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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.

  1. 4 gg fa

    GTM Engineer: Cliff @ Polaris

    Cliff Simon, CEO & Founder of Polaris Ops, walks Andy and Noah through structuring GTM engineering across the full lead-to-cash cycle. From uncovering six months of AI Apex slop in a client's Salesforce org to crafting custom signal plays that outperform off-the-shelf data, Cliff shares what he's learned across 20+ engagements in 14 months. Topics discussed: Build versus buy decisions for GTM engineering projects Total cost of ownership for AI-built GTM systems Structuring context layers with knowledge graphs and memory Positioning GTM engineers inside centralized RevOps teams Cleaning up AI-generated Apex slop in Salesforce instances Crafting custom signals that outperform commodity data vendors Running five go-to-market plays that reinforce each other Micro SaaS as a future path for GTM engineers

    GTM Engineer: Cliff @ Polaris

  2. 11 ago

    Agentic Sales: Amanda @ 1Mind

    Amanda Kahlow founded 6sense. Her new company, 1mind, deploys AI "Superhumans" that orchestrate roughly 15 agents to solution, demo, and close across the full buyer lifecycle, replacing the traditional SDR-to-AE-to-SE handoff chain. Amanda shares how her Superhuman "Nigel" joined a live meeting, held 31% of the talk time, and helped close a $262,000 deal in 11 days. She breaks down why solving for growth over efficiency changes how you build and staff your entire GTM org. Topics discussed: Orchestrating 15 agents per Superhuman across buyer lifecycleClosing a $262,000 deal in 11 days with AIShifting GTM from seller control to buyer controlUsing Superhumans for post-call sales coachingBuilding outbound without creating a bad buying experiencePricing Superhumans at the cost of a humanMeasuring deal cycle compression and ACV growthDeploying personalized Superhumans across ABM campaigns

    Agentic Sales: Amanda @ 1Mind

  3. 4 ago

    Agentic Sales: Philip @ Salesforce

    Philip Cooper, Chief Customer Officer, Agentforce Sales at Salesforce, explains how the CRM is being rebuilt around agents, ambient data capture, and headless architecture. With the Momentum acquisition powering a self-driving CRM and MCP exposure decoupling sellers from the UI, Philip lays out what happens when the system of record starts updating itself. Philip walks Noah and Andy through why the semantic layer still matters for consistent definitions, how Slack is becoming the bidirectional system of work where sellers forecast and invoke agents, and how revenue leaders can delegate business outcomes to orchestrated agent workflows. Topics discussed: Positioning Agentforce as a horizontal capability layer across clouds Auto-updating CRM through Momentum's conversation capture Exposing Salesforce headlessly through MCP for agent integration Using memory fragments to surface undiscovered deal insights Layering a semantic layer on unstructured data for consistent KPIs Turning Slack into a bidirectional system of work with CRM Orchestrating revenue outcomes by delegating to multi-agent workflows Shifting the build vs. buy calculus with opinionated agent deployment

    Agentic Sales: Philip @ Salesforce

  4. 29 lug

    GTM Engineer: Jai @ Deepline

    A mining safety company needed to find prospects, and no pre-built tool existed for that niche. Jai Toor at Deepline pointed a coding agent at the problem. It found a Reddit ML model, combined it with hiring signals, and delivered qualified contacts in under 30 minutes. Jai walks through Deepline's developer-first approach to GTM data infrastructure, from waterfall enrichment to deploying deterministic code-based workflows. He shares how close-lost analysis surfaces niche buying signals specific to each company. Topics discussed: Building code-native GTM infrastructure designed for coding agentsUsing waterfall enrichment across multiple data providers programmaticallyDiscovering niche buying signals through close-lost regression analysisDeploying deterministic plays from iterative agent testingAdopting pay-as-you-go pricing inspired by OpenRouter's modelCentralizing data while decentralizing agent-based rep interfacesGenerating org charts from multiple data sources at $7 eachPredicting cold call answer rates with ML-driven scoring models

    GTM Engineer: Jai @ Deepline

  5. 21 lug

    Agentic Sales: Austin @ Unify

    Austin Hughes, Co-Founder and CEO of Unify, joined Noah and Andy to make the case that offer based outbound is quietly beating the classic ask for a meeting. Austin walks through why Unify skipped Inbound entirely, how offer based outbound is beating classic meeting asks, and why he still believes reps need to own their own prospecting decisions even as agents take over the grunt work.   Topics discussed: - Building offer based outbound instead of meeting asks - Why Unify skipped Inbound as a use case entirely - Letting reps buy in individually before Rev Ops gets involved - Owning a company wide context graph in the data warehouse - Why AE demand surprised Unify more than SDR demand - Why teams now want predictability and cost over frontier models - Advice for hiring the first two outbound reps - Why rep agency still matters as automation increases

    Agentic Sales: Austin @ Unify

  6. 14 lug

    GTM Engineer: Gina @ Dust

    Dust keeps coming up across the GTM engineering community, and this episode explains why. Gina Kabasakalis, Founding US GTM at Dust, has a clear-eyed view of where AI adoption actually breaks down: not in the build, but in the handoff from one technical person to an entire team. She gets into the governance and permission infrastructure that makes multiplayer AI viable, with a concrete example of how RevOps encoding the right Salesforce schema field into a shared agent saves every sales manager from carrying that knowledge individually. She also covers what companies consistently get wrong about ROI measurement, and a new Dust feature called Pods that puts humans and agents in a shared workspace. If you're past the single-agent stage and trying to figure out how to scale this across an org, this one is worth your time. Topics discussed: Why RevOps and IT/CISOs are the two buyer profiles gravitating toward Dust Multi-model routing: matching model complexity and cost to the task rather than defaulting to the highest-reasoning model for everything Encoding institutional knowledge at the system level so ICs stop carrying it individually Piloting vs. rolling out: validating long-term fit vs. the executive air cover and embedded AI ops function a real rollout requires Why time savings is the wrong AI ROI frame and what the CRM hygiene argument actually proves Pods: a shared workspace for humans and agents, with use cases from new hire onboarding to funding round comms Four-component agent framework: trigger event, data sources, recipient and destination, output transformation Agent sprawl: when to consolidate duplicate skills and when distributed creativity is the point

    GTM Engineer: Gina @ Dust

  7. 7 lug

    GTM Engineer: Joe @ Primary Ventures

    Everyone wants to run GenAI workflows. The problem is they're running them on commoditized data, team sizes, job titles, basic firmographics  and wondering why nothing converts. Joe Lehr, Director of GTM Engineering & Innovation at Primary Ventures, works across a portfolio of companies from pre-seed through Series D, backed by a $625M fund five, and he sees this mistake at nearly every stage. Joe gets specific about how he builds out GTM infrastructure from scratch: which signals actually move deals, how Parallel.ai powers Clay's CLAGIN for enrichment at scale, and what it actually takes to move a sales team from single-player AI experiments to a shared multiplayer system built on Supabase and MCP. He also pushes back on a few widely held beliefs, including whether deep hyper-personalization still works, why your first GTM hire should not be a 23-year-old, and why CS may be the most under-automated function in the stack. Topics Discussed: Non-commoditized signal mapping before any AI motion is built Why creativity, not tooling, is the actual constraint at early-stage companies The right first GTM hire: what to look for and what to avoid How Parallel.ai powers Clay's CLAGIN for signal enrichment at scale Mid-funnel data capture and why multi-opportunity edge cases break most tools Build vs. buy: taking vendor sales calls before deciding what to build Going multiplayer with Supabase, MCP, and structured account objects Why deep hyper-personalization is losing ground to smarter segmentation CS as the next frontier for agentic automation, expansion, and churn scoring Observability gaps when vibe-coded internal tools skip engineering involvement

    GTM Engineer: Joe @ Primary Ventures

  8. 30 giu

    GTM Engineer: James @ Profound

    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

    GTM Engineer: James @ Profound
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Descrizione

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.

Informazioni

  • Creatore
    GTM Council and Frontlines.io
  • Anni di attività
    2026
  • Puntate
    16
  • Classificazione
    Contenuti espliciti
  • Copyright
    © GTM Council and Frontlines.io
  • Sito web del podcast
    Stacked GTM