Why 40% of AI Tools Get Fired and how She Built a 100% AI-Coded Brand

Why 40% of AI Tools Get Fired and how She Built a 100% AI-Coded Brand

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

Why Your AI Tools Are Making Your Pipeline Worse (Not Better)

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

Why and how You should run AI with NO Internet

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

Inside Perplexity's Revops, 3 AI Skills Replacing Admins

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

Your Top Rep Just Quit. Their $30M Brain Walks Out. AI Saves the Day

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