The 40% of AI tools getting fired (and why)
Here we are again.. on our own… (sorry song lyrics were coming to mind today ;)
Welcome back my friends, today I am pleased to feature a good friend of mine, the CEO and cofounder of Synapsa.ai Mrs. Maddie Bell.
We went deep into what she is doing in Claude Code building a brand and basically the entire product, well worth the time, so let’s dig in!
You can go to Youtube, Apple, Spotify as well as a whole other host of locations to hear the podcast or see the video interview.
The Soap-to-SaaS Operator Who Open-Sourced Her AI GTM System
Maddie Bell ran billion-dollar brands at Procter & Gamble before she became the co-founder and CEO of Synapsa. She calls the journey “from soap to SaaS.” I called her onto the podcast because she is the rare person who can do two things at once: build the AI, and tell you the truth about it.
She opened her actual repo on screen and walked me through the entire AI go-to-market system her team runs. Brand site, content engine, sales motion. Most of it AI-coded. All of it honest about where the AI ends and the human begins.
Here is what was worth stealing.
1. The reason your AI marketing isn’t landing: you made more noise, not more intelligence.
“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.” said Maddie Bell.
The data backs her up. One in five buyers is now less confident in their buying decision because of the AI-generated mess hitting them. Seven in ten say if you spam them, they never want to talk to you again. When sellers hear “AI for go-to-market,” they get excited. When buyers hear it, they brace for hundreds of spammy emails, needy chatbots, and blogs full of em-dashes.
The fix is not less AI. It is the return to brand-building fundamentals, run through AI instead of bolted onto old tools.
2. AI effectiveness is a pyramid, and almost everyone stops one floor too low.
She framed it like Maslow’s hierarchy. Three layers:
Protocol. Can the AI follow your playbook and do what you asked? This is the floor. The “I built a business in my closet over the weekend” crowd lives here. Great for an N of 1. Useless for the enterprise.
Personality. Here is the counterintuitive part. People react more negatively to no personality than to a personality they dislike. And the single most preferred personality for an AI is not “intelligent” or “professional.” It is the one that mirrors the buyer.
Persuasion. The art of moving a buyer’s focus from a state of need, to an accurate understanding of their problem, to a decision. Persuasion changes shape across the funnel. A blog needs curiosity and nurture. A bottom-funnel moment needs proof points and a value prop. This is where her team spends most of its time.
Most AI deployments get protocol working and declare victory. The advantage lives two floors up.
3. The five layers of an AI GTM system, built in order.
This was the meat. Maddie’s team built their system in a deliberate sequence, and the order matters because each layer feeds the next.
Layer 1: A load-bearing knowledge base. Not everything. The first document the AI reads every morning has disproportionate power over the 150th. So you codify only what is load-bearing: positioning, ICP, value prop, proof points. Plan on a week of real work here. Dump in 600 docs with no scaffolding and you get degradation, not intelligence. The technical name for the fix is progressive disclosure.
Layer 2: A design system. AI-first, so the brand shows up identically across every touchpoint. Drop in a call transcript, get a customer deck back that looks hand-designed. Hers are 100% AI-coded.
Layer 3: A content engine. Blogs, case studies, battle cards, comparison pages, produced end-to-end with human approval. The guardrail is sharp: new content has to pull three random samples from an approved library and score itself on a rubric. Does this sound authentically us? If not, it gets rejected before a human ever sees it.
Layer 4: The flip. Once the machine is humming, you become the bottleneck. So the agents start proposing. “When I wake up in the morning, the AI’s proposing, hey, this is what I think we should go do next. And I, as the human, am still deciding.”
Layer 5: The sales complement. Because the go-to-market knowledge is codified, the agents read every buying signal, de-anonymize, qualify into the right motion, route, book, and hand the rep a prep doc. Then, in Maddie’s words, the most important thing the AI can do in the live call is “get the F out of the way.”
4. Build vs. buy: pick your hard.
The cleanest decision framework in the episode. Single-player output with human approval (decks, docs, one-pagers) is now solvable yourself. Build it. The big tools are excellent at it.
Buy when you hit the multiplayer orchestration zone: buyer-facing, real-time, with handoffs, fallbacks, and security on the line. Her reason is blunt: 40% of AI products are getting fired because they are not reliable, secure, or able to bring humans in at the right time. You are hiring a system. You do not want to hire one you will have to fire.
And when you do buy, push hard. Everyone can claim everything now. Ask for production thresholds, grades, integrations. The businesses regretting their choices are the ones that did not pressure-test up front.
Why this matters:
The whole episode is one argument: AI did not kill the fundamentals. It made them worth 10x more. The operators who win are not the ones automating at volume. They are the ones crafting buyer journeys at volume.
Three moves for this week:
Find your top five load-bearing documents and write them like they matter, because the AI will amplify them everywhere.
Decide who in your org builds and who just uses. Not everyone should be a builder.
Run your current AI vendors through Maddie’s fire test: reliable, secure, multiplayer, humans in at the right time. If one fails, you already know.
Maddie said the quiet part out loud: “It’s a folder. We call it a repo because it makes us sound cool in front of our friends.” The tools are simpler than the hype. The thinking is harder than the demo. That gap is the opportunity.
My challenge to you: open one empty folder this week and build the first layer. Just the knowledge base. The rest only works once that one is true.
— Coach K
The 5-Layer AI Go-To-Market System
A build playbook for operators who are done with “AI-first” meaning nothing
Inspired by a conversation with Maddie Bell, Co-founder & CEO of Synapsa, on the GTM AI Academy Podcast. Distilled into a system you can build this quarter.
Start here: the standard
Good AI for go-to-market is three things. Instant. Intelligent. And damn near invisible to buyers.
If your buyer can feel the AI, you built it wrong. The goal is not to look like you’re using AI. The goal is to serve a better, faster, more human experience, with AI doing the heavy lifting underneath.
The data on why this matters:
1 in 5 buyers is now less confident in their buying decision because of AI-driven noise.
7 in 10 buyers say if you spam them, they’re gone for good.
40% of AI products get fired for being unreliable, insecure, or unable to bring a human in at the right moment.
AI didn’t kill the fundamentals. It made them worth 10x more. This playbook is how you build on the fundamentals instead of bolting AI onto your Frankenstack.
The diagnostic: the Pyramid of AI Effectiveness
Before you build, know which floor you’re on. Most teams stop one too low.
Floor 1 — Protocol. Can the AI follow your playbook and do exactly what you asked?
Test: Give it a task it should know cold. Does it follow your process, or improvise?
Most teams get here and stop.
Floor 2 — Personality. Does it sound like someone a buyer wants to talk to?
The counterintuitive truth: people react worse to no personality than to one they dislike.
The winning move: the best AI personality mirrors the buyer, not your brand’s ego.
Floor 3 — Persuasion. Can it move a buyer from a state of need → an accurate understanding of their problem → a decision?
Persuasion changes across the funnel. Top of funnel = curiosity and nurture. Bottom = proof points, value prop, testimonials.
This is where the advantage lives, and where you should spend most of your time.
Score your current AI setup 1–3. If you’re at a 1, you have a protocol system, not a persuasion system. That’s the gap.
The build: 5 layers, in order
The order is not optional. Each layer feeds the next.
Layer 1 — The load-bearing knowledge base
A repo is just a folder. Don’t let the word scare you or impress you.
The rule that separates a smart knowledge base from a slow one: the first document the AI reads has disproportionate power over the 150th. So don’t map everything. Map what’s load-bearing.
Your top 5 load-bearing documents:
Competitive positioning (how you’re different, how you’re the same as the category, what you own)
Customer value proposition
Product features and how you articulate them
Ideal customer profile (ICP)
Customer proof (the case studies you want the AI aware of at all times)
Do this:
Open an empty folder. Tell your AI: “I’m building a marketing AI repo. Set up the file format and structure for me.”
Write the five documents above like they matter, because they get amplified everywhere. Budget a full week.
Structure for progressive disclosure so the AI reads documents in the right order. Without this scaffolding, you’ll hit degradation around 60–600 documents.
Failure mode: dumping every doc you own into one folder and wondering why quality drops.
Layer 2 — The design system
So the brand shows up identically across every touchpoint, automatically.
Do this: build an AI-first design system once. Then a dropped-in transcript becomes a customer-ready deck that looks hand-designed. This is what makes “100% AI-coded” assets look like a human made them.
Failure mode: salespeople asking “why do we need a design system?” Answer: because you want the AI to represent you everywhere without a designer in the loop.
Layer 3 — The content engine
Now you scale production: blogs, case studies, battle cards, comparison pages, funnel optimization. End-to-end, with human approval.
The guardrail that makes this safe: before any new content reaches a human, the AI pulls 3 random samples from your approved library and scores the draft against them on a rubric. Does this sound authentically us? If not, it’s rejected. The human never sees off-brand work.
Do this: build the approved-sample rubric first. Quality control is the feature, not the afterthought.
Layer 4 — The flip (you become the bottleneck)
This one surprises people. Once the machine hums, the constraint becomes your own ideas and your need to sleep.
Do this: stand up agent teams that research, generate ideas, and push proposals to you for approval. You wake up to “here’s what I think we should do next.” You stay the decision-maker. The AI stops waiting for instructions and starts proposing them.
Layer 5 — The sales complement
Because your go-to-market knowledge is now codified, the system can read every buying signal.
Do this: de-anonymize visitors, start a guided selling conversation, qualify into the right motion (SMB vs. enterprise), route and book without friction, log the source, and hand the right rep an AI-generated prep doc.
The rule for the live moment: once the rep is in the seat, the most important thing AI can do is get out of the way. It supports with notes, logging, and system updates. It does not hijack the human conversation.
The decision: build vs. buy (pick your hard)
You can’t own everything. Use this litmus test.
Build it yourself when it’s single-player output with human approval. Decks, docs, one-pagers, internal analysis. The big tools are excellent at this now. There’s rarely a reason to buy.
Buy it when you hit the multiplayer orchestration zone. Check the boxes:
☐ Multiplayer — multiple humans and multiple AIs handing work back and forth
☐ Buyer-facing and real-time — no 5–10 minutes to figure out the right answer
☐ Must follow a strict process — certain steps have to happen before others, every time
If you check those boxes, the cost of failure is a lost opportunity. That’s a job for a supplier who eats, sleeps, and breathes that one workflow.
Bonus test: ask “what’s the buyer-first answer?” Build where your operators have unique, hard-won expertise (your own analytics dashboards). Buy where you want someone obsessing over a workflow full-time, with fallbacks for when an LLM goes down.
The vendor fire-test
You’re hiring a system. Don’t hire one you’ll have to fire. Before you buy, make them show you:
Did it actually work? Not “it can make 1,000 posts.” Were they good? What were the outcomes?
What are the production thresholds and grades? How is quality measured and enforced?
What are the integrations and fallbacks? What happens when a connector or an LLM fails mid-pipeline?
Where do humans come in? Reliable, secure, multiplayer, humans at the right time. If they can’t answer, that’s your answer.
Businesses regret not pushing harder up front. Push hard up front.
Your one-week starter plan
Day 1: Open one empty folder. Have your AI scaffold the structure.
Days 2–4: Write your 5 load-bearing documents. Make them excellent.
Day 5: Set progressive disclosure so the AI reads in the right order.
This month: decide who builds and who just uses. Not everyone should be a builder. 99% of your team’s job is serving a buyer who may not care about AI at all.
You don’t need to boil the ocean. You need Layer 1 to be true. The rest only works once it is.