I have had a few calls recently that went almost exactly the same way.

Founder: “We’re ready to scale. We just need to figure out how to do it using as much AI as possible!” (People are really sold on AI-Native GTM by now - yaas - the idea that something else will do the work and we don’t have to pay humans to do it is very appealing.)

Me: “Great. Who are we selling to?”

Founder: “Well, honestly, our product can help pretty much anyone who...”

And there it is. The sentence that kills more launches than I’d care to admit.

As a founder myself - I get it. I really do. When you invest your time and money into building something that you find genuinely useful, narrowing feels like leaving money on the table.

But it is not. Most of us don’t have the resources or product state that would equally serve everybody we could potentially help just yet. We have to narrow down our market and make difficult pricing, positioning, and ICP choices that will speak directly to it.

People usually get it when I show them this illustration 🍪

By the time most of us get involved in go-to-market, the product is already largely a given input. We can influence it at the margins, but it exists. Which means the customer decision is usually the first serious decision that is actually ours to make.

There are two ways teams get it wrong. Either they take it way too lightly, defining an ICP in a 60-minute persona brainstorm on a Tuesday afternoon with a branding consultant who knows absolutely nothing about your industry and calling it strategy. Or they take it way too seriously, disappearing into six months of research and emerging with a beautiful deck and zero customers.

There are only two roads I have seen work reliably:

1. Launch broadly, then reverse-engineer. Go wide, watch which segment retains and monetizes best, then double down. This works if you are PLG, can fund a launch with real reach, and have the capacity to service those early customers decently well. Plenty of great companies got here this way.

2. Do the digging first. Narrow the market before you start pumping out marketing and sales, using beachhead logic to find the segment most likely to convert in the next three to six months, with reasonable willingness to pay, healthy growth potential, and enough delight that they will actually refer you.

This article is about road two, because it is the one most teams need.

Here is what we’ll cover:

• Market segmentation - segment by job to be done, not by employee count

• Segment scoring - narrowing for winnability, not for size

• Research methods - why people doing beats people saying, every time

• The ECP prototype - your first answer is an ECP, not an ICP

• The GTM roadmap - when to expand

Let’s find the one 🗺️

Step 1: Market Segmentation (Who Could This Help?)

I have to poke fun at personas one more time, because most I see out there are hilarious.

The majority of ICP definitions I see in the wild are essentially industry plus headcount on a slide. “Mid-market SaaS companies, 200 to 1,000 employees, North America.” That could be roughly 3,000 companies - can you access them at the same time with the same messaging and serve them equally well? If 🫨- this definition is probably way too broad for you to operate with (and it is pretty narrow compared to most ICP slops I have the sadness to review).

The most useful basis for segmentation is almost always the job to be done, or the problem being solved. Something meaningfully more specific than smashing a random employee/revenue range onto your ICP deck.

Now, I am not anti-firmographics. Company size could correlate with willingness to pay, and in B2B you will absolutely end up with firmographic parameters eventually, because they help with targetability. But they are an output of this step, not the starting point.

The starting point is a much simpler question: who can this solution genuinely help, and what are they trying to achieve?

You should come up with between 10 and 20 segments fairly quickly (AI can massively help here - here are a few useful skills you can use right away). That is perfectly fine at this stage.

The only real quality bar is this: is each segment defined specifically enough that you could actually go and target it later?

Not “everyone who would like to reduce AI costs” but “Europe-based companies with a high volume of data to process with AI, operating with clients in regulated industries, with min $10M (Series A) in funding at the edge of scaling stage.” Specificity is your best friend here to unlock the next four steps.

That is then a workable answer at this stage. It is also completely useless on its own, which is exactly why step two exists.

Step 2: Segment Scoring (Where Can We Win?)

For most teams, targeting and properly exploring 15 segments at once is fantasy.

The real cost of trying isn’t just wasted effort and time. It is that you never reach critical mass in any single segment, which means you never gather enough evidence to say with confidence that you validated anything. You end up with 15 half-answers and still no decision.

So segment scoring answers one question:

Segmentation tells you who you could help. Scoring tells you who you can actually win in the next 3-6 months. Those are two very different lists.

This is where you bring in the specifics of your product and your sales process. The criteria I score against:

• Realistic conversion window. Is this customer plausibly going to convert in the next three to six months? Not “would they benefit,” but “would they buy, soon.”

• Sufficient willingness to pay. Can they afford you at a price that builds a business, not a hobby?

• Strength of the pain. How badly does this segment feel the problem? Are they motivated to solve it now? A problem they have comfortably worked around for years is not a problem you can sell into.

• Healthy market. Is it growing? Is there room?

• Proximity and access. Do I know these people? Can I reach them? Do I speak their language? This one gets skipped constantly and it is often the deciding factor. A slightly less perfect segment you have genuine access to beats a perfect segment you have no way in.

The Market Problem Map, which I built with Simon Belak, is better when you are under 10 and want to investigate each space with real depth.

I still believe the best way to do this is human-to-human. But you can use AI to pressure-test your scoring afterward. Running your results past AI for an objectivity check is a genuinely useful second pass, and it tends to make teams more comfortable committing to the answer.

The output of this step is your beachhead segment candidate to validate.

Step 3: Research Methods (Now Prove It)

One core governing principle here:

People do > People say.

Every single time.

The closer you can get to someone doing something real - signing up, paying, integrating, replying to an outbound message - the more confident you can be. And how far you can push along that spectrum depends partly on how much product you have to show.

Which brings me to synthetic personas and AI-only research. And maybe I am being a little old-school here, but talking to actual humans still hits differently than reading an AI-generated document or talking to a bot. When you sit with someone and feel their frustration, you build empathy. You get ideas you would never have thought to prompt for. They refer you to someone else. The AI version is competent and somehow empty.

I’m not saying AI/syntetic personas are useless. I’m saying: please don’t skip at least 10 real conversations (interviews, sales discovery calls) before making any final calls.

Here is a live example of what good looks like. I was in the pre-beta early access program for Grok Bot that launched this week, and watching that product team work was genuinely instructive. We signed an NDA, had a shared Slack channel, and I sent feedback as I used it. What made it work is that I felt like I was in it with them.

Compare that to receiving a cold LinkedIn message with a link and “tell me what you think?” If I like the person, I’ll send something back. Mostly I’ll forget. I can’t prioritize it.

For Grok Bot, it resulted in a stellar launch, with high-level opinion leaders like Lenny Rachitsky and Ruben Hassid posting about it for free, just because they felt involved being in a pre-beta program. One caveat: the product needs to be sincerely impressive (and this one is).

My favorite method once interviews are done is a small marketing or sales test that brings some real data in. Teams that I work with usually use these channels/tactics to run it:

• Founder branding - building in public on LinkedIn or X

• Advertising budgets - Google Ads usually works best, but go where the audience is (sometimes that means working with a niche publication, newsletter, or an influencer)

• Direct outreach - preferably to an email list of relevant event attendees - that works great

• Community launches - again - go where the audience is … and don’t be cringe

More info on that with many, many examples in this article if you need more context.

Ideally, you will get some traction to reverse-engineer really fast; otherwise, go back to the drawing board and try more validation or segments - it is not one shot, one opportunity. In practice, it takes 5-7 iterations to get it right.

Step 4: Build the ECP Prototype (Meant to Be Broken)

I hesitate to call it a persona, but I know you will 🙂

I call it ECP - Early Customer Profile. And it looks something like this.

One simple diagram, not a 40-page research report nobody opens.

It captures the main insights from previous stages and serves as a team alignment tool.

For now, in early stages, it’s the single source of truth, but it’s built to be broken. In my book, I said this holds for 3 to 18 months. I’m revising that down. In current AI cycles, I’d say three to six months, realistically. Markets are reshaping faster than our documents are.

Ideally, the ECP/ICP doc lives and evolves somewhere in your GTM brain and gets revisited automatically. At minimum, set a calendar reminder and ask three questions: what changed in the market, what changed in the product, and what changed in our traction? Is my ECP/ICP doc still relevant?

If you are further along the way and already have a real volume of customers, opportunities, and closed-lost data, Eddie Reynolds wrote the most in-depth piece on turning that reminder into an actual system: The AI-Driven Continuously Self-Improving ICP. His argument is that most mature ICPs are broad, shallow, and sitting in a doc nobody opens, with no feedback loop as deals get won and lost. Sound familiar? Same problem, later stage.

The difference is what you have to work with. At the earlier stage (that we focus on in this article), you have 10 sales call transcripts and a hypothesis. At the stage Eddie talks about, you have five full years of CRM data and can ask AI which signals actually separate your best customers from the rest.

Step 5: The GTM Roadmap (Earn the Right to Expand)

Your ECP is not your forever customer. These are the early customers who will give you valid insight and real money without demanding massive social proof first or dragging you through nine-month procurement cycles.

If someone still shouts at you that “we are leaving money on the table and we can help everybody,” show them this image and tell them Cursor, Clay, Harvey, Lovable, Uber, Slack, and Figma bake in the same logic to their early go-to-market roadmaps.

So come to peace with the fact that most companies don’t have resources to scatter across non-related segments. You choose one - the beachhead segment and then work your way up the market from here. Framing it this way makes narrowing feel like a smart strategic choice rather than a sacrifice, which is exactly what it is for most folks.

Now the danger zone 💀 Once you start getting traction, new opportunities will find you - you’ll get new ideas on sales calls, investors will pull you sideways, competitors will trigger your FOMO, inbound leads will try to lure you into 999 other directions, and the highest paying customer will demand one more feature - if not, they are gone.

When to move from the beachhead segment to other attractive segments or lean in to insights to change the direction?

In theory, you would commit to beachhead for min. three to six months or till you win a certain % of market share there - the answer is usually from 60-80% - this is how narrowly the beachhead should be defined.

If you have more resources to validate multiple segments independently at the same time and go all in on multiple fronts, you can absolutely do it - but do it quietly, in relative isolation.

The cost people underestimate is positioning drift. Public communication gets confusing fast when you’re serving several masters. Your marketing and sales materials need to reflect a vertical experience the buyer actually identifies with, and you cannot do that convincingly for three segments simultaneously.

One more thing while we’re here: be extremely careful not to take product requests à la carte from sales calls. At this stage, customers are still largely buying your vision of how this should work, so every “but it would be even better if it had/did this” should be taken with a grain of salt if it is really aligned with your product and go-to-market vision. If you lean in too much, you may lose direction and sink your resourcing into serving “a market of one”

So Where Does That Leave You?

The reason I wrote this is simple: to help you answer the question “how do I define my ICP?” And while I am triggered to write “You don’t - you find it in interactions,” I know that you deserve better answers.

It’s more work than a workshop. And it’s work that mostly can’t be delegated. Please don’t outsource this decision to someone with no knowledge of your industry, and don’t rely on AI slop that doesn’t survive contact with the market.

And even when you follow this process, your work in the ICP arena is never done. Things change constantly - a new launch from a competitor, new technology, or market saturation will shake your product-market-fit. These frameworks come in super handy whenever you’ll be forced to defend it.

There are 135+ more helpful frameworks, workshops, templates, examples and AI prompts in my GTM checklist that has helped 9,500 business owners plan & execute their GTM strategy. Get it here

If you’re interested in my other products:

📘 Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

Thanks for reading, and let’s go to market!

Maja

This newsletter is sponsored by Miro - the collaborative workspace where agents and teams converge to think, decide, and build together.

Miro’s MCP has become one of my favorite tools in the AI stack right now. Point Claude, Codex, or whatever agentic tool you’re already using at it, and instead of getting handed copy, you get a built board - ready to review with your team, right inside your workspace.

I use it to turn messy planning conversations and research docs into something that I can actually align on with my team members and clients. A perfect antidote for endless comment threads and AI-generated docs that no human will ever read :) You can check out how I use it in my latest video.

Try Miro MCP

Dear GTM Strategist,

You know this story. Maybe you are living it.

In 2025, you bought an AI SDR platform and then the coaching tool; both tools promised to “transform your GTM”.

Then you invested heavily in enrichment tools and orchestration, trying to bring all the pieces together until you realized in Q1 2026 that you should double down on context engineering and start building your own GTM brain.

Six months later, the pipeline number stayed pretty much the same, and the big boss is 😡: where is the ROI ???

Instead of fingerpointing about which vendor did not deliver on its flashy demo - here is something to REALLY think about: what if all the tools and concepts are just fine, and all we were missing is a layer of control/constrain that would finally bring order in our GTM AI stack chaos.

And before you roll your eyes, “is this another expensive tool?” read this one from Kyle Norton, CRO at Owner.com and host of The Revenue Leadership Podcast.

He calls the missing layer in our stacks the GTM Harness.

And you probably already have a harness - but did you build it on purpose, or is it some combo of prompts guiding the AIs?

In this post, you’ll learn:

• What a GTM harness is

• Which parts of it to build, and which to buy

• How two AI-native GTM teams (Owner and Cursour) built theirs in opposite ways

• Who should own the harness inside your GTM org

• A six-question audit to run this week

Gitty up 🏇

What Is a GTM Harness?

An LLM on its own knows very little about your business. It has never seen your pricing exceptions, your best rep’s discovery call from March, or the real reason you lost that deal in Q2.

Kai, Kyle’s OpenClaw, put it brilliantly (as quoted in his post):

An LLM is like a wild, powerful stallion that’s fast, strong, and highly capable. However, trying to ride it bareback (using a “naked” prompt) results in an uncontrollable, chaotic journey. The harness represents the reins, saddle, and bit. It doesn’t provide the muscle, but it gives the rider a predictable, repeatable mechanism to steer that raw power safely to a precise destination.

(An AI explaining what AI needs from you. I enjoyed that more than I should have.)

So the harness is the system around the model. In Kyle’s definition, it is what connects the model to your tools, prompts, files, databases, permissions, workflows, agents and state.

This means you most likely already have components of a harness, whether you intended to or not. Every AI tool you purchased, every Salesforce query you let reps run, every integration you set up. Those are components, and they add up to a system whether or not anyone drew it.

Kyle put it plainly in our chat:

As GTM leaders, what we are doing with our AI investment and now with our RevOps investment, we are building harnesses. We are building sets of tools and permissions and prompts around this superpowered intelligence.

Most teams are assembling those one tool at a time, but this is what raises the stakes. The harness isn’t merely how the model reaches your tools, it’s where your company’s knowledge lives.

Your ICP, your battle cards, your qualification logic, which objections actually matter in your market, which talk tracks sound good and never convert. And this is not something you add later.

Sarah Wooders made the point that memory is not a plugin, and Kyle’s take is that memory is part of the harness itself. Asking to bolt it on afterwards is like asking to bolt driving onto a car.

So the harness holds two things at once: how your AI works, and what your company knows.

Which is why the build vs buy question feels harder than it used to. You are not choosing a tool. You are deciding who gets to keep what your team learns.

Which Parts of Your Harness Should You Own?

Kyle’s take on this is (as voiced on stage at SaaStr): buy your infrastructure, build or own your intelligence.

The old build vs buy conversation was about cost, timeline, customization and whether engineering had the capacity. Those still matter. They stopped being what decides it, for two reasons.

The first is that building got cheap. What used to be a six-figure vendor contract and a two-quarter roadmap can now be a working prototype by Friday.

The second is somewhat more important. Software used to sit still: your dialer dialed, and it never got smarter. Now, with the intelligence layer, it does. The work and the learning arrive bundled.

Kyle’s test for this is one you can run yourself this afternoon. Take Claude: same model underneath, two products.

The consumer chat app is constantly learning about you. Your writing, your preferences, the things you keep coming back to. Ask it something six months in and the answer is shaped by everything it has picked up. It genuinely gets better. Now try to look at what it knows. There is no page that shows you your own memory.

Then go to Claude Code, running in a terminal on your desktop. It writes files directly to your computer. You can open them, read them, reorganize them. You can even sync them to a GitHub repo, so anything you build later can read them.

Same tool. Same intelligence. Opposite ownership.

One learns about you somewhere you cannot open. The other writes it down where you can read it, edit it, and point other tools at it.

Now scale that up to your revenue engine:

If you’re using a tool that is learning about your customer and learning about the messaging that sticks and learning about the optimal sequence to reach out to people, but you can’t look in and understand what those answers are and then take them somewhere else, I in general want to be pretty wary of that as an architecture.

Of course, sometimes renting is the best choice. Let’s say, when the software vendor is genuinely ahead and the alternative would be nine months building something worse.

Just make it a decision somebody made deliberately, rather than one that happened to you because the demo was good.

And one thing that gets misread constantly: own does not mean build from scratch. You can buy a component and still own what it produces, as long as you control the data, understand the logic, can export the memory and can move the learning when you need to. The inverse is also true, and this one stings. You can build something entirely in-house and still fail to own it, if what you built is a fragile one-off that nobody left in the building understands.

Two Teams, Two Very Different Harnesses

Let’s make this more concrete with an example. Take two of the most AI-native GTM teams in the market. Opposite architectures. Both own their intelligence.

Owner.com pre-loads. Their data team built a GitHub repo of skills that teach a model how to query Snowflake properly. Which tables are which, what the fields mean, how to interpret them. Kyle is candid that it is early days, “fairly grassroots” in his words, but the discipline is deliberate: everything goes into GitHub so a human can inspect what exists, and he will not run tools whose output he cannot see.

Cursor does the opposite. George Hou, their Head of Enterprise Growth, built an internal system called ChatGTM for a sales org of roughly 400 people, and explicitly rejected the repo approach. There is no pre-loaded context library. ChatGTM queries Salesforce, Gong, the warehouse and the open web at the moment a rep asks, pulling context on demand rather than maintaining it in advance. Brendan Short covered the full build here, and it is worth your time.

In short, this is when to use which:

→ Pre-load anything that is judgment-heavy but stable, e.g., how you sell, what good looks like, which of your fields lie to you.
→ Fetch live what changes often and already lives in your system of record, e.g., deal state, usage signals, call history, who joined the account last week.

Most teams need both. What you cannot do is copy one company’s architecture and expect the same result. Cursor’s version sat on top of years of context-engineering muscle they happened to have, because building Cursor required exactly that skill. Owner.com’s sits on a data team that had already done the unglamorous work of making Snowflake legible to a model.

The architecture is the visible part. The capability underneath it is what took the time.

Who Should Build Your Harness?

Short answer: not the person you are probably thinking of.

“GTM engineer” has come to mean someone good at Clay tables who builds lists and a few automations. Useful. Not this. Kyle calls his function applied AI on purpose, specifically to draw that line.

What the job actually needs is someone who can hold the whole system in their head:

You need somebody who can be an architecture thinker. They need to be able to understand and zoom out and think through how all of these things connect.

For instance, Owner.com hired a former founder who had previously started a couple of companies in GTM tech, someone who already understood the landscape. Kyle is blunt about the alternative:

Somebody junior and scrappy is probably not going to figure out this broader image. It has to be a real systems thinker.

But the trait that matters most is not technical at all. They have to want to sit with managers and reps, because that is where the requirements actually come from.

The obvious objection is cost. These people are expensive, and most GTM leaders assume they are a Series C problem.

Kyle’s math says otherwise.

Say you have ten BDRs at roughly $100k each. That is a million dollars a year already committed. An applied AI lead runs $250-400k. If that hire makes those ten BDRs 50-80% more effective, you have effectively bought yourself several hundred thousand dollars of BDR capacity without adding headcount. And every BDR you hire afterwards inherits the improvement.

So you have the harness, you have the person to build it, and you have the budget approved. That is when the actual problem shows up.

Building Is Not the Hard Part

Now, let’s get a little bit more practical here.

Kyle has long wanted a system that could tell a rep exactly what to say on a specific call. Not a generic script. The actual opening for this prospect, on this day.

The first version is deliberately simple. It gives the rep the customer closest in geography to the prospect, so they can name-drop someone local. It surfaces a pain tied to what the prospect is already using. It adds one observation from their website. The rep opens with something like: I see you’re using X, and the people I talk to on X usually tell me Y. Is that true for you too?

That system took about a week to build.

It took a few months to get adopted.

It turns out building is not the hard part. The adoption is.

This is where most AI investment quietly dies. The tool works, the demo went well, and six months later the pipeline number has not moved. Kyle’s diagnosis is uncomfortable:

You’re using AI to scale a bunch of stuff that is only mediocre.

Ask him where GTM leaders will waste money over the next year and he does not say the wrong category or the wrong vendor. He says tools that never get used. You are better off with fewer tools and much better adoption.

So how did Owner.com get theirs adopted?

• Set the frame before you ship anything. If your team believes AI is coming for their job, they will not lean into it. Kyle’s stance has been the opposite from the start: AI takes away the parts of the job nobody wanted, so reps spend more time with customers and closing. Almost every early investment at Owner.com was aimed at helping reps make more money, not at cutting headcount. That is a culture decision made long before the tool exists.

• Pilot with four top reps. Not volunteers, not the strugglers. The reps everybody else is chasing. If those four say this is great, adoption stops being a mandate and becomes something people want.

• Sit next to them while they use it. When Owner.com built pre-call research, the applied AI lead flew to Toronto and spent a week beside the reps. Hand them a batch of leads, watch them work it, hear “this is wrong, and here’s why,” go fix it, hand them a fresh batch. The feedback from those sessions is the product.

• Give them at-bats before they go live. Owner.com uses Avara so reps can practice a new motion with the AI rather than real buyers. Managers join them during rollout and coach in the moment.

• Measure it where it counts. Instead of logins or usage dashboards, keep track of call volume, conversion rates, meetings booked, pipeline. In the pre-call research pilot, those 4 reps made noticeably more calls and converted them at a higher rate, thus earning the rollout.

Which brings us to the part I keep thinking about.

Owner.com gets a lot of attention for the AI they have built. Kyle says that is not what makes it work:

Our secret sauce is our learning and development environment. It is the caliber of first and second-line sales leader that is able to bring these tools to the rep and change their behavior rapidly.

He knows people who have built things just as good as Owner.com’s and got none of the results, because they could not drive the change.

The harness is the easy half. The people holding the reins are the rest of it.

GTM Harness Audit Checklist

Everything above assumes you know what you are working with. Most teams do not, which is not a criticism. The harness got assembled over eighteen months of reasonable decisions made by different people for different reasons, and nobody was ever asked to hold the whole picture.

So before the roadmap, the vendor shortlist, or the hire, spend an hour on these six questions. Run them against what you already own, not just the next thing you are about to sign.

Most teams will not like their answers to three and four. That is fine. Knowing where you actually stand is a better starting position than a roadmap built on assumptions.

Where This Leaves You

The companies that win AI-native GTM will not have the most tools. They will be the companies whose systems get smarter every week because they own the memory that matters.

I’d add one thing. Owning the memory gets you nowhere if nobody touches the system. And a well-adopted tool is a liability if you can’t see what it’s learned about your customers.

You need both. Most teams have one.

So pick the thing you have been avoiding:

→ Nobody can name every tool touching your customer data? Start with the audit.
→ Reps quietly working around something you shipped? Go sit with them for a week.
→ About to sign a platform that will learn how you sell? Ask where that learning lives. Before you sign.

The only thing that helps is action. Pick one, do it this week.

Don’t build everything. But don’t buy your brain either.

Now I want to hear from you. What is the AI tool you bought that nobody uses, and do you know why? Hit reply, I read everything.

Huge thanks to Kyle Norton for the conversation. Go read Don’t Buy Your GTM Brain and subscribe to The Revenue Leadership Podcast. And if you want the full story on what Cursor built, Brendan Short’s piece on ChatGTM is worth your time.

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is supported by Swan AI, the AI GTM engineer.

Swan helped build something every GTM team should have in their toolkit.

GTM Skills is a free, open-source library of go-to-market skills - built with Pavilion and GTM Engineer School, and signed by the operators who got real results running them.

With GTM Skills, you get the actual system an operator ran - not a framework slide, not a prompt someone typed once. Install it, run it, and it executes the same way every time.

Wherever your stack lives - Claude Code, Cursor, Codex, or Swan - GTM Skills was made for you.

Human-crafted. Community-run.

Get the skills

Dear GTM Strategist,

If you’re like me, you’ve probably spent the last couple of years obsessing over prompts. Better prompts, longer prompts, prompts stolen from someone else’s LinkedIn post.

But here’s the thing.

A prompt is just a suggestion. It’s advice you give your AI once, and hope it does the right thing. That’s not a system - that’s you, doing the AI’s job for it, one chat window at a time.

The GTM operators pulling ahead right now are no longer working on better prompts. They’re engineering skills - reusable playbooks that their AI agent executes the same way every time.

I spent this week going through GTM Skills, an open-source, community-built skills library built by Swan in collaboration with Pavilion and GTM Engineer School. Some of what I found made me rethink things I thought were settled (pricing, account scoring, cold outbound) - which, as always, is the whole point.

Here are 5 skills I hand-picked for you.

In this post, you’ll learn:

• Why your ICP should refine itself against real win/loss data, not sit on a slide from last year’s offsite - from Ido Goldberg (Co-Founder & CPO, Swan AI)

• The R.E.P.L.Y. framework behind cold email that earns replies instead of just opens - my own system, finally turned into something your AI can run

• What actually separates a good sales call from a wasted one, from Kevin “KD” Dorsey (CRO, LeanScaper)

• Why your data warehouse should be talking to your GTM tools, not just sitting there, from Austin Hay (Operating Partner, Khosla Ventures)

• Bonus: How to prove what your AI agent delivered before the renewal conversation gets awkward, from Manny Medina (Co-Founder & CEO, Paid)

Let’s see my picks in action.

1. Define/Refine Who You Sell to

Author: Ido Goldberg, Co-Founder & CPO at Swan AI

Most ICPs I’ve seen are no more than a slide someone built to present once during a strategy offsite. After that’s done, they just sit there collecting dust and never get checked against what’s actually closing. Every quarter it stays wrong is a quarter of ad spend, outbound, and rep hours pointed at the wrong accounts.

Ido’s ICP skill fixes that by pulling your last 6 months of closed-won and closed-lost deals and holding the pattern up against what you say your ICP is - industry, company size, funding stage, geo.

The single most important output is the place where actual wins disagree with the stated ICP. Lead with that.

That’s the part that stood out to me. Not “here’s a fresh ICP” - a contradiction report, in the numbers that pay your reps’ quota. Where you’ve been telling the team to chase 1,000+ employee companies, but the wins concentrate under 200. Where losses cluster in an industry your ICP doesn’t even mention - which means your team’s been burning cycles on deals that were never going to close.

A few rules baked into how it works:

• Never fewer than 10 closed deals. Below that, it won’t refine - the signal is noise.

• Max 3-5 proposed changes per pass. More than that means you don’t need a refinement; you need to start over.

• Approve each change. Every new segment or persona edit gets shown back before it’s written anywhere.

• If the data says your ICP already holds, the correct output is “no meaningful drift.” Inventing changes to look productive is treated as a failure mode, not a feature.

2. Write Value-First Cold Email That Earns Replies

Author: Maja Voje, Founder at GTM Strategist (hi, it’s me 👋)

Full disclosure: this one’s mine, so take the enthusiasm with a grain of salt.

My Outreach Messaging skill is built on the same campaign structure that generated $1.5M in pipeline for one client. It’s based on one idea: give before you ask. Every cold email has to give the reader something real - an insight, a benchmark, an ungated resource - whether or not they ever write back.

The reply is what you earn after you’ve already been useful.

That’s the whole philosophy in one line. It’s built around a framework I call R.E.P.L.Y.:

• Relevance - a real why-now, not “hope you’re well”

• Empathy - their pain, in their words, not your product’s words

• Payoff - the value, delivered in the email, not promised on a call

• Low-friction ask - one question a phone can answer in one line, not a 30-minute booking

• You-focused - count the “you”s vs. the “we”s. If “we” wins, rewrite it.

The test I run on every draft: if they read it and did nothing else, was it still worth 20 seconds of their time? If deleting your email costs them nothing, you sent a pitch, not value - and pitches are exactly what’s flooding every inbox right now, which is why they don’t convert.

3. Build Behavior-Based Scorecards for Each Conversation Type

Author: Kevin “KD” Dorsey, CRO at LeanScaper

Ask five managers what a good discovery call sounds like and you’ll get five different answers - and a rep who has no idea which one they’re being graded against this week.

KD’s Call Scorecards skill turns “what good looks like” into 10-15 observable, gradeable behaviors for any call type. This applies to prospecting, discovery, demo, pricing, close, and more. Every score needs a moment in the transcript that earned it.

Weak programs grade on vibes (”felt good energy”), roll out a 40-item card nobody uses, or score calls without ever practicing the misses.

That single rule is why this works where most scorecards don’t. “Good energy” doesn’t localize a rep’s problem. “Never found out what matters to the decision-maker - scored 1” does, and it tells the rep exactly what to go fix before their next call instead of after they’ve already lost the deal.

The mechanics of the skill are pretty simple:

• Build small first. 10-15 behaviors, pulled from what your actual top reps do - not a 40-item card built once and never touched.

• Score on volume. SDRs running 2-3 calls a day, AEs running 7-8 demos a month - score every one, not a sample once a quarter.

• Chunk the practice. Don’t roleplay the whole call to fix one weak section. Drill the exact chunk a rep scores lowest on until they leave the session on a 5.

• Track the trend. A 90-day line that shows the team improving chunk by chunk - not a scorecard nobody looks at after the kickoff deck.

4. Turn a Scored Audience Into GTM Motion

Author: Austin Hay, Operating Partner at Khosla Ventures

Let’s assume you have a segmented, scored, and refined audience sitting in Snowflake. The ICP is refined, and a high-intent list is ready to be put to work. Yet, until it leaves the warehouse and lands in front of a rep, an ad platform, or a sequencing tool, it’s no more than endless rows of data.

Austin’s Reverse ETL activation skill puts all that data to work. It pulls accounts, contacts, and computed traits from your warehouse into your CRM, ad platforms, or sequencing tools where magic actually happens.

The failure mode this skill exists to prevent: a naive “sync the table” job that writes half-matched rows, re-writes unchanged records, blows past API limits, and pushes suppressed or non-consented contacts into a sequence.

It’s exactly where most AI-powered GTM stacks break. And this risk gets bigger as the rest of your stack automates. The faster you score and segment, the more damage one sloppy sync does downstream.

The guardrails that keep that from happening:

• It checks before it writes. Rows below a 90% match-confidence floor get flagged for review. No confident match - no new record.

• It only moves what changed. Untouched data doesn’t get synced for no reason (protecting your API budget).

• It respects opt-outs. Anyone who unsubscribed stays out, every time, automatically.

• It has a panic button. Try to delete too much at once, and it stops itself and flags you instead.

• It shows you the plan first. You see exactly what’s about to change before it actually changes.

5. Turn Agent Activity Into a Value Receipt

Author: Manny Medina, Founder & CEO at Paid.ai

If you find the previous four skills in this post useful and decide to adopt them, there’s one thing to keep in mind. Someone’s eventually going to ask what all of it actually bought you. And “the AI did a lot of stuff” is not an answer that survives a budget review.

Manny faced this problem himself, from the other side of the table - because AI agents are, in his words, cognitively invisible to the people paying for them. No one walks past an agent’s desk and notices it’s been crushing it. If nobody’s making the value explicit, somebody’s quietly deciding it’s not worth the line item.

Agents are cognitively invisible to the people paying for them, so their value has to be made explicit - continuously, not just at renewal.

His ROI proof generator was written for AI vendors proving value to customers - but the logic runs both directions. The same receipt format works whether you’re the one selling the agent or the one who just approved the budget for it internally. Either way, someone’s going to ask the same question at renewal time.

The skill is strict about what actually counts as proof. Three kinds of metrics exist, and only one closes the argument:

• Usage reporting (tasks run, tokens burned) - an engineering metric, not a business case.

• Performance analytics (uptime, latency) - a reliability metric.

• ROI reporting (time saved, cost avoided, revenue generated) - the only one anyone signs off on.

The receipt itself is four numbers, always shown with the arithmetic behind them:

1. Tasks resolved - in plain, specific language (”340 accounts re-scored, 12 ICP contradictions surfaced”)

2. Hours returned - task volume × real human-equivalent time

3. Cost avoided - hours returned × the real loaded rate

4. ROI multiple - value delivered ÷ what the tooling cost for the period

MUST separate customer-agreed benchmarks from assumptions, and label any assumption. NEVER fabricate a benchmark or a rate.

That single rule is what separates this from the AI-ROI slide everyone’s already tired of - the suspiciously round “10x productivity!” claim nobody can trace back to a real number. If you can’t re-derive the multiple yourself, it doesn’t belong in the deck.

GTM Skills is free, open-source, and ungated - built by the operators named next to their own work, not a vendor with a roadmap to sell you. Worth going through the full library, not just the four I picked.

Which one are you testing first?

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

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Dear GTM Strategist,

We talk endlessly about AI in sales, but most of it is people theorizing about what teams should be doing. I wanted the opposite: someone selling at a fast-growing AI-native company to show me exactly what sits on their screen every day. So I asked Alex Lindahl, who was one of the 1st GTM Engineers at Clay, to share how the team uses AI in their day-to-day.

Alex is one of my favorite people to learn from on this topic. He has been on the founding GTM team of eight Series A startups, four of which became unicorns, and he spends his days building the systems the rest of us read about. We first teamed up on “Is GTM Engineering the way to scalable growth?” back in 2024, and this year we published the 2026 State of GTM Engineering report together - the first real benchmark on how this role works in practice. He writes the GTM Engineering Newsletter and also just launched a YouTube show on AI in GTM.

Alex walks you through Clay’s full AI sales stack, the exact tools their reps reach for, plus 10 concrete ways they put AI to work. Some of my highlights include:

• generating on-brand decks from a single prompt,

• spinning up custom apps for each customer,

• clearing the Salesforce chores nobody wants to touch.

It is a rare look behind the curtain at a company most of us are trying to learn from. Copy the whole stack or steal one workflow. Over to you, Alex.

Day in the life

The alarm goes off at 6:47 am. Marcus doesn’t hit snooze. He’s a Clay sales rep, and he’s been in this game long enough to know that by the time he gets to his desk, the day is already in motion.

8:30 am: Before the First Sip of Coffee

There’s a Slack notification waiting. It lands every Monday at 8:30 am without fail. The message is from a Claude routine his team built in-house. Over the weekend, new users signed up at three of his target accounts. The routine found them, charted the signup velocity over time, enriched their profiles, and drafted the outreach hooks. Marcus reads the hooks. One of them is actually sharp. He makes a small edit, approves it, and keeps moving. This used to take him an hour. Now it takes four minutes.

9:15 am: The First Real Conversation of the Day

Marcus has a discovery call with the Chief Revenue Officer at a mid-market SaaS company. Before the call, Marcus fires up his Claude Skill that connects to Clay’s MCP. Marcus scans it on his walk from the kitchen to his desk. He already knows the company’s GTM tech stack, the VP’s last three public appearances, and where they’re likely bumping into friction. The call itself is easy, because Marcus isn’t winging it.

10:45 am: Building Something Bespoke

He’s in a POC with a fintech company. Their brand is dark navy, gold trim, serious, so they’re not going to be impressed by a generic slide deck. Marcus opens Lovable, pulls the call transcript from Granola’s MCP, and builds a mini app - their branding, their data from the test, their numbers. 20 minutes. No design team, no back-and-forth on colors. He sends the link before the VP’s next meeting starts.

11:30 am: The Legal Fog Lifts

The deal is moving, and then legal gets involved. The MSA redline comes in. Marcus would normally wait two days. Instead, his team routes it through Crosby - AI reviews the document first and flags what’s important, so the lawyer sees only what actually matters. Legal gets back in four hours instead of two days. The deal keeps moving.

12:20 pm: Lunch and a Question He Couldn’t Answer Yesterday

A CISO asked about Clay’s security documentation. Marcus types the question into Slack and tags Dust, which is connected to Notion, Google Drive, and Salesforce. The answer comes back in the thread - sourced, specific, ready to forward. He adds a line of context and sends it before finishing lunch.

This newsletter is sponsored by 1mind.

While mainstream B2B is still debating whether AI-native GTM is a real thing, Kyle Norton went and grew Owner from roughly $2M ARR to nearly $100M ARR in under four years by breaking almost every rule in the traditional CRO playbook.

45 days in, he cut half the sales team. He hired a VP-level RevOps leader when everyone said it was far too early. He audited his own hiring and found the polished case-study interviews were inversely correlated with who actually closed. And while every vendor was raving about “AI SDRs,” he called most of it a race to the bottom.

No wonder he’s one of the first names that comes up when people ask who’s actually getting results with AI-native GTM.

On July 29th, he sits down with 1mind’s Jonathan Kvarfordt for the 1mind Executive Playbook Series. He’s covering the roster he hires for in 2026 (two of the roles didn’t exist three years ago), the org formations that win in the AI era, why he manages throughput instead of activity, and the anti-patterns that are stalling “AI-forward” teams.

Live Q&A at the end, and there may be some skill md files and other goodies for the people who show up. Recording goes out afterward, but the questions are only answered once.

If you run a sales org and your forecast still lies to you - block the hour.

Register here

2:00 pm: The Slide Deck Problem, Solved

A QBR prep for a global account. Clay’s design team built their full style guide into Claude Design. Marcus prompts it and the slides come back - on-brand, structured, with the workflow baked in. He adjusts one section. The rest is ready to present.

3:15 pm: A Call That Matters

Marcus gets on a call with a founder. He does his best work here: present, asking real questions, listening the way you only can when you’re not thinking about what you have to do next. When it’s done, the call (routed to Gong) is scored on the 3Ts (Tailor, Teach, Take control). He reads the feedback and files it away. A second notification: recommended Salesforce updates based on the call - deal stage, next steps, contact role, company notes, all populated. Marcus hits approve with no need to enter Salesforce.

4:30 pm: An Event That Would Have Been Missed

There’s a Clay customer dinner next month in Chicago. Marcus covers a territory with a dozen companies headquartered there. He doesn’t have to figure out who to invite - Clay pulled the right executives (VP level and above, active) and the list surfaced in Salesforce. Marcus reviewed the names, approved the outreach, and added a personal note to two of them. The rest were handled.

5:45 pm: What Actually Changed

Marcus closes his laptop and thinks about what his day looked like two years ago. He was an AE and an SE. Simultaneously, he built demos, did research, and managed all of the sales admin overhead. The actual relationship work, the part only a human can do, got squeezed in around the edges. Now the overhead is handled with his judgment in the loop. The AI doesn’t close deals. Marcus closes deals. But the AI gives him almost a full day back each week to focus more time on moving deals forward.

Bonus use case from Clay’s top rep, Solange Levy

GTM Engineers on the team are system thinkers and tinkerers. One of my new favorite use cases comes from Solange Levy, who is Clay’s top rep. Her background is not what you’d expect either. She came into Clay from the private equity world with no sales background.

She built workflow to solve a problem every seller knows: deal context is scattered across Slack DMs, customer channels, texts, email, calls, and the CRM. After a full day of calls, reconstructing what happened and deciding what to do next meant piecing it all back together by hand.

Solange is using Codex to aggregate and keep all context for every deal in one place. Everything from internal and customer conversations, CRM data, web research, and custom signals built in Clay is aggregated, analyzed, and then turned into a daily action plan.

This is how it works. Each account gets a persistent workspace and a dedicated subagent. The subagent reviews primary sources, keeps the account’s deal folder current, and contributes to a daily refresh. A coordinating agent then aggregates every account update and surfaces the single most actionable next step to move each deal forward. She can also work directly inside any account to prep for a call, map the buying committee, build a tailored asset, or plan how to multithread. There’s no need to start from scratch.

Big to-dos are easy to remember. But it’s the deals that are usually won through smaller, well-timed actions. For example, sharing the right asset, answering a lingering question, giving a prospect a reason to re-engage. The daily summary keeps momentum across every deal, which is especially valuable when an account is still in an education phase rather than active buying.

Reps at other enterprises can replicate the system because the operating model is transferable: a persistent, consistently organized workspace for every account, agents grounded in primary sources with the supporting evidence preserved, context refreshed on a useful cadence, and a coordinating agent that turns updates into priorities. The two principles that you want to keep in mind are that you should separate recommendation from execution (AI can nail the strategy, but sellers need the underlying evidence to verify before acting). The next step is to make this multiplayer so AEs, BDRs, SEs, and sales leaders all work from the same shared context and institutional memory.

The stack behind the workday

Clay - Data enrichment, workflows, orchestration, agents, context layer.

Claude - Custom skills that connect to Clay’s MCP, Granola, and others

Claude Design - Collateral and slide deck generation with our built-in design system

OpenAI Codex - Operating system for building a personal context layer for deal execution

Granola - Call capture and generated summaries.

Lovable - Vibe-coded customer ROI, POC, and other bespoke apps.

Crosby - AI legal with counsel in the loop.

Dust - On-demand knowledge base, plugged into everything for rapid answers on account, product, company knowledge.

Superhuman - Faster inbox management.

Slack - Where all of it surfaces: approvals, briefs, scoring, updates.

None of these tools replaced Marcus. They just gave him his day back.

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is sponsored by Cello - referrals on autopilot.

I recently wrote about why CAC payback for public SaaS has hit 4.7 years - and which 5 channels are actually holding up under that pressure. User referrals and affiliate programs ranked highest, because you only pay when revenue shows up + referred customers tend to stick around longer.

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Dear GTM Strategist!

Last year, one of the pieces you told me you loved most was Emilia Korczynska’s “Lean ABM” playbook - the one where she showed, with real numbers and screenshots, how Userpilot built $900,000 in pipeline in 135 days without spending a cent on pricey ABM platforms. No fluff, no “$400M pipeline from seven emails” nonsense. Just LinkedIn ads, a scrappy toolstack, and a lot of discipline.

Well, she’s back, and the game has moved.

Since then, Emilia scaled that same motion to 5x the ad budget - and pulled it off with a leaner team than before, three people instead of seven. Her edge this time is a set of Claude skills she built to handle the ABM grunt work that used to eat entire afternoons: the reporting, the auditing, the “wait, is our messaging mix still on-strategy” math that nobody enjoys doing by hand every week.

When she offered to write the follow-up, I said yes before she finished the sentence. This is exactly the kind of thing I want us all wrestling with right now - how AI changes the actual day-to-day work, in the “here’s the repo, here’s what it does” sense, not the theoretical one.

Here’s what you’ll walk away with:

• The full ABM journey mapped as five stages - strategy, design, launch, audit, and report - so you can see where the effort actually goes.

• Four Claude skills you can copy, each owning one job: sizing realistic goals against your budget, executing the campaign, auditing live ads, and reporting on pipeline.

• A budget-sizing model that tells you whether your revenue goal is even possible with the money and numbers you have today.

• A system to keep campaigns on-strategy as you swap ads in and out, so your messaging mix doesn’t drift off-plan.

• The receipts: how this approach helped drive roughly $10 in pipeline for every $1 spent.

Emilia was also one of the first ones who believed in GTM Strategist, so it’s my personal pleasure to host her. I’ll let Emilia take it from here.

When I started our first ABM campaign (on LinkedIn) at Userpilot exactly two years ago - it was very successful and brought in $600,000 in pipeline in the first three months (I documented the whole journey in the Growth Unhinged article with Kyle Poyar) - and was already at $900,000 the following month (and was featured in this edition of GTM Strategist!). It sounded like a dream come true - over $10 in pipeline from every $ spent on LinkedIn - but the amount of work required to pull it all together was absolutely brutal, the revops were like eating glass, and we made strategic mistakes that probably cost us 50% of our ad spend.

We had a pretty big team working on the ABM campaigns back then as well: two full-time performance marketers managing LinkedIn ads, a GTM engineer, two graphic designers creating the ads and landing pages, myself, Director of Demand Gen, and Demand Gen Content Manager. Now - we’re spending 5 x more budget on LinkedIn ads than when we started (a six figure ad budget!) but we only have 3 people working on the ABM campaigns (1 performance manager, one content manager, and one graphic designer!)

What’s changed?

Claude Code entered the scene.

I’m far away from these click-baity posts saying “I replaced my entire marketing team with Claude Code.” But for various reasons, we had some big changes in our team over the last year or so - and when some team members left it simply turned out that we managed to make our processes so much more efficient with Claude Code agents and Cowork skills - that we didn’t actually need to replace them with new hires anymore.

Here’s what we’re automating now - and I’ve bundled some of the workflows as Claude Code/Cowork skills and plugins you can easily install with one command or by uploading a zip folder from this GitHub repo.

P.S. Fun fact - I actually haven’t used Claude Code to write this post - I still like to do some things *old school* - so if you spot an odd typo - that’s on me 😅

ABM Strategy Skill - are your revenue goals realistic with your current budget & numbers?

Before we launched our first campaign - I did a couple of months of research, tried different tools, and talked to a few LinkedIn ads agencies for about 2 months. There wasn’t really one “cut-and-dry” recipe for running an ABM program using LinkedIn ads (which is why I tried to document everything I did in this guide) nor a dedicated tool for this use case (which is why my husband built me a custom solution, which later evolved into ZenABM, and is now living a life of its own 😅 - P.S. I’m using it as the “ACME Inc” company in most of the skill outputs here because I can’t share any of Userpilot’s sensitive pipeline/deal data.)

So we made a lot of strategic mistakes:

• Splitting our audience into tiny segments that were too small and that cost us way too much to serve ads to;

• Creating too elaborate “ABM Stages” to move companies down the different “funnel stages” that didn’t reflect the real buyer’s journey - and were often too small to serve ads to, so companies were getting stuck in limbo instead of getting served more “Bottom of the funnel” ads.

• Spreading our budget too thinly across too many campaigns - running many campaigns with very small budgets to these granular, fragmented audiences meant we were severely underfunding every single campaign, losing most auctions and having really poor audience penetration

• Putting too many ads in the ad sets with a small budget - again - spreading our budget so thin that some of the ads we spent so much time creating weren’t serving at all - and the rest could get so few clicks per day that it took a very long time to actually deliver the campaign to enough people to learn whether it resonated with the audience or not.

At over $20,000 in ad spend per month and pretty good creatives - a lot of these problems could be masked for a good amount of time - but they were affecting us adversely and I bet we could have achieved even better results if we knew how to use the LinkedIn ad performance and conversion metrics to estimate a realistic budget and number of ads needed to achieve our revenue goals.

You may say it’s a “skill issue” - but since we launched, I talked to a few dozen other Demand Gen teams and a lack of clear strategy is a common problem.

So this is exactly where the ABM Strategy Claude Skill comes into play - it asks you about your campaign revenue goals, average deal size, your planned campaign budget, your website conversion rate to demo/trial and your qualification and close rates - and then fetches your LinkedIn ad metrics (free via ZenABM’s MCP server) like the average CPM, cost per click to landing page and the eCTR to calculate the realistic number of clicks you need to deliver to your website + how much they would cost you - and then calculates how many accounts and members you need to reach to realistically get this number, how many ads you can afford to run at a time (to avoid spreading your budget too thin) and how long it will take you to reach your goal with your budget!

It then proposes the LinkedIn Campaign structure (how many ads you can run) and which ad formats you should focus on based on your previous results:

Executing your ABM Campaign Strategy

OK, now the fun part - you know:

• how many ads you should create (and in what format),

• how much of your budget you should allocate to each one,

• what angle/job-to-be-done you should use for each based on your target audience.

Now it’s time to roll up your sleeves and actually create the ads.

This used to take us a long time (think weekly cycles) to do when we were doing it completely manually. Luckily I came up with this Notion Database because at our scale (at one point we were running 17 different campaigns at a time - and we’ve launched & tested 2000+ ads in the last 2 years!) we wouldn’t be able to do project management without it, keep track the production status of the ads (with database properties), or make sure the designer executes the right brief, and the performance manager uploads the right image and ad copy to the right campaign:

We initially used to brainstorm the ads & fill the whole database completely manually.

Then - I’ve built another Claude Skill that actually executes the campaign plan. It creates the whole campaign outline based on the strategy, ad briefs - and even the ad mockups (I would still advise working with a real, human designer for polishing those 😁). Again, you can download it from this repo or directly here, and install it in your Claude Cowork or Code.

Here’s what the Campaign Outline looks like (an example done for my husband’s company):

And here’s one of the briefs it actually generated for this campaign:

Auditing Your Campaigns and Reporting on Performance

Last but not least - ABM campaigns are never set-it-and-forget-it.

You need to monitor your campaign and ad performance at least on a weekly basis - and turn off underperforming ads (for me - typically ones with > 1000 impressions and < 0.4% eCTR).

But there’s a catch here - while turning the underperforming ads off, you need to remember to replace them with new ones - and to keep the campaign on track - keep the number of ads and the right ratio of messaging in accordance with your strategy.

That is - if in your strategy you planned to have eight ads, two of which were supposed to be about analytics, three about onboarding, and three more about MCP server, AI agents and in-app surveys - you need to make sure that ratio holds every time you swap ads in and out.

And then - are all your ads hitting the benchmark for their ad format? Are some decaying?

Which companies have engaged the most last week and are ready for sales outreach?

Which ad formats performed the best? Where did we spend the most of our budget? Did we allocate it to the most efficient ad formats?

I’ve created a Routine in Claude Code for myself to audit our account on a weekly basis and send me a report + a Slack update (especially useful given I have a weekly executive meeting where I need to report on all our results on a Monday). It also includes other sources (e.g. Google Ads, GA4 and Search Console - that require various API connections and service accounts), so it’s outside the scope of this article - but I’ve created two Claude skills included in this repo that will guide you further.

ABM Audit

/linkedin-abm-audit a diagnostic skill that evaluates your ABM Campaign(s) performance in the last 30 days.

The skill looks at the last 30 days versus the previous 30 days in your LinkedIn Campaign Manager and CRM (via the MCP I mentioned earlier) and hunts for problems.

What it does:

• It pulls spend, impressions, clicks, landing-page clicks and engagements, builds a scorecard (spend, ads live, CPC, CPM, CTR and effective cost per landing-page click), and runs an “ad-count model” from the strategy skill that compares how many click-driving ads you’re running against how many your budget can actually support.

• It classifies every ad by its ad format and compares each format’s performance against ZenABM’s 2026 B2B SaaS benchmarks (grading link formats on effective CPC-to-landing-page, not raw CPC)

• It tells you which formats influence most of your pipeline and ABM deals, watches for decaying ads and impression-hogging accounts, and outputs prioritized red/green flags plus a concrete, ordered fix list - all wrapped in a branded “[Company] LinkedIn Ads & ABM Audit” HTML and PDF. Its whole reason to exist is to tell you what’s broken and exactly what to change.

ABM Report

/linkedin-abm-report is the reporting counterpart - it uses the same data sources, benchmarks and math, but sends you a monthly recap for a stakeholder/exec audience rather than a to-do list.

It covers the last full calendar month compared to the month before, and summarizes what happened:

• total spend

• pipeline and deals influenced

• your best campaigns, formats and individual ads,

• the companies engaging with your ads most,

• How your different ad sets (by format) performed against the benchmarks

• month-over-month changes + a short set of recommendations and next steps.

Conclusion

The honest takeaway isn’t that AI replaced our demand-gen team - but it made us a lot more efficient and data-driven.

Running ABM on LinkedIn has always been about juggling many hard jobs together:

• getting the strategy right

• executing dozens of ads without losing the plot

• and auditing relentlessly so you kill what’s decaying and double down on what’s working.

Two years ago, each of those jobs took several people and days to get done.

What changed is that most of that work is now structured enough to hand to a Claude skill - the math, the briefs, the benchmarks, the weekly fix list - so a much leaner team can run a bigger budget without the strategy falling apart.

So if you’re staring down your own ABM program - whether you’re about to spend your first $5k or you’re already past six figures - start with the strategy skill to stress-test your revenue goals, use execution to get the ads built, and let the audit and report keep you accountable every month. Grab them from the repo (one command, or upload the zip into Claude Cowork or Code), point them at your own numbers, and skip the half-a-budget’s worth of mistakes I had to make first.

Get LinkedIn ABM skills

Maja here. Thanks, Emilia, for these insights and skills.

The ABM process has really changed a lot with AI and new tools - so here’s the summary of what we have learned today:

Before you go - my friend Ben Williams just published a report on the State of AI Search for Dev Tools. His team tested how five AI platforms (ChatGPT, Perplexity, Gemini, Google AI Mode, and xAI) answer real buying questions across 43 categories, then traced 126,814 citations to see which sources actually shape the answers.

It’s wild to see how each platform picks sources differently, so AEO is definitely not an easy game. We have covered the best AEO strategies in this Substack before, and we’re far from finished.

Let’s go to market!

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

Next Friday (July 17), I’m going head-to-head with my very good friend (and future nemesis) Jairo Guerrero, co-founder of Organic Hackers - scarily good at technical SEO and at digging up the hidden keywords worth ranking for.

My weapons: MCPs, a Claude custom project, AEO, and the SEO workflows we built for B2B content. His: everything technical I can’t touch. Someone has to win, so come stand in my corner and help me whip votes - and either way, you leave with a fresh stack of ideas for getting more out of your content.

Join us live

🎁 Bonuses: Jairo will share his client template Google Sheet for analyzing content gaps with everybody who attends, and I’ll update my AEO/SEO skill process on GitHub and share the link at the webinar ✌️

Dear GTM Strategist,

Design has always been my passion.

I’m kidding.

I have zero theoretical design knowledge, but I had to learn it on the fly. For two years, I wasted my weekends drawing shapes in Miro every Saturday and praying a human could decode whatever was in my head.

And yet design eventually became one of my biggest moats. English is my second language, so copywriting was never going to be the terrain where I could win globally within reasonable effort.

Design could be it. It carried a huge part of how GTM Strategist is positioned today, and it did more heavy lifting for our growth than I ever expected when I started.

So this is the piece where my husband and business partner Anže Voje and I open up our AI-assisted design process - the good, the bad, and the parts we still refuse to hand to a robot.

Big spoiler: I don’t think AI can do this job for us just yet. We still work with designers, on purpose, because branding is a moat and sometimes you need that final sparkle ✨, the 10% that makes it special.

My brother runs a men’s t-shirt ecom store doing serious volume, and he happily promotes it with AI-generated cartoon videos. For him, it works beautifully - because those creatives are meant to be broken - I mean, tested. If he has some winners from the batch, it is a win that pays huge bucks. It is almost a system in which we accidentally stumble into a winning creative you’d never have designed on purpose. Volume is the strategy.

Our visuals are the opposite. They’re built to (hopefully) last; we have a couple of shots a day to make or break it on LinkedIn or in a newsletter, and they are done with the intention of becoming learning assets people save and come back to.

That kind of work needs thoughtfulness, not thousands of iterations to test.

I would die of old age if I waited for legit test results from the velocity of publishing 1x a day on LinkedIn.

Some designs are made to be broken. Ours are made to last. That single distinction decides where AI belongs in your process - and where it doesn’t.

Our agenda for today:

• Why brand is still a moat in 2026, and how the “made to last vs. made to be broken” line tells you exactly where AI fits

• Our hybrid AI-human design system (big infographic), tool by tool, from idea to final file

• Anže’s head-to-head test of six AI design tools on the exact same prompt. Do you wonder which one is the winner?

Let’s get into it.

Brand as a moat kind of just happened

When I published my book, I made a decision that looks obvious in hindsight: I was going to compete on brand and design. Everything in our industry was so boring - I wanted to make it more fun and memorable. If everyone is designing as if they were still making a management consulting presentation full-time, I just wanted to stand out by investing more in the brand and bringing it to life. Oftentimes the brand 1.0 is just an extension of the founder’s personality and I don’t think that is bad because you personally have to stand by every design choice. And it attracts people - people who share your values, joie de vivre, passions and fears.

The design process actually started with my cousin having a summer internship with us - a junior industrial and material design student, extremely creative and tech-savvy. I believed in him so much - but we were all just figuring out how to do this (and had a bunch of fun doing it). Before you roll your eyes: I did not hire him because he’s my cousin; we hire for attitude. We were genuinely a good combo.

Then something very predictable happened. The work evolved from print into LinkedIn graphics, and I learned the hard way: almost no brand designer enjoys tweaking social media posts. It is hard to find people who find joy in nudging a headline 4 pixels to the left on a carousel for the hundredth time. So when my cousin eventually graduated and moved on to pursue his true passion in life (industrial design), the visual heavy lifting landed where it usually lands - on Anže and me 🙈

We don’t see our visuals as pretty decoration or funny memes. They’re educational design. They’re meant to teach something and be worth saving. That’s why I stayed so hands-on with the structure and the microcopy, even though I was constantly feeling guilty about how much time and energy it consumed.

At one point, design was almost a part-time job on top of everything else. And more often than not, I still wasn’t fully happy with the output (damn perfectionist), because I couldn’t perfectly express in a briefing what I could see in my head. I felt so frustrated and trapped.

Then came AI … and I was overjoyed when it became good at design - sort of.

In the beginning, I genuinely hoped I could DIY designs with the help of AI - that I’d sit there chatting with my AIs and hoping for a miracle. Then I saw the actual inputs and the reliability, and reality hit. For video, honestly, AI is already better than what most of us would produce by hand. For infographics - see the third section 🙈

(If you think we are there and I’m just testing the wrong tools, tell me in the comments what I should be trying. Maybe you know something that I don’t. )

Inside Our Hybrid: Humans + AI Design System

Our system explained in one breath: AI research of best practices + our own winning patterns + a human editor and designer + a learning loop that feeds our top-performing designs back into the process so the next round starts smarter.

The inputs that feed it are:

1. Our brand book. We run two winning formats: a dark system for the more technical content, and our good old purple with yellow highlights. Fun fact on the yellow: I found it while buying a car, when a salesman highlighted his phone number on a business card with a yellow marker. Eureka. I’m pretty sure people have copied that combo since, but I won’t take credit - the car salesman invented it, not me.

2. Best external practices. We manually save LinkedIn posts that use shapes and formats that clearly perform well, and we use AI to spot the winning patterns we couldn’t catch by hand. Colleagues also just send us good visuals, which - do that, it’s the cheapest R&D there is.

3. Our stored role-model graphics. This has been a massive upgrade in the last few months. We never start a brief from scratch. Every brief now carries an input, a couple of role-model graphics that have worked before, then the mockup, then the video instruction, then the back-and-forth. (You can get access to the full archive of my graphics by purchasing my GTM Checklist.)

4. Proprietary models - new ideas that I still hand-draw on paper or Miro. This is the most time-consuming part and the intellectual moat. Before I hand the brief over to design, I need to make sure the content holds up.

I uploaded my brand assets to Brandfetch, which makes collaboration super easy.

Now the tooling, which is where people always ask me what I actually use:

I brief in Miro. I’m not great in Canva/Figma - I’m much better in Miro, so that’s where the thinking starts. Then I do mockups in Claude, often in Claude Code, and prompt it further to get to a version one. Claude Code is amazing for design mockups, and here’s the unglamorous reason why: it builds on your previous outputs instead of starting from scratch, so it wastes far fewer tokens and is much faster than doing this in a chat.

For example, if I want Claude in chat to update the mockup it delivered, it will start drafting again. Meanwhile, Claude Code will just change a few lines of code in the previous output.

Claude Design is also a useful feature that works similarly to Claude Code and is purpose-made for design work. But it’s mostly focused on web design and slides and is less suitable for complex infographics; that’s why it’s not the focus of this newsletter.

When the mockup is ready, I record a video interpretation of the brief for the designer; the designer comes back with a v1, and we elaborate together over Slack and Miro until the graphic lands where it should. We use Notion to keep track of the process and the editorial calendar.

Is it perfect? Of course not. It’s still pretty heavy on labor. But the split is clear, and it’s held up for us:

AI structures the information and roughs out the mockup. The final 10% - the part that makes it unmistakably ours - is still humans, in a room, bouncing ideas and resolving creative frustration together. That friction is what makes it special. At least for now.

I want to be fair here, because someone will bring it up if I don’t. Our colleague Alex Lindahl, GTM engineer at Clay, produces basically all his visuals with zero design cost and zero designers, using Claude skills exclusively. He described exactly how here. So it can be done, and done well. I’m just not ready to give up the intimacy of the process yet - I’d have a bit of an integrity problem publishing fully AI-generated designs today. Ask me again in six months.

The Head-to-Head: Six Tools, One Brief

Every time the models leapfrog each other, I get twitchy and ask Anže the same question: “Should we switch to [new hotness] for our design mockups now?” Anže, being the calm and calculated one, doesn’t answer on vibes and FOMO. He runs the test.

So we pressure-test our system periodically for one reason: to stay honest and make sure we’re not clinging to something the market has already moved past.

Here’s how we kept it fair. Same design brief, given to each tool.

The six tools Anže put head-to-head: we first started with the most common ones: ChatGPT, Gemini, Claude (Chat), and Claude Code.

Then we added two wildcards: Mistral Vibe (European AI hope) and Z ai (Chinese rival that recently topped some benchmarks).

Here’s the prompt we used:

Draft me the mockup for a visual that will illustrate this LinkedIn post:
5 modes of LinkedIn for GTM, from a sharp profile to a full system. Pick the one that fits your goals and bandwidth right now:
Mode 1 - 𝐈𝐧𝐝𝐞𝐱𝐞𝐝 (The Foundation)
 → A profile that does its job the second someone Googles you or asks ChatGPT/Claude. Findable, credible, sharp. That's all you need if you're not running an inbound motion.
Mode 2 - 𝐎𝐩𝐢𝐧𝐢𝐨𝐧 𝐋𝐞𝐚𝐝𝐞𝐫 (The Engine)
 → A real POV plus the discipline to show up with it consistently. That builds trust and preference at scale - even if you only comment. Ideally, you do both.
Mode 3 - 𝐆𝐓𝐌 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 (The Converter)
 → Skip the content purgatory. Focus on signals - what your ICP complains about, which events they attend, how they change jobs. Build a signal system and turn it into outreach that actually lands.
Mode 4 - 𝐇𝐨𝐮𝐬𝐞 𝐨𝐟 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥 𝐁𝐫𝐚𝐧𝐝𝐬 (The Multiplier)
 → Several profiles amplify one core story. Team lift, huge dividends - but a long game, and turning your best experts into "content creators" carries real opportunity cost. Worth it for many. Not for everyone.
Mode 5 - 𝐌𝐞𝐝𝐢𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 (The Insurance)
 → LinkedIn can be a pain in the arse the second the algo shifts or they go on another automation witch hunt. One flag and you could lose a valuable asset overnight. Hedge it - build on channels you actually own (email), or diversify across social. Stop depending on one platform.

I was shocked to get such a variety of outputs with a single prompt. 🙈 Mistral, what on Earth did you do?! Nano Banana, 🍌 in 2025 we called you superior …

ChatGPT is the undisputed heavyweight for visual generation right now, mainly because it isn’t just spitballing pixels. It blends the creative with solid contextual research and microcopy. The result was a sophisticated infographic that, while perhaps too dense for a quick scroll, is objectively impressive in its depth.

Claude Code (running Sonnet 5 as a fast and versatile model) had an unfair edge. Despite my specific nudge to keep the layout neutral, it reached into our brand context and guidelines anyway. It handed out a mockup that was already one step closer to the final design.

That’s why there was a striking difference with Claude in chat, starting from scratch (also running Sonnet 5). The chat version was disciplined, following the brief to the letter without any extra flair. It delivered a clean, basic outline that synthesized the core points nicely. Which is actually not bad, because the best educational visuals don’t drown in text.

Gemini and its Nano Banana image generation model might be flashy for pure art, but for infographics, it over-engineered the solution. It was the most “creative” output in the batch, but it strayed so far into its own imagination that it became nearly useless as a functional brief for our system.

Mistral Vibe was the letdown of the group. It just put the information into a basic diagram with some summaries and called it a day. No real spark there.

On the other hand, Z ai (running GLM-4.7) was a pleasant surprise. It was my first time using it, and it did a solid job of both distilling the knowledge and proposing a coherent layout and color scheme. Still, a word of caution here: Chinese AI chatbots have significant data privacy vulnerabilities, so I would avoid putting sensitive or personal information in them.

Both Vibe and Z used HTML for their designs, much like Claude Code. This is a massive workflow win: we can just hand that code to a designer, ensuring the copy stays intact and nothing gets lost in the process.

Verdict? For our regular work, we will stick with Claude Code, because it’s so well connected with our business context, skills, and workflows. But when we need additional creative ideas, ChatGPT is the way to go.

So, Should You Go Full AI?

Here’s where I’ve landed, and I hold it loosely because this space moves fast.

If your creatives are meant to be broken - high volume, disposable, find-the-winner - lean into AI hardcore. Your business probably depends on it because everyone is using it atm. My brother is right, and AI-generated content helps him experiment much faster at a marginal cost, while he still combines it with three other content pillars: static photos, user-generated content, and high production.

If your creatives are meant to last - brand assets, educational design, the stuff people save - keep humans in the loop for inception and at least that final 10% of design touch - often referred to as “taste”. Use AI where it’s genuinely great: research, structure, and early mockups.

And here’s the timely part: we’re actually hiring a designer. Our production velocity is too limited right now, and Anže and I have taken this system about as far as the two of us can. We’d love someone to elevate it further.

It’s a global, remote role, part-time or full-time - we left it open on purpose so we don’t scare off great people over hours. The one real requirement: proven design work and content work for LinkedIn, newsletters, and blog posts - the good old B2B marketing stuff. If you vibe with this philosophy, apply here.

If you want to level up your design with AI, I encourage you to spend a little of this summer structuring your mood boards, finding your winning patterns, and tweaking your design system to play in your favor. That’s the part AI can’t shortcut for you.

Let’s make LinkedIn a little less nebulous - fewer random patterns and soulless gradients, a bit more human spark ✨ - so we can actually learn from each other and grow together.

Till next time,

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

You’ll walk out with an action plan you can execute over the summer, so your September launch (or whenever you’re shipping) actually lands. If you run a team or work agency-side, this is the alignment unlock: a joint session to pressure-test the ideas you co-created and get them defined well enough to ship. We’ll go deep on prioritization and roadmapping - and yes, MCPs, but Rory’s there to stop me if I start to AI-geek-out too much. Plenty of Q&A too.

Save your spot

🎁 Freebie: GTM Strategy & Roadmap blueprint FREE template in Miro

Dear GTM Strategist,

This week, I was in Vienna working with a $10M ARR SaaS company that counts FIFA, Google, and Ferrari among its customers. They have 9,000 customers; they are now laser-focused on enterprise, and when I presented the following chart to the team and asked the founder whether he was active on LinkedIn, he looked at me as if someone told me that I needed to lose 10 lbs before summer.

And I get it. Founders have 99 problems, and LinkedIn content is just one of them.

But from all the “let’s do LinkedIn” attempts, I have consistently seen the best results when the commitment came from the top down. Every time a lone marketer tries to motivate their colleagues to post more by offering a chance to win an Amazon gift card, the effort is doomed before it starts - the enthusiasm dies even faster than the budget.

The real question is this: how do you benefit (positive ROI) from content production, aka demand generation, without making it your primary job, your weekends, or the chore in your day?

That is what this post is about. Not another “How I gained 100K followers.” It is a fair take on what works vs what you are willing to put in - and sometimes, that hasn’t have to be much.

LinkedIn is not all-in or nothing. It is a spectrum.

You don’t have to choose between screen-sucking on LinkedIn five hours a day and writing the whole thing off as a scam. To help you navigate, I created 5 modes for using LinkedIn for GTM demand gen. Pick the one that best matches your GTM goals and capacity.

This edition will be especially useful if you are trying to build a personal brand and generate leads on LinkedIn. In this post, we’ll unwrap:

• The one sanity check to run before you spend a single hour on LinkedIn

• The five modes of LinkedIn for GTM, from a polished profile to a media machine, each with what it is, who it’s for, and what it takes

And summer ☀️ is the perfect time to do just that.

But first, a sanity check

Should you even do it?

Just answer one question honestly: is your ICP actually on LinkedIn?

I get a lot of backlash here, especially from technical teams. “But our audience is on X.” “Everyone’s on Reddit now.” Maybe.

In my experience, LinkedIn is where the money is for B2B tech companies.

And 66% of participants in my State of B2B GTM survey said the same.

Please check by asking them - see where your ICP actually is active. Just because they are not posting on LinkedIn does not mean they are not there.

The second trap is more subtle, and fatal for the success of the operation.

People obsess over the input instead of the outcome.

They see an influencer they like posting 10 times a week and take that as a proven recipe for success on LinkedIn. Then they:

1. Pump up a bunch of low-quality AI-generated fluff that does not work, and conclude that LinkedIn does not work whatsoever;

2. Fall into a depression that they are not “good enough” because they cannot keep the pace and abandon the mission.

Setting objectives is a strong motivator and a mission-critical ingredient in measurable GTM motions, but you can reach 200K followers or 1 million impressions - and still have an empty pipeline.

The ONLY objective that most people really care about is:

“I want LinkedIn to attract _____ ICP-fit leads from LinkedIn.”

This is money. Followers and leads are at best leading metrics.

It surely helps to look credible on LinkedIn (bigger profiles, higher engagement) - but at best these are leading metrics.

So how will you get the right leads?

Sometimes all you need is a sharp profile and a well-thought-out outbound campaign.

Other times, it requires a full-fledged content engine with multiple LinkedIn profiles (people) on board.

Based on winning patterns in my client portfolio, I structured 5 LinkedIn modes to generate demand on LinkedIn. They all work, and you don’t have to do them all.

Mode 1: Indexed

What it is: A profile that does its job the moment someone Googles your name (or asks ChatGPT). Something good comes up, you look legit, and you are well positioned for the keywords you want to be found for. Even if you solely rely on outbound, ICPs will likely check out your LinkedIn profile.

Who it’s for: Everyone, but especially the C-level at smaller companies and anyone who will land in someone’s inbox as a seller. This is the absolute must for squeezing new business out of LinkedIn - consider it to be your landing page.

What it takes: Start with a profile makeover, and you can do this easily with Claude. Find a few profiles you admire, ask Claude to analyze them, then adapt the winning patterns to your own context and roles. While you are there, add a few industry-relevant keywords, because being properly indexed by the search engines helps more than people expect. People are drawn to brands and to numbers, and they decide in <5 seconds whether your profile is worth their attention. Treat it like a conversion optimization project.

How to make it shine: make the most of your LinkedIn visual inventory (background photo, featured section & images under job experience). A human design adds credibility, and I think that still counts for something. (Call me old-fashioned.) You can just start with some Canva/Figma templates.

Treat the “Featured” section like prime real estate: highlight a few of your best posts, or more importantly, the links you want to drive demand toward. Keep your profile open and make it stupidly easy to contact you.

Pro tip: Most of us are stuck with networks from our “previous lives” - our colleague buddies and people that we met at work events may not be the best ICP, so before you even start thinking about content creation, make sure you are adding new ICP relevant people to your network; otherwise, you’ll have a problem with relevant reach. LinkedIn’s algo got better at showing your content to people who are most likely to care about it in the feed, but it also shows it to newer and most engaged connections. Make sure you are adding ICP-relevant people to your network non-stop (more about that in mode 3).

Mode 2: The Opinion Leader

What it is: You have something smart to say, and you actually write comments or post. This is the jump from “findable” to “followed” - or even “respected”

Who it’s for: Whoever’s GTM efforts can benefit from trust and preference, and who can commit real, recurring time (minimum 30 minutes/day). With AI flooding every feed and outbound more saturated than ever, the ability to build genuine preference and trust with your target audience has become a superpower. Some people call it the new moat.

What it takes:

2a) Engaging with other profiles

For value commenting, you’ll first need to identify posts worth commenting on - the easiest thing to get started is just to say “I need to write 10 comments a day and go through my feed” - that will help you put in the reps. Later on, you can either tie your efforts to another LinkedIn profile (content creator, opinion leader, competitor, target client) or build a system to identify relevant topics (content) that you want to pitch in on.

You hit the “bell” at the chosen profiles to get notifications on LinkedIn, but since those are pretty broken, you may be better off putting together a spreadsheet of profiles you’ll comment on, even an agent that will serve you their posts as Slack notifications, for example. I avoid AI-generated commenting like the plague because it makes smart people look really dumb, but I am big on using Wispr Flow for commenting because it is much faster.

Once the profile is solid, you can already start doing a little confident inbound through value commenting. Pick a handful of target profiles - customers, relevant influencers, the people whose attention your ICP already pays - and either join the meaningful conversations happening there or get some DMs rolling. Keep a simple spreadsheet of people worth commenting on. You can reverse-engineer it from whoever is already active and commenting in your domain, or just ask your audience who is relevant to them. It is not exactly effortless to build, but it is very doable.

The one hard rule: do not over-automate this. Your comments should be at least semi-decent, because a generic “Great post!” actively makes you look worse. This whole mode, a legit profile plus disciplined commenting, is the absolute minimum to squeeze new business out of LinkedIn and look credible while your outbound is landing.

2b) Posting your own content

The heavy lifting is producing content yourself. You need content pillars, which in plain language, is what you want to be known for. That is usually a blend of your domain knowledge, some industry insight, and trends, and anything involving AI tends to perform especially well right now.

Underneath the pillars, you need your strategic narrative and positioning nailed down, meaning your genuinely unique points of view. You can pull these from an interview you did or do an interview with yourself (just talk to Claude or ChatGPT for some time and ask it to identify your content pillars). Some questions that will help you here are: “what are competitors getting wrong?” and “what is the biggest misconception in our space?” That is enough to produce a strong first batch.

I would not pre-produce a giant vault of content and call it done. Content is a fast back-and-forth game, which is why I stay close to the signals of what is working. I study the subjects and the creators crushing it in engagement and comments right now, then feed the patterns into my own ideas. Call it pattern recognition.

My usual cadence with teams is three to five posts a week, and I would not overthink the clock: LinkedIn posts have a longer lifespan than they used to, so if the content is good, the exact time of day barely moves the needle.

Here is roughly how a week looks for me in this mode:

• 2 to 4 hours of content creation

• Design time, where Claude is genuinely excellent for early mock-ups, then I hand over the brief to my designer

• 1 to 2 hours of commenting (which is where Wispr Flow saves my sanity). A healthy diet is around 10 value comments a day on your target accounts, plus replying to the ones you get.

The signals I trust most for whether a piece is landing are saves and embedded comments; they tell me more than likes ever do.

Then I close the loop: I check analytics, do more of what worked, and hold an 80/20 split - 80% proven patterns, mine or other people’s, and 20% reserved for experiments that might become the next thing that lands.

Mode 3: The GTM Engineer

What it is: You add a few basic agents and automations to the flow so LinkedIn activity turns into a steady stream of new audience and fresh sales opportunities. If “GTM engineering” makes you twitch, cross it out and just think LinkedIn outbound.

Who it’s for: Teams ready to operationalize what Modes 1 and 2 surface, and who want to act on intent instead of waiting around for inbound leads to happen.

What it takes: You operate on signals. A signal is not just someone changing roles in the last 90 days. The richer ones are behavioral: someone engaging with a post on a topic central to you, commenting “send me X” on a lead magnet, or interacting with a specific creator or post inside your orbit. The simplest possible move is to start adding those people to your network. The more advanced mode is to coordinate an entire decision-making unit through multiple touchpoints, which is just ABM, and we have covered that before. Start with whatever adds value at the lowest level of complication, and build from there.

Now an all-important disclaimer, because it matters and I would hate for you to learn it the hard way: automation tools are strictly forbidden by LinkedIn. You can get flagged, and LinkedIn is genuinely on a witch hunt for automations. It sometimes reacts to nothing more than the number of profiles you open in a day. Honestly, I know more people who have happily participated in engagement schemes than people who got blocked for automation, but the platform’s tolerance is unpredictable.

If you want to be completely safe, do it manually within sane limits, somewhere around 30 DMs a day and 100 connection requests a week.

But then actually do it, seriously and consistently, or hire a VA to help, because this is the step that converts content into pipeline. (Pure data-scraping tools aka Exa are a different category, where the responsibility tends to sit with the tool rather than with you, but know the rules before you lean on anything.)

Mode 4: House of Personal Brands

What it is: Instead of one profile carrying everything, you activate several that support each other and cover different areas. One strong personal brand is impactful. A coordinated house of them is where it gets genuinely fun.

Who it’s for: Companies where content already has top-down buy-in and several people are willing to be on the journey together. The dividends are huge, but so is the work. It is hard, it requires constant pushing, and the orchestration and design coordination are consuming, especially early on.

What it takes: Start by teaming up with a few colleagues who are on the same content journey as you. Give the group a core brand strategy, the key news, launches, and narratives you will all amplify, and then help each person develop their own pillars and their own genuine point of view. You are not turning anyone into a billboard. These profiles are still real personal networks, and your job as the coordinator is to make every participant look good and keep them excited to take part. That is what makes it a real win-win instead of a chore people quietly abandon.

Attio is one of the companies where I see multiple high-level team members present on LinkedIn, which is especially visible at nicely orchestrated posts around key launch events. I asked Brian Da Silva, Social & Creator Lead at Attio, how they manage that:

“We treat social as an ecosystem, with the brand account at the center and profiles like our founder, our leadership team, and specific launch contributors amplifying around it. Each profile gives a different perspective you’d never get from the brand account, and having that range of content go out together is a big part of our social approach. That’s given us real surface area on LinkedIn, especially for bigger launches. It’s been important to remove as much friction as we can in this process by creating specific guides for the team to deliver content for their channels.”

Two traps to avoid, and both are bombs:

First, never copy-paste links straight from LinkedIn to share internally. The URL carries tracking parameters, and circulating those can flag your content. Strip the tracking portion and share the clean link somewhere else entirely, like WhatsApp or Slack, anywhere your people actually coordinate.

Second, engagement pods. There is a whole world of invite-only “boost each other” schemes out there. If your real goal is to signal to the algorithm that your content is mainly interesting to other founders and ghostwriters running engagement bots, by all means go ahead. It is genuinely not relevant to me, and it is a landmine I would much rather walk around.

Mode 5: Media Machine

What it is: You stop depending on a single platform. I love LinkedIn and have nothing bad to say about the opportunities it has handed me, but it has quirks, the algorithm shifts, and every algo change irritates me a little. That is not a reason to pull back, but you can make sure it’s not the only thing holding up your pipeline.

Who it’s for: Anyone whose demand generation has quietly become reliant on LinkedIn alone. The deeper the dependency, the more urgent the fallback.

What it takes: A real second channel that you can measure. I learned this the hard way. The very first time I got temporarily restricted, I was mid-workshop with a VC from Dubai, people suddenly could not tag me, and my heart almost stopped. It took LinkedIn about 2 days to come back and essentially say, “it is fine, carry on.” I appreciate the platform, but I never want you reliant on one channel for demand.

My best diversification so far is Substack. I have a newsletter list, I promote some of my Substack work on LinkedIn, but LinkedIn is not the sole driver of new subscribers, and that is the point. One honest caveat on the “newsletter for companies” debate: for personal brands, Substack and Beehiiv work great, and plenty of people will tell you to use them for companies too. I think that mostly holds for personal brands. For a company, a good old newsletter really needs to integrate tightly with your CRM, and that is harder to pull off cleanly on Substack or Beehiiv. Not impossible, just harder. The principle that matters most: go wherever you can analytically measure the results and connect them back to your effort.

A few other channels, honestly assessed. A lot of creators I talk to are doubling down on X, even though reach there has gotten weird, and breaking through is hard but not impossible. YouTube is a common and powerful brand-builder, but the production effort is in a different league. And the old Gary Vaynerchuk gospel of “record once, repurpose everywhere” largely breaks in the kinds of fast-moving spaces we work in. When trends shift weekly, you should be harvesting fresh insights to stay valuable and top of mind, not endlessly recycling the same clip into ten formats.

Pick your mode and start

So what will it be?

Find the one that matches your goals and your bandwidth right now, commit to it properly, and stick with it by September. Give it a fair 1-2 months to validate.

Summer is honestly one of the best periods to get your skin in the game or level up your LinkedIn activity. Most of us will have more time and bandwidth to work on what is “important but not urgent”. And reach drops a little so you can train your content muscles before the fall rush hits.

Let me know in the comments which mode you are in, what is working for you, and where you are getting stuck.

And if you are doing something I haven’t covered here, I genuinely want to hear it.

Let’s close some deals on LinkedIn 🤝

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is supported by Swan AI.

For over a year, I’ve followed Swan’s mission to build the first autonomous business - $10M ARR per employee with AI agents. In year one, 3 founders landed 200+ customers across 5 continents and $1.5M in monthly pipeline, with zero employees or SDRs. This year they raised $6M to build the first shared context and workflow systems purpose-built for AI agents.

The architecture matters more than the agents.

They just revealed how they structure it: the truths that repeat across your GTM - ICP, scoring, voice, routing - live in one place, and every workflow points to them. So when your ICP shifts, you fix it once instead of editing it across fifty plays and tools.

Get the workflows

(free, non-gated)

Dear GTM Strategist!

I often get asked, “We need a GTM slide for our pitch deck - can you please review this”?

Then I get something that more resembles a wishlist of GTM motions and a clusterf* of various marketing and sales channels that should or could work down the line.

And investors are pretty great at smelling insecurity. “All” you need to do is to make them believe that the team knows what they are doing and how they will get there.

How does the opposite of it show?

In a GTM slide that is full of vague goals, but shows little of what will actually drive the growth.

More than 2 years ago, I posted a guide to a killer GTM slide with an investor, Jonathan Crowder, and it became one of my best-indexed posts - but much has changed since.

More competition, AI native trailblazer startups setting bigger appetites with investors and in-built expectations that nearly every team should somehow use AI in their product and/or GTM - to name some. VC is now more selective, more AI-centric, and more concentrated in a smaller number of winners and mega rounds.

I’d immediately argue that this one is not useful just for startups fundraising but to all of us who are working on GTM presentations for clients, bosses and team members - investors can be internal or external :)

The sole mission of this article is to empower you with structural and tactical insights into how to make a compelling pitch to people that you need on your GTM journey.

The logic of the GTM Power Slide is simple - the purpose of the GTM slide is to create confidence and evidence that your GTM plan will work by providing traction data and projections that do the story for you - and it will be faster and better with their contribution, for which they will be handsomely rewarded. Not much different than sales, ain’t it?

This is why this article did not need a makeover - it needed a do-over.

We asked well-respected investors in the GTM space to share their best practices with us: Liz Christo from Stage 2 Capital, Paul Irving from GTMfund, Kyle Poyar from Growth Unhinged, and Ivan Landabasso from JME Ventures.

We got some really good insights and packed them into a “pitch perfect” GTM slide for 2026 that you can use as a template for your presentations.

So, let’s get into it.

What changed since the pre-AI native era?

The market split in two - and your GTM slide now decides which side you land on.

• AI took 80% of global VC in Q1 2026. Average AI deal sizes hit $51M versus $4.7M for non-AI - a 10.9x gap, up from 8.4x in 2024. Outside AI, capital is tight.

• Series A is a revenue test now. Investors want real revenue, not a demo, and they expect fast growth. Carta puts the median Series A post-money at $78.7M, and the top 10% of startups raised half of all 2025 capital.

• VCs are writing fewer, larger checks. Over 40% of seed and Series A dollars in 2026 went to rounds of $100M+. Everyone else faces a tighter, more scrutinized environment where financial discipline and a clear story matter more than ever. Your GTM slide is that story.

In a nutshell: investors are writing big checks to a select few while the rest have to live with scraps and “valuable feedback”. It is an especially painful problem on the periphery - outside San Francisco, London, and other VC power hubs.

How to convince them with the GTM slide?

Step 1: Show traction

We won’t focus on product-market fit, “problem-solution” slide and other parts of the presentation today. These come before and create the narrative that will make the GTM claims more believable.

And then there’s one slide where you can expect most discussion: your go-to-market plan. You need to nail it.

So more than anything, you need to show where you are starting from: your existing traction.

You need to give the confidence that you know what you’re doing. Your GTM slide should show a realistic picture of how you plan to acquire customers in your market, supported by clear assumptions. Show your existing traction and present a story on why this is an investment opportunity the investors should be part of.

Step 2: Outline a clear strategy to continue ahead

Here’s where the story part should continue. Anyone can draw a graph with projected growth. You need to explain what will drive it. Which GTM motions are bringing in revenue right now, and what are your bets that the investment will boost?

The most common mistake is to present too many options. Then it resembles a wish list, but no one will know what the focus should be. Instead, explain what your key drivers of growth are, why you make that assumption, and how you will measure it (what the goals and KPIs are).

Kyle Poyar (Growth Unhinged, previously partner at OpenView) explains: “VCs want to see that your GTM is repeatable, predictable, and efficient. You know your best customer, you have channels that are working, your sales reps are hitting quota, and you know exactly what you could do to accelerate growth even further.”

For the earliest stages (Pre-Seed & Seed), the GTM slide usually isn’t that deep - and it doesn’t need to be, says Paul Irving, general partner at GTMfund. Three things stand out to him:

1. An unfair advantage in distribution.

For example, a built-in audience, a media asset you own, strong advisors, or founders who came from the space. “It’s not a prerequisite, but it’s the first thing I look for, because it lets you iterate with customers faster than a competitor can.”

2. A motion that fits the business.

Where your customers are, how they buy, what kind of company you’re building. Too often it’s cookie-cutter and doesn’t match the business. For example, outbound email to restaurant owners who never open email. The strategy has to match the specific customer.

3. One to three channels you can own.

“I’d rather see one or two channels they’re going to win outright to get off the ground than a seven-channel distributed plan an early team can’t run,” says Paul.

Step 3: Let the numbers do the talking

Numbers are the foundation. It’s how the valuations are made and justified.

So, which metrics to focus on?

It depends on the round you are looking for:

First step (even if you’re not preparing your GTM slide yet): start measuring the numbers so you can then pull them together when needed. CRM is the best source of truth to do that.

Liz Christo (partner, Stage 2 Capital) says: “I see a lot of decks that are incredibly qualitative and subjective. They communicate how they are feeling during their sales process rather than reverting to the data. Often I think that’s because they don’t actually have the data. My big encouragement is whether you are a founder selling or have a large team, get the right CRM hygiene in place so you can track basic metrics: leads, opportunities, customers, ACV (annual contract value), go-live dates, etc...As the old adage goes, you can’t improve something you can’t measure.”

If you’re pre-revenue and want to focus on the traction, here’s what we have previously defined with Jonathan:

If you are already operating with revenue numbers, ARR (annual recurring revenue) is the gold standard. But for detailed talks about the valuation, investors will be interested in the economics behind the numbers. For example, how much does it cost you to acquire customers?

Kyle Poyar suggests that your gross margin adjusted CAC (customer acquisition cost) payback period should be healthy - ideally <12 months for SMB and <18 months for midmarket or Enterprise deals. But: “Make sure to zoom out; VCs aren’t looking for the details, they want to see that what you’re doing is ready to 10x.”

The killer GTM slide

The basics still hold. Your GTM slide has two jobs:

1. Communicate that you can predictably and successfully create traction.

2. To present the big opportunity you can pursue by securing funding.

Here’s what a template for the GTM slide in your pitch deck looks like in 2026:

Let’s break it down:

• Existing traction: a chart of your existing metrics: focus on the number of customers, ARR, and what GTM motions are driving growth. Explain key highlights of what’s working in a few bullet points. That could include learnings.

• Projected growth: continue with a projection of how this growth will look if you get an investment that will support expansion of your activities.

• Untapped opportunities: which are the additional opportunities that the investment will help you unlock.

“A company that is just getting started with founder sales and their first few customers should be focused on how they are going to get in front of their target audience, what they are learning, short-term milestones to prove value and product-market fit,” explains Liz Christo. On the other hand, a company with a team of 10 ramped sellers in seat should be sharing real data on the funnel, conversion, rep performance, and a believable path to scale that motion.

Where founders usually get it wrong

1) The math doesn’t add up

VCs talk in numbers every day, so the fastest way to lose them is numbers that don’t square. Paul Irving says: “ACV, customer count, revenue goal, and the pace to get there all have to line up, and often they don’t. Someone says they’ll hit $10M ARR; that’s 132 customers. They’re signing three a month — how do they get to twelve?”

Or the ACVs they’re projecting look nothing like the ones they have today. Sometimes there’s a real answer (they just turned on PLG onboarding, so velocity will climb). But often the numbers simply don’t square with the business.

2) Unrealistic targets

If you were on a first call with someone, this doesn’t mean pipeline yet. Liz Christo says: “I often see slides that make outlandish claims like “$15M in pipeline created last quarter” as a pre-seed company, and the company is layering in assumptions. For example, the first call with Bank of America enters pipeline at $3M, when in reality it probably isn’t even a qualified opportunity, let alone ready to have a $$ figure associated.”

Numbers also need to be put into context, explains Liz Christo. Random numbers on slide, like “Closed $300K in last 30 days” will raise questions on why that is important:

• Did you only start selling 30 days ago?

• Is this an acceleration?

• Is this ahead or behind your target?

3) Faking traction

Most VCs have a well-developed bullshit detector, says Ivan Landabasso (partner at JME Ventures and author of Startup Riders). “Don’t use the term “design partners” and other buzzwords when trying to build an argument around product-market fit or validation. You either have an early client who’s hopefully paying you (even if it’s a small amount), or a full-on client.”

If you have a “design partner” who’s not paying you after a few months, it will raise a question if:

a) they are taking advantage of you (if you are solving a real problem, there’s no reason not to pay you even if it’s a small amount), or

b) if you are solving a problem that is not important enough.

How to say “design partner” in non-jargon? “Temporary non-paying customer until we prove value”.

What to do this week

GTM slide is not an overnight task. Get it drafted, gather some feedback, test the narrative, practice the pitch. That’s the best bet for you to get it right and win investors in the end.

Also, there’s nothing wrong with showing weakness as well - frame them as opportunities when you give them the attention they deserve. For example: if you haven’t figured out paid ads yet, you might do it when you can devote some budget to it.

The same goes for learnings. Liz Christo shares an example: “You might have tested events last quarter, launched a new geo, or tested a new partnership - these are all opportunities to learn about what is/isn’t working. Experimenting and learning fast (even failures) are critical to growth. This behavior demonstrates the mindset of the founder or the founding team. I don’t need to believe you have it all figured out, but willingness to learn, being introspective, and keep getting better means you will find a path.”

And to wrap up, here are the highlights from the experts that contributed to this post:

Till next time,

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is sponsored by 1mind.

Buyers tell AI things they’d never tell a salesperson. “Send me more information” usually means “goodbye now” - and AI reads it correctly, while most sellers keep chasing a prospect who already checked out.

1mind analyzed hundreds of thousands of real enterprise buyer-AI conversations. Three findings stood out:

→ How someone talks to your AI predicts their seniority, function, and deal value within a few turns

→ In-market demand is far more concentrated than the “5% are ready” rule implies - and it shows in what buyers say, not a form

→ How buyers talk to your AI mirrors how they prompt AI to find you - conversation design and AEO are the same surface

Full report drops on a panel hosted by my good friend Jonathan Kvarfordt: AI Conversations that Convert.

Tuesday, June 30 at 12 pm EST. I’ll be there.

Register here

Dear GTM Strategist,

You’ve probably been there.

5,000 target companies that could benefit from your solution. A sequence you wrote and rewrote until it sounded exactly right to you. You’ve done housekeeping too - selected the most praised tools that promised great returns, verified emails and did everything the playbooks tell you to do.

Yet, when you hit play, nothing happened. No one cared.

Could we blame:

• The subject line?

• The sending tool?

• The offer?

Not really - in a world where decision makers are bombarded with emails, DMs, and robocalls, it takes 2 seconds to make a decision whether a message is relevant to them. Whatever sounds like: “Hi, congrats on building the X company - you growth is impressive, but I realized you are completely neglecting Y - this is exactly why we build Z - to help companies like X grow faster. Worth a 15min chat? You can book a demo here.”... will be largely ignored.

So what makes your message relevant to someone right now?

There are two points of failure in most outbound campaigns we are receiving and analyzing:

1. You emailed the wrong people. Only 0.1% of regular cold email campaigns convert to a meeting (source). The list wasn’t matched to anyone in real pain. It was just big.

2. The second failure is the one nobody admits. Even when you did reach a real buyer, you had nothing to say to them. You pulled the same signals everyone else pulls - the funding round, the job change, the LinkedIn post. Your “personalized” email opened exactly like the other nine in their inbox that morning. 🥱 This is not real personalization.

Everyone has the same AI models, the same enrichment, the same signals. The context you feed the machine is the only real moat left. And the only way to grasp relevant context is to DYOR - do your own research. That is the only way you’ll have something relevant to say to your people. Attention is deserved - not granted.

Yes - this is a post about the good boring research layer. About the 80% of outbound nobody talks about, and the half that makes a real difference in campaigns.

What you will learn (in this order):

1. How to identify the right segment that cares about your solution now

2. How to get, enrich, and filter the data

3. How to score it and prepare for campaigns

4. How to write the message that makes the buyer think, “wait, how do they know this about my market?”

Claude Code will be our super-assistant all along the way.

Quick note on the “we.” I handle the GTM strategy. My partner Anže Voje and his team build these workflows in Claude Code and Clay, on live client campaigns. What follows is the method we run daily.

It starts with a question most teams never think to ask.

1) Identify the segment with the highest pain point

Before you build a single list, answer this: where does proof of your buyer’s pain actually show up? How can you constantly monitor for it?

Don’t start by prompting “Build me a list with VP of Sales at a 200-person SaaS company.” Our goal is to find VPs who are, right now, drowning - because drowning is the only condition under which a stranger picks up.

So, where does drowning show up?

Picture a company that posts 12 “Account Executive” openings in six weeks. This is a signal that a sales org is scaling faster than it can possibly ramp. And this is the exact moment when an enablement or onboarding tool flips from nice-to-have to must-have. So a job board will hand you these signals - and Claude Code is how you can do research and then source the right data.

Signals beat demographics every time, because a signal has a clock on it. And the clock matters more than people think: a funding round from 150 days ago is not the same signal as one from yesterday. Fresh pain converts. Act fast.

Data you pull from public databases needs further work. Expect 30 to 40% of any pull to be miscategorized - the wrong part of the value chain, consultants disguised as buyers, media companies, outdated headcount. That’s where the next step comes in.

2) Map where your signal lives

There are many sources when you can find good data online. Claude Code will help you strategize and select the right sources and, if you can get your hands dirty, also access the data.

In Claude Code, describe your product and ICP, then have it build and populate this matrix:

Here are some common source categories you should check:

• Public filings and registries - permits, licenses, grants, regulatory disclosures, procurement records

• Hiring data - job postings are among the strongest “active pain” signals; companies hire right before or right after the pain becomes real

• Industry databases and association directories - membership lists, certified-partner lists

• Product and review data - G2/Capterra, app-store changes, integrations added or removed

• Self-reported activity - the company’s own site, press releases, conference appearances and exhibitor lists

• Data vendors/aggregators - broad coverage, but noisy and often stale; best for firmographics, weak for triggers

3) Find the right contacts with Claude Code

That hiring surge tells you a company is in pain. It does not tell you who to email. The 12 postings are signed by “Talent Acquisition,” not by the VP who owns the number and the budget.

This pattern repeats in almost every market:

No single source holds both “who the buyer is” and “what they are doing right now”.

You always operate with two separate lists:

1. First, you build a list of target companies

2. Then, you look for target contacts within these companies.

To find the right target contacts, ask Claude Code for a person - ask it to work backwards from the company. You already have the domain (URL) from step 2. Now point it at where decision makers are actually listed.

Here’s an example of a prompt you can use:

For each company in this list, find the person who owns the sales number. Check the leadership or team page first, then LinkedIn. Look for VP of Sales, Head of Revenue, or CRO. If the company is under 50 people, assume the founder or CEO owns it. Return name, title, LinkedIn URL, and the source you used for each one.

Claude Code goes company by company - reading the leadership page, cross-checking the title on LinkedIn, and flagging where it had to infer versus confirm. That last part matters: you want to know which names are solid and which are educated guesses before you spend a send on them.

If you subscribe to any databases, like Apollo, connect them to Claude Code (via MCP - just type /mcp), and Claude will include these sources in research.

My go-to tool for research in Claude Code is Exa. Exa was built for AI agents, not humans, so it searches by meaning instead of keywords. You can hand it a messy query like “companies that posted 10+ AE openings last month and just raised a round” and it returns the actual page content, not ten links to scrape - so Claude can work with the output right away.

If you target larger companies, it’s often not just one person, but a buying group. So tell Claude Code to map the group:

For any company with over 200 employees, identify 2 to 3 people in the buying group: the economic buyer who signs off on the budget, the champion who feels this pain daily, and the user whose workflow your product touches. Note each person’s likely role in the decision.

This is exactly the work Claude Code is the superior tool for this type of work - it reasons across mismatched sources to construct the person that is not listed elsewhere.

When you get the list, you can ask Claude to export it as CSV, to Google Sheet… however you want it.

4) Now score it, or you’ll drown in your own list

So now you have your list and your contacts. The next step is to get ready for the campaign. You will be tempted to just send a “email blast” to the whole list. But - take a deep breath. You don’t want to burn all your chances immediately. And you want to be especially prepared for the top potentials and personalize the message to them.

That’s where lead scoring comes in. Don’t skip it.

Here is an aggregate example of a scoring table that we use in our campaigns:

Adjust it to your needs, but make sure to keep two components that make this lead scoring model very powerful

1. Signal stacking: a Series B plus a new RevOps hire in the same week is worth more than either alone, because together they tell you budget exists and rebuilding is underway. T

2. Penalties: Noisy lists overflow with things that pattern-match to your buyer and are categorically wrong - the manufacturer when you sell to the installer. Punish those hard and the list cleans itself.

Then sort into four buckets: HOT goes out now with your deepest personalization. These emails deserve your manual review and tweaking. Think of contact-specific resources and information you can mention. Can you provide a report or a demo for them that can give them a taste of how specifically you can help them? Do it!

With WARM contacts, you can proceed with a lighter touch. At COLD contacts, you watch for a trigger - the right timing to send. SKIP you archive so you never spend another credit on it. Here’s an example for a 14-point model.

Earn the reply

All this research buys you one thing: the right to be specific. And specificity is the whole game, because everyone is fishing the same pond with half-baked messages. The fix is to go deeper than the surface signal everyone shares, until your message carries something they cannot get anywhere else.

We have to deserve their attention by providing something valuable to them at the right time. Aim for a message that is valuable on its own, whether they buy or not. Market intelligence disguised as outreach. And remember - the first message should be all about them. Don’t pitch slap, don’t write extensively about you and your company. Just earn the reply and the right to continue the conversation.

The bar is simple: would this email be worth their time even if you were selling nothing? If yes, you have earned the reply. If no, you are probably being ignored.

The reaction you are hunting for is one sentence: “How do they know this about my market?”

That’s why Claude Code is the best for research, because you can combine your knowledge of the industry (your knowledge base) with specific about the contact - and use that information for personalization.

For the full picture, reference the trigger, the names, the number, and try to name their exact situation.

Three layers in every first touch:

• Lead with what you saw.

• Support it with the figure that’s true at their size.

• Ask a genuine question - not a meeting request wearing a trench coat.

Good: “Am I reading this right, or does your team already have this handled?”

Weak: “Got 15 minutes Thursday?”

The first one opens a conversation. The second one asks a stranger for a favor.

We went deep on this idea with Jordan Crawford, the king of the PVP framework: building campaigns your prospects would pay to receive - if you want the full version.

How this runs in Claude Code

We already shared some prompts and examples, but now that you know the order, let’s get even more specific on how it happens in Claude Code - and how to make it compound instead of starting from scratch every campaign.

First, give Claude Code somewhere to think from. A plain project folder it reads at the start of every session. This will be your GTM repository:

We already published a detailed guide on how to build your GTM repository, aka your GTM brain - find it here.

Next, fill this folder with all your knowledge: ICP definitions, messaging documents, product decks, signals… All the research you’ll further do with Claude Code should write back here.

Research: turn the ICP into signals. Research based on job titles and company size is not enough.

Ask Claude where the pain shows up. Example of a prompt:

Read the ICP definitions. List 10 observable, time-stamped signals that prove a company is drowning in unramped reps right now. For each, name the public source and how to spot it. No demographics - only signals with a clock on them.

You get a ranked signal list and a source map, saved to the repo.

Then source the list: Point these signals at the live source and let it pull, clean, and cross-reference in one pass:

Example:

Take the top 3 signals from the list you just built. For each one, search the public sources you mapped - use Exa for web research and any databases connected over MCP - and pull every company currently showing that signal. For each company, return: name, domain, the specific signal it fired, the source URL where you found it, and the date the signal appeared. Only keep companies that match our ICP - drop staffing agencies, consultancies, media companies, and anything in the wrong part of the value chain. Skip any signal older than 90 days. If a company shows more than one signal, list all of them on the same row. Write the results to companies.csv, one row per company.

Next, find the right contacts at these companies. We covered that at step 3. The goal is to get contacts.csv file with the actual contacts you can contact.

And finally, score these leads according to the model you set.

Here’s an example prompt based on the logic presented earlier in the article:

Score every contact in contacts.csv against scoring-rubric.md. Apply both the positive factors and the penalties. For each row, show the points awarded per factor, the running total, the tier (HOT 10-14, WARM 6-9, COLD 3-5, SKIP 2 or below - including any negative totals from penalties), and a one-sentence reason. Flag every factor you’re inferring versus confirming from a source. Sort by total, highest first, and write it back to scored-contacts.csv.

HOT is the only tier you personalize by hand, so Claude Code’s job here is to draft, not send. You feed it the same evidence you scored on, plus your repo, and have it build each message on the one specific thing you already know about that company.

Example:

For each HOT contact in scored-contacts.csv, draft a first-touch message. Open with the signal and source on their row - lead with what we saw, not who we are. Pull one proof point or benchmark from the repo that’s true at their company size. Close with a single genuine question, not a meeting request. Keep it under 90 words, no pitch. For each draft, note which signal and which proof point it’s built on so I can verify before sending.

Four rules to hold the line:

• Draft, don’t automate. Claude Code gets you to 80%. The top tier earns the last 20% you add by hand - the detail the model can’t see from a row in a CSV.

• One signal, deep. Build on the single sharpest thing you found, not five shallow data points stacked together. Depth is what reads as “they actually looked at us.”

• Pass the test before it sends. Would this email be worth their time even if you were selling nothing? If no, it’s not ready.

• The first message is all about them. No company backstory, no feature list. Earn the reply, then earn the right to continue.

Then, and only then, think about automation

By the time automation is even on the table, the hard part is behind you. But here’s the line we don’t cross: the first 50 messages to your hottest leads get reviewed and sent by hand. Every single one. Hand that step to a machine and you’re back to low reply rates this whole process was built to escape.

Everything below HOT is what you can automate. Once the thinking is done, WARM and COLD can orchestrate through Clay as the execution engine, or proceed directly with the sequencing tool like Instantly for email or HeyReach for LinkedIn DMs. But that build is a story for another post. The full Signal-to-Pipeline playbook walks through how to take this into Clay and run it at scale.

The takeaway

Outbound stopped paying for volume. It started paying for homework.

The research used to take an analyst a week- most companies skipped it enirely and treated outbound as trial and error.

Now Claude Code does it in an afternoon. And if you want it to compound instead of starting from zero every campaign, you put it somewhere permanent - a context repository Claude Code reads automatically, so this quarter’s research makes next quarter’s sharper.

Do the thinking first. Don’t skip the research. Discover the patterns relevant for your industry and ICP. Then run it at scale. That order is the difference between a list that books a meeting 0.1% of the time and a pipeline that compounds.

We packaged the full method - including the templates, the source catalog, and the pre-launch checklist - into one free-to-access playbook you can run on your next campaign.

Get the full workflow

Now tell us: does your outbound start with a signal, or with a list? Let’s discuss in the comments.

Maja & Anze

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is supported by Attio, the AI CRM.

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Dear GTM Strategist,

When I invite someone as a guest writer, it’s because someone has a way of looking at growth that I think every senior B2B operator on this list needs in their head.

Mark Roberge is that person.

He was the founding CRO at HubSpot, where he scaled revenue from $0 to IPO. He co-founded Stage 2 Capital, which I think of as the most interesting VC firm in B2B - it’s run and backed by 1000+ go-to-market operators from companies like Anthropic, Lovable, Databricks, Zoom, etc. He teaches scaling at Harvard Business School. He wrote The Sales Acceleration Formula and recently published The Science of Scaling. If there is a person on the planet who has watched more B2B companies try to scale, succeed, and crater, it is probably him.

I asked Mark to share something most senior B2B operators are quietly getting wrong right now. The answer landed harder than I expected.

His read: Great businesses are failing unnecessarily due to their scale strategy.. They use gut feel where they should be using operational performance metrics. Half scale too late and too slow. Half scale too early and too fast. The cost of getting it wrong - in either direction - is enormous.

Scale too late and a competitor takes the market.

Scale too early, and you accelerate a broken business into bankruptcy.

What you’ll get in this piece:

• Why product-market fit is measurable - and the three-variable definition Mark uses with every Stage 2 portfolio company

• What go-to-market fit actually is - and the leading indicators that tell you it’s working before unit economics confirm it

• The Stay-Go-Slow framework - the operating cadence Mark uses with boards to scale at the fastest pace the business can absorb without breaking

If you’re planning a 2026 expansion - new product, new market, new motion - read this twice. Then send it to your board.

Over to Mark.

The Second Act Stumble

Steve had a way of making growth sound inevitable.

He had doubled revenue from $27M to $54M in twelve months. The board wanted another lap. Another 2x. Another miracle. They were polite about it, of course, but expectations have a way of sounding like commands when they come from the people who write the checks.

Steve stared at the numbers. The inside sales team was expanding in the U.S. mid-market and could squeeze another $30M out of the current motion. But not $54M. There was a $24M hole staring back at him from the spreadsheet, and Steve hated holes.

The fix, in his mind, was obvious. Build a new product. Sell it to the existing customer base. Dangle it in front of prospects. Slap “platform” next to the company name and watch the deals pile up. He unveiled the plan. Heads nodded. The board was sold.

Engineers abandoned the roadmap. Marketing tore apart the website and rebuilt it around the launch. Sales enablement ran boot camps so every rep could pitch the new product in their sleep.

The customer conference was the coronation. Except it wasn’t.

A year later, the new product had sold 10% of the forecast. Bugs, friction, broken promises. The slick collateral fell flat on impact. Meanwhile, the original engine - the one that had powered the prior year - had been neglected. An entire quarter was burned chasing pipeline for a product that couldn’t carry the weight. By the time anyone admitted it, the company was too far behind to catch up.

Steve didn’t just miss his number. He missed the point.

In the early years, founders understand they shouldn’t commit to a revenue goal too soon. They use design partners. They iterate. They tolerate uncertainty.

Then the company hits scale, launches a new product or enters a new market, and somehow forgets all of it. We assume the playbook from act one will transfer to act two.

It rarely does.

Some institutional knowledge persists. Far less than we assume. When we expand, we have to restart the product-market fit and go-to-market fit process from scratch. The journey should be faster the second time. It is never instantaneous.

So what does that process actually look like?

Product-Market Fit Is Not a Feeling

Every year, I ask the same question - to MBA students at Harvard, to founders in Stage 2 portfolio reviews, to keynote audiences. What is product-market fit?

The answers are always all over the place. A revenue milestone. A customer count. Inbound flow. A “feeling.” I disagree with every one of those.

Product-market fit happens when customers continuously realize the value they were promised when they bought the product. The cleanest quantitative proxy for that is long-term customer retention. When a customer renews, they have effectively re-bought. They are voting with their dollars that the value showed up. Across most categories, the bar is annual retention above 90%.

There’s one problem with that definition. Customer retention is a lagging indicator. By the time you know the cohort retained, you are six to twelve months past the moment you needed the signal.

So the best operators define a leading indicator of retention, or LIR. Some people call it the “aha moment,” a phrase Chamath Palihapitiya popularized at Facebook. The point is the same: identify the specific, instrumentable behavior that predicts long-term retention, and chase it relentlessly.

There is no universal LIR. It’s unique for every business. However, it is useful to think through your LIR using the above framework, simplifying the decision down to three variable: Percentage (P), Event (E), and Timing (T).

P - Percentage. The share of new customers that must hit the leading indicator for you to claim PMF. If your market has lots of well-funded competitors and a real first-mover advantage, set P lower (like 50%) and move faster. If you operate in a niche with high failure tolerance and a strong moat, set P higher (80%) and “nail it before you scale it”.

E - Event. The actual behavior that signals value realization. This is the variable that matters most. A good E has four properties:

1. Objective. Either it happened or it didn’t. “Processed the first transaction” qualifies. “Customer sees value” does not.

2. Instrumentable. You have to be able to track it automatically, at scale.

3. Tied to customer success. “Reduced processing time by 10%” qualifies. “Attended the QBR” does not.

4. Aligned with your unique value proposition. The whole company - marketing, sales, customer success - will be steered by this metric. It needs to push them toward your differentiation, not somebody else’s.

Consider starting with E oriented around “setup”, evolving it to “engagement”, and ending at “ROI.” Early on, “processing transactions” is enough. Later, “processed 100 transactions.” Eventually, “achieved 10% productivity gain.” The metric matures with the maturity of the business.

T - Timing. How frequently you measure it. Daily lets you learn fast. But not all product use cases call for daily usage or smooth engagement. Usually, a monthly view is a timeline that generates consistent usage metrics with a fast enough learning cycle.

Once P, E, and T are set, instrument the LIR by acquisition cohort. Each row is the month a cohort was acquired. Each column is the percentage of that cohort hitting the LIR by month of tenure. You now have a monthly readout of whether new customers are realizing value - quarters before you’d see it in the retention number.

Two things matter most in that chart.

Recent cohorts beat older cohorts. Old cohorts were acquired before your latest product, onboarding, and positioning improvements. They aren’t representative anymore. Limit how far back the analysis goes.

The LIR cohort chart belongs in your board deck and your company-wide dashboard. If you measure something this important quarterly in a private spreadsheet, the org will not align around it.

That is how you stop calling product-market fit a feeling.

Go-to-Market Fit: Can You Acquire Customers Profitably?

Hitting PMF gives you confidence that if you add 100 customers in a segment next quarter, most of them will realize the value. However, you have not proven that you can acquire and serve them profitably.

I call this go-to-market fit, or GTMF. The ability to acquire and retain customers consistently and profitably.

Unit economics, rather than GAAP-based profitability, are used to evaluate GTMF, as unit economics isolate the business costs associated with scale. Commonly used unit economics metrics in B2B software are:

• Payback period < 12 months

• LTV / CAC > 3

• Magic Number > 1.0

However, just like retention in the prior section on measuring product-market fit, unit economics are also lagging indicators. Fortunately, extracting unit economics back to leading indicators is far more formulaic and consistent across businesses.

Take LTV/CAC > 3. The figure below illustrates how we can extract the desired unit economic output back into short-term operational inputs.

You now have a tree of operational metrics: cost per lead, lead-to-customer conversion, leads per rep, ACV, gross margin, and annual churn. Set a goal for each input, plug them into the formula, and confirm that the LTV/CAC output you want falls out the other side.

Then watch them weekly. If the actual line stays above the target line, you are on pace to achieve GTMF. If it doesn’t, you can see exactly which input is breaking - lead cost, conversion, capacity, or churn - and fix that one specifically rather than guessing.

The same logic works for payback period and Magic Number. Decompose, set targets, and watch the leading indicators.

This leading industry approach allows you to operate your business many quarters ahead of your competition.

Scale Is a Pace, Not an Event

With PMF and GTMF in place, we are ready to scale the new segment. However, scaling at the wrong speed is an even more prevelant failure cause that the wrong time..

The classic mistake is to treat scaling as a single event that follows a fundraiser or an annual planning cycle. Set the annual number. Calculate how many salespeople are needed to hit the number. Hire all of those salespeople as quickly as possible, ideally in the next two weeks.

This strategy fails due to a lack of appreciation for the operational processes required to hire, ramp, and manage this new velocity of salespeople. Six months later, the majority of the new hires are under-performing, LIR and LIUE deteriorates, and the next board meeting is rough.

Scale is a pace, not an event.

Plan to add 10 salespeople a month for 12 months, not 120 salespeople next month. If the system absorbs that, accelerate to twenty. If it strains, pause, diagnose, fix, resume. The optimal pace is the fastest one that doesn’t break product-market fit and go-to-market fit. The leading indicator dashboards established in the prior sections serve as your speedometer.

At Stage 2 Capital, we use a quarterly operating motion called Stay-Go-Slow. We still build an annual plan. We also pre-agree on three questions with the executive team and the board:

• GO. What would we see by the end of next quarter that justifies going faster - and increasing the hire rate?

• SLOW. What would we see that justifies slowing down - and pausing hiring until the issue is diagnosed?

• STAY. What would we see that validates the current plan?

Each quarter, we check in. We evaluate where we landed. We adjust the next four quarters of hiring. We update the forward plan.

Most teams operationalize Stay-Go-Slow against three thresholds:

All three green: GO. Accelerate hiring. Update the forward plan up.

Mixed green and yellow: STAY. Hold the plan.

Any red: SLOW. Decelerate or stop hiring. Diagnose the issue. Fix it as soon as possible, hopefully in days and not quarters. Re-establish the hiring pace once the issue is addressed. Update the forward plan down.

Two things tend to come up the first time I introduce this with a board.

The first is the IRR worry. Investors get nervous that slowing down kills their return profile. It usually doesn’t. Successful exits happen on a five-to-ten-year arc. Acquirers don’t care about your Q2-to-Q3 growth rate from eight years ago. They care about the most recent two years, at most. The same lens is true for the next growth investor. Their perception of the scale capability of the business is weighted toward recent quarters versus 6 quarters ago. Disciplined pacing protects the long compound. Reckless pacing destroys it.

The second is the accountability worry. If you adjust the forward plan when conditions change, do reps stop hitting their numbers? Two answers. First, you keep tracking the original plan for performance management and comp. Otherwise, teams hide behind the revised forecast. Second, this isn’t about reducing accountability. It’s about managing the business with the latest information. Those are different things.

The Real Goal

Half the executives I meet scale too early and too fast. Half scale too late and too slow.

The risk of moving too slowly is just as real as moving too quickly. Competitors raise capital. Market windows close. Press releases pile up around someone else’s name. Movement matters.

But movement toward the wrong milestone is just expensive motion. Move quickly toward the right milestone, at the pace your organization can absorb. Instrument product-market fit and go-to-market fit. Then scale at the fastest pace those two instruments will tolerate.

The goal is not a short-term “triple, triple, double, double.” The goal is a long-term home run.

Good luck with the science of scaling.

Maja here.

If you read this and felt the small twist in your stomach that says we are doing this wrong - good. That is the most useful response you can have to Mark’s framework.

The most concrete thing you can do this week:

1. Write down your current LIR. Sit with your CRO or head of growth. Pick a P, an E, and a T. Don’t make it perfect. Pick something, instrument it, and revisit in 30 days.

2. Decompose the unit economic target your board cares about most - LTV/CAC, payback, or Magic Number - into the four to six input metrics that drive it. Set a goal for each. That’s your LIUE dashboard.

3. Run a Stay-Go-Slow conversation at your next board meeting. Even if you don’t change the plan, the conversation will surface where your team and your board actually disagree about pace.

This piece is based on Mark’s book The Science of Scaling. If you’re serious about scaling, I definitely recommend reading it, as it includes much more on using data to decide when and how fast to scale revenue. Mark is also donating 100% of the proceeds of the book to mental health, so get some copies for your team as well.

And for more GTM wisdom from Mark and his team, subscribe to the GTM in Practice Substack.

Until next time,

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is sponsored by Miro - where my frameworks actually come to life.

I’ve been a Miro power user since 2020 - workshops, planning, prototyping, inventing new models, you name it, I’ve got a board for it. This week, I ran my GTM Blueprint workshop live at Canvas 26 in London, and honestly, this is what Miro does best: an AI-powered workspace where teams come together to do the thinking.

Run these frameworks yourself, test Miro’s AI capabilities, and find the templates that turn you into a creativity powerhouse.

Get the free templates

Dear GTM Strategist,

Most of the go-to-market “strategies” landing in my inbox lately are AI slop.

You know it, I know it, so let’s not pretend otherwise.

So this time I want to bring the conversation back to something material and timeless: the four models I run with every single consulting client.

I invented them, perfected them over five years, and stress-tested them on AI companies at every stage you can think of - launching a product, expanding into a new market, even crafting that first pre-PMF plan. More than 12,000 companies have used them so far. That’s 15 years of work distilled into a handful of frameworks - but who’s counting? :)

Here’s what I love most about them: they are collaborative. Every single one is built as a workshop because I still believe that real human brains, in a room, on a board, together, create something beautiful that no model can spit out on its own.

And they age really well. They still deliver tons of value when we’re developing our go-to-market plans with teams. I revisit them yearly for strategic planning in my business practice as well.

Why does this matter more than ever?

Products have never been easier to build or copy. AI demos overpromise and underdeliver, so buyers walk in more skeptical than they were even a year ago. In that world, your edge isn’t more output. It’s focus and proprietary insight.

These four models are how I take teams from GTM overwhelm to GTM-focused execution, fast. The templates are open-source and free (video tutorials included).

Here’s what you’ll walk away with:

• A 60-minute diagnostic GTM Power Hour to map your whole motion and ship a v1 of your GTM plan

• The Market Problem Map to get you from “my product can help everyone” to one testable ICP

• A 5-stage Differentiated Positioning workflow so you stop sounding like every other “vanilla AI tool” that makes your target customer yawn 🥱

• My best seller now on AI steroids: GTM Blueprint. A reverse-engineered, prioritized quarterly plan you can actually execute

• The exact free templates, and where AI belongs in each step

Bon appétit 🍽️.

1. GTM Power Hour: Your first ballpark plan in 60 minutes

I invented this one in Belgrade during my Swiss Entrepreneurial Program, which was packed with back-to-back mentorship sessions. When I travel, I want to make the most of my time, but six 60-minute sessions in a day is heavy on context switching, even for me. So I sat down and thought hard about what would actually move the needle for entrepreneurs in the short window we had together.

After working on hundreds of GTM strategies, the pattern was obvious: founders bump into the exact same problems again and again (ballpark 90% of new products have ICP and positioning issues that need fixing before we turn on the marketing and sales megaphones).

But founders hate to hear that as a “go back and do your research homework” recommendation- what they need is an action plan, not another pile of “could-dos” that sinks them deeper into analysis paralysis. So I mapped an outline to help them spot the weak points in their current GTM and pinpoint the next opportunities to grab.

That’s how GTM Power Hour was born, and people love how simple and actionable it is. No heavy prior analysis required, so it works even when you’re just getting started. It helps you map the key elements of your go-to-market motion and ship version one of a plan to hit the traction that’s mission-critical for reaching your next stage.

One rule: constrain it to the next 3 to 6 months. Most people genuinely struggle to plan further out, and our landscape shifts so fast that half your assumptions go stale the moment the next AI model drops.

If your GTM plan reaches much further than six months out, it’s probably fiction. Plan for the next two quarters, then let reality (and the next model release) update you.

The output is a document you can hand to investors, colleagues, or partners. This one exercise pulls everything swirling around in your head into a clean, presentable structure.

You can get it here, with a video tutorial to walk you through it.

If you are already further ahead, share it with someone who needs this clarity. 🫶

2. Stop selling to “everyone”: Focus 🎯 - pick your beachhead with the Market Problem Map

Spray-and-pray (or its respectable-sounding cousin, “diversification strategy”) is a dangerous game in GTM. The number one mistake you can make is stretching yourself too thin. For most of us, GTM isn’t even the sole focus - we’re also wrangling finance, leading the team, shipping the product, and personal life, to name a few. The last thing you want is your attention and resources scattered across multiple bets.

If your AI BI tool “helps solo entrepreneurs, but also enterprises, and hey, why not the public sector too - and developers, yes, them too, because who doesn’t want to make data-driven decisions?! 😏” you are going to have a brutal time accumulating a critical mass of proof that your solution actually works in any one segment.

Why does that matter more than ever? Whoever you want to sell to wants to know who got results before them, and those reference customers need to be relatable in size, industry, and use case. Sure, radical innovations can catch the wings of virality by making innovators and early adopters fall in love. But most of us (sorry) aren’t building something differentiated enough to bet the company on “users are gonna love it, they won’t be able to shut up about it.”

Beachhead to the rescue. Your ECP (early customer profile) should not be “finance people in startups and scaleups” or “anyone with a budget,” but the specific, winnable slice of the market where your solution creates obvious, repeatable value. The beachhead segment.

A well-defined beachhead segment hits four criteria:

1. A pressing need for your solution (high pain point)

2. Adequate willingness to pay (budget already exists)

3. The ability to produce case studies, testimonials, and references that help you move into bigger adjacent segments (healthy markets, reasonable TAM)

4. Accessibility - you can realistically reach it from where you sit today

Usually there’s more than one beachhead hypothesis worth testing before you bet the next three to six months of GTM on winning it. Most teams come up with three to five ICP hypotheses, and it’s extremely hard to validate all of them at once. So we test gradually, and the Market Problem Map is the best tool I know for choosing which bet to test first.

How it works: you map the relevant industry problems you solve, the pain points, and how they manifest across your segments. Then you score on three criteria - level of pain, ease of reaching them, and ease of implementation. On the other side, you get either your best segment, or sometimes even the best problem to address across several segments.

This is the exercise that takes you from “who could we possibly help?” to “who do we test first?” - and you can start today.

That output is gold for early positioning and messaging, and everything around it: LinkedIn posts, outreach emails, sales decks, landing pages, all of it. And yes, the result will shift over time as your product matures and you learn more about your market. That’s expected. Iterate with it rather than trying to nail it on the first try.

3. Positioning: the 5-stage workflow that kills the “vanilla AI tool” vibe

Now that we roughly know who we’re talking to and what problems we’re solving, we can sharpen our communication with a legit positioning exercise.

Fair warning: this is usually the toughest one, especially for technical teams. Marketers have a slight head start here because we’ve been trained to think this way. But the real magic happens in the collision of both worlds, where you come up with something that absolutely does not sound like AI slop or another me-too AI tool.

I built this workflow by combining models from three people I learn from: Andrej Peršolja, Emma Stratton (her book Make It Punchy is excellent), and, believe it or not, Alex Hormozi - his value-prop formula is my cheat sheet, I use it constantly.

It runs in five stages:

1. Map the competitive alternatives. What are people actually using instead of you right now to fix this problem? Then map the evaluation criteria your ICP uses to decide whether something is the right solution.

2. Score how satisfied your ICP is with those alternatives. This shows you where you have the best shot to win. Usually you’ll find one or two criteria you can genuinely compete on, or an alternative people are so unhappy with that you can flat-out beat it (assuming you’ve got the capacity to solve it better).

3. Map your competitive assets. Careful here. “Great customer support” and “amazing team” are things anyone can claim, and that you should have anyway. Filter those out. What’s left, what’s genuinely and radically true for you, those are your real differentiators.

4. Translate differentiators into benefits and proof points. Proof points are increasingly non-negotiable. People have been burned by AI hype and enter the buying process far more skeptically than they were a year or two ago. Backing every claim is both smart and fair to your audience. Good business ethics.

5. Land your first version of positioning: a value proposition plus three benefits and features. This is exactly what feeds your landing page, your sales deck, and the context documents you’d drop into a Claude Code or Claude Cowork GTM setup.

The language always needs fine-tuning, and that’s where human plus AI iteration earns its keep, over multiple rounds, until it lands in the hearts and minds of your ICP.

Full honesty: This is a simplified scheme of the process, but in practice it’s a set of exercises that need to be adapted to the industry and GTM stage. You can find more examples (together with the templates and prompts for sales decks and landing pages) inside my GTM Checklist and GTM Bootcamp - links below.

4. GTM Blueprint 🛣️: reverse-engineer your next quarter of traction based on your objective

You’ve done the thinking. Now it’s time to bring in some traction - and oh boy, do I have a good one for this.

My starting move is always reverse engineering. Start with one measurable GTM objective, grounded in the next three to six months (locking plans in stone for 12 months is risky - at this stage we don’t know what we don’t know, and markets shift constantly). Then feed it two things:

1. The outside view - research on competitors, the market, and industry best practices.

2. The inside view - your team’s unique insights and experience.

That combination produces a proprietary, intelligent GTM plan instead of generic AI slop.

The winning move is to make your inputs actionable and testable. “Do a LinkedIn post” or “influencer collab” is directional, but not actionable. So prompt your team (and your AIs) in a way that yields ideas you can actually run and measure.

Say the objective is 500 new users by the end of next quarter. Your ideas might be:

• A PVP (personalized value prop) campaign in Clay and Claude Code. Good old outbound, done intelligently enough to actually convert.

• A performance campaign with an industry-relevant influencer who already has the audience and traction - assuming you nail the audience-influencer fit.

• Squeeze more value from existing users - growth loops for sharing, or even a referral program.

I love ideation. Generating all these exciting ideas sparks so much joy. But here’s the trap: you can do anything, but you can’t do everything. We all operate with limited resources, so the next move is non-negotiable - a reality check on who does this work and what will actually move the needle.

That reality check is prioritization, and I run it with a dead-simple matrix: value vs. effort.

• Value: If this works, how likely is it to move me toward my objective?

• Effort: How expensive, slow, or hard is this to pull off?

🪄 Pro workshop tip: I hate to admit it, but AI can be far more objective than humans at prioritization. I’ll always value gut feel and human oversight, but AI doesn’t “fall in love with its own ideas” or push something just because it’s really, really awesome. So I prioritize with AI first, then revise with the team - it saves time and a lot of unpleasantness.

Once you’ve prioritized, you’re ready to commit. Present the plan as a roadmap or a downloadable doc - whatever is most visible and actionable for your team. Avoid slides: they’re too static and never get updated IRL.

I packaged this whole workshop as a Miro Blueprint, free to use. Take it for a spin before the summer out-of-office auto-reply purgatory sets in.

Where to start (and where to go deeper)

My one piece of advice on where to begin: start where your customer journey is most broken right now.

No clear segment yet? Run the Market Problem Map.

Clarity but no traction? Jump straight to the GTM Blueprint.

You’ve got four frameworks and enough to run one this week - that’s real, and it’s yours for free.

If you want the full systems behind them (the templates, prompts, and worked examples that make them sing), they live in my GTM Checklist. Grabbing it is also how you keep this work going - which means more free frameworks and tutorials landing in your inbox.

Let’s go to market!

Love 💛

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

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Dear GTM Strategist!

We’ve been talking about AEO (Answer Engine Optimization) for over a year now, and honestly? Still a lot of talk, very little action.

The case for acting is obvious. Your customer has changed how they buy. AI search brings more qualified traffic that converts better. And for once, newer brands get a fair fight - because AIs don’t just reward the decades of SEO assets that make the giants nearly impossible to beat.

But here’s the question nobody can answer cleanly: how do you actually get AIs to take you seriously when your ICP types “What’s the best CRM for a Series B startup” or “compare Attio vs HubSpot”?

This isn’t a job for the SEO nerds and demand gen wizards anymore. The customer journey changed, your website traffic-to-conversion math changed with it, and that makes it a GTM problem - your problem.

And if you have no idea what to do about it, you’re in very good company. George Chasiotis, founder of Minuttia, asked 599 marketers about their AEO strategy. Only 14% felt very confident about it. The rest are running experiments they can’t validate, copying playbooks they’re not sure work, and watching organic traffic move in ways they can’t explain.

That gap between “this clearly matters” and “we know what to do” is exactly where most B2B companies are stuck right now. So I asked George to fix that.
In this edition, you’ll get:

• Where most teams get stuck with AEO, and why

• The 6 pillars to win AI search visibility

• Real examples of what works and what backfires, including a case study

• A practical way to measure AI search performance when the data is messy and the goalposts keep moving

Let’s hear it from George.

The State of AEO: What 599 Marketers Revealed

My team at Minuttia ran this survey with Kevin Indig and his Growth Memo newsletter. You already know the gist from the intro: lots of activity, not much confidence. So let me skip the “AEO is here” sermon and get into the numbers that should actually change what you do on Monday.

Almost everyone’s in - but most are just dabbling

82% say they’ve adopted AEO “to some degree.” That headline sounds intimidating until you look at the breakdown. Only 7.7% have a dedicated AEO team. Just 21.5% are actively implementing a real strategy. The biggest chunk, 25.9%, are running early experiments, which is a polite way of saying “poking at it.”

So when someone tells you everyone’s doing AEO, they’re technically right and practically wrong. Most “adoption” is tentative. If you build a committed motion with real ownership now, you’re not catching up to the pack - you’re ahead of most of it. That window won’t stay open long, though. 13% already call AEO their top priority, up sharply from a year ago.

Why nobody feels confident (and why that’s good news for you)

The intro gave you the 14% confident stat. Here’s what’s eating the other 86% - and notice that every single cause is something you can fix:

Measurement is the number one complaint. Not because tools don’t exist - we counted roughly 240 AI search tracking tools in a December 2025 study. The tools are everywhere; the good ones are rare. People can’t tell which numbers to trust.

18% can’t tie AEO to business value. No attribution means no ROI story, which means no budget when you go ask for it.

24.2% flat-out don’t know what works. They’re collecting playbooks and guessing.

Most companies are confusing being busy with AEO for being good at it. Doing the work and knowing the work is paying off are completely different things.

The takeaway is almost embarrassingly simple: fix measurement before you scale anything else. The team that cracks attribution wins the budget arguments and pulls ahead while competitors keep running experiments they can’t read.

Don’t just dump this on your SEO team

Most orgs hand AEO to whoever already does SEO. About 20% spread it across cross-functional teams, and a tiny 2.7% bring in outside help.

Defaulting to the SEO team feels natural, but think about what you’re actually doing: piling a brand-new, genuinely uncertain discipline onto people who already have a full plate, usually without new tools or a clear mandate. Then everyone’s surprised when there’s plenty of activity and no movement. Pick an owner on purpose, and give them what they need to actually win.

The traffic drop hiding under all of this

Nearly half - 47.2% - reported organic traffic falling. For 20.5% it was a moderate dip of 10-20%, and for 26.7% it was a serious drop of more than 20%.

Fair warning: it’s not clear how cleanly people separated “AI search ate my traffic” from everything else that moves organic numbers, so treat this as directional rather than gospel. But you don’t need a perfect attribution model to feel the floor shifting. Waiting until the data is airtight is itself a choice, and probably the wrong one.

Bottom line: this space is still messy and young, and that’s precisely why there’s room to win. Let’s get into how.

6 Pillars of AI Search

The problem with how some organizations approach AEO is that they’re trying to reinvent the wheel.

That’s why so many of them are ready to throw SEO out of the window, declaring AI search the new sheriff in town.

But search is still here, and it’s more relevant than ever.

Many of the best practices you used for search engine optimization are still relevant, but we can certainly say they have evolved.

Take a look at the following graph, which provides an oversimplified view of what SEO used to be about:

At Minuttia, we still rely on these six pillars to help our clients with AI search visibility, but with a few tweaks.

Let’s take a closer look at each of them.

Pillar #1: Topic breadth and depth

These two notions are interconnected.

Topic breadth encompasses all topics relevant to you and your business model.

Let’s say you’re a CRM company, and that’s the core topic you’re talking about, but there are also the related ones:

• sales pipeline

• customer support

• marketing automation

• and so on.

In its turn, topic depth describes a set of sub-topics and the questions people ask for each of the main topics.

Let us illustrate.

Ideally, you need both topic breadth and depth to lay the foundation for successful AEO and SEO strategies, but there’s a caveat:

You should keep these topics as close to your business model as possible. Let us show you what can happen if you stray too far from it.

Connecteam, a workforce management software company, created a clever (at the time) strategy to boost Google and AI search visibility.

They created a repository of review articles analyzing 500+ of their competitors (as of January 2026).

To add weight to these reviews and show authority to AI search engines, Connecteam created a review methodology page explaining why readers should trust them.

You can find an entire breakdown of Connecteam’s AEO cast study here.

However, right after Google rolled out its Core Update in December 2025, which flagged content unrelated to a website’s area of expertise, Connecteam’s experiment started going downhill.

Organic traffic was the first one to take a hit, but AI citations also followed.

Why did that happen?

The nature of these pages goes against what Connecteam is - a workforce management tool, not a review site, like G2.

Besides, 500+ pages isn’t a small number, so their impact on Connecteam’s visibility is real.

We’re not saying you shouldn’t give up on comparative and self-promotional content.

They simply shouldn’t constitute such a big portion of your content.

Connecteam posting 500+ review pages and listicles, sometimes 16 in one month, is a perfect segway into the next point: Does publishing velocity impact your AI visibility?

Pillar #2: Publishing velocity

Now, it’s well-known that publishing velocity impacts organic visibility.

But we believe it has as much impact on AI visibility.

You already know that Google gives preference to expert content related to the website’s subject matter (Google has doubled down on it lately, with its December 2025 and February 2026 updates).

Data shows that AI search engines behave the same. According to Ahrefs, the average age of content cited by AI assistants is 1064 days - 25.7% “younger” than content cited by Google, which is 1432 days old on average.

What’s more, Ahrefs’ study proves that ChatGPT and Perplexity order citations from newer to older.

We know what you’re thinking: 1,064 days is basically 2.9 years. In what universe is that ‘fresh’?

Sure, both Google and AI assistants seem to cite long-lived content that has passed the test of time. However, your new pages will reach that moment in time once, too; that’s why you should keep them coming, but don’t sacrifice quality for speed.

On that note, as your content inventory keeps growing, you’ll need to find a way to strike a balance between publishing new content and updating older pieces, which leads us to the next pillar: content optimization.

Pillar #3: Content optimization

We follow the same content optimization principles for AEO and SEO.

In a nutshell, the content optimization elements look like this:

Although the process is the same, the playbook for AEO has changed based on how AI search engines surface the results.

Here are some patterns we noticed:

• LLMs prioritize early passages, which is why the way the top of your page looks makes all the difference.

• Chrome has a limit on the number of passages for embeddings, so you should keep intros short and straightforward.

• Long reads don’t generate infinite citations; thus focus matters more than length.

• Google’s AI follows the semantic HTML structure of a page and uses headings to understand meaning, which is why they shouldn’t feel ambiguous.

• Clear heading structure, such as the one asking a question and immediately giving the answer, makes content more skimmable for AI search engines.

• An AI search engine may retrieve a separate section, which is why it should be logically structured but not isolated from the rest of the content.

• The main priority is the reader, so aim for a natural flow with a clear structure rather than optimizing content solely for LLMs.

Based on these patterns, we developed a set of best practices that you can see below.

Do these practices work?

We applied them when writing this piece on the best AEO agencies:

• a clear structure

• straightforward intro and headings

• succinct and focused paragraphs

• lots of tables

• and zero fluff.

Here’s what we’ve got in return:

Of course, all of these best practices become largely decorative if crawl efficiency optimization gets ignored.

Let’s talk about it next.

Pillar #4: Crawl efficiency

Our crawl efficiency analysis is a 4-step process.

First, we check if AI search engines can access your pages, which you can easily do by going to your website’s robots.txt file and seeing if it restricts any user agents from crawling your website.

Here you can see that the website disallows AI agents from training on and crawling the website information, mitigating its chances to appear in AI search engine results.

The next step is creating (or checking the correctness of) the llms.txt file (or the AI Info page).

We should emphasize that adding this page to your website is not a guarantee for better AI bot crawlability, and Google says this page isn’t necessary for better AI bot crawlability.

Nevertheless, our experience working with clients shows otherwise, and a properly structured AI info page can make an incremental difference to your AI visibility.

So, how do you structure your AI info page?

Essentially, it boils down to what AI search engines should know about your company in a specific order:

Now, there are a few things to zoom in on here.

First and foremost, pay attention to brand disambiguation: ensure that an AI search engine doesn’t confuse your brand’s name with something else.

Take Notion, for example. It’s a well-known brand, but it’s also a word on its own, so their llms.txt file has to specify the nature of this brand.

Another important insight here is to be specific about your target audience.

Make sure you include both who you want to target and who is not the right match for your product.

Here’s an example of how we interpreted this section for Minuttia’s AI info page:

You also want to add sources that prove and verify your credibility, such as case studies or research.

For Minuttia’s AI info page, we added three prominent client cases and referred AI bots to our Case Studies website page for more information.

On a general note, you should aim for incremental improvements that would have a tangible impact on how you’re cited by AI search engines, and not chase overnight AI wins.

As for steps #3 and #4, we want to zero in on the best practices for the URL structure that improve crawl efficiency:

For the URL architecture, we follow two different approaches for blog post URLs and for cluster URLs.

In both approaches, we use three different types of keywords to structure URLs.

Here’s how these keyword types compare against each other.

The rules are simple for a blog post URL - you simply choose the keyword type based on the main query your post is targeting.

Here are some examples with 3 types of keywords:

• https://www.meilisearch.com/blog/rag-for-structured-data

• https://viral-loops.com/blog/how-to-build-a-community-around-a-product

• https://viral-loops.com/blog/coming-soon-page-examples

The picture looks a bit different for the topic cluster pages.

Essentially, you have the same keyword types, but they apply to the URL slugs corresponding to each subtopic found in the given topic cluster.

Here’s a visualization to make things clearer:

If you want a real-life example for the topic cluster, the Product Launch 101 piece by Viral Loops shows how to structure URLs for the phrase match.

Basically, you have:

• Core page: viral-loops.com/product-launch

• Cluster page: viral-loops.com/product-launch/marketing-plan

• Cluster page: viral-loops.com/product-launch/plan-templates

• Cluster page: viral-loops.com/product-launch/strategy, and so on.

What you want for both phrase and term/semantic match is to keep things simple. Avoid numbers, dates, or anything that would confuse the crawlers.

Now, let’s move on to content monitoring.

Pillar #5: Content monitoring

Let us return to the aforementioned finding by Ahrefs that AI search engines tend to cite fresher content more often. That’s a point to the importance of publishing velocity.

However, focusing too much on new content while ignoring outdated posts is a bit like renovating the kitchen while the basement floods.

Just look at the Peanut app’s website.

According to Ahrefs, Peanut’s blog contains 2,441 pages, which means they built them at scale, most likely posting over 100 blog posts a month.

This effort didn’t go unnoticed, and their organic traffic peaked at 2.5 million monthly clicks in 2023.

However, as their content inventory started to age (some content pieces haven’t been updated since 2022), organic traffic also took a hit.

Essentially, too much focus on net new content created a dead weight of older, sometimes irrelevant pieces, making regular content updates a real necessity.

However, a content update doesn’t necessarily mean a full rewrite.

At Minuttia, we developed a signal-based system that flags content due for an update. This system assigns value, which determines how much of that content needs to be revised:

• Light refresh: we change < 30% of content, making improvements to the specific sections, such as outdated statistics.

• Content update: > 30% of content is changed, involving a full rewrite for specific sections, e.g., when rankings have significantly declined, or the SERP landscape has changed.

• Complete rewrite: we rework the entire piece, mostly when it’s too outdated to salvage, or the search intent for the target keyword has changed.

What is important here is not to fully automate this process and always have a human in the loop.

The cost of a poor update is too high to delegate this task fully to, say, an AI assistant, so there should always be a team member who does quality assurance.

Let’s move on to the last pillar.

Pillar #6: Brand experience and perception

Now, if you go back to the intro to this section, you’ll see that the sixth pillar for SEO optimization mentions backlinks.

However, since the introduction of AI search, backlinks have become only one part of that pillar, yielding to the following for elements, brand experience being the most important.

Now, where do you start with improving brand experience?

You start with what you can control: owned perception of your brand.

Here are some examples of how we manage it: being listed in the directory, including a positioning page on our website, creating relevant blog posts, adding the AI Info page, and so on.

How does that translate to AI search visibility?

The more you invest in owned channels, the more likely AI search engines are to cite them in a branded prompt.

So, it makes sense to invest in multiple platforms for owned perception, but you want to do it in a way that matches your positioning, messaging, and business model.

What about the risk of sounding overpromotional?

It’s a good practice to develop or acquire digital assets to help with distribution - a newsletter, asset library, knowledge base - anything that corresponds to your brand’s positioning.

Now let’s talk about multi-channel perception. Why does it matter for AI search visibility?

The following data insights lay out the reasons perfectly.

• According to Ahrefs, YouTube is the most cited domain in AI Overviews: its presence has increased by 34% over the past six months.

• Kevin Indig said on his LinkedIn that more G2 reviews correlate with higher AI visibility.

• Ahrefs claims that brands getting the most web mentions earn up to 10x more mentions in AI Overviews.

Essentially, it all boils down to the number of brand mentions, and there are quite a few ways to get them without appearing too self-promotional.

But here’s the thing: you don’t always control the narrative when it comes to multi-channel perception. Nevertheless, when a negative brand mention comes along, it doesn’t mean you shouldn’t react.

Take the following example.

Asana, one of the leading work management platforms with 4.4 stars on G2, still gets negative reviews on Reddit. The one below complains about the unintuitive UI, but instead of leaving it to other Reddit users to solve, an Asana representative chimed in to assist with the problem.

This is one of the correct ways to handle such blows to your brand image. And overall, if you want to join a conversation about your company, we suggest doing it from a branded account, so your participation is transparent and trust isn’t undermined.

The same goes for discussing your brand in communities. It’s a bad practice to flood subreddits with promotional posts, like in the example below.

Instead, build or help moderate a relevant subreddit, where both your marketing and support teams can actively contribute without breaking trust.

Obviously, developing owned and multi-channel perception alongside other components of AI visibility is pointless unless you can tie all that to business outcomes.

Yet, AI search ROI and measuring AI performance, according to our survey, proves to be a big challenge for most respondents.

How to Measure AI Search Performance

If you look back a couple of chapters, you’ll see that in our survey, 40.6% of respondents reported measurement and attribution of AI search performance as their biggest challenge, followed by the lack of best practices and inability to tie AI search visibility to ROI.

It’s not entirely unexpected!

If you run the same prompt several times and across multiple AI agents, the results will vary, and even if your brand appeared once or twice, there is no guarantee it will be there the third time. How can you properly measure that?

There’s one crucial caveat to mention when it comes to AI performance metrics: measurement needs repeated sampling and context.

You need to see how things change and evolve over time, and it should be done often, given the volatility of AI search results.

When it comes to specific AI performance metrics, we think the following ones matter most:

• Referral traffic from AI platforms (e.g., ChatGPT, Perplexity, Claude)

• Brand mentions and visibility in AI search engines

• Share of voice in AI search engines

• Average position your brand appears in AI search engines against other brands

• Number of citations and citation share in AI search engines

• Conversions and revenue attributes to AI-referred traffic

• Sentiment

• Incrementality (h/t Kevin Indig)

• AI Crawler Activity

• AI Overview Impressions

Here’s a short description of each metric (how we perceive them):

Now we come to the point where those 40.6% of respondents feel the most pain: setting up the tracking tools.

Let’s make one thing clear: you’ll need to use several platforms and assign them to different metrics, such as GA4 for AI referral traffic or Google Search Console for AI Overview impressions.

Below, you can see how we triangulate across multiple data sources:

From these sources, GA4, Google Search Console, Bing Webmaster Tools, and server logs are already familiar to you. The question is, how do you set up an AI search tracking report?

At Minuttia, we follow this 8-step process.

As you can see, the first two steps involve the client because we need to figure out all the relevant topics and related prompts (go to Topic Breadth and Depth to read more about it). Then we break down these prompts into two categories:

• Topical prompts related to a use case, problem, or category in which a brand competes.

• Evaluation prompts that directly test AI’s perception of your brand (pricing, integration, customer profiles, etc.).

Most AI tracking tools focus on topical prompts alone, but the problem with that is they often give results for synthetic prompts, which don’t always reflect the way how people communicate in AI search engines.

That’s why you need a combination of topical and evaluation prompts to see the whole picture, but while topics are quite straightforward, it’s not always clear how to get evaluation prompts.

Allow us to let you in on how we do that.

The process runs like this:

And here’s the template for an evaluation prompt:

Evaluate [Brand] on the [platform/product/solution]’s ability to [specific evaluation criterion], [desired outcome or business impact].

You can type it into ChatGPT and then check whether the response matches the reality and if there are discrepancies to be fixed.

Note that here you’re measuring perception, not visibility. The primary goal of evaluation prompts is to see how AI search engines see you.

We also recommend checking the cited sources to understand if anything needs to be fixed or created to fill the perception gap, and do that for each AI search engine separately - different search engines mean different retrieval methods, sources, etc.

Which tools do we recommend to track the prompts and generate reports?

Honestly, there is no single tool that would do this job perfectly.

A go-to platform for any brand would be GA4, but what we found is that the data it shares often deviates from the real picture.

For example, in our experience, some AI-driven referral sessions are flagged as direct.

What we recommend you do instead is to create a separate AI Search channel in GA4 that groups ChatGPT, Perplexity, Claude, and any other AI search engines you want to track. But even then, the data can get misrepresented.

So, our closing point is that you shouldn’t take this data at face value and treat it as directional.

No decimal-point precision reflects reality but can indicate some trends, which ultimately make a difference for the bigger picture.

Maja here. The truth about AEO is that nobody has it fully figured out yet. The mechanics shift every month, the tools are still maturing, and what worked last quarter might not work next quarter. But the teams that start building muscle here now will have a real lead 12 months from now.

A few things worth doing this week:

• Pull up your robots.txt and check whether you’re accidentally blocking AI crawlers. Takes 5 minutes, and I’ve seen teams discover they had agents blocked they didn’t even know about.

• Pick two evaluation prompts (the ones that test how AI describes your brand and category) and run them across ChatGPT, Claude, and Perplexity. Compare what gets cited. That’s your perception gap, sitting right there in plain text.

Big thanks to George and the Minuttia team for putting this together. Drop your questions in the comments, or feel free to contact them directly.

If you want to dive deep in AEO, check out previous GTM articles on this topic:

• How to Win the New SEO Game: AI Search, GEO, and the Future of Visibility

• AEO: How to Make AI Recommend Your Product

See you next week.

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

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Dear GTM Strategist,

I used to think selling would be the last human frontier. The relationship work, the trust-building, the reading-the-room work - I assumed AI would help around the edges while humans stayed in the seat.

Then I met Nigel, 1mind’s newly launched Ride-Along Superhuman. My honest reaction: “I thought this would be possible by 2030. And yet, here we are.”

Here’s the thing most GTM leaders don’t talk about openly: the Solutions Engineer (bottleneck is the silent killer kof B2B deal velocity.

The median AE:SE ratio is 4:1. In many orgs, it’s 10:1. Which means most live sales calls have zero technical support in the room. The buyer asks a hard question, the rep stalls, and the deal goes cold. Qualified pipe doesn’t lose to competitors. It loses to friction.

Teams try to patch this with duct tape: AI listeners, Slack-based help channels. Fine for nudges, useless when the prospect asks something off-script.

The old answer is to hire more SEs. The smarter one is a two-layer SE org: AI handles the repeatable 60-70% of Q&A live on the call, humans handle the strategic 30-40% (where most SEs want to be anyway).

That’s the bet 1mind is making. They are aiming to scale the SE motion, not replace it. Boston Consulting Group predicted this in their August 2024 The Future of Sales with AI report. 1mind is one of the first to deliver on it.

Nigel reads the room like a seasoned pro, stays focused on what he was trained for, and never bluffs. Which is more than I can say for most human-led demos I’ve sat through.

In this edition, I invited Jonathan Kvarfordt to join forces again. Jonathan has a great eye for winners and is one of the sharpest minds in our space.

Here’s what you’ll learn in this edition:

• Why the standard SaaS buyer journey is structurally broken, not just slow

• The OAR Matrix: how to decide whether to Optimize, Amplify, or Reinvent with AI

• The four lanes every GTM team will pick from in the next 18 months

• Three concrete moves every GTM leader should make this quarter

• What “AI-first GTM” actually looks like in practice, with proof points

Jonathan, the floor is yours 🎤

The buyer journey was built for 2018. Your buyers live in 2026.

Walk through your own funnel as a buyer. The pattern is painfully consistent.

A prospect researches their problem on Perplexity or ChatGPT. They have recommendations from AI and then they land on your site, like what they see, and fill out the inbound form. A 22-year-old BDR who started six weeks ago calls them and asks the same qualifying questions Perplexity already answered.

They get scheduled into discovery with an AE, who asks most of the same questions again.

Meanwhile the buyer is pinging peers and former users to self-educate, because nobody on your side has actually answered their question yet. Eventually, a demo happens. Then come the follow-ups, the stakeholder alignment calls, and - if everyone is still awake - a close.

Sound familiar? It should. This is the standard SaaS playbook, and it has a clear lineage. Predictable Revenue in 2012 gave us the SDR/AE split. SalesLoft, Outreach, and 6sense around 2014 gave us sequenced cadences, lead scoring, and intent data. 2020 forced the entire industry online. 2022 added the PLG layer.

By 2018, the model had settled: deterministic workflows, MQL-to-SQL handoffs, BANT-or-MEDDIC qualification gates, seat-based pricing, and 80-90% gross margins that made it all economically viable. That model was beautifully tuned for its world.

And then came AI-led growth (AiLG) and changed everything. Buyers self-educate before they ever talk to us, AI has compressed research time from weeks to minutes, gross margins on AI-native products have collapsed to 50-60%, and the buyer increasingly knows more about your category than your rep does.

We are running a playbook designed for a world that no longer exists. It is yielding diminishing returns and is expensive to run.

How to fix it?

Humans are only capable of so much and 75% of the drag is internal friction anyway

Here is the part of this conversation almost nobody is naming.

When my team at Momentum ran the analysis, 75% of the friction slowing down sales cycles came from inside the company, not from the buyer. It wasn’t RFP processes, security review, or buyer indecision dragging deals out. It was internal handoffs, approval queues, and CRM hygiene. SDRs reminding AEs, AEs chasing SEs, SEs waiting on implementation.

The deal moves slowly because we move slowly.

And the cost model is breaking right alongside it. Depending on stage and industry:

• 10-50% of CAC goes to sales and marketing

• 20-50% of revenue is spent on sales alone

• 50-80% of cash burn is driven by headcount

So we’re running an expensive, slow model in a market that rewards the opposite.

Most GTM teams aren’t built for the AI world. Handing your 2018 playbook a ChatGPT license is not transformation. You do not transform from writing emails faster.

Most “AI in GTM” conversations stop at the buyer-facing surface - chatbots, AI demos, automated outreach. That’s treating the symptom, not the system. You cannot fix what’s broken by trying harder on top of it.

Let me be very clear here. The old world would say, “hire more humans” but countless thought leaders, analysts, and companies in the space have already identified that growth at all costs does not work.

Here is the other side of the coin: even if you could have all the funding in the world, humans have capacity constraints. Knowledge limits, only work at certain times, talent quality variability, scheduling nightmares, etc. These constraints cause, as we have talked about, which is the horrible buying experience. So what if we looked at the issue from first principle thinking? What if we ask the question, what is best for the buyer and customer experience?

After working with thousands of individuals, hundreds of companies, and working on AI strategy and implementation since before ChatGPT was released, I can tell you, you can only optimize a human so much because of the constraints, at some point, you hit a ceiling. When I was at Momentum, we released the book called Ignite, where we interviewed 30+ experts in the space about this and everyone came to the same conclusion, which is to think in systems and that means balance between humans and AI, not only AI and not just only humans - it’s a harmony between the two. Architecting the intelligence of both humans and AI, not one replacing the other.

At 1mind, every use case we solve for is something that humans do not do right now anyway. For example, how many companies do you know of that have an expert human on their website (not an SDR), ready to jump on a Zoom call at any time and able to answer deep technical questions 24/7?

The answer is… no one. Why? Because no one can afford it.

How many companies have 1:1 coverage of AE to SE? Not many… why? You got it, no one can afford it, let alone find enough talent to fill the gaps, even if they could.

How many companies have a product expert ready to answer deep technical questions right after someone buys a product? Again, not many.. Most give you a few hours of onboarding or some sort of onboarding doc and send you on your way…

How many Customer Success teams have deep technical support anytime they need it?

If anything, every customer success leader and team I have been on has been begging for more help, not less.

But what if we ask the question, what if we gave a genuinely delightful experience? What if we gave our BEST to our customers from the start? What if we used technology as a way to make the customer experience better? Is that not a good thing?

This is me as a human actually typing a phrase that AI usually does, but this is NOT about replacing humans, it is about doing things that humans are not doing right now in the first place.

What to do instead?

The OAR Matrix: put AI where it actually belongs in your org

Most GTM teams I work with want to jump straight to “AI transformation.” The smarter move is to know which level you are actually playing at.

At GTM AI Academy, which has helped over 10,000 leaders on their AI Adoption Journey, I teach a simple framework called the OAR Matrix to help them figure out where they are and where they want to head next.

1. Optimize. Same process, made faster with AI. Realistic gain: 10-30% time savings. For a large org, that is a serious ROI number. Start here if your team has low AI maturity or low appetite for disruption.

2. Amplify. Real structural changes. More revenue, real cost savings. Realistic gain: 20-40%. You are not blowing up the function, but you are seriously rewiring it.

3. Reinvent. Burn the playbook. Rebuild from first principles. Realistic gain: 100x on the right function.

Most organizations are optimizing where they should be reinventing. You may be asking, but will humans actually talk to an AI and get value? Will businesses actually use this?

To make that concrete, look at HubSpot - one of the most sophisticated demand engines in B2B.

HubSpot could have gotten stuck on the Optimize path: better sequences, ChatGPT licenses for the SDR team, faster routing. Instead, they put an AI Superhuman named Fiona at the front of their funnel and let her handle the buyer conversation end-to-end.

The results speak for themselves:

• 88% of buyers who landed on the page engaged with Fiona

• 32% had a meaningful conversation - averaging nearly 8 minutes, with 90% surfacing real pain and 55% getting into pricing

• 78% increase in free trial conversions

• 25% increase in influenced pipeline

But the number that should stop every RevOps leader cold is this:

HubSpot estimated it would have taken 83 SDRs and 19 sales engineers to deliver the same volume and depth of buyer conversations.

Remember, this putting AI in a place that HubSpot could not hire for in the first place, so this comparison is not saying they replaced this many people, they identified how many people it would have taken them to hire to be comparable.

That is the gap between Optimize and Reinvent. Optimize gives you 20% more from your current 102-person team. Reinvent reimagines the entire process where AI gets put into the mix for a better buyer experience and allows humans to do more of what you need your humans to do, like build better relationships, stakeholder management, and connect with customers the way that only humans can.

Reinvention always feels like a slingshot pulling backward. It takes time to reinvent and build from the foundation up. So you feel like a rock in a sling shot and while everyone is on Linkedin posting about the latest agent that made them $2 million in 2 days that they built over the weekend, you are feeling like you are falling behind. But trust me, when you do this right, you accelerate faster than ever. You don’t catch up to where you were, you launch past it.

In 2027, buyers will choose how they want to buy from you

Up to this point, I have been talking about what AI does inside your GTM team. Here is the harder question: what AI does to the buyer’s experience of you.

Within the next 18 months, every company will land in one of four lanes. There is no neutral position, and the lane you pick becomes part of your brand whether you intend it to or not. Because the BUYER is the one who will control how they want to interact with you, because now they have options. Which means we as companies need to think about giving them a “choose your own journey” type of experience.

For example:

1. Human only. The buyer demands human only experiences. For whatever reason some humans will reject AI and demand a human only experience. No one knows the size of this group, but it will be a reality at least for the coming future. If this is the case, those human buyers will expect the best of the best. Mediocrity will not be tolerated because just like how Amazon released 2 days shipping and you expect every company you work with to have 2 day shipping, buyers will have the same expectations when it comes to overall experience. The Tide lifts all boats and that tide is a better buying experience is a minimum requirement.

2. Human, with AI assist. The buyer goes through a similar experience to what most have right now where the seller stays in the seat; AI handles the back-office work. This is Optimize on the OAR Matrix. Most GTM teams I work with are aiming here - realistic, achievable, a solid place to operate from. AI is here and there in the process, but it focuses on human-to-human interaction.

3. Human to AI. This is where things start to shift into AI Led Growth. Gartner surveyed 646 B2B buyers and identified that 67% PREFER a rep-free buying experience. If someone is willing to work with your company but is tired of the old-school buying process that we already walked through, why not give them the option of doing more with AI? HubSpot is running this. Seismic is running this. ZoomInfo is running this. The bet is that buyers want optionality, and the data so far says they do not only in surveys, but in real life.

4. AI to AI. This is the one most people are not ready for. Within 12 to 18 months, a meaningful share of your inbound traffic will be AI agents - OpenClaw, Hermes, or self built Claude Code or Cowork or ChatGPT AI Agents all acting on behalf of a human buyer, doing the research, comparing vendors, and in some cases making the purchase recommendation. Is your website, your pricing page, your demo flow designed to be evaluated by an AI agent in 90 seconds? The future will be where the human buyer has a fleet of agents not only doing research, but has a wallet with a budget that they can pay for solutions. Are you ready?

The strategic question is not “will AI replace my salespeople.” That is the wrong question, and it makes leaders defensive. The right question is: what buying experience does our brand want to be known for? Pick your best fit. Design for it.

The principle underneath it all: design around the buyer, not the seller

The single biggest mindset shift required to operate in any of the four lanes above is this: stop designing your GTM motion around what the seller does, and start designing it around what the buyer needs at each step.

Once you do, you discover that a lot of seller activity exists only because the seller’s workflow demanded it - the BDR call, the AE re-discovery, the qualification gate. AI handles the parts the buyer never needed a human for. Humans show up where buyers actually want them: judgment, negotiation, edge cases, relationship.

That is the actual reorganization. Not your org chart. Your workflows.

Three things to do this week

You do not need to rebuild your GTM org by Friday, but you do need to start. Three moves:

1. Walk your own funnel. Pick a friend in your industry and have them go through your buyer journey end to end - inbound form, BDR call, AE call, demo, follow-up. Measure how much time it takes to get to the end goal. Have them rate the experience 1 to 10 and answer one question: would they recommend it to a friend? Most GTM leaders have never done this exercise on their own funnel. Most are stunned by what they find.

2. Map the internal handoffs, not just the buyer journey. Where does the deal slow down inside your walls? SDR to AE? AE to SE? Approval queues? Quote desk? Pricing exceptions? Map every handoff and time-stamp each one for your last ten closed deals. The pattern will be obvious within an hour, and it will tell you exactly where AI belongs first especially when you map these frictions to your buyers journey. Optimize in areas where these frictions overlap.

3. Pick one workflow to redesign around the buyer. Don’t try to fix everything. Pick the single most expensive workflow in your funnel - your AE discovery process, your trial-to-paid motion, your renewal flow - and ask which steps exist because the buyer needs them, and which exist because your process demanded them. In that area, give the buyer and customer the option of AI. You will be surprised how many will not only use the AI, but prefer it.

A year from now, the GTM orgs that win will look smaller, faster, and weirder - fewer humans, doing the parts only humans can do, with AI doing the rest. The org chart won’t look like 2018. Neither will the revenue per head. This will cause disruption of the old world

And one more thing

You can read about AI Superhumans all day. The only way to understand what one actually feels like in production is to have a conversation with one. Mindy at 1mind, Fiona at HubSpot, and others are running live - go talk to them for five minutes.

It will tell you more about where GTM is headed than another article will.

Explore the Customer Superhumans →

The question worth sitting with

What buying experience does your brand want to be known for in 2027 and beyond?

Because if you don’t decide, your 2018 playbook is deciding for you.

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is sponsored by Miro.

Miro’s MCP server connects your boards to the AI tools you already use: Claude, Cursor, Lovable, Replit, ChatGPT, and more. This turns your collaborative workspace into a live context that AI agents can read, research, act on, and build from.

Imagine sending your prototype, which you agreed on with your team in Miro, directly to Claude Code to build it in minutes — that’s a reality with Miro’s MCP server (and much more).

I’ll be at Canvas 26 London 🇬🇧 on June 2 to be the first to know what Miro ships next. I will also host a workshop there at The Studio on how I use Miro. If you’re going, let’s totally shake hands. I’d love to compare notes on how you’re putting collaborative AI to work in your GTM motion. The San Francisco keynote will also be streamed on Miro’s YouTube account, which is free to watch.

Save your spot

Dear GTM Strategist,

It is easy to spend your time looking at people further ahead. But every milestone deserves a pause. I just crossed 33K subscribers on Substack and 83K on LinkedIn, with 1 million monthly impressions, in two and a half years of consistent content creation.

None of it was an accident, and none of it was linear.

It started in November 2023 when I launched my book, The Go-to-Market Strategist. I had spent months prepping for the launch, and I had no idea what would happen. Luckily, it became a bestseller. That was the aspiration. But then: now what?

So I started a newsletter on Substack, mostly because the platform had embedded growth features and a few content creators I admire were using it. 🤷 No sophisticated agenda there. I also kept up the habit of posting three to five times a week on LinkedIn, since it had worked well for the book launch sign-up list. Why stop?

I want to be honest about how this actually happened, because most “how I grew my audience” content makes it look linear and inevitable. It was not. There was a 6-month period in mid-2025 where I genuinely lost the plot. I will get to that.

In this article, I will walk you through:

• The 7 growth levers behind GTM Strategist, in the order I built them

• The mid-2025 identity crisis that broke the pattern and forced a rebuild

• A simple 3-layer framework for thinking about your own lever stack

• What I am betting on next in AI-native GTM

So let’s get started. We have a lot to cover.

7 levers in detail with useful how-to content

Before I walk through each lever, here is the shape of the journey. Every lever, once added, kept compounding. None of them replaced what came before.

What is interesting in this curve is what happened between February and September 2025. I was doing all the right things, and the growth flattened anyway. That was the period that broke me a little, and it was also the period that taught me the most.

Let me walk you through it.

Lever 1: Voice and Consistency (The Longest Reps)

The first 6 months after the book launch, I was just learning by doing.

Looking back, I’m not sure what I was doing exactly. But I was doing the work. One of the great things about GTM is learning to play to your strengths and compensate for your weaknesses. My strength is being extremely hardworking and disciplined. Copywriting was never my passion.

I am a long-form girl. I don’t spend my days thinking about hooks, and I have a philosophical barrier to copying what direct-response copywriters do - it feels repulsive to me as a person. I acknowledge the discipline is highly effective for conversion-centric copy, and I will happily pay someone else to do it, but I don’t have it in me.

Luckily, social media and newsletter content are a different beast. You need something proprietary to say - and in the world of AI-generated everything, that matters more than ever.

My first instinct was to promote sections of the book in the newsletter. That worked for a while, but I quickly realized that not all the interesting GTM knowledge in the world lives between two book covers. So I started exploring topics that came up with my consulting clients and inside our community.

What I underestimated back then was how long it takes to find a voice that actually sounds like you. Not a “brand voice document” voice. A voice where people read three sentences and know it is you.

Today, you can shortcut a lot of this by embedding call recordings into your content AI engine. Back then, AI-assisted writing was barely a thing - ChatGPT had just properly launched - and I tried 6 different copywriters before I gave up. None of them could mimic my voice well enough for me to be happy with the output. So I sucked it up, developed just enough copywriting muscle to get by, and committed to writing everything myself, every single day for six months.

The reps are the moat. There is no version of this where you skip the early uncomfortable months.

🔗 Dive deeper: I documented the full first year of building GTM Strategist here - a useful read if you are prepping for a launch or building your own content engine backed by digital products.

Lever 2: Design as a Moat

When copywriters and AIs could not replicate my voice, I made a strategic call: I would write everything myself and double down on design instead.

This turned out to be one of the best decisions I made. Back then, most content creators were either DIYing something in Canva or paying stunning amounts ($200+) for a single infographic that honestly looked the same to me every time. I get it: don’t fix what’s not broken, but also innovate because people are getting saturated with formats.

At the same time, most newsletters in our space looked the same - blocks of text with the occasional stock photo or screenshot. Investing in custom illustrations, frameworks, and visual systems made my content instantly recognizable in a LinkedIn feed.

Design became my first real differentiator, and it still is.

These days, the GTM Strategist design system is pretty sophisticated. We have fundamental brand assets and a brand book, and we are capturing great trends all the time. As we experimented with new formats, topics, and visual aesthetics, it became a little design lab.

🎁 Gift number 1: Based on the analysis of my best-performing LinkedIn posts in the last 90 days, here is a cheat sheet for designs that work well for me. The analysis was done with Miro’s MCP in Claude. Get it here.

The Big Lesson: When everyone else is racing toward the same AI-generated aesthetic (currently, orange Claude-generated images, even though ChatGPT became amazing at infographics), the things that are harder to copy (aesthetic illustration, complex graphics, atypical layouts) become more valuable. While I know plenty of people who are doing great work with AI-generated designs, I am continuing to invest in human-led design and branding.

Lever 3: Collaborations and Cross-Recommendations

You can go far on your own. You can go further with the right people by your side. Or, more simply: go where your audience already is.

I was such an underdog when I started. I am originally from Europe, English is my second language, and we have a slightly unconventional take on GTM here. Some would call it scrappy. I call it extremely pragmatic - we get the job done. The first year, I was not really taken seriously by the US-centric GTM crowd. The “big guys” mostly looked past or down at me.

But I was eager to show them I was not a force to be taken lightly. Not only can I outwork most people in the space, I genuinely believe I have good advice beyond theoretical fluff, built on solid fundamentals. Expanding the community and evolving from scrappy, bootstrapped startup roots toward an enterprise-relevant, GTM-first approach became a really important mission.

I am forever grateful to Kyle Poyar, Aakash Gupta, Aatir Abdul Rauf, David Pereira, and Pawel Huryn - among the first “big guys” who took me seriously. They invited me to co-create content, gave me real advice on how to grow, and helped me develop a more sustainable content flow. Not one that was rooted only in my product portfolio, but one that was starting to become a recognizable voice in the space.

People ask me all the time how to find this kind of person. My rule: I personally have to love their content and admire them as professionals.

How to find your people:

1. Ask your existing audience who else they follow and learn from in the space.

2. Use desktop research or AI research to map adjacent creators and operators.

Identifying them is not rocket science. Deserving their attention is the harder part. Sometimes it takes me months, even years, to build these relationships. Dozens of multi-channel follow-ups. The slow grind of becoming useful before becoming visible.

But once you find a real synergy, those partnerships become invaluable. They are the partnerships that shape ecosystems.

If you are building a brand right now: do not optimize for the algorithm before you have optimized for peer trust. The algorithm changes every quarter. Peer relationships compound for years.

I am still in touch with nearly everyone who supported me early on. We learn and grow together. Some sweet collabs coming up in the next couple of months. Stay tuned. 🫶

Lever 4: Free Assets That Feel Like Paid Products

In 2024 and early 2025, the “comment to get X” era on LinkedIn was in full bloom. Most of those posts were thinly veiled email harvesters. The asset itself was rarely worth the comment.

I took the opposite approach, almost by accident. The principle, in retrospect, is product-led growth applied to content: give away something for free that people would happily pay for.

This brought real, relevant attention to my profile. Not vanity engagement. The kind of attention that turned into clients, partners, and long-term subscribers.

There was a temptation around this time to go mainstream. The content creators I admired when I started out were shifting toward career, leadership, and personal-growth content. The reach of that kind of content was undeniable. But that was never my passion. I am obsessed with GTM.

Fast forward to 2026. “Comment to get X” is making a comeback because AI has made deep research available to an average student. Anyone can ship a free guide in a weekend. Which means the bar is back to where it should have been all along: the asset has to actually be good.

When people want something, it usually pays off to go the extra mile and create a special asset for them. Be selective. Not all followers are equally valuable to your business. Plenty of people would love anything for free.

What matters more than ever is ICP fit. If you have to go the extra mile to make something deeper, more meaningful, more researched - do it. If you go with mainstream blockbusters like “Comment to Get My Productivity OS,” and you work on neural computing consulting, it will not help grow your business.

The point is to find the right audience, not to boost vanity metrics.

🎁 Gift number 2: A little stash of my past “comment to get X” assets, in case you missed them on LinkedIn.

🔗 Dive deeper: My 5-step LinkedIn viral post framework - “comment to get X” does not work the way it used to, but when you nail the topic, it is still magic.

Lever 5: You Are the Niche (And Why I Almost Walked Away)

This lever overlaps with Lever 3, but it deserves its own section because it almost made me quit the whole thing.

The temptation to broaden the range of topics was real. You can easily get thousands of likes on LinkedIn by oversharing your personal life, riding generic leadership trends 🥱, or quoting Steve Jobs for the millionth time.

Big content creators call them “audience growers”.

I call them “if you see these three times, hit unfollow”.

What kept me grounded was a simple realization: I am obsessed with GTM. The day my content stops being about GTM is the day I stop showing up with conviction, and readers can feel that.

This cost me time. It probably cost me follower count. But it built the only thing that actually matters long-term: a reputation as a thought leader in the space. You cannot build that by accident, and you cannot build it while chasing every adjacent trend.

And then, for a couple of months in 2025, I lost touch with that.

When everything stopped working 🤯

Now we get to the part most growth stories skip.

Mid-2025 was very difficult for me, not only as a content creator but as a person. I was struggling to find my voice because mainstream AI had arrived with these grand promises of agents doing everything for us. I am a big believer in AI technologies, but I was not (and still am not) convinced we are at that point yet.

Around the same time, the LinkedIn algorithm shifted. Posts that used to hit 20K impressions consistently started struggling to reach 10K. The same effort. The same topics. The same care. Half the reach.

I was tired. I was frustrated. The constant algorithm games took a real toll on me. It became a negative spiral.

The Substack growth flattened too. You can see it clearly in the chart above. February to September 2025 is a near-plateau, even though I was publishing consistently.

But there is no nice way to say this: I lost a lot of my enthusiasm. I did not know what I was doing wrong. The honest answer, in retrospect, is that I was not doing much wrong. I just could not figure out fast enough how to adjust. The ground had shifted underneath me, and the system I had built was no longer the right system for what was about to happen next.

I needed a break.

My husband and business partner Anže, and I packed our bags and spent a month in Indonesia. It was fun. We came back with a clearer head, but also a little empty-handed. I was not sure what came next.

The answer came from two directions at once.

Lever 6: Curated Brand Partnerships (That Add Value)

Before I go further, I want to be transparent about something.

Brand partnerships are how I keep this content free and accessible to everyone who needs these insights. No paywall, no premium tier, no “subscribe to unlock.” Just one direct trade: I work with a small, curated group of partners, and in exchange, the work stays open for the entire community. Huge thanks to all our partners 🫶

That is the deal. And because that deal matters to me, I take partnerships very seriously.

After Indonesia, I went back to first principles: give people what they want.

I talked to community members. I reflected on what content I actually enjoy consuming. The conclusion was simple. Everything has to be useful, hands-on, and well-researched. Especially now that creating content has become so easy, having something smart and useful to say is the only sustainable way to remain relevant.

But the bigger shift was how I started thinking about brand partnerships.

I stopped seeing partners as sponsors who get a shout-out in exchange for a banner. I started seeing them as part of a GTM Strategist ecosystem - a curated tool stack that produces real ROI and sustainable work for the community.

Sourcing from our consulting work, I invited a small group of tools (up to 6 a quarter) we already use and trust to build long-term partnerships with us. Not for one-off campaigns, but for ongoing collaboration. In return, we get a steady pulse on what is new in their ecosystem, and our community gets something genuinely useful: tactical, practical content built around tools that actually work together.

Some of our best-performing content in the last 90 days has been partnership content - the “how to use X” deep dives, the workflows, the real implementations. Every piece is researched. Some experiments will work, some will not, and that is fine. The decisive call was this: stop shying away from naming the tools, and be ruthlessly honest about which ones we recommend.

The results have been strong on the partnership side, too. UTM links never capture the real value, even though we are consistently in the top 3 to 5 content creators for almost every brand we collaborate with. That is not me bragging. That is me saying we hold ourselves to a measurable standard, and we walk away from partnerships that do not clear it.

I get pitched at least three times a day for brand collaborations. Most of them, I redirect to other creators who would be a better fit. It does pain me to say no, especially to smaller tools and to people I personally admire. But sustainability and trust come first.

It took me years to build trust with this audience. It would take one or two bad partnerships to lose it.

So I am almost annoyingly selective. I try every tool myself. I will not feature anything I would not personally recommend to a client or use in our business for years.

Lever 7: Riding the Claude Code Wave (Early)

The second answer came at the start of 2026. And it changed everything.

Claude Code went mainstream. I was lucky to get into the ecosystem early - and I made a deliberate choice to share what we were learning openly as we figured it out, instead of waiting for a polished playbook.

The result: 3 to 5x my usual growth benchmarks in 90 days.

Some of my best-performing assets in that window have been built around Claude Code, context engineering, and what I am now calling the GTM brain - a proprietary AI-native GTM system that compounds rather than degrades with every model update.

🔗 Dive deeper:

• The GTM repository for Claude Code

• The GTM guide to AI context engineering

• Build your GTM brain (a demo with Attio)

That last part - “compounds rather than degrades with every model update” - matters more than it sounds.

For content writing specifically, we moved everything into the Anthropic ecosystem and built a system that consistently produces great results without getting worse every time a new model ships. I still firmly believe in human editing, but for the first time, I am genuinely happy with how we are innovating in content creation.

Did we burn too many nights and weekends in Claude Code? Yes.

Was it worth it? Yes.

The other unlock was the rest of the stack. Claude Code for mockups. ChatGPT for infographics. Wispr Flow for voice-to-text. I can speak faster than I can type, and Wispr Flow lets me move at the speed of thought. Not affiliated - just genuinely obsessed.

The pattern here is the same as Lever 3 with collaborations: be early to the platforms that are about to matter for your audience, and share what you learn openly as you figure it out.

Major Unlocks on this journey: The Lever Stack, in 3 Layers, One Moat 🏰

Looking back, I can group the 7 levers into 3 layers.

This is the framework I would hand my November 2023 self if I had a time machine. I do not, so I am handing it to you instead - in the hope that your demand generation journey will be a little smoother than mine.

The foundation layer - voice and design - is the part you cannot skip. Everything else rests on it. (Great candidates for your .md files, by the way. 😉)

The distribution layer - collaborations, free assets, niche discipline - is how your work reaches the right people.

The compounding layer - curated partnerships, platform bets - is where the brand starts working for you instead of the other way around.

Most people try to start at the top of the stack. They want partnerships before they have a voice. They want platform bets before they have an audience. It does not work that way. Each layer rests on the one below it.

Together, they compound into a distribution moat 🏰.

This is the holy grail of AI-native GTM. Products have never been easier to copy and build, so the speed of traction is the name of the game. And almost no one builds that moat overnight. It takes commitment, iteration, and the kind of dedication that does not photograph well.

That is the annoying part. It is also the part that makes the moat real - and the part AI can actually help you with, if you let it.

In the meantime, take care, and good luck building your own stack.

Catch you at the next milestone (50K newsletter x 100K LinkedIn, hopefully) 🤞

Maja

✅ Need ready-to-use GTM assets and AI prompts? Get the 100-Step GTM Checklist with proven website templates, sales decks, landing pages, outbound sequences, LinkedIn post frameworks, email sequences, and 20+ workshops you can immediately run with your team.

📘 New to GTM? Learn fundamentals. Get my best-selling GTM Strategist book that helped 9,500+ companies to go to market with confidence - frameworks and online course included.

📈 My latest course: AI-Powered LinkedIn Growth System teaches the exact system I use to generate 7M+ impressions a year and 70% of my B2B pipeline.

🏅 Are you in charge of GTM and responsible for leading others? Grab the GTM Masterclass (6 hours of training, end-to-end GTM explained on examples, guided workshops) to get your team up and running in no time.

🤝 Want to work together? ⏩ Check out the options and let me know how we can join forces.

This newsletter is supported by Attio, the AI CRM.

I helped build something every founder should have in their toolkit.

GTM Atlas is a free resource for early-stage teams building from scratch, and I wrote an entry on building a GTM brain that gets smarter over time, using context engineering to make AI permanently smarter about your ICP.

With Atlas, you’ll get a point of view on your growth strategy, frameworks you’ll return to, and the creative latitude to build the right motion for your business, not someone else’s.

Wherever you are, Atlas was made for you.

Mapped by operators. Curated by Attio.

Read my entry now

Dear GTM Strategist,

Lately, we have been so deep in Claude that we might have slightly neglected the “strategy part” of the go-to-market strategy in our conversations.

Reality check:

If we spend all our time copying each other’s repos, workflows, and skills- who is really driving our GTM? AIs?

Time to take a mini break from terminals and .md files and shift our focus from how to do X (tactics) to what to do (strategy) instead.

As the world shifts into the third wave of AI and AI-native companies disrupt our imagination of what can be done in record time with a handful of people (and agents), it is harder than ever to stand out, stay relevant, and build any moat.

To sharpen our strategic muscle, I curated 3 thought-provoking ideas from GTM Atlas:

1. Your product is the pitch by Elena Verna

2. Your website is middle of the funnel now by Emily Kramer

3. An AI’s job is to remove wasted motion by Kyle Norton

Some snippets made me feel really uncomfortable (no spoilers), which is the whole point of learning something new.

My sole intention for this piece is to slightly provoke your mind, prompt you to think differently, and arm you with relevant insights that could help you build a more effective GTM system.

Let’s see if I can make it happen.

Here are the big ideas I hand-selected for you:

1. Your Product Is the Pitch

Author: Elena Verna, Head of Growth at Lovable

The companies winning right now are giving the most away upfront - product, access, experience - before asking for anything in return. Decision makers (CISOs, CTOs, VPs) are doing their own evaluation. If you’re not letting them touch the product, you’re already out.

But here’s the part I keep coming back to.

“Stop trying to minimize the freemium. Treat it as the line item that replaces your Google and Meta spend.”

Lovable gives away millions of dollars of software every year. Hackathons, schools, book clubs - no requirements, no forms. Elena says it’s their best acquisition channel by a significant margin compared to any paid spend they do.

This is a complete reframe of how most founders I talk to think about freemium. They treat free usage as a leak to plug. Elena treats it as a marketing budget. Same dollar amount, totally different mental model.

A few other ideas from her chapter that I dog-eared:

• Satellite apps are replacing gated PDFs (and that is great for AEO 👌). Nobody wants to trade their email for a whitepaper anymore. Elena is seeing a rise in interactive, clickable mini-apps - real experiences with a back-end, where prospects enter their own information and get something useful back. It puts marketing in an engineering position, but the engagement is incomparable to flat content.

• Build in public is now an acquisition channel. When software becomes commoditized, what differentiates you is the team building it. Lovable’s first few million in ARR came largely from their CEO being publicly transparent about what was working and what wasn’t. Founder social brand is no longer a vanity project.

• AI agents are part of your ICP now. This one stopped me cold. Elena’s hypothesis is that B2B software is evolving to be consumed by machines, not humans. Humans will own the agent interface. Agents will perform the tasks. Your future buyer profile includes the machines doing the work.

If you’ve been treating PLG as something you’ll get to “after we lock in sales-led motion and start generating predictable revenue,” this chapter might make you feel uncomfortable. Which is the point. In the AI-first GTM world, speed is of utmost importance.

2. Your website is middle of the funnel now

Author: Emily Kramer, founder at MKT1

I’d never say that marketers are lazy. They are usually one of the hardest-working people I see in the organizations we work with. But all the busyness could be a trap! Many of them are so busy thinking about the next campaign, event, and webinar that they fail to see that the nature of their work has changed tremendously. The big question here is - are we busy doing things that still work?

Emily Kramer placed a fair warning: RAM - Random Acts of Marketing. It’s what happens when teams have access to more tools, more channels, and more AI-assisted ways to create content than ever before, and they respond by just doing more of everything. Copying what other teams do. Saying yes to every founder request. Never stopping to ask which box is actually worth checking. Emily’s argument is that the teams pulling ahead aren’t doing more. They’re doing less, better. How?

Her solution is the gen marketer: a generalist who can orchestrate across all the marketing sub-functions, run “fuel + engine” campaigns (where fuel is the content and engine is how you distribute it), think in big bets that differentiate, create leverage by tapping into ecosystems, and is genuinely fluent in generative AI workflows. The big question here is not how to do more, but how to extract more value from what we are already doing.

Of course, you want an example: The role of the website in the AI-first GTM world.

LLMs scour the internet and find your pricing, whether you put it on the page or not. Traffic is fundamentally lower because people are getting answers from chatbots before they ever click through. So when someone lands on your site as an arguably higher-intent lead, the stakes for converting them are higher than ever. Emily offers some practical tips: Stop gating. Put pricing in a structured format. Put meeting booking in the demo flow. The website is no longer where you figure out who your customer is - you should already know.

And this change has two massive implications for GTM down the line:

• Accounts before contacts. Emily is direct about this: we’re so obsessed with leads that we don’t think nearly enough about the accounts we want. It’s a multi-person sale. Stop net fishing. Do account research, niche down, and systematically pursue the ones that fit. Stop thinking about lead capture and qualification - start thinking about which accounts are even aware of you and what stage they’re at.

• Kill MQL and SQL. When you’re going after whole accounts with multiple stakeholders, MQL/SQL labels create territorialism between marketing and sales that’s completely unnecessary. The buying journey isn’t linear. These are milestones, not handoffs. The fight over “marketing didn’t deliver enough” vs. “sales isn’t converting enough” is a distraction from the actual job: getting qualified, hand-raising buyers and going full force on them together.

Maybe sales and marketing will finally become best buddies in this new world … or maybe agents will take over the customer journey - who knows.

3. AI’s job is to remove wasted motion

Author: Kyle Norton, CRO at Owner.com

And for those two who are 🥱 - too much growth and marketing - Kyle comes to the rescue with some thought-provoking numbers 🍽️ next:

“Our ACV is only $8 to $10k, so how do you make cold outbound work in that world? By obsessing over the quality of the data, and stripping every wasted motion out of your rep’s day.

Outbound won’t work if your BDRs are spending 70% of their day researching leads and calling contacts who aren’t the real decision makers. You can maybe book a meeting every two days. You press reps so hard on a dial number of a few hundred a day that they can’t find enough high-quality leads to hit it. If you tiered those leads, 25% might be A, 50% B, 25% C. Tier C is who you will never close. The dial number gets hit, but you’ve wasted your time.”

What did Owner do about it? They built what Kyle calls AI PCR - pre-call research - that captures everything between calls automatically. Their outbound BDRs now make 150 to 250 calls a day and stay hyper-prepared throughout. Kyle’s top BDR last month generated $174K in closed-won ARR. Cold. The historical average was $72K per rep per month. They’re now at around $120K per BDR. How? By centralizing AIs.

A lot of teams think the answer is giving every rep their own Claude skill and letting them generate their own lead lists. Kyle says no. One specialist owns the system, builds it to production quality, and deploys one version at scale. Expert vs. non-expert in applied AI isn’t 50% better or 100% better - it’s 20x better. You need the expertise concentrated, not scattered.

Your AEs don’t need to be AI-savvy. This was the most controversial line in his chapter. Kyle wants to encourage AI fluency everywhere, but he’s clear: making your AEs better at AI doesn’t move the needle the way people assume. Fixing the data and the system does.

A lot of that gain didn’t come from better scripts. It came from giving reps only the leads worth calling.

If you’ve been telling yourself that hiring more BDRs is the answer to your pipeline problem, this chapter is going to be uncomfortable reading. In the best way.

What All Three Have in Common

When I finished the Atlas, I noticed something: Elena, Emily, and Kyle are talking about completely different parts of the funnel - capture, qualification, outbound - but they’re saying the same thing.

Stop adding more. Start removing waste.

• Elena: stop minimizing freemium, give away the product so the buyer can actually feel it

• Emily: stop running random acts of marketing, pick fewer bets and make them count

• Kyle: stop cranking dial volume, fix the data so reps only touch leads worth calling

The 2026 GTM AI playbook isn’t about doing more things faster with AI.

It’s about using AI to strip everything that doesn’t compound.

The teams that figure this out first are the ones we’ll be writing about next year.

The full GTM Atlas has six more chapters I haven’t covered here - Nicolas Sharp (Attio’s founder & CEO) on why there is no universal playbook, Roniesha Copeland (Vercel) on building the system before the message, James Pastan (Framer) on activation, Rati Zvirawa (Intercom) on letting AI own (not just assist), Josh Epstein (Coder) on retention as a culture problem, and yes, my chapter on building a GTM brain.

It’s free, non-gated, beautifully designed, and definitely worth your time.

I read it twice. Your turn.

Explore the GTM Atlas 🌍

Talk soon,

Maja

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Dear GTM Strategist,

In my ideal world, my content team would consist of 8 AI agents that would transform my call recordings into snappy LinkedIn posts and long-form Substack. 🤡

In the real world, I spend 20 hours/week working on content, we have an editorial and design team, and I am still scratching my head about how to speed up our content production with AI without compromising the quality and brand voice.

Claude Code was a big unlock on this journey since I could finally create context once, and then it knew our business, ICP, and branding essentials without dramatic flights in prompts and projects setups that produce various inputs.

Still, I keep hunting for more structured, end-to-end examples of how GTM operators are doing this - and ironically, I found one of the best in the LinkedIn comments.

Benjamin Gibert is the CMO of Base Operations, a B2B SaaS platform selling street-level threat intelligence to corporate security teams and government agencies. It is a series A startup with ~40 people. Ben does not have a dedicated content team. He is building an AI-native GTM strategy using agentic systems, but he can not get away with AI slop in a field that requires high trust.

Yet, his best marketing coworker is Claude Code.

Benjamin has built a content system that takes structured company knowledge, regularly enriches it with real customer conversations, and turns it into SEO articles, LinkedIn posts, ABM personalization, and even product strategy inputs.

Stuff that we like to call content engineering - the post you’re about to read is the most concrete walkthrough of how to actually do it that I’ve come across. Directory structure, agent specs, where the human review gates sit, which integrations matter and which don’t. He’s also open-sourced the whole thing on GitHub, so you can pressure-test it on your own setup.

Everything that we’ll present is done purely with the Claude Code and some power-up tools in the price range of SMEs. In other words, you can do it too!

Benjamin, how can we set up AI content workflows that produce content that is factually true and actually produces pieces that our ICP appreciates, instead of calling them AI slops?

Everyone in GTM is trying to figure out how to build content workflows that drive pipeline and revenue with AI. The problem is that if your AI-generated content is indistinguishable from your competitor’s AI-generated content, it won’t make any difference.

I run marketing for Base Operations, a B2B SaaS platform that provides street-level threat intelligence to corporate security teams. Most of my working hours are inside Claude Code. Over the past 14 months, I’ve learned (mostly through painful trial and error) how to build a content system that takes sales call transcripts and produces SEO/AEO articles, LinkedIn thought leadership posts, ABM personalization inputs, product strategy insights, and ultimately helps refine our targeting and GTM strategy.

Sales calls have always been a goldmine of customer insights. AI gives you a unique opportunity to turn that into the fuel for your GTM engine, but how you set up your system is the difference between sophisticated slop and differentiated content that converts your ideal buyer.

This post walks through how the system works: the knowledge foundation, the intelligence extraction, the pipeline stages, and the tool integrations that close feedback loops. I’ll also share the framework that puts it all in context, and point you to an open-source repo where you can build your own version.

The pipeline is the second thing I built. The first was the knowledge foundation underneath it, which is the context that matters most.

The Sophisticated Slop Problem (And What Solves It)

I’ve seen a lot of B2B teams embrace AI for content and fail. They build elaborate AI workflows that produce output that looks good only on the surface. I’m sure you’ve seen it. It reads well, hits the right keywords, and feels well-researched. And it says absolutely nothing a competitor couldn’t copy-paste, swap in their logo, and publish tomorrow.

I call this “sophisticated slop.” It passes every surface-level quality check, but it ranks nowhere and converts nothing, because search engines and buyers can both tell when content lacks genuine specificity. It erodes trust in humans and it doesn’t get ranked by algorithms, which are quickly adapting to the flood of AI content.

I made this mistake myself early on. My first attempt at a content pipeline had impressive multi-step workflows, carefully written prompt files, and zero strategic or tactical context beyond basic company information and target audience. After over a decade writing B2B content and managing content teams, I was so excited about building the pipeline architecture that I didn’t pause enough to evaluate how differentiated the output actually was.

Then I realized 90% of output quality comes from what you feed the system, not from the sophistication of the agents. This sounds obvious, but it’s easy to miss. Simple agents reading rich, specific context will outperform complex agents reading thin context every time.

A useful gut check: could a competitor copy-paste this output and change the logo? If yes, it’s slop regardless of how impressive the pipeline or how sophisticated the content sounds. That test has everything to do with whether the system contains genuine intelligence about your market, your customers, and your competitive position.

The fix is context engineering: building something substantial before you build the first agent. If you take one thing from this post, it’s that the quality of what you feed the system determines output quality more than any skill architecture. And the act of writing that context (documenting your ICP with real data, articulating positioning based on differentiated value, codifying voice rules with anti-patterns) forces the strategic clarity that makes everything downstream specific.

The Knowledge Foundation

The foundation of the system is a modular context architecture that lives in Github: a structured set of interconnected files that agents consume selectively.

The master knowledgebase is the source of truth. One document containing positioning, ICP definition, persona profiles with buying psychology, voice and style rules, competitive landscape, proof points with metrics, and messaging framework. For Base Operations, this covers everything from our three core points of view to prohibited terminology to how we talk differently to a CSO versus a security analyst.

From the master, I derive scoped module files that agents actually consume:

• icp.md -- firmographics, technographics, tiered customer profiles, buying committee roles, team structure, external signals that indicate a company is in-market

• personas.md -- per-persona buying psychology, pain points in their language, decision criteria, common objections, content preferences, and where they spend time online

• positioning.md -- strategic narrative, points of view and insights that ladder to them, differentiators with proof points, value propositions mapped to specific pains

• voice-guide.md -- tone rules, voice guidance, style and editorial standards, anti-patterns (no “it’s not X, it’s Y”), a list of prohibited terms (no “seamless” or “revolutionary”)

• competitive-landscape.md -- competitor mapping, battlecard data, positioning gaps to exploit

The cascade principle makes this maintainable. When positioning changes, I edit the master knowledgebase, run a sync skill, and every derived module updates. Every agent that reads those modules produces different output on its next run. Maintain in one place, consume in many. Without this, you end up maintaining the same positioning language in 15 different prompt or skill files, and they drift within a week.

Why modular instead of one big file? Each skill specifies exactly which context files it reads. The research agent reads ICP, competitive landscape, and customer intelligence. The writer reads voice guide, positioning, and the approved outline. Tight context scoping produces better output, uses fewer tokens, and reduces the chance of an agent pulling irrelevant information into its reasoning.

The customer intelligence layer is where the real differentiation lives. We’ve extracted hundreds of structured insights from sales call transcripts across six dimensions: jobs to be done, customer problems, workflow reality, buying decisions, competitive intelligence, and product-market fit signals. These are structured JSON with exact customer quotes, speaker context, pain triggers, cost estimates, and urgency signals for every call.

This is the raw material that makes content sound like a practitioner wrote it instead of a marketing team. When a security director at a Fortune 500 retailer says “We have 80,000 employees. Realistically, we have budget to monitor threats for maybe 50 of them. The executives. Everyone else, we just hope nothing happens,” that quote, properly anonymized, does more work than any marketing copy I could write. It names the impossible math that every security leader knows but nobody talks about in vendor conversations.

The intelligence layer also includes a customer language lexicon: the specific terms buyers use versus the terms marketing teams use. In our market, customers say “single pane of glass” while marketers say “end to end platform.” Customers say “I need to know what’s happening on this block” while marketers say “hyperlocal situational awareness.” When your content uses the customer’s language, it feels like a peer wrote it. When it uses marketing language, it feels like a vendor wrote it.

The proof library completes the foundation: case studies with quantifiable metrics (”70% reduction in assessment time,” “5x increase in location coverage”) mapped to specific value propositions. Every claim the system makes in an article is backed by a specific number from a specific customer outcome. Claims without proof are invisible to both search engines and skeptical buyers.

All of these files are in markdown. This is the most effective format for AI agents to search and cross-reference while minimizing token costs. Plain text, version-controlled in git, readable by any agent without special parsing.

Here’s what the directory structure looks like:

A side benefit I didn’t anticipate: once your GTM assets are structured like this in a repo, every file becomes searchable and cross-referenceable by Claude Code. You can ask research questions across your entire customer intelligence base (”Which pain points come up most often in enterprise healthcare calls?” or “What do prospects say about Competitor X’s pricing?”) and get answers grounded in your actual data. The structured repo becomes a research tool in its own right. And you aren’t locked into your call recording vendor’s credit system, pricing, or schema markup.

One Input, Multiple Outputs: The Customer Intelligence Multiplier

This is the strategic idea that changed how I think about content engineering. A single structured extraction from a sales call produces intelligence that feeds every GTM motion.

The call analyzer takes a raw transcript and produces structured JSON. The extraction runs through a Claude Code skill with a detailed system prompt specifying the exact output schema. You drop a raw transcript from any call recording tool (Gong, Fathom, Otter, or even manual notes) and the skill produces the structured JSON. The prompt and example output are both in the repo. The JSON output covers:

• Jobs to be done: what the customer is trying to accomplish and why, with desired outcomes

• Pains with context: specific problems, triggers, costs (time, risk, money), urgency signals

• Workflow reality: current processes, manual steps, spreadsheet hacks, brittle integrations

• Competitor mentions: brands trialed, workarounds used, sentiment toward each

• Customer lexicon: exact phrases customers use versus marketing-speak, including “deviant” terms (the non-obvious ways buyers describe your category)

• Keyword candidates: scored by buyer intent (35%), differentiator fit (20%), SERP weakness (15%), business priority (10%), mini-volume plausibility (10%), and authority fit (10%)

That structured output feeds:

SEO and AEO articles via the content pipeline (the detailed example below). Pain-point keywords, buyer language, and evidence from the call become the raw material for articles that rank because they contain genuine specificity.