5/12/26: A CMO's AI Playbook for 5X Marketing Output
Welcome once again for yet another episode of the GTM AI Podcast. Apologies for the delay from last week but we have a couple of amazing episodes this week, starting with this one today with Amy Osmond Cook the CMO of Fullcast.
As a reminder we give goodies and deep dive podcasts every week for you to enjoy, we also have tons of content in our paid level like Claude Code workflows and walkthroughs, AI education for revenue leaders, and tried and true strategies to scale AI.
Todays goodies is all about AI Visibility from Amy.
On to the podcast!
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.
I had a guest on the show last week who said something I have been waiting for someone to say out loud for two years.
“Fire your agency.”
That was Amy Osmond Cook. She is the CMO of Fullcast, a PhD in organizational rhetoric, and one of the few marketing leaders I have met who can argue both the philosophy of language and the physics of pipeline in the same breath. She is not the type to say something for shock value.
Here is what you need to know before you read another word.
Amy is running the marketing org of the first AI-native go-to-market platform on earth. Fullcast acquired Copy.ai (AI workflows), Ebsta (revenue intelligence), Atrium (sales performance), and Commissionly (compensation), and stitched them into a single system. They are punching multiple weight classes above their size. And the numbers she walked me through were the kind that make you stop and re-read the slide.
So I asked her how.
This is what she said, and what I think every GTM leader needs to do about it this week.
The numbers she brought to the show
Amy did not hide behind hypotheticals. She brought the receipts.
“We scaled, we reduced our salary by several hundred thousand, and we increased our productivity 5X. In one month we went up 10 points in AI visibility. The results were insane.”
“Copy.ai’s website was in existence for three years, and they went from zero to 84 domain strength with zero human content. Zero.”
Let me sit with that for a second.
Three years. 84 domain authority. Zero human writers.
For context, that is the kind of domain strength that takes a content team of 8 humans about 5 years of disciplined publishing. Copy.ai got there with AI agents and a workflow system. And before anyone yells “but Google will penalize you,” I asked the same question. Here is her answer, and it is the one that should make every CMO take a long look at their content team’s budget.
“That’s the answer to whether Google’s going to downgrade your site if you use AI. There’s the answer.”
Google does not care if it is AI. Google cares if it is good.
The whole game has changed.
The thesis: the AI agency is here
Amy’s frame is the one I have been trying to put words around for months. She just nailed it.
“Using Copy as an AI agency extends your team like an external agency does, only you do it with AI agents. So it doesn’t mean that you get rid of your team. It doesn’t mean you only use robots. It just means your efficiency goes way up.”
Read that twice. The AI agency does not replace your humans. It replaces the external humans you have been renting at $200/hour.
That distinction matters. Because the wave I am watching right now is not “AI is taking marketing jobs.” The wave is “AI is collapsing the agency layer.” Internal teams keep their best people and let them operate at agency-level scale. Agencies that do not pivot to AI-native delivery are getting compressed from both ends: clients want lower cost and faster output.
If you are an agency right now, you have about 18 months to become an AI-native agency or become a feature inside someone else’s stack.
The 3 plays I pulled from the conversation
1- FAQs are the secret sauce of AI visibility
This was the line that stopped me cold.
“The FAQs is the secret sauce for being visible in AI. If there’s one thing I can tell marketers, it is this is good for AI visibility.”
Most marketers treat FAQs as a footer afterthought. Amy treats them as the primary surface area. Why? Because LLMs are trained to match questions to answers. Your FAQs are literally the format the bot is looking for. Every page on your site should have one. Every product. Every category. Every persona.
If you do nothing else this month, audit your FAQs.
2- The combo that creates magic is public + private cloud
I asked Amy what makes Copy.ai different from just using ChatGPT. Her answer is the most important sentence in the episode.
“You take what’s in the public cloud, you combine it with your own Copy.ai digital asset management system that provides all of the private information. You sync it together, and then all of a sudden magic actually happens.”
This is context engineering in plain English. The LLM is the engine. Your private data is the fuel. Most teams are running their engine on premium-grade nothing.
You will get average output until you give your AI access to your CRM, your call recordings, your win/loss notes, your win patterns, your brand voice, your customer language. Then it stops sounding like ChatGPT and starts sounding like you.
3- If it is AI, say it is AI
This one is going to be unpopular with the “AI SDR at scale” crowd. Amy is not having it.
“Marketing has done go-to-market a disservice by pretending that we’re real people when we’re not, when we’ve been trying to personalize at scale. People got wise to it. People were like, ‘Oh, they’re not real. I thought they were real. Now I’m not even gonna listen to them.’”
The fastest way to lose trust in 2026 is to pretend an automation is a person. The fastest way to build trust is to be radically transparent. If a chatbot is doing the work, say so. If a human is on the other end, prove it.
This is not just ethics. It is conversion math. People convert higher when they know what they are talking to.
What this means for you this week
The pattern Amy ran is replicable. You do not need to acquire Copy.ai to do this. You need to make 3 calls.
1- Audit your FAQs. Does every important page have one? Are the answers structured? Are they pulling search traffic AND showing up in ChatGPT and Perplexity?
2- Inventory your context. What private data could you feed your AI tools that you currently are not? Sales calls, brand guidelines, win notes, customer reviews, product docs. Pick the top 3 and load them this week.
3- Run the transparency test. Open your last 3 outbound emails. Open your last 3 chatbot flows. Is it clear when the human ends and the AI begins? If not, fix it before the prospect figures it out themselves.
The AI Visibility Playbook
How to Get Your Brand Found Inside ChatGPT, Claude, Gemini, and Perplexity (Without Losing Google)
By Coach K (Jonathan Kvarfordt) : Founder, GTM AI Academy Inspired by my conversation with Amy Osmond Cook, CMO of Fullcast, on the GTM AI Podcast.
Why I built this
Copy.ai’s website went from zero to 84 domain authority with ZERO human-written content. In one month, her team gained 10 points of AI visibility. Her marketing org is 5X more productive, and they shaved several hundred thousand dollars off salary spend.
I have been studying AI visibility for over a year. I have read every paper, talked to a friend at Google, run my own experiments on the GTM AI Academy site, and built playbooks for clients in the Fortune 500. What Amy said in that conversation crystallized everything for me into a system I could finally write down.
That system is what you are about to read.
If you are a CMO, a head of content, a head of demand gen, or a marketer who suspects the rules of being-found just got rewritten, this is for you. The hard truth is that the old SEO playbook still works for Google. It does not work for ChatGPT, Claude, Gemini, or Perplexity. Those engines do not rank pages. They reason over content. The marketers who figure that out first are going to own the next decade of pipeline.
The marketers who do not are going to wake up one morning and find that their organic traffic disappeared because their prospects stopped clicking links.
Here is the system. I call it the FOUND framework.
The FOUND Framework: 5 Plays for AI Visibility
# Play What it does
F : FAQ Everywhere Match how LLMs were trained: question → answer
O : Own Your Context Combine public + private cloud so AI sounds like YOU
U : Use EEAT, Hard Experience, Expertise, Authority, Trust signals on every page
N : Narrate For The Bot Structure content for parsing, not just reading
D : Distribute Where Bots Train Show up in the corpora the models actually learn from
Each play has a tactical step, a real-world example, a self-test, and a “try this week” rep. Use them as a checklist.
Play 1: F : FAQ Everywhere
The principle
LLMs are not search engines. They are autoregressive question-answering machines. They were trained on billions of Q&A pairs. When a user asks ChatGPT “what is the best sales performance management platform,” the model is doing pattern matching on every Q&A pair it ever ingested.
The structure the model is looking for is the FAQ structure.
Amy said it on the podcast: “FAQs are the secret sauce for being visible in AI.”
She is right. I have audited 47 client sites in the last 6 months. The sites that show up in LLM citations have one thing in common. They have hundreds of FAQs scattered across pages, products, categories, and persona hubs. The sites that do not show up have FAQs buried in a footer link.
The play
Add a structured FAQ block to every meaningful page on your site. Not “Contact us” pages. Every page that matches a buyer question.
The structure matters. It must include: 1- A clear H2 or H3 question (phrased the way a human would ask it) 2- A 2-4 sentence direct answer (lead with the answer, not the setup) 3- Schema.org FAQPage markup for crawlers 4- A short follow-up that names alternatives or related concepts
Example
Bad FAQ:
Q: Tell me about your platform. A: Our platform leverages cutting-edge AI to unlock revenue potential.
Good FAQ:
Q: What is sales performance management software? A: Sales performance management (SPM) software is a category of tools that combines territory and quota planning, commission management, sales analytics, and rep coaching into one platform. Most companies use SPM to align go-to-market plans with actual rep behavior and to forecast more accurately. Categories of SPM include territory and quota tools (like Fullcast), commission tools (like CaptivateIQ and Commissionly), and analytics tools (like Atrium).
Notice the second one names competitors. That is not weakness. That is what LLMs are looking for. They want connection density.
Self-test
Go to your homepage right now. Count the FAQs. If the answer is fewer than 3, you have work to do. Go to your top 5 product or solution pages. Count. If most of them are at zero, you have a structural problem.
Try this week
Pick your top 3 highest-converting pages. Add 5 FAQs to each. Use the buyer’s actual language (mine your sales call transcripts for the exact phrasing). Add FAQPage schema. Republish. Watch what happens in 30 days.
Play 2: O : Own Your Context
The principle
Most teams are using AI as if it is a public utility. They pour the same prompt into ChatGPT that every competitor is pouring. They get the same generic output. Then they wonder why it sounds like everyone else.
Amy explained this beautifully on the show: “You take what’s in the public cloud, you combine it with your own Copy.ai digital asset management system that provides all of the private information. You sync it together, and then all of a sudden magic actually happens.”
The public cloud is the LLM. The private cloud is your data. Magic is the intersection.
If your team is not feeding private context into your AI workflows, you are running a Ferrari on regular unleaded.
The play
Build a private context layer that your AI tools can access for every output. Minimum viable context layer:
1- Brand voice document (with examples of what is on-brand and off-brand)
2- ICP definition (with anti-personas, not just personas)
3- Top 20 customer quotes (verbatim language from sales calls and reviews)
4- Top 10 win/loss notes (why deals closed, why they did not)
5- Your last 50 blog posts indexed for retrieval
6- Your top 30 sales conversations transcribed (Gong, Otter, or equivalent)
Tools that do this well: Copy.ai (workflows), Claude Projects, ChatGPT Custom GPTs, and several agentic platforms. Pick one. Get it running this month.
Example
I ran a test last quarter with two CMO clients. Both wrote a launch announcement for a new product feature. Client A used vanilla ChatGPT. Client B used Claude with a Project containing their brand doc, 5 customer interviews, and their last 10 launch posts.
Client A’s draft: 6 hours of editing before it sounded like them. Client B’s draft: 20 minutes of editing before it shipped.
The difference was not the model. It was the context.
Self-test
Ask yourself: if I gave my AI tool a blank prompt today and said “write a blog post in our voice,” would the output be 80% there or 20% there? If 20%, you have a context problem, not a model problem.
Try this week
Build a single brand voice document. Two pages max. Three examples of how you sound. Three examples of how you do NOT sound. Load it into your AI tool of choice. Test the difference.
Play 3: U : Use EEAT, Hard
The principle
Google introduced EEAT in 2022: Experience, Expertise, Authority, Trust. It was originally a search ranking signal. It turned out to be the most prescient framework of the AI era.
LLMs care about EEAT for exactly the same reason Google does. They are trying to figure out which sources to weight when they generate an answer. Sources with EEAT signals get cited. Sources without EEAT signals get ignored.
Amy nailed this on the show: “The EEAT acronym, experience, expertise, authority, trust. Those things, if you can pull those into your content, even if it’s AI, it’s fine.”
The marketers who lose at AI visibility are the ones publishing AI content with no human evidence layer. The marketers who win are the ones publishing AI-assisted content backed by named experts, real client work, citations, and proof.
The play
Audit every piece of content for the 4 signals:
Experience: Does the author have firsthand experience with the subject? Is that experience visible on the page?
Expertise: Does the author have credentials, certifications, or a body of work that proves they know the topic?
Authority: Is the author cited by peers, mentioned by other reputable sources, or recognized by their industry?
Trust: Does the content include sources, dates, transparent disclosures (including AI assistance), and verifiable claims?
If a page hits all 4, the LLM is more likely to cite it. If a page hits 0, the LLM treats it as noise.
Example
Compare two blog posts on “How to forecast pipeline accurately.”
Page A (low EEAT):
No author photo
Generic byline “Marketing Team”
No citations
No dates
Stock images
AI-generated body with no real examples
Page B (high EEAT):
Author photo with name (Amy Osmond Cook)
Author bio: “CMO of Fullcast. PhD in organizational rhetoric. Scaled marketing through 3 acquisitions.”
4 citations to peer-reviewed pipeline research
“Last updated May 2026”
Original screenshots from a real client engagement
3 verbatim quotes from named sales leaders
Page B wins in AI citation 100% of the time.
Self-test
Open your last 5 blog posts. Score each on the 4 EEAT signals (0 to 1 each). If your average is below 3, your AI visibility is leaking.
Try this week
Add author bios with credentials and photos to your top 10 content pages. Add a “Last updated” date. Add 3 verifiable citations per page. This is the lowest-effort, highest-impact move in this playbook.
Play 4: N : Narrate For The Bot
The principle
Human readers scan in F-patterns. They read headlines, skim first sentences, and bounce. They forgive bad structure.
LLM readers do not scan. They parse. They tokenize. They build semantic relationships across an entire document at once. They do not forgive bad structure. Bad structure means the wrong thing gets cited, or worse, your content does not get cited at all.
This is the biggest mental shift for content marketers. You are no longer writing for two audiences. You are writing for three. Humans. Search crawlers. Reasoning agents.
The play
Adopt the “answer-first paragraph” pattern across all content: 1- Lead with the direct answer in the first 2 sentences 2- Add context, nuance, and proof in the next 3-4 sentences 3- Close with a what-this-means-for-you bridge
Then layer in these structural moves: 1- One H2 per major idea, written as a question or a declarative answer 2- Bullet points for parallel concepts (LLMs love these) 3- Tables for comparison content 4- Numbered lists for sequential steps 5- Bolded key terms (acts as a semantic signal) 6- Clear topic sentences in every paragraph
Example
Old paragraph (LLM-unfriendly):
“When you think about pipeline forecasting, there are many factors at play. Some companies use historical data, others use signals from the buyer journey, and the best ones combine both approaches in a unified system that gives them what they need to make decisions.”
New paragraph (LLM-friendly):
Best pipeline forecasting combines historical data with real-time buyer signals. Companies that use only historical data underperform forecasts by an average of 23%. Companies that use only buyer signals overcorrect on small inputs. The winning approach (used by leaders like Fullcast, Clari, and Gong) blends both into a single confidence score. This is the model your forecasting team should be building.
The second one will be cited. The first one will be skimmed over.
Self-test
Read your last blog post aloud. Does each paragraph have a topic sentence? Could a stranger reading only the topic sentences understand the full argument? If no, restructure.
Try this week
Pick your top 3 pages by traffic. Rewrite each opening paragraph as an answer-first paragraph. Add 2 bulleted lists and 1 table to each. Republish. Measure citation rate in ChatGPT and Perplexity 14 days later.
Play 5: D : Distribute Where Bots Train
The principle
LLMs do not browse the web in real time the way Google does. They are trained on snapshots of the internet, then augmented with retrieval. The places they were trained on (and the places they retrieve from) are not your blog.
They are: Wikipedia, Reddit, Stack Overflow, YouTube transcripts, podcast transcripts, major publications, GitHub, and a handful of high-authority industry sites.
If your brand does not appear in those sources, you are invisible to the model regardless of how good your own site is.
The play
Build a distribution strategy that targets the LLM training corpora directly. Minimum viable distribution layer: 1- Wikipedia: get your founders, your category, or your major customers cited (NOT a page about your company, those get deleted) 2- Reddit: get organic mentions in industry subreddits (without astroturfing) 3- YouTube: publish video content with full transcripts and chapters 4- Podcast: be a guest on shows whose transcripts get indexed 5- Industry publications: contribute bylined articles to top trade outlets 6- GitHub or Hugging Face: publish open-source tools or datasets if you are technical
This is slower than running ads. It compounds.
Example
I tested this with a client last year. We took their CMO and got her on 12 podcasts in 6 months. Every one of those podcasts published transcripts. By month 8, when prospects asked ChatGPT “who is the leading expert on go-to-market for fintech,” her name appeared in the answer.
That is the compounding effect. Twelve podcast appearances cost her about 20 hours total. The lifetime traffic and inbound from being cited by ChatGPT and Perplexity is worth orders of magnitude more.
Self-test
Open ChatGPT. Ask: “Who are the top 5 thought leaders on [your category]?” Are you in the answer? If not, you have a distribution problem.
Try this week
Make a target list of 10 podcasts in your space. Pitch 5 of them this week. Even one acceptance starts a flywheel that will pay off for 24 months.
The 30-Day Action Plan
If you do nothing else, do these 4 weeks of work.
Week 1: Audit and structure
1- Run a FAQ audit on your top 10 pages 2- Add author bios and EEAT signals to your top 10 content pages 3- Rewrite the opening of your top 3 blog posts to be answer-first
Week 2: Context build
1- Write a 2-page brand voice document 2- Pull your 20 best customer quotes from sales calls and reviews 3- Load both into your AI tool of choice 4- Test by generating one piece of content and comparing to your baseline
Week 3: Production sprint
1- Publish 5 new FAQ-rich pages targeting LLM-friendly buyer questions 2- Add FAQPage schema to all of them 3- Add internal links between them
Week 4: Distribution
1- Pitch yourself or your CMO on 5 podcasts 2- Publish one bylined article on a major industry publication 3- Run a Reddit AMA in your top relevant subreddit
In 30 days, you will have moved more on AI visibility than 90% of your competitors will move all year. I have watched this play out across 7 clients. The pattern holds.
The Self-Test Scorecard
Use this every quarter to track your AI visibility maturity.
Play 0 : Not started 1 : In progress 2 : Live and measurable F : FAQ Everywhere No FAQs on key pages FAQs on top pages FAQs on every key page + schema O : Own Your Context Vanilla AI prompts Some private context loaded Full context layer in production U : Use EEAT, Hard Anonymous content Author bios present EEAT signals on all content N : Narrate For The Bot Wall-of-text content Some structure added Answer-first across all content D : Distribute For Training Own site only Some external presence Wikipedia, podcasts, publications
A score of 7 or higher is the leadership tier. Most companies are sitting at 2 or 3 today. The gap is wide open.
My challenge to you
Pick ONE play this week. Not five. One.
The marketers who win the AI visibility race are not the ones who do everything. They are the ones who pick the right thing and execute it with discipline. Amy did not transform her marketing org in 90 days by trying to do everything. She picked the workflow layer (Copy.ai), went all in, and let the compounding do its work.
If you tell me which play you picked, I will help you map the first 30 days. Reply to the newsletter, comment on my LinkedIn, or send me a DM. I read every one.
I hope this helps you the way it helped me. The conversation with Amy changed how I think about my own content engine, and this playbook is the receipt.
The future of marketing is already here. It is just unevenly distributed. Go close the gap.
Coach K