https://www.gtmaipodcast.com

https://www.unionsquareconsulting.com

Coach K (Jonathan Kvarfordt) sits down with Eddie Reynolds, founder and CEO of Union Square Consulting and host of the Go-To-Market Science podcast, for a masterclass in go-to-market efficiency in the age of AI.

Eddie has carried a bag his entire career, spent 3 years inside Salesforce, and has now spent a decade helping CROs and RevOps leaders find the quick wins hiding in their revenue engine. In this conversation he shares the Go-To-Market Efficiency Pyramid, the exact model his firm uses to figure out what to fix first, and why AI only works when the foundation underneath it is solid.

If you're a CRO, VP of Sales, RevOps or GTM leader trying to figure out where AI actually fits, this one is for you.

What you'll learn:

• Why "what should we do with AI?" is the wrong question, and the question to ask instead
• The 4 layers of the Go-To-Market Efficiency Pyramid (fundamentals, adoption, optimization, acceleration)
• The pipeline truth test: how deals older than 2x your sales cycle are lying to your forecast
• Why you should fix your pipeline before you generate more of it
• The capacity-planning math behind the "magic number" of 200 accounts per rep
• Why AI tools fail without a defined sales process (and how to fix it)
• Centralized vs. decentralized AI, and the trap of every rep vibe-coding their own agent
• Why adoption, not the tool, is the real competitive moat

CHAPTERS00:00 Intro: meet Eddie Reynolds01:00 Eddie's story: sales, RevOps, and Union Square Consulting03:25 Why great RevOps needs sales empathy05:41 Why go-to-market is "an unscientific endeavor"07:16 The Go-To-Market Efficiency Pyramid (overview)09:13 Layer 1: Fundamentals (ICP, personas, process, data)10:34 Layer 2: Adoption (the hardest layer)11:01 Layers 3 & 4: Optimization and Acceleration (where AI lives)12:05 The pipeline truth test: deals 2x your sales cycle are dead14:14 Fix your pipeline before you generate more15:13 Capacity planning and the "magic number" of 200 accounts16:00 Why AI tools fail without a defined sales process19:02 Running a go-to-market / pipeline council24:46 The #1 pattern CROs miss: focus26:41 Why AI strategy takes time (no magic button)30:55 Centralized vs. decentralized AI (the vibe-coding trap)32:15 Professional vs. tinkerer: the "3 hours vs. 25 hours" story33:35 The wrong question every leader asks about AI36:27 Where to find Eddie and his frameworks37:01 The Salesforce story that went viral (260K views)40:00 Wrap

CONNECT WITH EDDIE REYNOLDSFrameworks: https://www.unionsquareconsulting.com/frameworksPodcast: https://unionsquareconsulting.com/podcast/LinkedIn: https://www.linkedin.com/in/edwardreynolds/

#GTMAI #RevOps #AIinSales #GoToMarket #RevenueLeadership #Salesforce #PipelineManagement #SalesStrategy #CRO #ArtificialIntelligence

https://www.gtmaipodcast.com

Most companies bolted AI onto their old systems and got more noise, not more intelligence. Maddie Bell figured out why, and what to build instead.

Maddie is the co-founder and CEO of Synapsa, and she came to AI the long way: from running billion-dollar brands as a brand director at Procter & Gamble. "From Soap to SaaS," as she puts it.

In this episode she opens up the actual repo and walks Coach K through the AI go-to-market system her team built, including a brand site that is now 100% AI-coded.

We get into why buyers eye-roll the second they smell AI marketing, the three-layer pyramid that decides whether your AI actually works, the five layers of an AI GTM system you can copy, and the build-versus-buy line that 40% of AI products are getting fired for crossing.If you lead go-to-market and you are tired of "we're an AI-first company" meaning nothing, this one is for you.

⏱️ CHAPTERS

00:00 Intro: the husband-and-wife AI startup

02:14 From Procter & Gamble to AI CEO ("Soap to SaaS")

03:49 The big idea: meetings are the atomic unit of business growth

05:46 The thesis: instant, intelligent, and invisible to buyers

06:10 Why brand-building fundamentals just 10x'd in value

08:18 The Frankenstack problem: AI created more noise, not intelligence

08:47 Why your humans are the real moat

09:56 Why hallucinations stopped being the real problem 12 months ago

10:43 The Pyramid of AI Effectiveness: Protocol, Personality, Persuasion

11:54 The personality nobody admits buyers want

12:54 Persuasion: moving a buyer from need to decision

14:51 The messy middle where the real work lives

17:00 What this means for you if you are not a builder

21:13 Why buyers eye-roll AI marketing (and the data behind it)

23:55 Anatomy of an agentic system

24:46 "It's a folder. We call it a repo to sound cool."

25:28 Load-bearing content and progressive disclosure

33:39 Where to actually start (an empty folder)

34:42 The 5 layers of an AI GTM system

35:45 The 100% AI-coded brand site

37:57 The content rubric that rejects off-brand work before a human sees it

38:17 The surprise final layer: you become the bottleneck39:16 The sales side: signals, guided selling, and prep

41:09 Build vs. buy: how to pick your hard

45:50 Why 40% of AI products are getting fired

47:20 How to pressure-test any AI vendor before you buy

TAKEAWAYS

1. Good AI for go-to-market is three things: instant, intelligent, and damn near invisible to buyers. If buyers can feel the AI, you built it wrong.

2. AI effectiveness is a pyramid: Protocol (can it follow your playbook), Personality (people hate no personality more than a bad one, so mirror the buyer), and Persuasion (move them from need to decision). Most teams stop at protocol.

3. There are five layers to a real AI GTM system: a load-bearing knowledge base, a design system, a content engine, a self-improving idea layer, and a sales complement. You build them in order.

4. Single-player output with human approval (decks, docs, one-pagers) is now buildable yourself. Multiplayer, buyer-facing, real-time orchestration is where you should still buy.

5. 40% of AI products are getting fired for being unreliable, insecure, or unable to bring humans in at the right moment. You are hiring a system. Don't hire one you'll have to fire.

Quotes

"Meetings are the atomic unit of business growth." — Maddie Bell"

In our excitement to scale AI, many companies overlaid it onto traditional Frankenstack systems. And rather than creating more intelligence, we've created more noise." — Maddie Bell

"It's a folder. We call it a repo because it makes us sound cool in front of our friends." — Maddie Bell

Maddie Bell, Co-founder & CEO, SynapsaWebsite: https://synapsa.aiLinkedIn: https://www.linkedin.com/in/maddiebell/#GTMAI #AIStrategy #RevenueLeadership #AIagents #GoToMarket

https://www.gtmaipodcast.comStop running siloed GTM experiments. Start building a flywheel.Dan Rosenthal went from a failed Cambridge PhD application to $5M in biotech sales with zero systems — and that frustration became the fuel behind Workflows.io. In this episode, Dan walks me through two complete playbooks his team uses with YC companies, Fortune 500 logos, and everyone in between.We cover the GTM Flywheel (how to connect every channel so they feed each other) and the ABM Playbook (what to do when you have fewer than 10,000 target accounts and can't afford to miss). This is one of the most tactical, systems-level conversations I've had on this show.What we get into:Why AI is raising the bar for GTM — and what to do if you're below itThe GTM Flywheel: traffic, lead capture, nurture, and conversion all connectedWhy your best-performing content should become your ads (and vice versa)The ABM infrastructure that helped 3 reps book 6 meetings in one dayICP modeling done right: tiering based on concentric circles, not point systemsSignals: first-party, second-party, third-party — and why most people use them wrongAwareness scoring from "Identified" to "Selecting" — and why it changes everythingClaude Code and the future of GTM automation (real talk on what's hype vs. ready)Why old-school sellers with new-school systems are the most dangerous combo in B2BTimestamps:00:00 — Intro & Dan's background (biology master's to sales to Workflows.io)03:20 — What makes Workflows.io different from Clay agencies07:30 — The GTM Flywheel Playbook walkthrough19:00 — AI's real role: web research agent for lead qualification22:50 — Website conversion and de-anonymized visit tools25:00 — When the GTM Flywheel doesn't fit: entering ABM mode33:00 — Building the ICP model the right way37:00 — TAM mapping: why Clay alone isn't enough42:00 — Signal tracking: 3 categories and which ones actually move the needle45:00 — Awareness scoring and the client story that says it all49:00 — Infrastructure vs. quick wins: you need both51:00 — Workflows.io services breakdownConnect with Dan:Website: https://www.workflows.io/LinkedIn: https://www.linkedin.com/in/dan-m-rosenthal/More from Coach K:GTM AI Podcast: https://www.gtmaipodcast.com

https://www.gtmaipodcast.comYour AI conversations are sitting in someone else's vault. In this episode, 20-year GTM operator John Williams shows exactly how he took his back: archiving every chat locally, running AI models offline on his own laptop, and setting hard guardrails on what his agents can buy and agree to without him.This is the GTM and AI Podcast, where real operators show the receipts. One rule in this kitchen: you actually have to cook.WHAT JOHN SHOWS LIVE ON SCREEN:• Chat Archive: a free, open-source browser extension that exports any AI conversation (Claude, ChatGPT, Gemini, Groq, Perplexity) to JSON or markdown, with zero outbound calls. Nothing leaves your machine.• Exporting a full Claude conversation and continuing it inside Groq with full context intact• Why he runs local models with Ollama, and how open-source models let you switch models mid-conversation without losing context• Agent Commerce: an open spec for what your AI agents can spend and what terms they can accept on your behalf• The AI Acceptable Use Policy: an open-source starting point so shadow AI doesn't run your company• How to vet any open-source AI tool before you trust it (the one question: would your security director approve?)TIMESTAMPS:00:00 - Welcome to the kitchen: the one rule of this podcast01:00 - Who is John Williams? 20 years in GTM, 5 as an independent operator02:30 - Why your GitHub repo is the new resume04:00 - The AI Acceptable Use Policy: fixing the shadow AI problem05:30 - Agent Commerce: spending limits and guardrails for your AI agents09:00 - Chat Archive: why owning your conversation history matters14:00 - Building a digital twin from a year of AI conversations16:00 - Local models 101: moving past being a "prompt jockey"19:00 - GPU brownouts and token authority: why local inference is your backup plan22:00 - LIVE DEMO: exporting a Claude conversation locally25:00 - Porting full context from Claude into Groq, zero loss28:00 - Token economics: the W-2 cost didn't disappear, it moved31:00 - Data portability use cases: audits, regulated industries, federated intelligence36:00 - OpenClaw: the power and the security risks of autonomous agents39:30 - Where to find John + why he'd apply for his next job in publicFIND JOHN WILLIAMS:GitHub: github.com/fxops-aiHugging Face: huggingface.co (johnwilliamsatl)If this episode saved you from losing a year of AI conversations, subscribe and come cook with us next week.#GTMAI #AIAgents #LocalAI #DataOwnership #Ollama #GoToMarket

https://www.gtmaipodcast.com

He stopped hiring for RevOps tasks and started building Perplexity skills instead. Here's exactly how.

In this episode of the GTM AI Podcast, Coach K sits down with Nate Follen, Head of Enterprise Ops and Systems at Perplexity, to walk through the three AI skills his team actually runs every day and every week. Nate helped scale RevOps at Ramp, advised teams at Momentum, and now leads the internal go-to-market systems engine at Perplexity. He doesn't talk theory. He shares his screen and shows the real workflows.

Key topics covered:

• Why Nate now asks "do I have to hire for this?" before adding headcount, and where the answer is still yes (hint: the Salesforce admin role isn't dead)

• The Voice of Customer dashboard he built with two prompts, an API key, and live-refreshing call transcripts, that tells the product team not just what customers said, but what to do about it

• The weekly RevOps deck-prep skill that pings the sales team in Slack, pulls live data from Snowflake via Claude Code, checks Linear, and assembles a deck that used to take an hour

• The CRM hygiene system that cleans account ownership and Salesforce hierarchies nightly, builds Polytomic data models without logging into the tool, and DMs Nate every error with a severity score and a fix

• Why orchestration across 400+ connectors beats single-model lock-in, and where Perplexity sits next to Momentum and Salesforce instead of replacing them

• The mindset shift: from "find the two things to focus on" to running dozens of projects in parallel with a team of agents

If you lead revenue operations, enablement, sales, or marketing, this is the tactical breakdown of what AI-run GTM systems actually look like inside one of the fastest-growing AI companies in the world.

Resources mentioned:

• Nate Follen on LinkedIn: https://www.linkedin.com/in/follen/

• Perplexity: https://www.perplexity.ai

• Momentum.io (call recording and transcript analysis)

• Salesforce, Slack, Linear, Snowflake, Claude Code, Polytomic, Hightouch

• GTM AI Podcast & Newsletter: www.gtmaipodcast.com

Get the free lead magnet from this episode, The Perplexity RevOps Skill Stack, with the exact prompts and build steps: www.gtmaipodcast.com

https://www.gtmaipodcast.comhttps://www.fluint.comIn this episode of the GTM AI Podcast, I sit down with Nate and John, co-founders of Fluint, for a kitchen-deep look at how they built Ollie, an omni-agent that captures tacit sales knowledge and surfaces it across an entire enterprise sales org. We cover:The tacit knowledge problemWhy a small pocket of reps wins 50-60% of pipeline while everyone else sits at 17-19%, why "clone your top rep" has been said for 15 years and never delivered, and what happens to your $30M deal architect when they take a new job two weeks from now.The implicit signal exampleA real story of two reps, two POC readouts, two procurement follow-ups (one at 9pm Friday, one Wednesday during business hours), and why top reps will negotiate completely differently on the same data while average reps miss the signal entirely.The photo vs. video architectureWhy most AI tools treat data as a snapshot (LLM context = one photo), and why Fluint's event-driven, time-series architecture treats it as a video. The 10-Second Tom analogy from Fifty First Dates that explains why LLMs alone cannot solve this problem.The ML + LLM stackJohn walks through the architectural decision: ML for pattern recognition and judgment layer, LLM for human-to-human interaction. "Using the right tool for the right job." This is the most underrated decision in enterprise AI right now.Ollie's omni-agent designWhy one AI teammate beats 130 task-specific agents. The 75% of users who gender their AI. The trust dynamics that make a sales rep follow an agent's advice when it runs counter to the playbook.The racehorse modelHow Fluint runs a global baseline model and a customer-specific model in parallel, evaluates them nightly, and promotes the winner. Continuous evaluation as the moat.The "data is a product of people" answerJohn on why perfect data is a logical fallacy and what to do instead. The single line that changes how you approach AI readiness.Real outcomes+$28K added to ACV per team per year. 32 days off the median sales cycle. The maturity curve from Q1 (win existing deals with less discount) through year-end (win deals you would have lost).GUESTSNate, Co-founder & CEO, Fluint (the "second brain")Repeat enterprise sales leader and repeat founder. Built Fluint from a problem he could not solve as a sales leader. Author of two books on tacit knowledge and executive sound-bite communication.John, Co-founder & CTO, Fluint (the "first brain")Technical co-founder. Builds the systems that turn Nate's crazy ideas into shipping product. Specialty: event-driven architectures and ML-as-judgment-layer for enterprise sales.LinkedIn Nate:https://www.linkedin.com/in/natenasralla/Linkedin John:https://www.linkedin.com/in/jon-crawley-3797a8100/Blog: fluint.io/blogJohn's recent technical guide: building enterprise AI agents (just published on the Fluint blog)GitHub repo (DIY resources for time-series-data agent architecture): linked from blog

https://www.gtmaipodcast.com

Most AI agents are "good enough.. until they're not." Julia MacDonald, SVP of GTM at Haize Labs, shows how she sells 99% reliable AI agents to banks, insurers, immigration lawyers, and healthcare orgs with a 2-person GTM team. Plus the use-case filter that decides what's worth building in the first place.

Deep dive:In this episode of the GTM AI Academy Podcast, I sit down with Julia MacDonald, SVP of GTM & Solutions at Haize Labs, the only company I have seen solve the AI agent reliability problem at the enterprise level. We cover:

The "Mostly Fine" trap that kills enterprise AI deals

Why your agent telling Customer A "no discount" and Customer B "20% off" is a GTM problem, not a tech problem, and why the enterprise buyer is pricing the worst 1% of conversations, not the average.

The hypothesis-first use case filter Julia uses to pick where to play

Document-heavy. Cannot be outsourced cheaply. Right vs. wrong answers. High stakes. Human accountability. The almost-mathematical criteria she runs every use case through before her 2-person GTM team touches it.

The Code of Conduct + Supervisor Models + Adversarial Red Teaming stack

The actual methodology Haize Labs uses to engineer 99% reliability into voice debt collection agents, immigration paralegal agents, and insurance claims agents. Includes how their red teaming hit 15,000 adversarial queries on a single agent.

The 2-person AI-native GTM motion

Julia's full prospecting flow: hypothesis → use cases → companies → executives → enrichment in Clay and OpenAI → outreach in Dripify. Plus the pre/post-call brief workflow that makes her "smarter today than I was six months ago" on every call.

The honest gaps Julia named

What Haize Labs is not yet doing on closed-loop attribution, and why that's a real tradeoff for any 2-person GTM team.

Sneak peek of "Lossless RAG"Julia teases their new hallucination-free retrieval product (we go deeper on this in next week's episode).

GUESTJulia MacDonald, SVP of GTM & Solutions, Haize LabsLinkedIn: https://www.linkedin.com/in/juliamacd/Haize Labs: https://haizelabs.com

https://www.gtmaipodcast.com for free goodies!

https://www.wrench.ai

To connect with Dan-- https://www.linkedin.com/in/danbaird/

Most "personalized" AI outreach isn't personal at all. It's a name swap on a template.And that's why your reply rates are flat even though your stack tripled.

This week on the GTM AI Podcast, I sat down with Dan Baird, the founder and CEO of Wrench.ai (and one of the original co-founders of Lovesac). Dan has been building in AI for nine years, and he runs one of the most quietly impressive ML+LLM stacks I've seen in the GTM space. He calls it the "RoboCMO."

We got into:

→ Why personalization and relevance are not the same thing (and why most teams confuse them)

→ Why most sales personas are built in conference rooms, not from data

→ The 27-million-person study on what actually predicts a top-performing seller

→ Why LLMs alone will never solve your outbound conversion problem

→ How the "early adopter vs. late adopter" split changes your messaging completely

→ The 2-axis "Relevance Map" that shows you what your buyers want to hear (that you're not saying)

→ What he thinks about Open Claw, agent-to-agent commerce, and where this is all heading

If you run revenue, marketing, or sales enablement in 2026, this one will change howyou think about your AI stack and your outbound playbook.

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📥 FREE LEAD MAGNET INSPIRED BY THIS EPISODEThe Personalization-vs-Relevance Audit: 12 questions that score whether your AIoutreach is built to convert or just to send. Takes 6 minutes.👉www.gtmaipodcast.com

━━━━━━━━━━━━━━━━━━━━━━━━━━━━📌 CHAPTERS

00:00 Cold open + Open Claw take ("I'll let other people make the mistakes at scale")

04:09 Who is Dan Baird? (Lovesac, ConAgra, and the 3D-printed Allosaurus skull)

07:31 What problem Wrench.ai solves (and why it won't go away)

11:42 How Wrench is different from Clay and the AI SDR category

15:30 LLMs vs. machine learning: the cake recipe analogy

19:01 Live demo: the Relevance Map and how it scores you in real time

26:31 Personalization is not relevance (the core distinction)

27:31 The 27-million-person study on top-performing sellers

30:50 Why most personas are BS, and what to build instead

35:00 Where AI-to-AI commerce is heading (and what to do about it)

39:25 The "AI slop" problem and how to actually differentiate

41:30 Wrap💬

SUBSCRIBE for weekly GTM AI breakdowns, frameworks, and tool reviews from insidethe room with the operators actually shipping this stuff.#GTMAI #AIOutreach #SalesEnablement #RevenueLeadership #AIStrategy #DanBaird #Wrench

www.gtmaipodcast.comwww.fullcast.comMost marketers are using AI like a typewriter. Amy Osmond Cook is using it like a band.In this episode of the GTM AI Podcast, I sit down with Amy Osmond Cook, CMO of Fullcast and one of the sharpest marketing leaders in the AI-native SaaS world. Amy walks us through how Fullcast's acquisition of Copy.ai rewired her entire go-to-market motion in 90 days. We talk about how her team is producing more, spending less, and getting found by the LLMs that are quietly replacing Google search.A few of the stats Amy dropped that you do not want to miss:5X productivity increase after standing up Copy.ai as the AI agency layerHundreds of thousands of dollars in salary savings, redeployed into growth10 points of AI visibility gained in a single monthCopy.ai's own site: zero to 84 domain authority in 3 years with ZERO human-written contentWe also get into Amy's incredible origin story (single mom in her parents' basement, a $10K tax-return bet against her husband, four acquisitions later, now co-founding the first AI-native GTM platform), the death of "Hi {first_name}" marketing, FAQs as the AI visibility cheat code, and where agentic GTM is heading next.If you lead marketing, run go-to-market, or are wondering whether your agency contract is about to become a line item you cut, this one is for you.⏱️ Chapters00:00 Cold open and introductions01:00 Inside the Fullcast acquisition strategy (Copy.ai, Ebsta, Atrium, Commissionly)03:00 Amy's origin story and the $10K bet that started a 15-year career06:00 Fullcast's three-pillar bet on AI-native GTM08:00 Fire your agency: the AI agency thesis11:00 Why FAQs and EEAT are the secret sauce of AI visibility14:00 Personalized landing pages, demo environments built in one hour17:00 Writing workflows that actually capture the mind of a sophisticated writer20:00 Pressure-testing the Fullcast blog live on the show23:00 The Copy + Ebsta + LLM context stack24:00 A day in the life: marketing four years ago vs. today25:00 Why "Hi {first_name}" is finally dead28:00 The new rule: if it is AI, say it is AI30:00 The agent-to-agent future and how marketers must adapt32:00 WebMCP, agentic search, and writing content for the bots🔗 About AmyAmy Osmond Cook is the CMO and co-founder of Fullcast, the first AI-native go-to-market platform. She has scaled marketing through three acquisitions (Simplus to Infosys, PathologyWatch, Onboard to Conservice) and previously ran Stage Marketing for 15 years. She holds a PhD in organizational rhetoric and taught writing and rhetoric at the university level for 25 years.🔗 ConnectFollow Amy: https://www.linkedin.com/in/amyosmondcook/Subscribe to the GTM AI Newsletter on SubstackLearn more about Fullcast: https://fullcast.com#GTMAI #AIMarketing #CopyAI #Fullcast #AIAgents #AIVisibility #RevenueOperations #SalesPerformance #CMO #MarketingLeadership

www.gtmaipodcast.com For more from Ryan: https://superhumanrevenue.beehiiv.com/p/hiring-an-ai-transformation-leadAnd Ryans Linkedin: https://www.linkedin.com/in/ryan-staley/Ryan Staley built a division from zero to $30M with four salespeople and no marketing budget. He's taught 800+ CROs how to use AI. On this episode, he pulled back the curtain on the agentic operating system he built to run his entire business — from CEO decision-making to content creation to pipeline management — using Claude Code, Obsidian, and API-connected tools like Fathom and HubSpot.This wasn't theory. Ryan screen-shared his actual system, showed real outputs, and walked through the folder structures, memory layers, and agent orchestration that let him operate "at the speed of thought."