elio @ Scalestack

Tuesday, June 2, 2026 • Duration 48:20

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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Agentic Sales: Matt President @ Regie

Tuesday, May 26, 2026 • Duration 49:11

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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Stacked GTM – Details, episodes & analysis

Podcast details

Recent rankings

Apple Podcasts

Spotify

Shared links between episodes and podcasts

RSS feed quality and score

Publication history

Latest published episodes

Related Shows Based on Content Similarities

Apple Podcasts

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Stacked GTM – Details, episodes & analysis

Podcast details

Recent rankings

Apple Podcasts

Spotify

Shared links between episodes and podcasts

Youtube1

Spotify1

LinkedIn6

RSS feed quality and score

Publication history

Latest published episodes

Similar podcasts and content

Related Shows Based on Content Similarities

Apple Podcasts

Spotify

Related Shows Based on Content Similarities

Youtube1

Spotify1

LinkedIn6