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

✅ 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.

I’ve helped 10,000+ companies build their GTM plan. I know exactly where they get stuck. Every time, it’s the same three things:

• Too many GTM ideas, no way to cut through them

• Everything feels important, so nothing gets prioritized

• Great strategy session, zero follow-through

So I rebuilt my #1 GTM planning workshop inside Miro - with an AI agent “GTM Strategist” Sidekick trained on my book and checklist to power up the process.

I’ve run this workshop more times than I can count. The framework never changed.

What changed is that now you don’t need me in the room.

Take the workshop for a spin

(free access)

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.

LinkedIn thought leadership through a parallel pipeline. The same call produces anonymized customer quotes, tension hooks (”80,000 employees, budget for 50”), and persona-specific framing for founder content.

ABM personalization through persona-specific pain points, industry patterns, and objection mapping that flow into outbound sequences. When an outbound email references the exact workflow problem a prospect’s peer described on a sales call, the response rate is categorically different from a generic insight and ask.

Product and GTM strategy through JTBD pattern analysis, feature request clustering, and competitive intelligence aggregation across calls. When three different security directors independently describe the same gap in their workflow, that’s a signal your product team needs to hear. The structured extraction makes that pattern visible.

There’s also a compounding effect on your strategy itself. Run the call analyzer systematically across all of your sales conversations and patterns emerge that inform your ICP and persona definitions upstream. We discovered that mid-market security teams react to AI in security fundamentally differently from enterprise teams, which led us to split a single persona into two with different content strategies. That insight came from structured analysis of 150+ calls, not from any individual conversation.

A note on cadence: this kind of strategic update to ICP and positioning should happen every 3-6 months based on accumulated evidence, not in reaction to every call. Run the analysis, surface the patterns, then take findings through a structured review with your team. AI surfaces what’s changing; humans decide what to integrate.

One conversation with multiple structured outputs. Each feeding a different system that consumes only the dimensions it needs. The call analyzer produces comprehensive structured intelligence, and each consuming system takes the slices it needs. Before building this, I was treating each GTM motion as a separate content creation problem. Reframing it as an intelligence distribution problem improved the compounding impact of the whole system.

The Content Pipeline: Stage by Stage

Here’s the SEO/AEO pipeline as one detailed example. This is the system that produces differentiated articles that rank, grounded in sales call intelligence no other company has.

The system can produce articles as fast as you can review them. The bottleneck is the human in the loop at the outline stage and final review, which can take at least 15-20 minutes per article. The results have been meaningful: organic traffic doubled over 3-4 months, AEO citations increased 500-600%, and we’ve ranked #1 on Google for core terms within weeks of publishing, resulting in inbound increasingly driving more demo calls and pipeline.

Research and Intelligence Layer

The pipeline starts before any writing happens, with two agents doing the strategic work.

The Call Analyzer (built on Grow & Convert’s Pain-Point SEO methodology) processes a transcript and produces the structured JSON I described above. It prioritizes buyer intent over search volume, captures exact customer phrasing rather than generic summaries, and maps every finding to specific keyword types: category searches, comparison queries, JTBD queries, and deviant terms.

The Research Agent takes the call analyzer output across hundreds of calls and produces two things: a prioritized topic backlog scored by pain-point SEO criteria, and detailed content briefs for selected topics.

The brief is the part that tends to surprise people. It’s a structured JSON document specifying everything a downstream agent needs:

{

  “meta”: {

    “topic_id”: “GC-202501-security-platform-guide”,

    “priority_score”: 4.3,

    “stage”: “BOFU”,

    “format”: “guide”

  },

  “keyword”: {

    “primary”: “security intelligence platform”,

    “variants”: [”threat intelligence software”, “risk assessment platform”],

    “searcher_intent”: “Category”

  },

  “audience”: {

    “persona”: “Global Security Director”,

    “pain_summary”: “Spending 20+ hours manually gathering threat data”

  },

  “value_props_to_weave”: [

    {

      “pain”: “Manual research bottleneck”,

      “prop”: “5x faster threat assessments”,

      “proof”: “Case Study: Fortune 500 Security Team”

    }

  ],

  “originality_nuggets”: [

    {

      “type”: “quote”,

      “source”: “Sales transcript - Fortune 500 retail”,

      “owner”: “Content team”

    }

  ]

}

The brief specifies the keyword, the searcher’s intent, the target persona, the specific angle, a section-by-section outline, which value propositions to weave in (mapped as pain to proposition to proof), originality requirements, internal link targets, and SEO metadata.

The research agent consumes icp.md, personas.md, competitive-landscape.md, and the customer intelligence directory. It never sees the voice guide or the full knowledgebase. It gets exactly the context it needs for strategic research decisions and nothing else.

Enrichment and Outline

Stage 1 (Enrichment): The brief gets enriched by mining five tiers of context sources in priority order:

1. Structured JSON insights (the fast path: already tagged by persona and use case)

2. Case studies (metrics, customer outcomes, before/after comparisons)

3. Use cases (workflow demonstrations, product capabilities)

4. CSV insights database (pattern-level evidence across multiple calls)

5. Full transcripts (deep path, last resort for specific unfilled gaps)

Evidence gets mapped to each section of the brief with specific quotes, metrics, and source attribution. The actual quote, the actual number, the actual speaker role and industry context.

Stage 2 (Outline): The enrichment output merges with the brief structure into a writer-ready outline. Each section includes word count targets, specific proof points assigned with full text, AEO formatting directives (definition boxes, comparison tables, numbered processes), and entities to mention.

Then the pipeline stops.

This is deliberate. The human review gate sits between research and execution. You review the outline, adjust the emphasis, approve or reject evidence choices, and rename the file to _outline-approved.md before the pipeline continues. Human judgment at the strategic inflection point, not at the copy-editing stage where it’s too late to change direction.

Write, Edit, Link, Publish

Stage 3 (Writer): Writes from the approved outline, consuming only voice-guide.md, positioning.md, and the outline itself. The outline carries all the evidence forward, so the writer agent never needs to search the knowledgebase. The outline is the information boundary. Everything the writer needs is already in it. No searching, no improvising, no pulling in context that wasn’t approved.

The writer follows specific patterns. Feature to Benefit to Proof: mention a capability, explain why it matters to the reader, prove it with a specific metric or customer outcome. Value propositions as undercurrent: threaded throughout the piece at natural moments, never isolated into a “Why Our Product” section. Customer anonymization applies during writing: “Bob Rendle, Director of Crisis Management at Compliunce” becomes “the Director of Crisis Management at a Fortune 500 insurance and risk management company.” Keep the title, the industry, the company scale. Remove the name and the brand (unless you can get approval).

Stage 4 (Editor): Style enforcement, terminology standards, prohibited language removal. Reads only voice-guide.md. Catches any remaining drift from brand voice, eliminates weak qualifiers, scrubs obvious AI-writing tells, and ensures consistent terminology. If Stage 3 did its job, Stage 4 makes minor adjustments.

Here’s where another compounding loop lives. After every human editorial pass, everything is tracked in git. A Claude skill tracks the diff between the AI draft and my edited version, and the system analyzes what I changed: which phrasings I rewrote, which claims I softened, which sections I restructured. Those patterns get fed back into the agent’s prompt and workflow rules. The editor that produces draft 15 has internalized lessons from my edits on drafts 1 through 14. This is slow, unglamorous improvement, and it’s the kind of thing that adds up over months.

Stage 5 (Internal Linker): Calculates a link budget based on word count (an 800-1,500 word article gets 5-8 links; a 2,500+ word piece gets 10-15), then distributes priority URLs across the article. Core product pages get priority, then case studies, then other articles. Reads only the sitemap. Hard rules: max one link per paragraph, no duplicate destinations, anchor text must already exist naturally in the prose.

Stage 6 (Publisher): Markdown to HTML conversion, FAQ JSON-LD schema generation for rich snippets, category detection based on content analysis, related article suggestions via keyword matching against the sitemap, and Webflow CMS draft creation. Reads only the SEO assets YAML file generated back in Stage 2.

Each stage loads the minimum context it needs. The pipeline architecture enforces this: no stage has access to more than it requires. This prevents context pollution, where an agent with too much information starts making connections the outline didn’t authorize. I learned this the hard way when an early version of the writer agent had access to the full knowledgebase and kept inserting product mentions that the outline hadn’t approved that didn’t make sense. Everyone who has built with AI knows this pain. Constraining context solved it overnight.

Where Tool Integrations Close the Loop

The pipeline works with Claude Code and local files. No external integrations are required. But the system gets meaningfully more powerful when you wire in data sources through MCP (Model Context Protocol) or CLI (Command Line Interface).

Firecrawl gives the research agent automated SERP analysis. Instead of guessing what’s ranking for a target keyword, it can crawl the top results, identify content gaps, and build briefs informed by what’s actually out there. The enrichment stage can pull competitor content analysis into the evidence map.

HubSpot connects deal data to content prioritization. Which pain points correlate with deals that closed won? Which personas engage with existing content? This turns topic selection from educated guessing into one informed by the fastest closing deals in our CRM.

Clay enriches companies mentioned in transcripts to surface industry patterns across the customer base. When you see that the same workflow problem appears across retail, financial services, and healthcare, you know it’s worth a dedicated article rather than a throwaway mention.

Lemlist (or any outbound sequencer with an API) connects the intelligence layer directly to outbound execution. The same persona pain points, customer language, and objection mapping that feed your content pipeline can also draft multi-channel outbound sequences. Combine structured call insights with an outbound email skill that knows your voice rules and you can go from “interesting pattern in our sales calls” to “personalized 3-step sequence loaded in the sequencer” in a single session. The intelligence does double duty.

Webflow is already in the pipeline. Stage 6 creates a CMS draft directly, with FAQ schema, category detection, and related article suggestions. No copy-paste or manual formatting. The human in the loop is for editorial standards, not copy-pasting markup into your CMS.

Analytics feedback is the loop that makes the system compound. Which articles drive pipeline? Which briefs produce content that ranks? Which topics drive demo requests versus bounces? That performance data flows back into the research knowledgebase, updating priority scores and informing future topic selection. The research agent building brief number 120 has access to performance data from briefs 1 through 119. It knows which angles worked, which personas engaged, which content formats converted. This is where the system starts to learn, and it’s the difference between a content production line and content infrastructure.

The core pipeline works today with Claude Code and local files. Every MCP integration is an optional enhancement. You don’t need all of them to start. You add them as your system matures and the feedback loops become worth closing.

The Bigger Picture: From One Motion to a Compounding System

The content pipeline is one motion in a larger system. I want to zoom out briefly because the architecture pattern transfers to other GTM motions.

I’ve come to think about AI-native GTM as four layers:

Layer 1: Human Strategy and Creative Direction. The modular context files I described. Positioning, ICP, personas, voice guide, competitive landscape. Written by humans, consumed by AI. No agent replaces the judgment that goes into sharp positioning.

Layer 2: AI as Thought Partner. Using AI to pressure-test strategy, explore alternative angles, deepen thinking, test against persona councils. Augmenting human reasoning, not replacing it.

Layer 3: AI-Powered Systems. The content pipeline lives here. So does signal-based outbound, demand gen campaigns, customer intelligence extraction, competitive monitoring, and lifecycle campaigns. Each motion follows the same pattern: signals trigger workflows, workflows produce outputs, quality gates enforce standards.

Layer 4: The Practical Architecture. Claude Code, MCP integrations, skill files, the directory structure that makes it all work. The architecture compounds: every reference file makes every skill more effective. The marginal cost of the 20th skill is far lower than the first.

The learning loop connects execution back to strategy. Content performance data updates the research knowledge base. Outbound response rates refine persona models. Customer intelligence from new calls enriches the foundation. Feedback flows upstream, and the system gets better with each cycle.

The critical insight at every transition between layers: the jump is never about discovering a better tool. It’s about restructuring how work is represented. From conversations to files. From files to skills. From skills to pipelines with learning loops.

One thing that makes the system genuinely extensible is that clear structure in the repo means adding new inputs is straightforward. When I added dedicated competitive intelligence pages for each of our main competitors, every skill that consumed competitive-landscape.md immediately had richer context. That opened the door to competitor-specific content pipelines (comparison articles, alternative pages) that would have required a separate research effort before. The structured foundation means new capabilities often emerge from adding a new file to the right directory.

A helpful way to think about where you are today:

• Level 0 (Ad-hoc): Copy-paste into AI chat. Re-explain your company every session.

• Level 1 (Documented): ICP, positioning, voice rules exist as files AI can read in Claude projects or Custom GPTs.

• Level 2 (Assisted): AI drafts following your strategy. Skills extract insights, write articles.

• Level 3 (Systematic): Signal-to-workflow-to-output patterns for each GTM motion.

• Level 4 (Compounding): Performance data updates strategy. The system learns from every execution cycle.

The content pipeline I walked through operates at Level 3, moving toward Level 4 as the analytics feedback loop matures. The jump from Level 0 to Level 1 is the hardest, and also the most valuable even if you stop there. Writing a detailed ICP document for an AI to consume forces you to actually have a detailed ICP. Articulating positioning with proof points for a skill file to reference forces you to actually have sharp positioning. The foundation-building work makes your GTM better whether or not you ever build the pipeline on top of it.

Try It Yourself

I’ve open-sourced a generalized version of this system as a GitHub repository. It includes:

• Example files from a fictional B2B SaaS company showing what good looks like at each stage (fully written examples you can study, not placeholders)

• Strategy templates that Claude Code walks you through interactively to build your knowledge foundation

• Execution skills: call analyzer, research agent, the full 6-stage SEO pipeline, LinkedIn insight extraction

• A sync skill that cascades master knowledge base edits to all derived module files

• An onboarding agent (via CLAUDE.md) that detects your setup state and guides you through building your own version

Clone it. Open Claude Code. The system walks you through creating your knowledge foundation first, then running the pipeline.

Get the repo by Subscribing to Dirty Hands.

The repo is designed to grow. The content pipeline is the first fully built motion I’m offering for free.

I get asked for advice on AI all the time, so I’m launching the Dirty Hands GTM newsletter to share what I’m building for GTM (and selfishly to force myself to clarify my thinking on this). It’s one of the wildest and most exciting times to be in B2B marketing if you love getting your hands dirty and building. It’s a cliché because it’s true.

Every week, I’ll break down one system like this in Dirty Hands, a newsletter for GTM operators who build with AI. Real systems with catalogued results and failures.

Future issues of the newsletter will add new motions to the repo: signal-based outbound, content repurposing, customer intelligence extraction, competitive monitoring. Each new motion adds skills, so you have a reason to keep coming back as the system expands.

If this walkthrough was useful, the newsletter goes deeper.

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Dear GTM Strategist,

Claude mania has taken the GTM AI space by storm in Q1 2026. People who have never written a line of code are now spending their nights and weekends staring at a console, shipping GTM systems that used to require a full engineering sprint.

And Anthropic, the company behind Claude, just crossed $30B in ARR - in record time:

But is the hype real? Are GTM operators actually getting value from all their cool builds, or are we entering another “crypto” era - lots of talk, energy, questionable ROI?

Time to find out.

Kyle Poyar (Growth Unhinged) and I surveyed 200 GTM AI operators and curated their best insights into the 2026 Claude for GTM Pulse Report, focusing on how they use Claude Cowork and Code, the value teams get from these tools, and their limitations.

In this article, I am presenting my 5 key takeaways from a 37-Page report:

1. Claude Cowork is not (just) Claude Code for Dummies. Claude Code and Cowork split GTM operators almost 50/50, but they are not complete substitutes.

2. 80% use Claude for productivity. Content creation (29%) and GTM engines (23%) deliver the most impact at the moment, but it feels like we are just scratching the surface of what is possible.

3. GTM engineers are wiring Claude Code into stacks and systems, not using it solo but in combination with other tools. Most common use cases so far: 79% are building SDR research tools and 71% adapting messaging dynamically by segment.

4. 92% reported that Claude Code and Cowork help them “save time”, but more interestingly, 67% have reported building workflows that were previously impossible. But just 27% have replaced a GTM tool so far - the SaaS apocalypse crowd might be getting ahead of the data.

5. But nothing is perfect - even Claudes. Anthropic is a pretty savvy monetizer - even Max plan ($100-$200/month) users hit credit walls 2-3x a day. Memory loss, integration gaps, and a lack of team skill-sharing round out the list of frustrations. We’ve also gathered some intel on how best-in-class users bypass these limitations.

A massive thank you to every community member who participated in the survey 🙏 - this article would not have been possible without you. You are the best!

Want more insights?

Here you go.

Insight 1: Is Cowork Just Claude Code for Dummies?

When Anthropic introduced Cowork in January 2026, my first reaction was: “OK, this is Claude Code for people who don’t want to open the terminal.” I was somehow wrong. We went ahead and bypassed Cowork to go all in on Claude Code, but after talking to colleagues who built legitimate, serious use cases in Cowork, I revised my opinion.

For example, one of the well-known Substackers in my circles let go of three VAs because Cowork could just absorb their workload for content research and repurposing.

Cowork is not just a lite version of Claude Code - it is a different tool optimized for a different kind of work, and it feels like they are just getting started, so we should not dismiss them as some “inferior version” of Code.

Our data backs this up: just three months after launch, Cowork (32%) has already surpassed Claude Code (31%) as the primary Claude product among GTM operators we surveyed.

Claude Chat sits at 30%, and many of us use all tools at the same time, which tells you something important: there is no “one to rule them all” - they do different things in different time and cost differently - here is a quick breakdown on when to use each.

A reality check: this survey sample skews toward advanced users, so the share of Claude Chat users in the broader population is much higher. Don’t let the numbers intimidate you - use them as inspiration instead.

The core insight from the data: 93% of Claude Code users rely on detailed company context1 as a core foundation, vs. 72% of Cowork users. That gap tells you who is building a compounding system and who is running sophisticated tasks.

For advanced users: The other thing where Claude Code outshines its siblings is interconnectivity with other tools for complex use cases and GTM systems. If we are somehow limited with native apps and MCPs within chat and Cowork, CLIs and API integrations fuel powerful, customizable Claude Code builds at optimized costs. Bring agents into the mix and you can unlock the kind of agentic AI systems Canva, Descript, and Linear have built2.

Insight 2: Most Teams Are Still in Productivity Mode - but #1 Use Case is Content

The most widely adopted use case is productivity at 80% - analyzing data, preparing for meetings, and creating documents. Content creation comes in second at 69%, followed by product marketing at 64%.

That is the adoption story. But here is the more interesting cut: which use case do people say has the biggest impact?

Content creation jumps to #1 impact at 29%, while productivity drops to 15% despite leading on adoption. GTM engines punch way above their adoption weight, cited as the #1 impact use case by 23% of respondents despite only 54% adoption.

Long story short: people use Claude most for productivity, but they get the most value from content and GTM engines. Specifically, Claude Cowork shines for content creation, while for Claude Code users, GTM engines are the primary use case.

Within content, the top workflows are:

1. Creating social media posts (72% of content users)

2. Building websites, landing pages, and micro-sites (66%)

3. Creating structured content pipelines (52%)

4. Auditing SEO performance (38%)

5. Analyzing traffic and content performance (34%)

The thing that separates content in Claude Code/Cowork from content in Claude Chat is context engineering. With Code or Cowork, you can encode your brand voice, your ICP, your top-performing posts, your sponsor briefs - and Claude pulls exactly what is relevant for the task at hand. The more you feed the system, the better the output gets.

But remember - with AI content creation, it is often “garbage in, garbage exponentially out”. Strategic narratives and unique points of view are more important than ever:

Without something original to say, the best architecture in the world just produces polished noise. The system amplifies your point of view. It does not create one for you. - Amos Bar-Joseph, CEO at Swan AI

To help make LinkedIn a better place for the intelligent carbon life still being there, I am sharing a core idea of how we structured the content system in our Claude Code. It is already producing amazing LinkedIn pieces (still human edited!) - design briefs are OK but need final touch and AEO/SEO. Well, we still have room for improvement there - so if you have some advanced builds, send them my way, and I’ll consider sharing them in our future content.

Now back to survey results:

Insight 3: GTM Engine Builders Are Playing a Completely Different Game

The 54% of operators using Claude for GTM engines are not just doing things faster. They are building workflows that did not exist before. Here is what the top GTM engine use cases look like:

What makes this category different is the combination of tools. GTM engine builders are not using Claude in isolation. They are wiring it into a stack. The tools that Code and Cowork users love the most are ChatGPT (#1, mentioned by 26% of respondents), Clay (#2 at 21%), HubSpot (#3 at 19%), LinkedIn (#4), and Apollo (#5).

The top GTM AI tool stack in 2026 is Claude + CRM + orchestration. These tools are not competing. They are combining.

Claude handles the intelligence layer. Clay handles data enrichment and workflow automation. It connects the CRM layer. LinkedIn and Apollo feed the signals. The builders who get this are not asking “which AI tool should I use?” They are asking “how do I architect a system where Claude sits at the center, reads my context, connects to my stack, and runs while I sleep?”

Insight 4: Show me the money - what value are users getting from Claude

Every reasonable human being at this point feels like - OK, sounds like an interesting opportunity, but it does take a lot of work and changes. Is it worth the trouble or are GTM nerds just raving to each other to have something new to say? In other words - is the hype real?

Respondents are practically unanimous (92%) that Claude Code and Cowork have saved them time. But what really sparked my attention is that 67% said that it enabled them to do something previously impossible. Enabling previously impossible workflows is a different category of value - that is where compounding GTM intelligence lives.

What’s really interesting is whether Claude replaces existing tools. 55% of respondents said yes, but the breakdown was more intriguing.

Lower qualified work and non-core contractors have taken the larger hit so far 😢 - remember the Substack guy who could fire 3 VAs (yeah, that is the spiel ATM)? While having a good content person is invaluable, it is mission-critical to learn AI for the lower-qualified tasks. I’ve been saying that to every young person out there: if you cannot compete on quality yet, compete on speed and productivity - make AI your bestie.

On the tool replacement side, only 27% have replaced an existing GTM tool so far, 30% say they will soon, and 43% say no. Most at risk are contractors, SEO agencies, ad agencies, spreadsheets, manual outbound work, ChatGPT, Perplexity, analytics providers, and third-party automation tools.

Some are loudly predicting a SaaS apocalypse driven by vibe coding. Considering previous findings, this is not exactly true. I personally believe in combining the tools with strong ecosystems into new stacks that empower GTM systems.

Insight 5: The Frustrations Are Real, Documented, and Partially Fixable

This section exists because Emily Kramer (MKT1 Newsletter) suggested we ask about frustrations when she reviewed our survey. Best suggestion we got. The responses were juicy.

The reported limitations of Claude, ranked by number of mentions:

1. Credits, cost, and usage limits - running out of credits, throttling, usage limits even on the Max plan, unpredictable usage on small tasks.

2. Memory limitations - losing context between sessions, no persistent long-term memory, manual workarounds required.

3. Context window and token limits - running out mid-task, large operations failing, duplication across sessions.

4. Integration and data gaps - weak Google and Salesforce integrations, limited connectors, restricted access to company data sources.

5. Reliability and consistency - hallucinations, shallow outputs, inconsistent results across sessions.

6. Team collaboration - skills are not easily shareable, updating a skill does not cascade to teammates, poor team workflow support.

“I’m on a Max plan and I still run into model usage restrictions 2-3x per day.”

“If a coworker comes up with a skill that’s useful, we’ll have to build the skills separately rather than sharing.”

Some practical “hacks” that help:

• Open new chats regularly instead of continuing long threads - reduces context bloat

• Ask Claude to plan before executing - use plan mode before burning tokens

• Move from MCP to CLI or direct API calls for reliability - ScaleKit benchmark data shows CLI hits 100% reliability vs around 72% for MCP, at a fraction of the token cost

• Mix Sonnet model into your workflows instead of Opus for simpler tasks - it is about 20% of the Opus cost

• Build an external memory system - a CLAUDE.md or history.md file Claude updates proactively

• Test on a small sample before running full campaigns - do not burn credits on untested logic

• To improve output quality, ask Claude to critique itself before you accept the output

“The biggest trap with Claude (or any LLM) is that it’s a yes-man by default. You bring it a plan and it’ll tell you it’s brilliant, maybe suggest a few tweaks around the edges. That’s not useful. I started building a habit: create the plan in Claude, then explicitly ask it to red team the thing.” - Andy Nester, CMO at PERSUIT

In the Future, Everybody Will Be an Engineer

In 1968, Andy Warhol famously said, “In the future, everyone will be world-famous for 15 minutes.”

In GTM AI in 2026, the equivalent might be: in Q4 2025, everybody became a ______ engineer (place your buzzword of choice to fill in the blank).

The trend started with the GTM engineer role, then the term content engineering started trending; oh, and have you ever heard of vibe coding? And just last week I heard about a marketing engineer for the very first time. It’s getting really hard to catch up with all the new engineers - it used to be easy (front-end/back-end). 🙈

It’s an incredibly exciting time to be building in this space and if these strategic narratives usually get great buzz on social media - so by all means, do it - but is all these engineering making non-technical people anxious, excluding women (lower representation in the engineering skill pool) and turning off competent operators who actually know their s*** and favoring tool jockeys instead?

Maybe …

But there are a lot of great things going on in the space, too. There are great companies, teams, solo contributors and content creators pushing the space forward. And sometimes that means having a critically acclaimed discussion or open-sourcing something outside the comfort zone.

I feel we are just getting started here and that Claude’s domination will continue throughout 2026. 🤞 So please keep track of what is new in the space and share your best practices.

The gap between AI power users and those who use GPT chat to prompt outbound emails and blog posts is getting wider and wider, and while you are still early in the space, the AI-powered teams are a force to be reckoned with, not something to smirk at.

Kyle and I hope this report is a high-quality contribution to what is actually happening with Claude in GTM - not just another hype piece. If it was useful, hit that heart on Substack - it helps me convince Kyle to do more of these collaborations 😉.

The full 37-page “Claude for GTM Pulse Report” is available to premium subscribers of Kyle Poyar’s newsletter. It includes more survey data and insights on Claude Code and Cowork adoption, how top GTM teams use Claude, limitations to be aware of, and advice to get the most out of Claude.

Get it here

And if you like what I do and want to support my free content stream, you can do that by buying my digital solutions 👇

✅ 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,

Here’s a question that should keep every B2B founder up at night: What does AI say when someone asks for the best tool in your category?

The new “homepage” for your buyers is no longer your website. It’s a ChatGPT prompt. And if you’re not in the answer, you don’t exist.

ChatGPT, Perplexity, and Google AI Overviews are the new front doors for buyer research. According to Forrester, 94% of B2B buyers now use AI during their purchasing process — and most companies have no idea what’s being said about them (or worse: nothing is being said at all).

Prospects visit your website later in the buying process, but are better informed.

So it’s no surprise that 51% of GTM practitioners said they plan to increase their investment in AI discovery and Answer Engine Optimization (AEO) in 2026 (source) — the top channel bet by a wide margin.

But the playbooks are still being written in real time. It’s part gold rush, part black box, and part “nobody actually knows what they’re doing yet.”

So I rolled up my sleeves and did the research on how to do AEO in 2026 - for B2B products. This article gives you:

1. Why AEO is the most urgent GTM priority right now — the data that makes this undeniable

2. What AEO actually is (and what it’s not) — cut through the naming chaos and snake oil

3. A 6-step playbook to audit and improve your AI visibility starting this week

4. Real case studies from companies getting 10-25% of signups from AI discovery

5. The tools and stack to monitor your “Share of Model” and track AI-referred pipeline

Let’s get into it.

The shift is no longer theoretical. It is measurable, accelerating, and directly impacting pipeline.

AI search is massive — and growing fast. ChatGPT now has 800 million weekly active users processing 2.5 billion prompts per day. Google AI Overviews appear in 13% of all searches — double since January. Gartner predicts that by end of the year, 25% of organic search traffic will shift to AI chatbots and virtual assistants.

The conversion quality is insane. This is what really caught my attention. Webflow reported that their ChatGPT traffic converts at 24% — 6x higher than Google. Ahrefs found that AI search accounts for just 0.5% of their visitors but 12.1% of signups — a 23x higher conversion rate. Surfer SEO sees roughly 25% of new customers coming from AI assistants.

AI-referred traffic is a fraction of the volume but disproportionately high-intent. These buyers show up pre-sold because the AI already told them you’re the best option.

And yet almost everyone still feels in the dark when it comes to optimizing for AI search. A 10Fold study of 400 senior marketing execs found that only 11% of B2B firms have the majority of their content ready for AI discovery. There’s a massive gap between awareness and execution — and that gap is your window.

What AEO Actually Is

AEO (Answer Engine Optimization) is about making your product the answer when buyers ask AI for recommendations. Not ranking on a results page. Not getting clicks. It’s about being cited, summarized, and recommended inside a conversation.

The naming situation is a mess - I’m sticking with AEO, but it’s also called GEO (really? 🌍), LLMO, AIO (all in one?), SXO, AI SEO… But the name matters less than the concept.

Here’s the mental model shift that matters:

In SEO, you compete for rankings. In AEO, you compete for mentions. The new metric isn’t “Position 1” — it’s “Share of Model,” which measures how often your brand is the recommended answer across AI platforms. Webflow tracks this obsessively and has grown their AI category share from 1.2% to roughly 60%.

What AEO is not:

• It’s not “new SEO.” AEO extends SEO — it doesn’t replace it. Late-funnel SEO is the foundation. If you don’t rank for “[your category] for [use case],” you probably won’t show up in AI answers either. AI models pull from the same sources that rank in traditional search.

• It’s not gaming LLMs. There’s a lot of snake oil out there — prompt injection tricks, hidden text, keyword stuffing for AI. This is short-term thinking at best and will get you flagged at worst.

• It’s not just a content project. This is what Kyle Poyar warns about most: “AEO isn’t a content thing or an SEO experiment. It’s a GTM capability.” It touches product marketing (how your product is described), content (what you publish), and brand (what third parties say about you).

• It’s not about publishing more. Volume without structure and authority doesn’t improve AI visibility.

The AEO Playbook: How to Get AI to Recommend You

Here’s what I’d recommend based on what the fastest-moving companies are actually doing — not theory, but deployed and producing results.

Step 1: Audit Your AI Visibility

Before you optimize anything, you need to know where you stand. This takes 30 minutes and will probably surprise you.

Run the prompts your buyers would run. Open ChatGPT, Perplexity, Gemini, and Google AI Mode. Type the questions your ICP actually asks: “What’s the best [your category] tool?” “Compare [you] vs [competitor].” “Best [your category] for [specific use case].” Do this for 15-25 variations across the buying funnel.

Track three things for each prompt:

• Are you mentioned at all?

• Are you recommended or just listed?

• How are you described? Is it accurate? Is it how you’d want to be positioned?

You might be the #1 result on Google and completely invisible in ChatGPT. These are different systems with different signals. One company can dominate in ChatGPT and be absent from Perplexity — each platform has its own retrieval logic and citation patterns.

Tools that can help: HubSpot’s Search Grader (free baseline), Profound, Xfunnel, Ahrefs Brand Radar. But for the start, manual testing will do. There’s no substitute for seeing exactly what AI says about you right now.

Step 2: Build Your Prompt Library

Once you’ve done the initial audit, systematize it. Identify the 15-25 core prompts that define your category across the buying funnel:

Top of funnel: “What is [category]?” “How does [category] work?” “Do I need a [category] tool?”

Mid-funnel: “Compare [you] vs [competitor].” “Best [category] for [industry/use case].” “What are the pros and cons of [you]?”

Bottom of funnel: “[You] pricing.” “[You] vs [competitor] for enterprise.” “Is [you] worth it?”

Map yourself and each competitor against every prompt. This becomes your AEO scoreboard — your “Share of Model” tracker. Update it monthly because AI outputs shift with model updates and new content entering the training data.

This is not unlike building an ICP or a messaging framework — you’re defining the battleground. Except instead of a sales pitch, you’re defining the conversations AI is having about your market without you in the room.

Choosing the right prompts: a view from the practice

Not all AI searches are equal, says José Velez, co-founder and CEO of Reach, an AI search research lab running R&D on how AI search engines actually decide what to recommend. “The ones that matter are the high-intent queries across your buyer journey: problems your ICP is searching for solutions to, the specific solutions and categories they're evaluating, and the comparisons they're running between specific vendors. Volume metrics from traditional SEO don't apply here; what matters is intent and proximity to a buying decision.”

So after preparing your list of core prompts, you should reverse-engineer what’s actually in the answers. “For each target prompt, study what the models are citing in that case. Is it a listicle? Owned content from vendors? Review sites? What categories of companies are included? What comparison factors matter? This tells you exactly what your content must cover, what format it should take, and whether you can realistically influence the answer.”

Step 3: Win at Late-Funnel SEO First

Here’s the thing that should relieve a lot of anxiety: you don’t need to start from scratch. Late-funnel SEO is the foundation for great AEO.

AI models rely on live web search (through RAG — retrieval-augmented generation) to construct their answers. They pull from the same content that ranks in traditional search. If your best comparison pages, use case pages, and product pages rank well for high-intent keywords, you’re already ahead of most competitors for AEO too.

What to prioritize:

• Comparison pages: “[You] vs [Competitor]” — these are gold for both SEO and AEO

• Use case pages: “Best [category] for [specific industry/team/problem]” with specific, citable details

• Product pages with clear definitions: Open with what you are, not marketing fluff. AI pulls the first clear definition it finds

• ROI/pricing pages: Detailed, transparent, and structured — these directly feed AI responses about your product

• Technical docs and integration guides. This one is wildly underrated.

Talal Syed leads AEO and SEO strategy at GrowthX. They ran a study across 1 million AI responses and found something that should change how teams prioritize content:

“Docs pages - like technical docs and integration guides - were 8.6x more likely to get cited than other pages on a site when buyers were actively evaluating products. The long tail has exploded with AI search, and these engines are hungry for high-quality content that goes deep, is well organized, and answers the specific questions people have about products. The brands filling that gap on their own sites are the ones showing up in AI.”

If you’re a B2B SaaS company and your docs are an afterthought, you’re leaving citations (and pipeline) on the table.

For product companies, José Velez consistently sees the same category of underproduced, high-intent content driving outsized results: competitor pricing guides, competitor reviews tied to specific use cases, jobs-to-be-done alternatives (”How to embed tables in Notion” - with your product positioned as the faster option), and “How to cancel [Competitor]” articles that intercept buyers already looking to switch.

“These capture buyers at the exact moment they’re evaluating alternatives, which is exactly what AI models surface as answers, but most teams don’t produce them because they’re not intuitive. Disclaimer: don’t apply this blindly without first validating there’s real search demand in your space,” says José.

Step 4: Structure Content for AI Interpretability

AI systems don’t read content the way humans do. They extract, chunk, and reassemble. Your content needs to be built for this.

Freshness is critical. Research from AirOps found that 83% of AI citations come from pages updated within the past 12 months, with more than 60% refreshed within six months. If your best content is a year old and untouched, it’s losing AI visibility every quarter.

Practical formatting changes that matter:

• Lead with direct, definitional answers. Don’t warm up for three paragraphs before defining what your product does. AI pulls the first clean answer it finds. Start every key page with a single, citable sentence: “[Product] is a [category] that does [specific value].”

• Use clear, semantic headings that mirror the questions buyers ask. Not “Our Approach” but “How [Product] Handles [Specific Problem].”

• Modular content structure. Each section should stand alone when extracted from the page. AI systems break content into passages, not pages.

• Add FAQ sections with schema markup. These are directly extractable by AI. Use real customer questions, not marketing-speak.

• Implement structured data (Schema.org) — Product, FAQ, HowTo, Organization schemas make your content machine-readable. This isn’t optional anymore.

Case Study: Webflow’s FAQ + Schema Markup Automation

Josh Grant, previously VP of Growth at Webflow, shared how a focused experiment on just six core feature pages (Design, CMS, SEO, Shared Libraries, Interactions, Hosting) drove outsized AEO results - with no content overhaul and no agency.

His team built an AI workflow in AirOps that used Perplexity to deep-research Google’s “People Also Ask” results, Reddit threads, and niche forums to surface the exact questions buyers were actually asking. From there, the workflow identified gaps in existing FAQ content, generated high-intent Q&A pairs, wrote on-brand answers, and auto-structured everything into clean schema markup.

The results: +331 new AI citations (57% of all new citations across Webflow.com that period) and +149K SEO impressions, a 24% lift. As Josh put it: “AI didn’t change the game here. It just raised the bar for how precisely you can answer the questions that already exist.”

Advanced Move: Make Your Product Machine-Readable

AEO in 2026 is evolving beyond optimizing for crawlers toward declaring structured intent for AI agents. Dima Durah, Product Growth at Toloka.ai, argues the highest leverage move is to publish a machine-readable “source of truth” about your product - pricing, capabilities, and constraints - structured, explicit, and owned by you.

For example, in addition to a typical pricing page written for humans, expose a machine-readable version alongside it: a /pricing.json or /pricing.md that an agent can reliably parse.

“Agents don’t want pages, they want structured truth they can act on. The teams that win are the ones that make their context explicit, machine-readable, and impossible to misinterpret.” - Dima Durah, Product Growth, Toloka.ai

This is forward-looking but increasingly practical. If you don’t define your context for AI agents, they will infer it - and they might get it wrong.

Dima previously shared her workflow for creating an AI knowledge graph for AEO. Find the exact prompts and instructions in this GTM Strategist article.

Step 5: Build Citation Signals and Authority Off-Site

Here’s what a lot of teams miss: AI answers are heavily influenced by what other sources say about you. It’s not just about your website — it’s about your entire digital footprint.

Third-party review platforms matter enormously. G2, Capterra, and TrustRadius are among the most-cited sources when AI recommends B2B products. A complete, well-maintained G2 profile with recent reviews isn’t just for your sales team’s social proof — it’s training data for the AI that your buyers are consulting.

Other signals that feed AI recommendations:

• LinkedIn content. LinkedIn recently reported being the #1 most-cited domain for professional queries across AI search platforms. Your expert content on LinkedIn is increasingly being cited by AI systems. This is a case where employee-generated content and thought leadership have a direct, measurable impact on AEO.

• Mentions in reputable publications. Guest posts, expert quotes, original research cited by others — these are authority signals for AI, just as they were for SEO. But for AEO, they’re often more important because AI systems prioritize consensus across multiple credible sources.

• Community presence and user-generated content. Reddit threads, community forums, and Slack discussions are all sources that AI scrapes for recommendations. This is where your power users become your AEO army.

• Integration ecosystem mentions. If you’re mentioned on partner websites, integration directories, and marketplace listings, you’re building citation density that AI picks up. (This connects directly to the GTM ecosystem model — ecosystem actors generate citation signals you can’t create alone.)

José Velez shared an example of Indie Campers ($140M+ ARR): “We grew organic revenue 8.8% YoY and AI search revenue 254% in the first 5 months of working together. But to rank for competitive prompts specifically like “best RV rental company” and “RV rental Tomorrowland”, the strategy was completely different from the under-explored formats. Competitors were already covering those, so we had to create specific types of content tailored to the patterns the models were citing and secure earned mentions on the third-party sites the models pull from.”

Step 6: Monitor and Iterate (This Is Not “Set It and Forget It”)

If there’s one mistake that will waste all the work above, it’s treating AEO as a one-time project. AI outputs are probabilistic, not deterministic. They shift with model updates, competitor content changes, and citation pattern changes.

What to track:

• Share of Model: How often your brand is mentioned and recommended across AI platforms. Track monthly against competitors.

• AI-referred traffic and conversions: Set up referral tracking for chat.openai.com, perplexity.ai, and other AI sources in your analytics. Measure not just visits but conversion rate and pipeline contribution.

• Framing accuracy: Is AI describing your product accurately? If it’s wrong or outdated, you need to fix the source material. Several companies are now actively correcting misinformation that AI surfaces about their products.

• Citation sources: Which third-party sources does AI cite when mentioning you? Strengthen the ones that drive good positioning; fill gaps where competitors are getting cited and you’re not.

The companies that are winning at AEO treat it like a living system - more like product marketing than like a content calendar. They test prompts weekly, refresh key pages quarterly, and track their Share of Model the same way they’d track pipeline metrics.

The AEO Stack: Tools to Get Started

The tooling landscape is evolving fast, but here’s a practical starting point:

AI visibility monitoring:

• Profound — tracks brand mentions and citations across ChatGPT, Perplexity, Gemini; backed by $35M Series B from Sequoia

• Ahrefs Brand Radar — tracks brand visibility in AI search across major chatbots

• HubSpot Search Grader — free tool for a baseline AI visibility audit

Content optimization for AI:

• AirOps — content engineering platform helping teams see highest-impact pages and act on AI visibility data

• Surfer SEO — increasingly focused on AEO alongside traditional SEO

Manual testing (free, effective, start today):

• Run prompts yourself across ChatGPT, Perplexity, Gemini, Google AI Mode

• Track AI referral traffic in your analytics (look for chat.openai.com, perplexity.ai as referrers)

• Set up a monthly “AEO audit” cadence — 30 minutes, 15-25 prompts, document what you find

Here are also Google’s recommendations for optimizing your content for performance in AI Overviews.

AEO Is a GTM Capability, Not a Content Project

If there’s one thing I want you to take away from this article, it’s this: AEO is not a one-person content project. It’s a cross-functional GTM capability.

It touches product marketing (how your product is described and positioned), content (what you publish and how it’s structured), brand (what third parties say about you), product (what users share and how integrations are listed), and sales (the language used in decks and on calls ends up in the digital ecosystem AI scrapes).

LinkedIn recognized this when they formed a cross-functional “AI Search Taskforce” spanning SEO, PR, editorial, product marketing, and brand. They saw their B2B non-brand traffic drop up to 60% on awareness-driven topics and realized traditional SEO alone couldn’t solve it.

An important part of your AEO strategy is localization. Your buyers use AI chatbots in their natural language, so make sure that your AEO content is available and translated in the languages of your target markets.

“AEO is a compounding channel. Early wins come from listicles and competitor alternatives content, but the real compound effect shows up around months 4-6 once the models start consistently associating your brand with the category.” - José Velez, Founder & CEO, Reach

The window to build early AEO advantage is open right now. With 89% of companies not yet AI-ready, the early movers will own disproportionate share of AI recommendations in their categories. Webflow’s journey from 1.2% to 60% category share didn’t happen overnight — but it happened in months, not years.

As Dima Durah said, we’ll soon be optimizing not just for answer engines but for autonomous AI agents that browse, compare, and purchase on behalf of buyers. Companies with machine-readable, structured product truth will have a massive head start.

Here’s your minimum viable next step: Run 10 prompts today.

Open ChatGPT. Type the questions your buyers ask. See what comes back. That 30-minute exercise will tell you more about your AI visibility than any strategy deck.

Then come back here and work through the 6-step playbook. You’ve got everything you need to start.

✅ 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.

50% of software buying decisions involve 5 or more people - and 66% of those buyers shift priorities mid-process. Yet most ABM motions still roll everything up to a single account score and treat the entire buying committee the same way.

The result? Sales reaches out to the wrong stakeholders with irrelevant messaging. Real champions go untouched. And “orchestration” becomes just a workflow that ran.

To help you navigate this complexity, Influ2 is hosting a live panel on Tuesday, May 6 - with Devin Reed (The Reeder) and Ed VanderBush (LiveRamp). They’ll break down the 3 failure points that silently kill buyer momentum - latency, fake personalization, and false attribution - and what contact-level ABM looks like in practice.

If you’re serious about orchestrating GTM around how buyers actually buy, don’t miss this one.

Register here

Dear GTM strategist!

Claude has 10x-ed its revenue in 10 months (ChatGPT “only” 2.5x-ed it) and it took our LinkedIn by the storm.

Yet, it feels like everybody is talking about it and only a few people are harvesting massive value that Claude Code can bring to GTM teams. And it was not only a hunch - as Kyle and I are surveying our community members most people use Claude for content creation.

To do the same thing as we’ve always done more effectively (productivity). But that is just stretching the potential that Claude Code can bring to GTM - the true mastery of AI GTM comes with remaging processes and building native agentic flows.

Previously we have covered context engineering, GTM campaigns, and today I want to bring you to the weeds on how best-in-class teams such as Linear, Descript, and Canvas are building signal-driven GTM systems that actually compound over time. And we won’t stop there - you’ll get a neat GitHub repo that you can use to build your v01 of truely agentic GTM systems without relying on 20 other expensive internal tools.

So let’s go for this ride. Meet your driver Nico Druelle from The Revenue Architects and let’s do this!

When we start working with a new GTM team, we do the same thing every time: we ask to see how they use AI in their day-to-day work.

The answer is almost always the same. Someone opens a chat window. They paste in their company description, a brief ICP summary, maybe a persona or two, and then type the task. Claude produces something reasonable. They paste a slightly different version next week. The context is rebuilt from scratch every single time, then disappears when the conversation closes.

The teams we work with — at a developer infrastructure company, a design platform used by professional studios, and a half-dozen others in between — don’t work that way. Their AI has context before the session starts. It knows their ICP, their tier definitions, their signals, their competitive positioning, their current campaigns. The team doesn’t explain their GTM to Claude — Claude already knows it.

The difference isn’t the AI model or the tools. It’s that they built a repository.

What a GTM Repository Is

The concept is simpler than it sounds: a set of markdown files in a git repository that captures your GTM institutional knowledge in a structured, queryable form. Your ICP definition. Your signal library with detection methods and scoring. Your competitive battlecards. Your buyer personas with outreach hooks. Your workflow documentation.

Claude Code reads these files automatically every time it starts a session in the repo. Once the context is there, any task — research an account, score a list, build a campaign — becomes a one-line prompt.

The engineering analogy is exact: you wouldn’t rebuild your data model in a chat window every time you needed to write a query. You wouldn’t re-explain your API contracts to every new engineer. The knowledge lives in the repo. People and tools read from it.

The best GTM teams figured out that revenue knowledge should work the same way.

Why Most Teams Haven’t Built This

Because there was never a forcing function. You can paste context into a chat window and get a usable output. It works, sort of, and the alternative — actually structuring your GTM knowledge into a maintained repository — feels like overhead.

What changes the calculation is accumulation. The first time you run a signal campaign with proper context behind it versus a generic prompt, the output is noticeably better. After six months of updating your signal library with performance data, your scoring model is calibrated to actual outcomes. After a year, a new hire can open the repo on day one and understand your ICP, your signals, your competitive positioning, and your current priorities without a single onboarding call.

That compounds. Generic prompting does not.

The Architecture

A well-built GTM repository has five layers.

CLAUDE.md — The Brain

The single most important file. Claude Code reads it automatically at the start of every session. It is the persistent context layer: ICP summary, top signals, positioning, team, current priorities. Everything else in the repo is referenced from here.

The failure mode: making it too long. CLAUDE.md should be scannable in two minutes. Deep detail lives in the context files it points to.

Context — Institutional Knowledge

Six files that capture your GTM strategy in structured form.

context/profile.md — Company overview, product, deal profile by segment, and reference customers. The foundation every other file builds on. If Claude needs to describe what you do in a research brief or a sequence, it reads this first.

context/icp-definition.md — Tier definitions with explicit criteria. Not “we target Series B SaaS companies.” That is a category, not a definition. A real ICP definition includes the employee range and why, the technographic signals that indicate fit, the organizational signals (does this function exist?), explicit anti-ICP exclusions, and a qualification framework with must-haves separate from red flags.

One pattern consistent across every high-performing team we’ve seen: they maintain an ICP evolution log. Every time the ICP changes — a filter tightened, a segment removed because it’s not converting — they record what changed and why. Six months of that log is more useful than the current definition.

context/signal-library.md — Signals with detection methods, point values, decay curves, and message hooks. Not “recently funded companies.” A real signal is: “Series B announced in last 60 days, detected via Crunchbase webhook into Clay, worth 30 points, decay to 15 points after 60 days, message hook: ‘Series B is the inflection point where the ops layer either scales or becomes the bottleneck.’”

Two concepts worth understanding before you build your signal library.

Signal decay. A funding event from 150 days ago is not the same signal as one from 10 days ago. The repo scores them differently — a Series B at announcement is worth 30 points; at 90 days it’s worth 15; at 180 days it’s worth 0. This matters because most teams don’t decay their signals, which means their “active list” quietly fills with accounts that were relevant six months ago and aren’t anymore. Decay forces the list to reflect actual urgency, not historical interest.

Signal combinations. Two signals together are more predictive than either alone, and score accordingly. A Series B plus a new RevOps hire in the same week isn’t a 65-point account — it’s an 80-point account with a combination bonus. The combination tells you something neither signal tells you individually: budget exists AND someone is actively rebuilding. The repo includes a combination table with bonus scoring rules and recommended actions for each pairing.

The signal library is also where performance gets tracked. Which signals are actually generating meetings? Which ones look good in theory but produce no pipeline? A signal library that isn’t updated with results is a hypothesis document. Updated with outcomes, it is a learning system.

context/positioning.md — The source of truth for how you talk about what you do. Value pillars with proof points, a messaging matrix by persona, and a “what not to say” section that prevents Claude from generating copy that sounds good but misrepresents your actual differentiation.

context/competitor-radar.md — Battlecards that are honest about where you win and where you lose. The most useful part of a battlecard is not the feature comparison. It is the pattern: “we win when X, we lose when Y, here’s the exact language that works when they bring up [Competitor] in a call.”

context/personas/ — One file per buyer persona. Title, decision role, what they measure themselves on, how they buy, what gets their attention, what gets ignored. The sample outreach hook calibrated to each signal is what Claude draws from when building any first touch.

Skills — Repeatable Execution

A skill is a markdown file that tells Claude how to run a specific GTM task using your context. Four skills cover most of the work.

Account Research — given a domain, produce a full intelligence brief: company snapshot, stakeholder map, signal score, competitive context, and a recommended angle with a specific first-line hook. This is what goes in front of the AE before any Tier 1 conversation.

Signal to Sequence — given a signal and target segment, produce a complete campaign: trigger logic, audience definition, full sequence copy across all touches, measurement targets. Ready to load into your outbound tool. A message passes if the prospect would find it valuable even if they never buy from you. Remove the CTA. If the message still has value, it passes. If it’s pointless without the ask, it’s a pitch — rewrite it. This single filter eliminates most of what makes outbound bad.

ICP Scoring — given an account or a list, score each one against your ICP definition and assign a tier. Runs on a single account in a minute or a batch of 500 in one session.

Weekly Update — reads the current state of the repo, identifies what’s stale, and drafts every section that needs updating. Run Monday morning. Claude produces the diff; you confirm the changes. Ten minutes instead of two hours.

Workflows — The Connective Tissue

Three documents that capture how the operational processes actually work. These are not for Claude to execute — they’re decision trees and process specs for the humans running the system.

workflows/enrichment.md defines the data waterfall: which sources run first, what data quality threshold triggers the next layer, what fields are required before an account moves to active sequences. It also covers email deliverability infrastructure — domain warming, sending limits per mailbox, bounce rate thresholds — because no amount of signal intelligence survives a blacklisted domain.

workflows/signal-routing.md is the decision tree that runs when a signal fires. Customer? Suppress. Active opportunity? AE owns it. Contacted in the last 45 days? Cooldown. Still active? Score, assign tier, route to the right sequence. Without this document, signals fire and outreach happens inconsistently — some get actioned immediately, some sit for a week.

workflows/campaign-build.md maps the full campaign process from audience definition through launch to measurement review: who approves the trigger logic, who writes the copy, what QA checks run before the first email goes out, when the first review happens and what you’re looking for.

Outputs — The Archive

Every research brief, campaign brief, and sequence that comes out of the repo lives in outputs/. Six months of outputs alongside the context files that produced them is a feedback loop: you can see how your thinking evolved, which campaigns worked and which didn’t, and how your ICP has changed over time.

What This Looks Like When It’s Working

Three patterns from teams we’ve worked with, anonymized.

The signal that rewrote the ICP. A developer tooling company had been targeting “VP of Engineering” as their primary persona for 18 months — it was the intuitive choice and it had always been in the deck. After building a signal library and tracking which signals actually converted, a pattern emerged, in a weekly-update run: accounts where a “Platform Engineering” or “Developer Experience” team had been created in the last 6 months converted at 4x the rate. Not a different company type. A specific organizational moment. The ICP update that followed — adding this as a Tier 1 organizational signal — changed the shape of their outbound entirely. They found it because they were measuring.

The battlecard that got rebuilt. A security software company had lost three consecutive deals to the same competitor without being able to explain why. Their battlecard was 14 months old. After a structured loss analysis — asking the losing AEs what the competitor said, what the champion reported back, what tipped the decision — they found that the competitor had made two pricing changes and was leading with a capability they had previously deprioritized. The updated battlecard changed the next four competitive evaluations. They won two.

The new RevOps hire who didn’t need onboarding. A B2B SaaS company hired a new Head of RevOps in January. Their GTM repository had been running for 8 months. On her first day, she opened the repo: ICP definition with a log of every change made over the prior 6 months and why, signal library with performance data attached to each signal, persona files with specific outreach hooks, and a full archive of campaigns run during her predecessor’s tenure with results documented. She ran the account research skill on her first 20 priority accounts in her first week. The onboarding conversation was about strategy, not context.

None of these are dramatic wins. That is the point. The repository compounds quietly. The gains show up in ramp time, in consistency, in the patterns that only surface when you’ve been systematically tracking outcomes.

Building Yours

We built an open-source starter kit with everything described above: the templates, five skills, three workflows, two playbooks, and a fully built-out example company (Relay, a fictional workflow automation platform) with every context file populated, real sample outputs, and a campaign with actual performance data attached.

Clone it. Run the setup skill. Start from a repo that already knows your company.

The order that works:

1. Run the setup skill first — provide your domain and Claude researches your company, writes every context file, and marks what was inferred versus confirmed. The repo is 70–80% complete before you’ve answered a question. If you want to sharpen the inferred fields, the skill offers a 3-minute refinement pass after showing you the results.

2. Run the ICP Scoring skill on your current pipeline — the gaps between what you expect and what it produces tell you where your ICP definition needs work. This is the fastest way to stress-test the context Claude just built.

3. Run the Account Research skill on your top 10 accounts — use the outputs as prep for your next conversations with those accounts. See what changes.

4. After your first few campaigns, update the signal library with results — this is what turns it from a hypothesis document into a learning system.

What it will not do: Write your strategy. The ICP definition template is structure, not thinking. The signal library is only as good as your knowledge of your actual win patterns. The repo amplifies what you know. It does not replace the work of knowing it.

Keeping It Current

Setup is one afternoon. The compounding happens in the maintenance — and it’s lighter than it sounds.

Weekly (5 minutes). Update the “Current priorities” section of CLAUDE.md. This is the only file that needs to change every week. What are you focused on this week? Which campaigns are live? What’s the most important thing Claude should know before any session this week? Five minutes keeps the entire repo oriented to your current state.

After every campaign (15 minutes). Add results to the campaign output file — reply rates, meeting rates, which signals converted, which touches drove responses. This is the step most teams skip, and it’s why their signal library stays a hypothesis document instead of becoming a learning system. The signal library performance log only gets useful after you’ve run three or four campaigns and can see patterns across them.

After every win or loss (30 minutes). Update context/competitor-radar.md. What did the winning or losing AE hear? What did the competitor say? What tipped the decision? A battlecard updated in real time after a competitive deal is worth ten battlecards written in a quarterly planning session from memory.

Quarterly (1 hour). Review context/icp-definition.md and add an entry to the ICP evolution log. Did anything change? A segment that’s not converting? A new company type that’s showing up in deals? The evolution log — what changed and why, with dates — is one of the highest-value artifacts in the repo. After a year, it tells you more about your ICP than the current definition does.

The total ongoing time commitment is less than two hours a week. The question is whether that time gets spent or not. Teams that maintain the repo have a self-correcting system. Teams that don’t are running on stale context, which is almost worse than no context — it generates confident but wrong outputs.

Automating the update loop. The manual version of this workflow is: open your outbound tool, pull the week’s reply and meeting numbers, open the repo, update the signal log, update the campaign results table, run the weekly-update skill. Twenty minutes if you’re disciplined. Zero minutes if you’re not.

OpenClaw closes that gap. It’s an open-source AI agent that runs on your machine, can read and write files, browse the web autonomously, execute shell commands, and message you on Slack. The pattern: configure OpenClaw to run on a schedule, have it pull metrics from your outbound tool via browser automation, update the signal performance log and campaign results files directly, then ping you on Slack with a summary and a prompt to run the weekly-update skill. You open Slack, see “here’s what changed this week, here’s what I updated, here are the three things I need you to decide” — and respond from your phone.

The human still makes the judgment calls. Whether to change the ICP, whether a signal is underperforming enough to cut, what happened in the competitive deal on Thursday — OpenClaw can’t know those things. But the data collection, file updates, and context preparation that surround those decisions become automatic.

The /weekly-update skill included in this repo is built for exactly this workflow. Run it manually on Monday morning, or let OpenClaw trigger it on a schedule. Either way: Claude reads everything, drafts every section that changed, and asks you to fill in the parts it can’t know. Ten minutes, not two hours.

The Bigger Point

The GTM teams pulling away right now are not running more outbound, spending more on paid, or writing better copy. They have built better context. Their AI has something to work with. Their signals are calibrated to real outcomes. Their new hires don’t start from zero.

The repository is not the point. The institutional knowledge is the point. The repository is just where you put it — so it doesn’t walk out the door when someone leaves, doesn’t disappear when the Slack thread scrolls away, and doesn’t have to be rebuilt every time you run a new campaign.

Build it once. Update it when things change. Let it compound.

Get the Starter Kit

GitHub: github.com/KarlRaf/gtm-starter-kit

What’s included:

• CLAUDE.md template (pre-structured, not a blank form)

• 6 context templates: company profile, ICP definition, signal library, positioning, competitor radar, persona

• 5 skills: Setup (auto-populate from domain), Account Research, Signal to Sequence, ICP Scoring, Weekly Update

• 3 workflows: Enrichment (with deliverability), Signal Routing, Campaign Build

• 2 playbooks: New Signal Response, Competitor Switch

• sync/ folder with starter scripts for pulling campaign data into the repo automatically

• Complete example company (Relay) — every context file populated, two sample outputs, a live campaign brief with sequences and performance tracking

If you enjoyed this article, you can explore more about Claude Code and context engineering for GTM in these recent articles:

• How to Build GTM Campaigns Your Prospects Would Pay to Receive

• The GTM Guide to AI Context Engineering

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🏅 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.

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Dear GTM Strategist,

You know those outbound messages that make you want to throw your laptop out the window?

“Congrats on your new role!”

“I noticed your company is growing.”

“Company X deserves more SEO backlinks.”

- followed by a pitch for something you’ll never buy from someone who clearly knows nothing about you.

Here’s why it won’t end anytime soon: mass outbound “spray and pray” tools - now powered by AI - generously include such templates, and sales noobs and lazy sellers eat them up.

But if you send out 500 emails and book zero meetings, treat that as a sign that something needs to change.

Claude Code and Jordan Crawford to the rescue. Jordan is the founder of Blueprint GTM, where he helps teams learn and work with Claude Code, and a co-creator of Cannonball GTM - a Substack that I cannot recommend enough!

And he’s here to help

Instead of sending out 500 “congrats on your recent promotion to the VP of strategy at X…”, you will learn how to craft an email that intelligent life will actually reply to because it talks to their pain points and adds value.

So in today’s post, I want to share what we learned and share more tips on how you can apply it to build better campaigns.

What you’ll learn:

• Context engineering - how to load AI with deep, specific knowledge about your ICP, market, and to find data sources to get 10x leverage.

• How to set up Claude Code as your GTM operating system with compounding context, persistent memory, and the ability to query databases, write scripts, and deploy sub-agents.

• Why permissionless value prop (PVP) - a message that is independently valuable to the recipient, whether they buy from you or not - beats cringe personalization.

Why Most AI-Generated Outreach Fails

Problem: Everybody has access to the exact same signals and tools - and those are not the best.

Job changes, funding rounds, LinkedIn activity, technographics - you can get all of this from a dozen enrichment tools. And so can every one of your competitors.

When everyone enriches from the same signal pool, the “personalization” is identical across the board. You’re all writing the same email to the same person with the same data points.

Solution: Take a step back - deep dive into your ICP, buying committee, and fall in love with their problems

The generic enrichment layer has become the baseline. The teams pulling ahead are the ones who layer proprietary context on top: their own win/loss patterns, domain-specific data sources, industry signals that no enrichment vendor packages out of the box.

As you build your proprietary intelligence, you unlock first degree signals.

Jordan Crawford put it perfectly: “We are currently limited not by our tools anymore, but by our imagination. What’s the greatest thing you can imagine with the best possible data?”

Most teams use AI to write faster. Very few use it to think deeper about what would actually be valuable to say.

The question worth asking: what is the most valuable thing I could say to this person, and what data would I need to say it?

Often in GTM, you have one shot to do it - so you better do it right

That’s a fundamentally different starting point - and it leads to fundamentally different messages. And it motivates you to put in more thought and love instead of thinking that changing names and companies is the level of personalization you need.

Context Engineering: The Skill That Separates 10x From 1x

Context engineering goes way beyond prompt engineering. A clever prompt still works with whatever the model already knows. Context engineering is about loading an AI system with enough specific knowledge - ICP docs, call recordings, competitive intel, product data, market signals, database access - that it stops guessing and starts reasoning like your best rep.

Why Claude Code specifically? Because, unlike a regular Claude or ChatGPT chat, Claude Code offers compounding context:

• Persistent memory. It remembers what you’re working on across sessions. Each client gets a folder with accumulated knowledge that builds over time.

• Skills. These MD files are reusable workflow instructions - think of them as playbooks that teach Claude Code how to perform a specific set of tasks - like validating a LinkedIn profile, querying a specific database, or structuring a particular type of analysis. (Here’s Anthropic’s official guide for building Skills).

• Sub-agents. Claude Code can spawn specialized agents for subtasks without polluting the main context. Think of it like a manager delegating to employees - the manager doesn’t need to know every detail of how the floor got swept, just that it’s done.

• Script execution and API calls. It can write and run actual code, query databases, call APIs and write to other systems, and save results locally. Think of it as an operating system, not a chatbot.

The first campaign is the heavy lift. Every one after that builds on the context you’ve already loaded - so the intelligence compounds while the time to deploy drops.

The setup on your computer should be simple: one folder per project or client. Inside that folder lives everything Claude Code needs - the company context, the ICP definition, access to relevant data sources. When you open that folder, Claude Code already knows what it’s working on and it doesn’t mix it with the context and memory from other projects.

“The process matters more than the prompts. Long prompts are the past. What matters is your goal, the order of operations, and the context you load before you start building.”

Example: A Campaign for GAF That You’d Actually Send

To demonstrate what building a GTM campaign with Claude Code looks like, Jordan picked a deliberately unexpected company: GAF, one of the largest roofing manufacturers in the world. GAF sells roofing tile through independent contractors. About as far from the B2B SaaS bubble as you can get.

Why? Because great outbound isn’t limited to B2B SaaS. If you can find discoverable pain and public data, you can build a killer campaign for anyone.

Here’s what happened in roughly 30 minutes:

Step 1: Context loading. Jordan told Claude Code to research GAF’s website and understand the business - who their customers are, how they tier their contractors, what their reward programs look like. No manual briefing doc. Just: “Go understand this company.”

Claude Code ran web searches, built a markdown (MD) file summarizing GAF’s business model, contractor ecosystem, and competitive landscape - and saved it as persistent context.

Because context windows in AI tools are limited (and can fill fast with data-intensive research), storing relevant data in MD files ensures that Claude can always reference key information while you clear the context window for a new task. You can even ask Claude to save important info to its memory.

Step 2: The plan. This is where it got interesting. Think of where you could find relevant, unique data that is not sourced in standard enrichment tools and can give your messages an edge - like public databases or industry associations. For the GAF case, Jordan used the Shovels database, which contains 185+ million building permits. He told Claude Code: figure out what intelligence in this data would be incredibly valuable to GAF.

That’s the magic of Claude: you can tell it your goal and it will find the best way to do it.

Claude Code found 19.6 million roofing permits in the Shovels database, with 276,560 permits mentioning GAF. Based on this, it identified the play on its own: upsell targets. Contractors who already use some GAF products but give 60–90% of their business to competitors like Owens Corning and CertainTeed. Claude Code even identified GAF’s competitors from the permit data without being told who they were.

Still, use your brain. You should be the final judge of the plan and ruthlessly tell Claude where it went wrong. In our example, the pitch it initially suggested was wrong and Jordan told it how to correct it.

Step 3: Data queries and message generation. Claude Code queried the database multiple ways - loyalty splits, geographic white space, competitive displacement targets, brand share trends over time. It found 154 high-quality contractors that represent massive upsell opportunities.

Then it identified the right decision-makers and drafted messages with actual data:

“There are 200+ roofing contractors who’ve used GAF in at least 10% of their permitted jobs, but they’re giving 6y just haven’t committed. Here are the top five - with their revenue, Google reviews, total permits, and exact brand mix.”

That’s market intelligence disguised as outreach. And it compels a response because it’s independently valuable - the recipient benefits from reading it whether they take a meeting or not.

Jordan calls this a Permissionless Value Prop (PVP) - a message so valuable that prospects would literally pay to receive it. You’re giving away an insight, and the meeting is just a natural next step. For this, you don’t need a large database of potential leads - but a highly focused one.

“I’m giving you valuable data on your market. That’s worth a response. Nobody ever called market intelligence ‘creepy.’”

👉 Watch the full livestream recording to see how Jordan Crawford built this whole campaign live.

The Playbook: How to Build Your Own Context-Engineered Campaigns

Ready to try this yourself? Here’s the framework Jordan uses with every client.

1. Invert from your best deals

This is Charlie Munger’s advice applied to GTM. Download all your call recordings (or ask Claude Code to create a script to get them for you). Deploy Claude Code against every transcript and ask: what likely made this person convert? Compare closed-won against closed-lost.

Then ask the harder question: what public data could have predicted this? For each conversion signal, search for publicly discoverable evidence. If your best customers tend to be companies with outdated tech stacks going through rapid hiring - can you find that signal before they ever hit your website?

2. Find discoverable pain

Not every company is a good fit for this approach. You need two things: a differentiated product and high public data availability. Regulated industries - healthcare, construction, finance - have a treasure trove of public filings, permits, compliance data, and regulatory signals.

But even in lower-data environments, you can often find something - job postings, technology stack signals, press releases, SEC filings.

3. Build the context folder

This is the “context engineering” part. Before Claude Code writes a single message, load it with everything your best rep knows:

• ICP documentation

• Call transcripts and win/loss analysis

• Competitive intelligence

• Product positioning and objection handling

• Access to relevant data sources (Exa.ai, Blitz API, OpenWeb Ninja, Apify, or whatever fits your market)

4. Plan before you execute

Jordan uses Claude Code’s plan mode religiously. This will get Claude to draft the strategy right before burning tokens on heavy lifting. What data do you need? How should you query it? What’s the angle? The plan will be saved as an MD file, so Claude can reference it even as its memory resets across tasks - so it has to be airtight.

5. Go deep, not wide

Jordan’s “drill, not peanut butter” analogy is the best mental model here. Don’t spread thin across thousands of accounts. Pick a specific segment where you have deep data and a strong value prop. Build one killer campaign. Prove it works. Then expand.

“Scaling this to a thousand companies without doing the hard thinking first is like pointing a thousand cars off a cliff. The best campaigns start narrow and go deep.”

But I’m Not Technical - Can I Still Do This?

If you can write a Google Doc, you can use Claude Code.

The terminal might seem scary at first glance, but you will quickly get used to it. There are just a few commands you actually need:

• cd + drag and drop a folder (to navigate to your project)

• claude (to start Claude Code)

• shift + tab (to switch to plan mode)

• clear (to reset the context window)

• ctrl + V (to paste - note that it differs from cmd + V even on macOS)

That’s it. Everything else, you just write (or even talk - speech-to-text tools like Wispr Flow can be a huge time-saver). You describe what you want to do. Claude Code figures out how. If you don’t know how to do anything in the terminal, just ask Claude in chat and it will guide you through every step.

The Bigger Picture

Any SDR tool can send a thousand emails in an hour. The real unlock is deep research that is scalable.

The analysis Jordan did live - querying building permits, identifying competitor usage patterns, finding geographic white space, building data-rich messages for specific decision-makers - would have taken a research team weeks. Claude Code did it in 30 minutes.

The teams that win in 2026 won’t be the ones sending the most emails. They’ll be the ones sending messages that make prospects think: “How do they know this about my market?”

That’s the bar now. Context engineering gets you there.

Here are your next steps:

• Open Claude Code this week and start with one thing: load it with context about your ICP. Upload your best call transcripts. Ask it to identify what made your best customers convert. Work backward from there.

• Grab my 12 Claude Code skills for GTM. I turned my entire GTM methodology, tested with 1,000+ companies, into a set of Claude Code skills that cover everything from competitor analysis and positioning to pricing, launch assets, outbound, and ABM. 100+ tasks across 12 phases, where each phase builds on the last. Add them to Claude Code or Cowork, ask for what you need, and the right skill activates with tailored output based on your product context. Get them here for free on GitHub.

The AI model is table stakes - everyone has access to the same LLMs. The context you feed it is your moat. Start building it.

Claude Has Taken Over GTM: Now Tell Us How You’re Actually Using It

Q1 2026 made one thing crystal clear: Claude didn’t just enter the GTM stack. It started eating it.

Teams are using it to scrape niche directories for non-obvious prospect lists, build trigger-based outbound sequences, create programmatic AEO pages at scale, reverse-engineer competitor ads, build custom lead scoring logic, and clean up CRMs without touching a single line of code. And that’s just what we already know about.

Kyle Poyar (Growth Unhinged, 80K subscribers) and I are running the first-ever Claude Code Pulse Survey to map what GTM teams are actually building with Claude right now, in production, not what looks good in a YouTube demo.

This is what you get for 5 minutes of your time:

• Early access to the full results before anyone else, including real workflows and setups from practitioners across outbound, content, CRM, growth, and sales

• A chance to be featured as a guest contributor in Growth Unhinged or GTM Strategist. If your setup is interesting, we want to write about it.

We’re going all-in on Claude use cases and workflows in Q2, and we need your help figuring out what’s already working (and what’s still broken) so we can curate content that brings tangible value to your business.

👉 Take the survey

It takes under 5 minutes. I did it in 3 minutes. Can you beat me?

✅ 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,

Everyone talks about how AI will make GTM cheaper because you can just vibe code your own tool and cancel expensive subscriptions. But an interesting trend is happening instead.

AI tools are becoming more expensive to run, not less. And the reason has very little to do with pricing decisions. It has to do with how AI software actually works.

Clay’s recent pricing change is a good example.

When they announced the update, reactions were all over the place. Some users celebrated the fact that data costs dropped significantly. Others quickly calculated that the same workflows would now cost much more.

Both reactions were technically true.

Clay did not simply change prices. They changed how costs are exposed inside the product.

For the past two decades, SaaS followed a predictable model. Companies sold access to software, usually priced per seat, while the cost of serving each additional user was close to zero.

AI tools break that model. Every AI-powered workflow consumes something real behind the scenes. Tokens. API calls. Model inference. Compute.

As usage grows, so do the underlying costs. Which means the economics of AI products look very different from traditional SaaS.

Clay’s pricing change simply makes that reality visible. And it highlights a much bigger shift happening across the AI software landscape.

In this post, we will look at:

• Why AI tools are forcing new pricing models

• Why credits and usage-based pricing are spreading across SaaS

• How teams are tackling variable costs of AI tools

• and what this means for the future economics of AI-powered GTM stacks

Let’s get into it.

Clay’s Pricing Change Is a Signal

Clay introduced a new pricing mode last weekl. At first glance, the announcement sounded simple. Data costs inside the platform dropped significantly. In many cases, the reduction was between 50 and 90 percent.

That part was easy to understand. Data enrichment has always been one of the most expensive parts of running Clay workflows, so cheaper data was welcomed by many users.

But the rest of the update revealed something more interesting.

Clay separated the costs of two things that used to be bundled together:

• Data credits

• Workflow actions (platform usage)

Data credits now pay for enrichment and external sources. Actions pay for running workflows and executing tasks inside the platform.

In other words, Clay is no longer charging only for access to the product. It is charging for the work the product performs. As Kyle Poyar pointed out in his analysis, this pricing now splits out the value/platform piece (high margin) vs. AI tokens (low margin).

The rollout itself was a masterclass in comms: coordinated internal/external messaging, CEO posts, video walkthroughs, and all existing customers grandfathered on the legacy model.

It created two very different reactions in the community.

Some users calculated that their workflows would now be cheaper because enrichment costs dropped so much. Others realized that heavy automation would now generate higher bills.

Both interpretations were correct. It also depends on how they are using Clay.

The pricing model did not simply go up or down. It exposed where the real costs actually sit. And those costs are not primarily inside the Clay product itself. They come from everything happening underneath the interface.

Every automated workflow triggers multiple external services. Data providers charge per record. AI models charge per token. Infrastructure charges for compute and storage.

The more work the workflow performs, the more resources it consumes. Traditional SaaS products could hide those costs because they were small and predictable.

AI products cannot.

Clay’s pricing update simply makes those economics visible.

AI Products Cannot Be Priced Like Traditional SaaS

To understand why Clay made this change, it helps to zoom out. For most of the past two decades, SaaS companies followed a predictable model.

They sold access to a product, usually priced per seat or per account. Once the software was built, the cost of serving each additional user was relatively small.

That is why SaaS margins became so attractive. Revenue scaled faster than costs and ARR (Annual Recurring Revenue) was the name of the game.

AI products behave differently. Every time a user runs an AI workflow, the workflow consumes AI tokens. Those costs do not disappear as usage grows. In many cases they grow directly with usage.

This is one reason pricing teams have become unusually active recently. According to the PricingSaaS Trends Report, pricing changes across SaaS companies increased roughly 15 percent year over year, while packaging changes increased more than 20 percent as companies experimented with new pricing structures.

One trend stands out in particular: credit-based pricing models grew more than 120 percent.

Credits allow companies to connect price more closely to consumption. Instead of charging only for access to the product, they charge for the work the product performs.

This shift is already visible across many AI products. Figma introduced AI credits for certain features. Data platforms charge per request or per record processed. Some AI assistants are priced per resolution or per automated task.

Kyle Poyar recently described this shift as moving from selling software access to selling completed work. He also analyzed how

The pricing unit is no longer a seat. It is the task the system performs. And this is reflected in AI-based pricing models.

The Hidden Cost of the AI GTM Stack

Once you start looking at AI tools through the lens of usage, something else becomes visible. Most GTM teams are no longer buying a single product.

They are assembling a stack of services that work together.

A typical outbound workflow today might look simple on the surface.

• A contact gets enriched.

• The company is analyzed.

• A personalized message is generated.

But behind the scenes, that workflow may trigger several systems.

An LLM analyzes the company or contact. Another model generates the message. Automation tools move the data between systems, and users can “bring their own keys” - connect the their preferred tools with API keys to source the data directly.

Each of those steps has a cost attached to it.

• Data providers charge per record.

• AI models charge per token.

• Infrastructure charges for compute.

None of those costs is visible when the workflow is first built. They only appear once the system starts running at scale. This is why many teams are beginning to notice their AI budgets growing faster than expected. As 2026 SaaS Management Index shows, 61% of IT leaders have already cut projects due to unplanned SaaS cost increases.

A workflow that feels inexpensive during testing can become costly when it runs thousands of times per week. At that point the GTM stack starts to behave less like traditional software and more like infrastructure.

And infrastructure is almost never priced per seat.

Pricing Models Are Starting to Reflect This Reality

Once you understand the economics behind AI products, the recent wave of pricing changes makes sense. Companies are searching for ways to connect pricing to the actual work their systems perform.

Several models are emerging.

Some companies introduce monthly credit allowances that reset each billing cycle. Others charge once usage exceeds predefined limits.

Hybrid models are also becoming common. A base subscription provides access to the product, while usage-based components capture the cost of AI features.

A few companies are going even further and pricing based on outcomes.

Support platforms charge for resolved conversations. Automation platforms charge for executed workflows. AI assistants charge for generated outputs. All of these approaches attempt to answer the same question.

What is the most honest unit of value for AI software?

The industry has not settled on a single answer yet - ideally completed outcomes, but that’s not always feasible to measure. However, one thing is becoming clear. Seat-based pricing alone will not capture the economics of AI systems where usage directly drives cost.

How Top GTM Teams Are Tackling AI Costs

I asked world-class companies in my network what the variable and sometimes unpredictable AI costs mean for them and how they are tackling this.

Amos-Bar Joseph, CEO and co-founder of Swan AI, famously shared his $50K monthly Anthropic bill.

However, a month later, he shared how he optimized the costs - slashed the bill to $27K while doubling AI usage (Swan grew from 200 to 400 customers that month).

What changed? Swan’s AI agent had been operating through a translation layer - converting every action into human-language instructions that then had to be decoded into code. By removing that layer and rebuilding the agent to operate directly in code - calling APIs and databases natively - they cut costs by nearly half while doubling usage.

The lesson from Amos: the biggest cost savings don’t come from prompt optimization or model switching, but from rethinking the AI architecture itself.

Kareem El Muslemany (senior monetization manager at Zoom and author of Pricing Code) highlighted that AI is novel, and costs can escalate unpredictably - 80% of companies in a recent survey were 25% or more over their AI budget. Customers can use cost and ROI calculators from vendors to budget for AI costs. Variable costs can be optimized by using lower-cost services or AI models when they suffice, using internal data/tools, and having fewer steps in workflows. As a final check, spend caps can be implemented to avoid unplanned overages.

But as Kareem also points out, pricing leaders should be mindful of pure-token costs. In his post, he describes that for pure coders using Claude’s API alone, raw token costs don’t always capture the true value. Code Review, one of its highest-value developer workflows so far, is billed on token usage at $15–25 per review. That logic made sense when Claude was mainly infrastructure, but Code Review is valuable IP. Monetizing on credits alone undersells its incremental value.

Fynn Glover, co-founder and CEO of Schematic, said that how you price against rising AI costs is a strategy question. “It depends entirely on two things: your capitalization and your pricing philosophy based on your stage and your capitalization.”

If you’re well-funded and in a land grab, your philosophy might rightly be, “for now, let’s not let input costs drive our pricing.”

If you’re bootstrapped or undercapitalized and focused on demonstrating pricing power, then yes, cost discipline matters a lot.

“The mistake I see most often: companies defaulting to cost-plus pricing because their AI costs are rising. I think classic pricing theory is quite instructive here. The moment you anchor your price to your costs instead of your customer’s value, you’ve told the market exactly how to think about you and it’s not favorably. Cost-plus can work for pass-through add-ons. For your core product? It erodes perceived value and gives up pricing power,” said Fynn.

The New Skill GTM Teams Will Need

This shift creates a new challenge for GTM teams. The efficiency of AI workflows suddenly matters.

In the past, teams rarely needed to think about the internal mechanics of the tools they used. You bought the software, added users, and focused on adoption. With AI systems, the structure of the workflow itself becomes important.

A poorly designed workflow may trigger unnecessary enrichment calls. Prompts may be longer than needed. Automation loops may multiply the number of model calls.

None of this is visible in the interface. But it shows up very clearly in usage-based pricing.

This is why some teams are starting to treat AI workflows almost like engineering systems. They monitor how often processes run. They optimize prompts to reduce token usage. They simplify automation chains to avoid unnecessary calls.

The goal is not only to build workflows that work. The goal is to build workflows that work efficiently.

That is a new skill set for most GTM teams.

It sits somewhere between operations, data, and product thinking. Teams that learn to design efficient workflows will get more output from the same tools. And over time, that advantage compounds.

AI software is changing the economics of the SaaS model. The future GTM stack will not only be evaluated based on features.

It will also be evaluated based on how efficiently it converts compute into outcomes.

The teams that understand those economics early will build systems that scale without surprises.

To hear directly from top pricing experts how they’re tackling AI monetization in 2026, join me on March 26 for a livestream with Kareem El Muslemany (Monetization at Zoom), James D. Wilton (Monevate, ex-McKinsey, author of Capturing Value), Jasdeep Garcha (Product & Monetization at Vercel), and Fynn Glover (CEO, Schematic).

We’ll dig into credits vs. usage, predictability vs. alignment, outcome-based vs. output-based pricing, and the growing gap between the value your AI product creates and the revenue you actually capture. No slides, no pitches - just what’s working and what’s not.

Join here

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Dear GTM Strategist!

Before we jump into another exciting round of AI playbooks, we need to talk about the infrastructure we are operating in. Most conversations about AI in go-to-market focus on tools and tactics. How to automate outreach. How to build agents. How to scale workflows.

These conversations are exciting, but they often miss the bigger issue.

Artificial intelligence did not fundamentally change GTM.

It exposed how many of our systems were already broken.

For years we built revenue engines that relied heavily on human effort. People writing cold emails. People updating CRM records. People manually interpreting customer feedback. It worked when capital was cheap and headcount was easy to justify.

Those systems are now under immense pressure.

Jacco van der Kooij, the brilliant GTM strategist leading the Winning by Design movement, frames the shift in a very intriguing way.

“For years, we talked about keeping the human in the loop. Today the real challenge is to close the loop.”

Closing the loop means building systems where learning happens continuously. Insights move quickly through the organization. Decisions are based on structured data rather than fragmented opinions.

That requires something many GTM teams would rather avoid. It requires looking at the infrastructure behind how revenue actually works.

After reading Jacco’s chapter in Momentum’s Ignite Your GTM with AI, I spent months trying to get him to talk about this topic with us. If AI is going to work inside GTM systems, we first need to understand the architecture those systems run on.

And that architecture is often messy.

Teams are building AI workflows on top of fragmented data, disconnected tools, and processes that were never designed to scale. Instead of creating leverage, this often creates more complexity.

This conversation is about fixing that foundation.

It is not a light read. But if you are responsible for leading GTM teams in the AI era, it will change how you think about your systems.

In this edition, we’ll cover:

• Why three “black swan” events colliding at once created the perfect storm for GTM

• How AI-native companies build compounding revenue machines instead of linear sales motions

• The four data crises you must fix before AI can actually help you

• Why the excavator analogy should change how you think about your team

• How to optimize for buying instead of selling, and why that distinction matters more than ever

Let’s get into it.

The Macro Shift and the AI Revenue Machine

Artificial intelligence did not change go-to-market overnight. It simply exposed what was already broken. And this massive shift did not happen in a vacuum. Jacco points out that it is the direct result of what he calls the Three Black Swan Convergence - three compounding macro events colliding at the exact same time.

COVID-19 fundamentally changed how we work and interact. The sudden end of zero interest rates completely changed how we fund businesses. And artificial intelligence changed how we build and scale products. Each of these on its own would be disruptive. Together, they exposed a massive systemic flaw in how software companies operate: for the last decade, we over-indexed on cheap human labor instead of building truly intelligent systems.

When money was practically free, founders could raise millions on a mediocre idea and hire armies of salespeople to brute-force their way to revenue. We bloated our recurring revenue models with manual, unstructured work. Humans writing cold emails. Humans manually updating CRM records and parsing through customer feedback. Every single time you insert a human into a repetitive data process, you introduce delay, bias, and error. For years we compensated for these slow feedback loops with more effort and more headcount. The system was highly inefficient, but it was still moving. Now that inefficiency is completely visible.

Jacco explained this perfectly:

“The main change that we see over the last two years is that AI has revealed how recurring revenue is supposed to work. And it did that by removing the influence of people in an unstructured way, that it actually slows the system down.”

AI-native companies operate entirely differently. They strip away unstructured human influence and build closed-loop systems. If a new user segment starts converting faster today, the system detects it immediately and automatically routes more resources to that specific segment. Learning takes minutes or hours, not months.

This speed is driven by what I’d call User-Decider Convergence. The old software model relied on top-down procurement - you sold to the executive, and they forced their team to use the tool over a nine-month sales cycle. AI natives flip this entirely. The person using the product is also the champion and the decider. They experience the pain, find the solution, and feel the impact instantly. They swipe their own credit card. Adoption spreads organically from the bottom up, and then they upgrade their own account and invite their entire team to join.

This is how you build a compounding revenue machine. This is how a company scales from $1M to $100M without hiring a thousand salespeople. You don’t just buy a new AI tool - you change your entire operational architecture.

The Four Data Crises

Before AI can act as your growth engine, you must fix your data infrastructure. You cannot scale a broken system, and if your underlying data is a mess, adding artificial intelligence will only help you make expensive mistakes much faster. Most traditional software companies are currently paralyzed by their own tech stacks, trying to deploy advanced AI models on top of a broken foundation.

Here are the four critical data crises you have to solve first:

Unstructured Data. Ninety-five percent of your customer conversations remain unformatted and completely unused. Your sales team has hundreds of calls every single week, and they take highly subjective notes based on gut feelings. This unstructured data is completely useless to a machine - AI cannot parse a vibe. You must use a strict methodology like the SPICED framework to turn human conversations into structured, machine-readable data.

Incompatible Data. Your tools do not speak the same language. Sales call recordings live in one platform. Pipeline revenue lives in your CRM. Customer support tickets are stuck in a completely different system. They exist in isolated silos, and you cannot build a unified growth engine if your data is fragmented across six different dashboards that refuse to integrate.

Disconnected Data. This is exactly what breaks the compounding growth loop. A customer success manager might learn exactly why a major client churned on a Tuesday, but that critical insight never flows back to the marketing team running the acquisition campaigns. The system is entirely disconnected - the right hand has no idea what the left hand is learning.

Incomplete Data. Gaps in your data capture are incredibly dangerous in the AI era. If you feed an AI model incomplete data, it will not just misfire - it will actively mislead you. It will confidently tell you to target the exact wrong segment and burn your entire marketing budget in the process.

To fix these four crises, you need to build a real-time data infrastructure. This is the core difference between the old way of operating and the new AI-native approach: you need a closed-loop system where data flows continuously. Learning from a customer cannot take an entire quarter. It must take hours.

Here is how Jacco van der Kooij explained this urgency:

“What do I mean with real time? Well, look, if you learn something happens in a region or if you learn a particular conversion rate is in a particular state and it moves down. It cannot take months before that propagates and leads to action. It is got to take minutes, hours.”

If a conversion rate drops abruptly, your system needs to adjust that same afternoon. If a new and highly profitable buyer persona suddenly appears in your funnel, you need to throw more resources at that fire immediately. You can no longer afford to wait for a monthly reporting meeting to figure out what the market wants.

The Excavator Analogy and the 30/70 Talent Split

We are currently forcing our most expensive talent to do manual labor. We pay human beings to write generic outreach messages, scrape lead lists, and update pipeline stages. This makes absolutely no sense.

Jacco uses a brilliant construction analogy to explain this shift. If you need to dig a massive hole, you can hire four people with shovels - they will eventually get the job done. Or you can bring in an excavator that digs faster, deeper, and with far more accuracy. AI is the excavator. But here’s the important nuance: when you bring the excavator onto the site, the humans don’t just disappear. Their roles completely change. You suddenly need an inspector to make sure the machine doesn’t hit a gas line. You need an architect to verify the foundation is square.

This is how Jacco explains it:

“The human beings are taking away the role of physical labor, the shovel, and are now becoming humans that oversee if the work is done properly.”

Your team should not be writing the spam. They should be managing the AI agent that generates the campaign - reviewing the target list, tweaking the context of the messaging, approving the final output. Once they perfect that process, they build an AI agent to check the first AI agent. This is a fundamentally different way of working, and it requires fundamentally different skills.

This shift brings us to a harsh reality about talent. AI will not fix your mediocre performers. Most leaders assume that AI will magically elevate the bottom 70% of their team - that it will turn struggling reps into top performers. It will not. If a rep doesn’t understand the buyer’s pain points, giving them an AI tool just helps them send irrelevant messages much faster.

The real magic happens at the top. AI gives your top 30% superpowers. These are your relationship builders, the complex problem solvers who actually understand the market. When you remove their manual data entry and administrative tasks, they operate at unprecedented levels. So stop trying to use AI to save the bottom 70%. Instead, architect systems that eliminate the need for routine execution work entirely.

Avoiding the Sales Tech Sidestep

Sales technology took a massive wrong turn over the last decade. The earliest tools were built to help us use data to work smarter - they were optimized for intelligence. Then we took a detour. Later tools completely abandoned intelligence and optimized for pure volume instead. We built massive machines to automate cold outreach and scale email spam. AI is now forcing us to return to that original principle: using data to reach the right people at the right time with genuinely valuable information.

This is where early-stage founders have an unfair advantage. If your company is under $5M in revenue, you are in the best possible position. You don’t have to unlearn bad habits. You don’t have a massive legacy tech stack to dismantle. You can build a clean AI-native system from scratch.

Jacco shared the exact tactical workflow with me, and it is incredibly simple:

1. Use LinkedIn Sales Navigator. Set strict filters to pull a highly targeted list of 200 prospects.

2. Export that list and feed the profiles into an AI model like Claude. Ask the AI to analyze those 200 people and find the exact common problem they all share right now.

3. Use a tool like HeyReach to build a highly contextual campaign based on that specific research.

A few years ago, this level of research and execution would take a team of human SDRs several days to complete. Today, a single founder can do the entire thing in three hours. You don’t need a massive team to compete anymore - you just need a smarter architecture.

Optimize for Buying

We need to talk about buyer psychology, because this is where everything connects. People absolutely hate being sold to - but they love to buy. If you stop someone on the street to aggressively sell them a timeshare, they will run away. If you put that same person on Amazon, they will happily spend hours filling their cart. The motivation is completely different when the buyer is in control.

“Most people hate being sold but they love to go buy. And that means that the sales process should not be based around selling and optimizing selling; it should be based on buying and optimizing buying.”

The old software model completely misunderstood this dynamic. We built massive, complex processes optimized entirely for the seller - mandatory discovery calls, artificial demo gates, aggressive outbound follow-ups. We tried to control the timeline. The new AI-native model uses technology to optimize the buying process instead. It removes friction. It uses AI to figure out exactly what specific research or proof the customer needs to make a purchasing decision today. It doesn’t blast them with templates - it finds the patterns in their problems and offers the exact solution they are actively looking for.

This is where true scale happens. When you optimize for buying, you activate compound growth loops. You achieve exponential growth when your current user essentially sells your product to the next prospect. They experience a frictionless buying process, get immediate value, share the tool with their internal team, and invite external collaborators. The adoption loop feeds itself, and AI simply accelerates how fast that loop spins.

Value Versus Impact

The final piece of the AI revenue machine is how you communicate, and most B2B companies get this completely wrong. They all sound exactly the same - saving time, reducing costs, boosting productivity. They focus entirely on the value of their product, but value is simply what your product can do. It’s a feature. If you sell a car, the value is that it drives fast or uses electric energy. The impact is entirely different: the impact is that the car allows you to visit your mother who lives a hundred miles away.

Jacco summarized this dynamic:

“The impact is an outcome, the value is a promise. We often say value is the promise of impact. Impact is the realization of that promise.”

When you sell value, you’re just making a promise. Buyers don’t care about your promise - they care about their own reality. This means you have to completely shift your messaging. Look at what your early customers actually achieve with your product. Listen to the exact words they use to describe their experience.

And here’s where most marketing teams ruin everything: they take raw, powerful customer feedback and try to make it sound professional. They sanitize it. They polish it until it sounds “clean and sexy.” When you do that, you lose the touch - you lose the exact verbiage that actually resonates with your buyer. Don’t polish your copy until it sounds like every other software company. Use the exact words your early customers use in your marketing campaigns. Keep it in their natural language.

When you are trying to differentiate your brand in a noisy market, you will have to make hard choices about which pain point to attack and which messaging to scale. This is where the human element becomes your ultimate advantage in an AI world. You can’t just wait for a dashboard to tell you what to do - you have to rely on your intuition to find the starting point.

Jacco van der Kooij left me with one final piece of advice that I think captures the whole conversation beautifully:

“Your gut tells you where to look for the data, then the data will narrow it down and make sure that you get it right.”

Trust your gut to find the initial direction. Then use your data architecture to prove it’s actually right. This is how you win the next era of go-to-market.

Live stream: Clay for Growth & Marketing

On March 19th, I was joined by Davide Grieco, Head of Growth at Clay. He walked us through how growth and marketing teams are using Clay to move faster, act on signals in real time, and finally get credit for the ROI they generate.

We demoed real playbooks - from auto-refreshing ad audiences and signal-based campaigns to personalized landing pages at scale - the exact workflows teams at OpenAI, Vanta, and Verkada are running today.

If you’re tired of slow campaigns and budget justification loops, watch the replay of the live stream.

Watch here

Launching a product is hard.

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→ Customer research and positioning

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Dear GTM Strategist!

It is time to revisit one of the most controversial themes in our space: GTM Engineering.

Over the last year, the discussion has been heated. Some claim the title exists because GTM tech is broken. Others argue that GTM Engineers are simply tool jockeys with more love for automation than for real business fundamentals. There are even voices suggesting the entire role is vendor hype designed to sell more software.

Despite all that, the demand keeps growing.

GTM Engineers are being hired across companies and continents, and many of them are earning serious money while building the infrastructure that powers modern revenue teams.

Even if the concept irritates you a little, it is difficult to argue with the data.

So what does the data actually say?

Together with Garrett Wolfe and Alex Lindahl, we ran the largest GTM Engineering Report to date to understand what this role really looks like in practice.

Thanks to our wonderful partners who supported the report: Rho, Wiza, Juicebox, Watt Data, and Exportly.

• 228 respondents

• 30+ countries represented

• The first benchmark study of compensation, tooling, and org design for GTM Engineers.

Some highlights from the data:

• US median compensation sits around $135K

• 72% report direct revenue impact

• 84% of GTM engineers use Clay

• Nearly 68% hold no meaningful equity

• Coding skills create a $40K compensation premium

Thank you to everyone in the community who contributed data, experience, and insights. This report would not exist without you 🙏.

I want to walk through the findings and answer the questions that keep coming up in every conversation about GTM Engineering:

• How much can a GTM Engineer actually earn?

• Is this role just an agency hype, a Clay trend, or is it becoming core infrastructure for in-house teams as well?

• Do you really need to know how to code to get into GTM engineering?

• And which tools are actually powering this new wave of revenue builders? Because I do love tools as much as you do.

Now, let’s dive into the weeds of GTM Engineering.

How Much Do GTM Engineers Actually Earn?

The first thing everyone asks about GTM Engineering is simple.

Is this a real role with real compensation, or just another fancy title for someone building outbound workflows?

The data makes one thing very clear. This is a very real role and the compensation is already significant.

Across the 228 respondents in our survey, the median base salary for in-house GTM Engineers in the United States sits at around $135K.

But the distribution is extremely wide.

At the lower end, salaries fall between $60K and $90K. These are typically junior operators focused on tooling execution, list building, and basic automation workflows.

At the top end of the market, compensation climbs well beyond $200K, especially for operators who combine technical depth with ownership of pipeline generation and revenue systems.

This spread reveals something important.

The market has not figured out how to price this role yet.

Two people with the same title can be doing completely different work. One might be managing enrichment workflows and outbound tooling. Another might be designing signal engines, building automation layers, and architecting the entire revenue data stack.

Those roles are not even remotely comparable, yet the market keeps calling both of them “GTM Engineers.”

The geographic compensation gap is equally striking.

While US-based operators report a median base salary of $135K, their non-US peers sit closer to $75K. That is a difference of roughly $60K, or an 80% premium for US talent.

Even in an increasingly remote world, location still plays a major role in compensation.

Company maturity also matters.

Mid-size companies with 201 to 1,000 employees currently pay the highest median salaries for GTM Engineers. Funding stage also influences compensation, with Series B and Series D companies leading the market at roughly $145K median base salary.

Early-stage startups tend to offer lower base salaries, with pre-seed and seed companies averaging closer to $85K, often expecting equity to compensate for the lower cash component.

Except that is not what the data actually shows.

Equity distribution across GTM Engineers is surprisingly low, even in early-stage environments. Nearly 68% of respondents report holding little or no meaningful equity, despite directly influencing pipeline generation and revenue outcomes.

In other words, companies are asking these operators to build their revenue engines without always giving them a meaningful share of the upside.

This mismatch becomes even more obvious when you look at the technical side of the role.

Because there is one factor that consistently moves operators into the highest compensation bands.

Coding. And it makes me wonder - is the next GTM system a structure of folders and GTM as a code?

Do GTM Engineers Need to Know How to Code?

The short answer is no. You can absolutely enter the field without coding skills. But you are likely to earn much more if you do know how to code.

Our data shows that technical depth has a very real impact on compensation and scope.

When we analyzed the survey responses, a clear pattern emerged. GTM Engineers fall into three broad categories based on their technical proficiency.

1. Low-code operators.

2. Mid-level technical builders.

3. High-code engineers.

Low-code operators typically rely on no-code platforms and workflow builders. They connect tools, manage enrichment pipelines, and run outbound programs using existing platforms. Their work focuses on configuration rather than engineering. Typical tools in this category include Clay, Zapier, Make, Airtable, and no-code CRM automation inside HubSpot or Salesforce.

These operators report a median salary of around $90K.

The next group sits somewhere in the middle. These professionals understand scripting, basic APIs, and data manipulation. They might write small pieces of code, automate tasks, or build lightweight integrations when needed. They often combine platforms like Clay or HubSpot with scripts written in Python, JavaScript, or SQL to extend what existing tools can do.

Their median compensation rises to roughly $105K.

Then there is the top tier.

High-code GTM Engineers regularly work with Python, JavaScript, SQL, and modern AI coding tools. They build custom workflows, write scripts, create data pipelines, and sometimes bypass traditional vendors entirely by building their own internal tooling. Tools in this group often include Python environments, data warehouses like Snowflake or BigQuery, and AI-assisted development tools such as Cursor or Claude Code.

This group reports a median salary closer to $135K.

That is roughly a $40K to $45K premium compared to low-code operators.

In practice, the role is splitting into two different types of operators.

Some GTM Engineers focus primarily on operating the modern sales stack. They configure tools, manage enrichment workflows, and run outbound systems using existing platforms.

Others spend most of their time building the infrastructure behind those systems. They write scripts, build custom automations, and design the data pipelines that power the go-to-market engine.

Both roles are valuable, but they require very different skill sets and experience levels.

Interestingly, the rise of AI-assisted coding is already changing the skill requirements. Tools like Cursor and Claude Code allow GTM Engineers to generate scripts, build automations, and experiment with custom workflows without needing the background of a traditional software engineer.

This does not eliminate the value of technical knowledge. If anything, it increases the leverage of operators who understand how systems work.

But the data suggests that the closer you move toward engineering, the more valuable and better compensated your role becomes.

GTM Engineering in Agencies vs In-House Teams

One of the most common questions around GTM Engineering is where this role actually belongs. Is it something companies should build internally as your core capacity - if so, where should GTME sit in an org, or is it better suited for agencies and external specialists to test the grounds?

The data suggests that both models exist and are growing quickly, but they operate very differently. A large part of the early momentum around GTM Engineering came from the agency ecosystem.

Over the past two years, a wave of specialized agencies and fractional operators started offering GTM engineering services. The barrier to entry was relatively low, demand from founders was extremely high, and the tooling ecosystem made it possible for a single operator to build fairly complex outbound infrastructure.

Our data reflects this rapid growth and the chaos that comes with it.

Monthly agency retainers range from as little as $1K to as high as $33K per month. The median minimum fee sits around $5K, while the median maximum retainer lands closer to $8K.

This is an enormous spread.

The reason is simple. Buyers and sellers are often talking about completely different services while using the exact same label.

Some agencies provide done-for-you outbound. They build lists, run enrichment, and launch campaigns.

Others operate as systems architects, designing signal engines, building data pipelines, and integrating the entire revenue stack.

These are fundamentally different services.

Yet they are both marketed as GTM Engineering.

At the same time, the role is increasingly appearing inside companies themselves.

A growing share of respondents work in-house inside revenue teams, not as consultants or agencies. These operators sit directly within marketing, RevOps, or growth teams and own the internal automation layer that connects the entire go-to-market motion.

Instead of running campaigns for multiple clients, they focus on a single company and build long-term infrastructure.

• Signal monitoring.

• Pipeline automation.

• Data enrichment systems.

• CRM architecture.

This is where the role becomes far more strategic.

In-house GTM Engineers are not just launching outbound sequences. They are designing the systems that determine how pipeline is generated, routed, enriched, and converted across the entire funnel.

Agencies often help companies get started. In-house teams are where these systems are maintained and scaled over time.

So while agencies helped popularize the category, the role itself is quickly becoming core infrastructure inside modern revenue teams.

The most mature organizations are not outsourcing this capability.

They are building it internally.

The GTM Engineering Tool Stack

If you want to understand a new role, look at the tools the operators actually use.

The GTM Engineering stack reveals a lot about how modern revenue teams are evolving.

CRM platforms remain the foundation. Salesforce and HubSpot still dominate the ecosystem, with roughly 88% adoption among respondents. Almost every GTM Engineer is building automation layers on top of one of these systems.

But the real story sits above the CRM.

Clay has quickly become the default infrastructure for this function. Across the survey, 84% of GTM Engineers report using Clay, making it the most widely adopted tool in the entire stack.

Among agencies the adoption climbs even higher, reaching 96%.

This level of dominance is rare for such a young platform.

Clay essentially acts as the connective layer between enrichment providers, data signals, outbound workflows, and automation logic. For many operators it has replaced large portions of the traditional outbound stack.

At the same time, Clay is also the most polarizing tool in the ecosystem.

Operators love the flexibility. They can combine dozens of data sources, trigger workflows based on signals, and build highly customized outbound engines.

They also complain about the complexity.

Workflows often rely on multiple APIs, fragile integrations, and complex logic chains. Running a single campaign can require stitching together several tools and enrichment providers. When one piece breaks, the entire workflow stops.

This creates a strange paradox.

Clay is the most powerful platform in the stack and also one of the most frustrating to maintain.

Meanwhile, AI-native development tools are spreading quickly across the GTM engineering community.

Tools like Cursor and Claude Code now show adoption rates approaching 70% among respondents. These tools allow operators to generate scripts, build small internal tools, scrape data, and automate workflows without relying entirely on third-party vendors.

This shift toward AI-assisted development is changing how GTM Engineers operate.

Instead of waiting for vendors to build new features, many operators simply build their own solutions.

When we asked respondents what they value most in their tools, the answers were very consistent.

• 44% pointed directly to AI capabilities.

• Another 24% highlighted automation power and speed.

At the same time, the frustrations are equally clear.

• 26% complain about poor integrations and closed ecosystems.

• 18% struggle with clunky interfaces.

• 11% cite expensive platforms that deliver limited flexibility.

Despite the massive popularity of Clay and the rapid rise of AI tooling, a clear gap still exists in the market.

About 12% of respondents say they wish a true all-in-one outbound platform existed.

The demand for a unified system is obvious.

So far, no vendor has fully solved it.

For now, GTM Engineers continue to build their own stacks.

The Maturity Gap and the Hype Train

GTM Engineering might be one of the fastest-growing roles in modern revenue teams.

It is also one of the youngest.

When we analyzed the age distribution of survey respondents, the results were surprising.

Nearly 30% of GTM Engineers are between 18 and 22 years old. Another 25% fall between 23 and 26.

That means more than half of the talent pool entered the workforce only a few years ago.

This explains a lot of the confusion surrounding the role.

Many operators entering the space are extremely strong technically. They know how to use modern sales tech. They can build workflows, connect APIs, and automate data enrichment pipelines.

But deep experience with complex sales processes is still relatively rare.

That experience gap matters.

GTM Engineering sits directly at the intersection of marketing, sales, RevOps, and data infrastructure. The operators building these systems are shaping how pipeline flows through the entire organization.

When someone with limited business experience is suddenly responsible for designing revenue systems, mistakes are inevitable.

This does not mean the role is flawed.

It simply means the category is still very early.

The tooling ecosystem exploded quickly. Clay, enrichment APIs, signal platforms, and AI coding tools dramatically lowered the barrier to building automation systems. Suddenly, a single operator could do work that previously required an entire RevOps team.

Naturally, a wave of ambitious builders rushed into the space.

Some are incredibly talented. Others are still learning the fundamentals of how revenue organizations actually operate.

This creates a chaotic market.

Companies are hiring junior tool operators and expecting them to perform like senior revenue architects. Operators are experimenting aggressively without always understanding the long-term implications of the systems they are building.

The result is a wide maturity gap across the industry.

Some GTM Engineers are quietly becoming some of the most valuable operators inside modern revenue teams.

Others are still figuring out what the role actually requires.

This is exactly what early categories look like.

Messy. Fast-moving. And full of experimentation.

Where GTM Engineering Goes From Here

For all the discussion about tools, AI, and automation, the biggest constraint facing GTM Engineers is surprisingly simple.

Time.

In our survey, 25% of respondents said bandwidth is their number one bottleneck. Most GTM Engineers operate as a team of one, responsible for designing automation systems, maintaining the data layer, supporting sales workflows, and experimenting with new go-to-market infrastructure.

At the same time, many companies are still figuring out what this role actually is.

Only 45% of respondents say their organization clearly understands what a GTM Engineer does. Another large group reports only partial understanding across leadership and revenue teams.

This combination creates an interesting moment.

The tools are evolving quickly. The talent pool is growing. But the organizational structure around the role is still catching up.

The companies that figure this out early will have a real advantage.

Because modern go-to-market is becoming increasingly technical. And the operators who can connect systems, automate workflows, and build revenue infrastructure are quickly becoming some of the most valuable people inside revenue teams.

So there you have it. A snapshot of where GTM Engineering stands today, what the market pays for it, and which skills will make you indispensable in the years ahead. If you want more information, download the full The State of GTME Report here. What I shared in this article are my key takeaways, the report itself is 80-slides long - and you can get it FREE:

Get the full report

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As a trained marketer and someone who spent 10 years working as Marketing Manager and CMO before ever having to learn RevOps/Sales for my own business, I have a hot take ♨️ on how nearly all cool technologies GTM technologies are tailored to “sales and RevOps” while marketing is massively lagging behind GTM AI transformation.

When Kyle Poyar and I launched The 2026 State of AI in GTM, I was shocked at how marketing GTM AI plays were mainly focused on productivity - how to do the exact same thing we were doing for the last 10 years faster, allegedly better, and in larger quantities. Some plays were more advanced, but so vendor-specific that I was gagged by how we lack imagination and ideas for reinventing the marketing process with the powerful AI tools we’ve had at our disposal for the last couple of years.

And I am not blaming marketing here for a sec - marketers are among the hardest working people in any organization. They run campaigns, take care of the brand, manage relationships with many stakeholders, organize events, manage advertising budgets, and handle the creative side of businesses. They were thrown AI at them and told: “Now be more productive and create better ROI.”

With increasing pressure on opex and an “adopt or die” mindset to AI, those people started to panic. FOMO on tools, feeling like their department is falling behind, and overselling on demos - best they could do was to create a couple of projects in ChatGPT/Claude to speed up content creation, bought some tools for mass generation of creatives, and at best started playing with some agents to be able to prove to their bosses that they too are AI-transformed now.

But that created problems - deeper problems that we’d expect. As a result of local AI implementations, new silos emerged within the organization. And it is even worse when you go out there and see them in person.

Mainstream B2B marketers are still sold on the idea that their job is to do “inbound marketing” - a concept HubSpot pioneered in 2006. People, that was exactly 20 years ago. This idea is dead. In 2026, we should be holistically thinking about integrated buyer experiences and ensuring that Marketing and “Sales” are elegantly aligned to drive demand toward business outcomes. And that means outbound, data enrichment, and the notion of redefining what we call Marketing Qualified Lead (MQL) with signals we have at our disposal beyond “downloaded an ebook”, “liked our LinkedIn post”, and “registered for a webinar”.

People don’t like it when I say that marketers should move into the role of business developers, sharing tools with RevOps, but that is exactly how we enable marketers to squeeze more out of the activities they are already putting so much time, money, and effort into. By working hand in hand with Marketing leaders in my clients’ orgs and running 50+ Clay tables for them, I put together a starter pack of 8 plays that show you how to use Clay across your entire marketing stack.

These aren’t edge cases or anything new to add to an already full to-do list. These are plays marketing teams are running right now, and this is how you squeeze more value out of them. People love them - I published an earlier version on LinkedIn, and the post went viral.

In this article, I’ll walk you through the concept behind each play and when to use it. For the full step-by-step implementation guide with prompts, scoring formulas, and integration details, grab the complete playbook here.

8 Marketing Clay Plays

Here’s what we’ll cover:

• Play 1: Newsletter Audience Intelligence — Turn a flat email list into segmented, personalized content

• Play 2: Competitive Ad Intelligence — Pull ad spend and messaging data across 50 competitors in one table

• Play 3: Event & Conference Intelligence — Walk in prepared, walk out with scored follow-up lists

• Play 4: Influencer & Creator Vetting — Vet 200 creators by content relevance, not follower count

• Play 5: Community & Forum Intelligence — Capture competitor complaints and buying intent from Reddit, automatically

• Play 6: Partnership & Co-Marketing Discovery — Replace “who should we partner with?” gut feelings with data

• Play 7: Programmatic Personalized Landing Pages — Generate 1:1 pages per account at scale

• Play 8: Podcast & Speaker Pitch Lists — Pitch 200 shows in the time it takes to manually research 10

Let’s dig in 🔎

Play 1: Newsletter Audience Intelligence and Segmentation

Marketing function: Email / Content Marketing

Difficulty: 2/5

You’re sending the same newsletter to everyone. Your VP of Marketing subscribers get the same content as your RevOps ICs. Your SaaS readers see the same topics as your e-commerce readers. And you have no idea who any of these people actually are — you have email addresses, maybe first names, and that’s it.

The play: Import your subscriber list into Clay, enrich every subscriber with firmographic and professional data (company, title, seniority, industry, revenue band), then use an AI column to cluster your audience into meaningful segments — not by demographics, but by content needs.

Think segments like “SaaS Leaders” (VP+ at SaaS companies, cares about strategy), “Startup Operators” (any role at sub-50-person companies, needs tactical how-tos), or “Agency/Consultants” (needs frameworks they can apply to clients).

Three outputs from one table:

• Segmented email lists synced back to your email platform — different content for different audiences

• Content intelligence — you know exactly which topics each segment cares about, so your editorial calendar is driven by data, not guesses

• Sales-ready leads — subscribers who match your ICP, already warm, exported to CRM with full context

The marketers getting this right are seeing 45%+ open rates on segmented sends. Same list. Same effort. Different results — because they know who’s reading.

Bonus: If you sell sponsorships, the enriched data is a goldmine for pricing. “38% of our subscribers are VP+ at SaaS companies doing $50M+ in revenue” justifies 2-3x higher CPMs than “we have 10,000 marketers on our list.”

Play 2: Competitive Ad Intelligence

Marketing function: Advertising / Competitive Intel

Difficulty: 2/5

Be honest — here’s how most marketing teams do competitive ad research: Check Facebook Ad Library. Screenshot a few ads. Check LinkedIn. Maybe Google Ads Transparency Center if you’re thorough. Do that for competitor #2, #3... and by #4 you’re bored and skipping. You end up with a folder of screenshots and zero pattern analysis.

The play: Build a Clay table with 50 competitors and pull structured ad data using Clay’s native integrations — Adbeat for display and video ads across 80+ networks, Semrush for paid search intelligence and traffic sources. Then use an AI column to classify messaging themes across the entire landscape.

This is where it gets interesting. Instead of knowing what one competitor’s latest ad looks like, you can answer questions like:

• What messaging themes are overused in our space? (If everyone says “Scale faster with AI,” maybe don’t.)

• What pain points are under-addressed? (Nobody’s talking about implementation pain? That’s your angle.)

• What offer types dominate? (If everyone offers demos, maybe a free trial stands out.)

• Who’s spending the most and where? (Actual budget benchmarks, not guesses.)

Output: A competitive positioning brief with real spend data, messaging pattern analysis, and 3 specific recommendations for how to differentiate. Updated whenever you want — not once a quarter.

Good news - Clay just launched an Ads feature so you can build ad audiences directly in Clay, enrich them, and with that achieve a much better match rate (up to 90%, compared with standard 30% if you work only with firmographics) - and drastically lower your cost-per-lead. Then you can push them directly to LinkedIn Ads or Meta Ads using the Clay integration.

Watch my Clay Ads demo:

Play 3: Event and Conference Intelligence

Marketing function: Events / Field Marketing

Difficulty: 3/5

Events are the biggest line item in most marketing budgets. And the follow-up is always the weakest link.

Pre-event, you LinkedIn-stalk 20 people the night before and hope for serendipity. Post-event, badge scans sit in a spreadsheet and someone sends a generic “Great meeting you!” email two weeks later. You spent $30,000 on a booth and can’t prove ROI.

The play has two halves:

Before the event: Import the attendee/speaker list into Clay, enrich with company data, then — and this is the key step most people skip — define your trigger signals and scoring priorities. Not generic firmographics. Your signals. Are they hiring a RevOps person (signal: building the team that buys your tool)? Using a competitor’s product? Recently raised Series B? New CMO in the last 90 days?

Use a Claygent column to research each attendee against your custom triggers. Then generate personalized talking points that reference their actual situation.

Instead of walking up with generic “How’s your team scaling?”, you get “I saw you’re hiring a RevOps lead and just moved off [Competitor] — how’s the transition going?” That starts a real conversation.

After the event: Import engagement data (badge scans, conversations, demo requests) and score attendees by interaction depth. A badge scan gets a generic nurture. A demo request gets AE handoff the same day. Everyone in between gets follow-up matched to their engagement level, with messaging that references their specific situation.

Output: Walk in prepared with a ranked “who to meet” list. Walk out with prioritized follow-up that hits within 24 hours.

Play 4: Influencer and Creator Vetting at Scale

Marketing function: Influencer Marketing

Difficulty: 3/5

B2B influencer marketing is exploding, and vetting is 90% of the work. Find a creator. Go to their LinkedIn. Scroll through posts. Check engagement. Are the comments real? Do they actually post about relevant topics? Now do that 50 times.

Most teams vet 5-10 creators manually and pick based on gut feel and follower count. Follower count is a vanity metric. A creator with 5,000 engaged ICP followers beats one with 100,000 random ones every time.

The play: Feed 200 potential influencers into Clay. Use enrichment providers (Apollo, Clearbit, Modash) for structured profile data — title, employer, follower counts, engagement rates. Then use Claygent for thought leadership research — searching the open web for their articles, podcast appearances, conference talks, and blog posts.

Score by content relevance (do they write about your topics?), professional credibility (what’s their background?), content volume (are they actually active?), and platform reach (weighted last, not first). Add a red flag detection layer — are they employed at a competitor? Have they gone inactive?

For everyone scoring above threshold, generate personalized pitches that reference their specific content. Not “we’d love to leverage your audience” but “Your article on signal-based selling aligns with what we’re building — here’s a specific collab idea.”

Output: A data-driven influencer selection in hours, not weeks. Decisions based on what creators have actually published, not their follower count.

Play 5: Community and Forum Intelligence

Marketing function: Community / Competitive Intel

Difficulty: 1/5

Reddit threads, industry forums, public community discussions — people are literally typing out their problems, frustrations with competitors, buying criteria, and feature requests. In public. For free. And nobody monitors them systematically.

The play: Use Clay Signals (a free, native feature) to automatically monitor Reddit for mentions that match your criteria — competitor names, pain-point phrases (”looking for a tool that…”, “frustrated with…”, “alternative to…”), and buying intent signals. When Signals flags a relevant discussion, use Claygent to visit the specific thread and extract deeper intelligence.

Each mention gets classified by signal type:

• Competitor complaint → Feeds your battle cards and competitive positioning

• Pain signal → Becomes your next blog topic or ad angle

• Feature request → Goes to the product team

• Buying intent → Alerts your content team or community manager to engage (with a helpful comment, not a sales pitch)

• Market insight → Monthly intelligence digest for the team

This is a market intelligence play, not a lead generation play. You’re capturing what the market is saying to inform your marketing — not identifying individuals behind anonymous posts.

Output: A continuously updated competitive and market intelligence feed that runs on autopilot. Monthly digests with top competitor complaints, trending pain points, and content topics the market is actively asking about.

Play 6: Partnership and Co-Marketing Discovery

Marketing function: Partnerships / BD

Difficulty: 3/5

“Who should we partner with?” is a question every marketer asks. Nobody has a system to answer it. You scroll LinkedIn, think of companies your customers also use, ask around, and hope someone has a warm intro. You end up partnering with whoever said yes, not whoever would be the best fit.

The real question isn’t “who should we partner with?” — it’s “who has our audience but doesn’t compete with us?” That’s a data question.

The play: Start with a real data source — account overlap from Crossbeam (which companies share customers with you?), AI lookalikes from Ocean.io (which companies serve the same buyer in adjacent categories?), or tech stack matches from BuiltWith (whose customers use similar tools?).

Import candidates into Clay and enrich with compatibility signals: Are they actively marketing (blog, newsletter, webinars)? What’s their audience size and social presence? Are they a similar company stage? Use Claygent to visit their website and assess their marketing activity level.

Score by partnership compatibility — audience overlap, complementary offering, size match, marketing activity level — and generate personalized co-marketing pitches for the top-scoring candidates. Not “let’s partner” but a specific idea: “Let’s co-create a benchmark report on [shared topic] — your analytics audience and our automation audience would both benefit.”

Output: A scored partner list built from real data, not guesswork, with pitches ready to send.

Play 7: Programmatic Personalized Landing Pages

Marketing function: Content / ABM

Difficulty: 4/5

“Personalized landing pages” has become meaningless. Most teams swap the company name and logo on a template and call it personalization. “Welcome, Acme Corp!” and then the same page for every visitor. Nobody falls for it.

Real personalization means different messaging for different accounts — industry-specific value props, relevant case studies, company-specific challenges, tailored CTAs. But creating truly personalized pages is brutal at scale. 50 target accounts × 1 custom page each = 50 pages nobody has time to write.

The play: Start with your target account list in Clay. Enrich with company data and use Claygent to research each account’s likely business challenge (from job postings, recent news, public strategy signals). Then generate page copy per account — headline, value proposition, case study match, and CTA — all tailored to that account’s situation.

Push everything to Webflow’s CMS using Clay’s native integration. One page template in Webflow with dynamic fields. Clay populates the fields differently per account. Each gets a unique URL: yoursite.com/for/[company-name].

Use these pages across channels — ABM campaigns, event invitations, partnership proposals, outbound sequences. Track per-page engagement and alert your team when accounts show high interest.

Output: 50+ landing pages where the content actually IS different. Not “Hello {company_name}” mail merge. Real relevance — a fintech company sees a compliance-focused value prop and a finserv case study, while a SaaS company sees a scaling-focused message with a matching case study.

Play 8: Podcast and Speaker Pitch Lists

Marketing function: PR / Business Development

Difficulty: 2/5

Podcast guesting is one of the most underrated marketing channels. One good appearance puts you in front of a captive audience for 30-60 minutes. No other channel gives you that kind of attention.

But podcast outreach is 95% research and 5% pitching. Find a podcast. Check if it’s still active. Find the host. Find their email. Listen to an episode. Write a pitch. At that pace, you pitch 5 podcasts a week if you’re dedicated.

The play: Start with a real podcast database — Listen Notes has 3.7M+ indexed shows searchable by topic, with structured data on host, episode count, and popularity. Export to CSV and import into Clay. Then use Claygent to research each podcast’s website — host names, guest booking pages, recent episode topics, typical guest caliber. Use enrichment providers (Apollo, Hunter, Findymail) to find host emails.

Score by topic relevance, guest caliber match (do they host people at your level?), recency (active vs. dormant), audience signal, and contact availability. For every podcast above threshold, generate a personalized pitch that references their specific recent episodes and proposes 2-3 concrete topic ideas.

Output: 200 podcasts researched, scored, and pitched in the time it takes to manually research 10. Each pitch references the host’s actual content, not a generic “I’d love to be on your show.”

Where to Start

Unlike ABM plays that build on each other, these marketing plays are independent. You don’t need Play 1 to run Play 4. Pick the ones that match your biggest pain:

• Spending too much on events with weak follow-up? Start with Play 3.

• Newsletter engagement is flat? Start with Play 1.

• No idea what competitors are saying in their ads? Start with Play 2.

• Need influencers but can’t find the right ones? Start with Play 4.

My recommended order: Start with Newsletter Intelligence + Competitive Ads (week 1 — fastest to build, you already have the data). Then move to Event Intelligence + Influencer Vetting (week 2-3 — high-ROI plays affecting big budget lines). Add Community Intel + Partnerships (week 4-5 — signal plays that compound over time). Then tackle Landing Pages + Podcasts when you’re ready for more complex builds.

There are also natural synergies between the plays — community intelligence (Play 5) reveals pain points that inform your competitive positioning (Play 2), which feeds your personalized landing pages (Play 7). Event attendees (Play 3) enrich your newsletter intelligence (Play 1). Influencer vetting (Play 4) surfaces creators who also make great podcast guests (Play 8).

Start with 2-3 plays. Build the muscle. Add more as you see results.

And do it with patience and love. Some marketers might be a bit overwhelmed at first - set them up for success - double down on what is already working well for them and extract leads from that. Help them help you should be the guiding principle here. They are not “less likely to understand our tools” - they are just 2 years behind in adopting them, and they can catch up really fast. I’ve seen it work. Hope you’ll do it too

For the full implementation guide with step-by-step instructions, Clay prompts, scoring formulas, and integration details for all 8 plays, grab the complete playbook here.

If you like what I do, you can find more of such assets, playbooks, workshops, and awesome examples on my 100-Step GTM checklist.

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Dear GTM Strategist!

Earlier this year, Kyle Poyar and I published our 2026 State of AI for GTM report. The results were brutal. 53% of GTM leaders reported little to no impact from AI. Only 24% are seeing real returns. Nearly half don’t have a single AI agent in production.

Why is the gap so big? It isn’t the models. We all have access to the same LLMs. It isn’t prompt engineering. We all have the same cheat sheets.

The difference is structural. The 53% are treating AI like a chatbot. One-off conversations. Zero memory. Starting from scratch every time. The 24% are building Context Systems.

This shift has a name: Context Engineering.

It is the practice of designing the entire information environment around your AI, not just the prompts you type into it. It is the difference between asking AI clever questions and building a system that actually understands your business.

We are seeing a massive rise in GTM Engineering teams adopting this approach. And right now, the primary environment for building these systems is Claude Code.

In this post, I’m breaking down:

1. What context engineering is and why it changes the AI equation for growth teams

2. How Claude Code makes it practical, even if you’ve never opened a terminal

3. The four building blocks that turn AI from a chat window into a compounding knowledge system

4. Where to start (simpler than you’d think)

The Problem: AI Without Context Is Expensive Autocomplete

Here’s what actually happens in most companies right now.

A marketing manager opens ChatGPT. Types “write me a cold email for our product.” Gets back something that sounds polished but could have been written for any B2B SaaS company on earth. No understanding of the ICP. No awareness of the positioning. No knowledge of what worked before and what flopped.

So they try to fix it. “Make it more specific to fintech CFOs.” Better, but still generic. “Add a reference to regulatory compliance challenges.” Getting warmer, but the tone is off. “Make it sound less like AI wrote it.” Now it’s bland. “Actually, go back to version two but keep the compliance angle.” Five, eight, twelve rounds of back-and-forth, each prompt trying to inject a piece of context that the AI should have known from the start.

After 30 minutes of this prompt ping-pong, you end up with something that’s... acceptable. Not great. Acceptable. The next time you need an email? The exact same process. From zero. All that refinement you just did? Gone. The AI learned nothing from the exchange.

Most growth and marketing teams live in this loop, and it’s the reason behind those survey numbers. Every interaction starts from scratch. No memory, no accumulated knowledge, no understanding of your business. You’re re-onboarding a team member every single conversation. Except this team member has amnesia and forgets everything the moment you close the tab.

Imagine you hired a senior marketer, a really good one. Instead of briefing them once and building on that knowledge over time, you wiped their memory clean every morning and started over. You’d fire that person in a week.

Most teams are doing exactly that with AI. Then wondering why 53% see no impact.

Enter Context Engineering

In mid-2025, Shopify CEO Tobi Lütke posted something that caught the attention of the entire AI world:

“I really like the term ‘context engineering’ over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.”

Andrej Karpathy, one of the most respected AI researchers alive (former Tesla AI lead and OpenAI founding member), immediately agreed:

“Context engineering is the delicate art and science of filling the context window with just the right information for the next step.”

These aren’t fringe voices. The CEO of a $100B+ company and one of the most cited AI researchers in the world are saying the same thing: the way we work with AI is about to look very different.

Here’s how the shift breaks down:

Prompt engineering was about crafting the perfect question. Getting your words just right. Finding the magic sentence that made the AI produce good output. Focused on the input, one message at a time.

Context engineering is about designing the entire information environment around the AI. Not just what you ask, but what the AI already knows when you ask it. Your strategy, your ICP, your brand voice, your past learnings, your tool stack. Structured, persistent, and available at the moment it’s needed.

The simplest way to see the difference:

Prompt engineering is writing a clever brief for every single task. Context engineering is building the team that doesn’t need a brief, because the institutional knowledge, the playbooks, the quality standards, and the tool access are already baked into how the operation runs.

One scales with effort. The other scales with knowledge.

Why Growth and Marketing Teams Should Pay Attention

If you work in growth, marketing, or GTM operations, context engineering isn’t an abstract technical concept. It’s the answer to a specific, expensive problem you’re probably already experiencing.

The problem: Your team uses AI for content creation, email copy, competitive research, lead scoring, or campaign analysis. The output is fine. Usable. But generic. It “sounds like AI.” You end up rewriting half of it to match your voice and add the specifics only you would know.

The root cause: The AI doesn’t know your business. It doesn’t know your ICP is mid-market B2B SaaS companies with 50-200 employees who just raised Series A. It doesn’t know your brand voice is direct and practitioner-first, not corporate and safe. It doesn’t know that your outbound campaigns perform better when you lead with the industry-specific pain point instead of the feature list.

All of that context exists somewhere: in your head, in scattered docs, in your CRM, in the lessons your team has learned through trial and error. But the AI can’t access any of it. So it defaults to generic. Every single time.

Context engineering fixes this by building persistent structures that give AI access to the right knowledge at the right moment. Not dumping everything into a prompt. Not copying and pasting your brand guide at the start of every session. A system that loads the right context automatically and accumulates knowledge over time.

The 24% of teams seeing real AI impact and the 53% still getting nothing are using the same underlying models. The difference is the infrastructure around them.

Claude Code: Where Context Engineering Gets Practical

Let’s get tangible.

Claude Code is Anthropic’s command-line tool for working with Claude. If you’ve only used AI through chat interfaces (ChatGPT, the Claude website, Gemini), Claude Code works differently. That difference is the whole point.

It runs in your terminal (the text-based interface that developers use). If you’ve never opened a terminal, that might sound intimidating. What matters for growth and marketing teams: Claude Code is the first AI environment actually designed for context engineering.

In a normal chat interface, you type, the AI responds, and eventually the conversation gets long enough that the AI starts “forgetting” earlier details. No persistent knowledge. No structure. No way to say “always know these things about my business before we start.”

Claude Code changes that with four building blocks that turn disposable conversations into a persistent knowledge system.

I’ll walk through each one.

The Four Building Blocks

1. CLAUDE.md — Your Strategic Brain

Your strategic onboarding document for the AI, read automatically at the start of every session.

CLAUDE.md is a text file where you write down everything the AI should know about your business. Your positioning. Your ICP definition. Your brand voice guidelines. Your pricing model. The frameworks you use. Navigation rules so it knows where to find what. How your tools connect and when to use which one.

If you’ve ever onboarded a new marketing hire, you know the drill. The first two weeks are pure context transfer: brand guides, competitive landscape, audience research, past campaign performance, what worked and what flopped. CLAUDE.md is that entire onboarding compressed into a document the AI absorbs in seconds.

What makes it powerful is persistence. Unlike a chat conversation that evaporates after you close the tab, Claude Code reads your CLAUDE.md at the start of every session. The AI never needs to be re-briefed. It shows up already understanding your business, your standards, and your preferences.

It also evolves. When you learn something new (a sharper ICP insight, a campaign angle that outperformed, a positioning shift), you update the file. Over time, the CLAUDE.md becomes a living strategic document: your institutional knowledge, accessible to every AI interaction going forward.

As your system matures, the CLAUDE.md naturally evolves from holding everything directly into a navigation layer, pointing the AI to separate, dedicated files for your ICP research, your voice guide, your competitive analysis. You start with one well-organized document and gradually build it into a structured knowledge base. The CLAUDE.md stays at the center, but the depth behind it grows.

The companies getting real ROI from AI aren’t writing better prompts. They’re building better knowledge files.

2. Skills — Your Specialist Playbooks

If CLAUDE.md is the strategic brain, Skills are the specialist methodologies it follows.

A Skill is a set of instructions that tells the AI how to perform a specific type of task. Not “write an email” but “write a cold outbound email using our 3-line framework, apply signal-based personalization from the prospect’s recent activity, use the messaging angles that worked best in last quarter’s campaigns, and follow our brand voice rules.”

Your team already has SOPs for this. The content writer follows a different process than the paid ads specialist, who follows a different process than the conversion optimizer. Skills are those SOPs, encoded so the AI can follow them with the same consistency as your best team member on their best day.

What this looks like in practice:

→ Content creation skill — knows your editorial voice, follows your formatting standards, applies your quality checklist, runs a readability review

→ Outbound optimization skill — applies signal-based prospecting methodology, personalizes based on buying triggers, structures sequences using proven frameworks

→ Conversion audit skill — loads your product positioning first, benchmarks pages against conversion standards, recommends specific copy changes with rationale

→ Competitive analysis skill — knows where to find competitor intelligence, applies your analysis framework, outputs in the format your team actually uses

Skills compound with CLAUDE.md. A content creation skill doesn’t just follow generic writing best practices. It follows YOUR best practices, informed by YOUR brand voice and YOUR ICP definition from the CLAUDE.md. The layers feed each other. The more complete your system, the more specific and useful every output becomes.

3. MCP (Model Context Protocol) — Your Tool Connections

MCP is how Claude Code connects to the tools you already use. It’s the integration layer that lets AI pull data from and interact with your existing tech stack in real time.

Your marketing stack probably includes some combination of HubSpot, Clay, LinkedIn Sales Navigator, Google Analytics, your CMS, and more. You’ve already spent time and money connecting them through native integrations or automation platforms like Zapier or n8n. MCP does the same thing, but for AI.

Without MCP, the AI can only work with what you manually paste into the conversation. With MCP, it can pull live data from your CRM, reference documentation from your knowledge base, check analytics dashboards, or enrich prospect data through your prospecting tools. All as part of the natural workflow.

A concrete example: you ask the AI to figure out why your latest outbound campaign underperformed. Without MCP, you’d need to export data from three different tools, copy it into the chat, and explain what each metric means. With MCP, the AI pulls the campaign data, audience segments, and engagement metrics directly from the source. It analyzes them together, with full context of what your previous campaigns looked like.

MCP doesn’t mean the AI takes control of your tools. It means the AI can see what your tools contain when that information is relevant. Read access to your existing infrastructure, not a rip-and-replace of your stack.

4. Hooks — Your Automated Quality Gates

Most guides skip this building block entirely. For teams that care about quality and consistency, it might be the most important one.

Hooks are automated checks that trigger before or after the AI takes an action. Quality gates that fire automatically to enforce standards without anyone having to remember to manually review.

In a well-run marketing operation, there’s always a review process. Content gets edited before it publishes. Emails go through brand compliance. Campaign spend needs approval above a certain threshold.

Technically, a hook is a simple rule you set: “when the AI does X, automatically run Y.” Claude Code lets you configure these through a quick setup command. You define a trigger (before the AI writes a file, after it finishes a task, when a session starts) and an action (run a formatting check, send a notification, block the operation if it violates a rule). No coding required beyond describing what you want - Claude helps you build them.

What this looks like:

→ Auto-format everything the AI writes: Every time Claude creates or edits a file, a hook automatically applies your formatting standards. No more inconsistent document styles or manually fixing headers and spacing.

→ Protect files you don’t want touched: Set a hook that blocks the AI from modifying specific files: your approved brand guidelines, your finalized campaign briefs, your production configurations. It can read them for context but can’t change them.

→ Get notified when the AI finishes: Running a long analysis or batch task? A hook sends a desktop notification the moment Claude completes, so you can work on something else instead of watching the screen.

→ Enforce rules automatically: A hook that prevents mixing data between different client accounts. A hook that flags content missing source citations before it’s presented as final. A hook that checks every new document follows your naming conventions. Whatever your team’s quality rules are, hooks make them automatic.

Hooks encode quality expectations into the system itself, so consistency doesn’t depend on someone remembering to check every output manually. The same way a pre-publish checklist catches errors before they go live, hooks catch them before the AI even presents the work.

Claude Code walks you through the setup with examples. You can also type /hooks inside Claude Code and it will guide you through creating your first one interactively.

How It All Works Together

A growth marketer sits down Monday morning and asks the AI to draft cold outbound emails for a new campaign targeting fintech CFOs.

Behind the scenes:

CLAUDE.md loads automatically — the AI already knows the company positioning, the detailed ICP profile for fintech CFOs (their specific pain points around regulatory reporting, their buying triggers tied to annual compliance deadlines), the brand voice guidelines, and the insight from previous campaigns showing that compliance-focused messaging outperforms feature-led pitches for this segment.

The outbound skill activates — it follows the team’s proven three-email sequence framework, applies signal-based personalization, and structures each email around documented best practices for this segment.

MCP connections pull live context — the AI checks the CRM for recent fintech engagement data and pulls relevant industry triggers from the prospecting tool to inform the personalization layer.

Hooks run quality checks — before presenting the drafts, automated gates verify brand voice compliance, confirm that every claim is substantiated, and check that the emails follow the approved format and length.

The marketer receives three emails that sound like their best copywriter wrote them after an hour of prospect research. Not because the AI is magic, but because the context system made the task, to borrow Lütke’s phrase, “plausibly solvable” before the first word was generated.

Multiply that across every task the team runs: content creation, lead research, campaign analysis, competitive intelligence, conversion audits. Each one drawing from the same persistent context, each one refined by accumulated learnings.

That’s when the compounding kicks in.

The Compound Effect

With traditional AI (chat interfaces, one-off prompts), knowledge doesn’t accumulate. Every session starts from zero. You might get marginally better at writing prompts over time, but the AI never gets better at understanding your business. Session one and session one hundred produce the same quality output.

With context engineering, the trajectory looks different:

→ Week 1 — You set up your CLAUDE.md with positioning, ICP, and voice guidelines. The difference is immediate. Output goes from generic to on-brand. That 30-minute editing ritual after every AI interaction? Gone. The AI sounds like your team because it understands who you are and who you’re talking to.

→ Month 1 — You’ve built your first skills, connected your CRM, and the system is learning from real work. Claude starts suggesting new skills based on the types of tasks you keep running: “I notice you do competitive analysis often, want me to create a dedicated skill for that?” You say yes, describe how you approach it, and in minutes you have a repeatable playbook the AI follows every time. Your team is producing in hours what used to take days.

→ Month 3 — Your system now has learnings from multiple campaigns baked in, ICP definitions refined against real conversion data, messaging angles tested and documented, competitive intelligence accumulating in the background. The AI knows which pain points drive replies for each segment, which positioning angles your enterprise buyers respond to versus mid-market, which content formats generate pipeline versus just engagement. Every new piece of work starts from a higher baseline than the last.

→ Month 6 — Your context system has become something that didn’t exist in your company before: a living, structured knowledge base that captures everything your team learns and makes it instantly accessible. New hires onboard through it. Campaign post-mortems feed directly into it. Competitive shifts get documented the day they happen. The AI surfaces patterns you’d miss manually, flags when messaging drifts from what works, connects dots across campaigns, segments, and quarters. Need to research five competitors at once? Claude can spin up parallel agents that work simultaneously and synthesize the findings into one report. You’re not saving time. You’re operating at a level that wasn’t possible before, regardless of team size.

The gap between teams that build this and teams that don’t widens every week. A compounding advantage that gets harder to close the longer you wait.

You Don’t Need to Code Any of This

At this point, you might be thinking: this sounds powerful, but I’m not a developer. Claude Code runs in a terminal. There are files, configurations, tool connections. Is this really for me?

The hardest part of context engineering is knowing your business well, not knowing how to code.

If the terminal part still scares you, I have you covered. I spent the last week curating the best non-technical resources (free courses, setup guides, and “copy-paste” skills) specifically for GTM teams.

👉 Access the full Claude Code Resource List here

Writing a CLAUDE.md is writing a document. If you can write a strategy brief, you can write a CLAUDE.md. Skills are playbooks in text format. If you’ve ever documented a process for your team, you already know how to create one. MCP connections? Closer to setting up a Zapier integration than writing software.

What changes the equation: Claude Code builds itself with you.

Don’t know how to structure your CLAUDE.md? Tell Claude about your business and it will draft one. Not sure which skills you need? Claude will suggest them based on what you actually use it for: “You’ve asked me to write outbound emails five times this week. Want me to create a dedicated skill so I do it your way every time?” Need to connect your CRM? Describe what data you want access to and Claude walks you through the setup.

You don’t need to have it all figured out before you start. Open Claude Code and start writing the way you’d brief a new team member on their first day: everything about your company, your customers, your positioning, what campaigns worked and what flopped. Don’t worry about structure or format. Claude takes what you give it, asks follow-up questions, and helps you shape it into a proper context system that gets smarter every time you use it.

Over the past year, development has been turned into conversation. The technical barrier hasn’t disappeared. It’s been absorbed by the AI itself. What’s left is the part only you can do: clearly articulating what you know about your market, your customers, and what actually works.

Where to Start

You don’t need to build everything at once. Here’s a realistic first month:

Week 1: Your CLAUDE.md

Start here. Copy this structure, fill it in, and you’re already ahead of most teams using AI:

→ Company & Positioning - Who are we? What do we do? Who do we serve? What makes us different?

→ ICP Definition - Who’s our buyer? What pains them? What triggers a purchase decision?

→ Brand Voice - 3-5 concrete rules. Not “professional and friendly” but “direct, practitioner-first, no corporate fluff, every claim backed with data”

→ What Works - Learnings from recent campaigns. Which angles drove results? What messaging fell flat? What surprised you?

→ Tool Stack - What tools does your team run on and how do they connect?

Don’t overthink it. Tell Claude about your business, type it out like you’re explaining to a colleague, and let it help you turn that into a structured document.

Week 2-3: Your first skills + tool connections

By now you’ll notice patterns in how you use the system. Claude will too, and it’ll suggest skills based on your most common workflows. Let it create them. Review, adjust, and suddenly the tasks you repeat every week are running on documented playbooks instead of starting from scratch.

This is also when you connect your first tool: your CRM, your prospecting platform, your analytics, whatever your team touches most. Ask Claude to help set it up. Start with read-only access and build trust from there.

Week 4: Review and compound

Look at what the system produced this month. What was strong? What missed? Update your CLAUDE.md with those learnings. Every correction, every insight, every pattern you add now informs every future interaction. The compound effect kicks in here.

This is also a good time to set up your first hooks. After a month of using the system, you know what quality issues come up: formatting inconsistencies, files that shouldn’t be edited, notifications you wish you had. Tell Claude what you want to enforce and it will create the hooks for you.

Even a basic CLAUDE.md with your positioning, ICP, and voice guidelines will produce noticeably better output than starting every AI session from zero. Once you experience that difference, once you see the AI produce something that sounds like your team wrote it, you’ll understand why context engineering matters.

The Bigger Picture

Context engineering isn’t just an AI technique. It’s a forcing function for strategic clarity.

Building a context system requires you to precisely know (not assume, not vaguely sense) who your customer is, what your positioning is, how your brand sounds, and what works in your market. Most teams think they know these things. They try to write them down and realize the gaps are bigger than expected.

Which is exactly the point. The act of engineering your context forces you to get clear on your strategy.

In a world where AI handles more of the execution layer every quarter, strategic clarity becomes the ultimate competitive advantage. The teams that build context systems will compound their knowledge, their output quality, and their speed. Every week. Every month. Every quarter. The teams that don’t will keep starting from zero and wondering why AI hasn’t delivered on the promise.

This isn’t about a single tool or a trend. It’s a shift in how teams capture, structure, and apply what they know. The companies that figure this out first don’t just get better AI output. They get better strategy. Better onboarding. Better institutional memory. A compounding knowledge asset that grows more valuable with every interaction and never leaves when someone does.

Infrastructure, not hype. If you’re reading this thinking “we should probably start,” yes. You should.

Happy context engineering!

Fast-Track Your Context System

The hardest part of Context Engineering isn’t the tech. It’s writing the source material. You need to document your GTM strategy, your playbooks, and your frameworks before the AI can learn them.

You can spend weeks writing this from scratch. Or you can feed my brain directly to your AI.

I brought back my GTM in a Box bundle for a limited time. It includes my full 350-page GTM Strategist book (PDF), my 100-step launch checklist, my GTM Masterclass course, and all my core frameworks.

The Context Engineering Hack: Upload the PDF book, the and the Checklist and the transcript of the videos (they are included in the package) directly to Claude and say: “You are now an expert GTM Strategist trained on Maja Voje’s methodology. Use this context to draft my CLAUDE.md file.”

You get an instant, world-class GTM brain for your AI.

Get GTM in a Box

vFairs is a flexible event management platform that powers everything from event registration and ticketing to event apps, badge printing, and hybrid events. The platform can be configured for any type of event—from conferences and trade shows to job fairs and virtual experiences. As a Gartner Magic Quadrant Leader and three-time Gartner Peer Insights Customer Choice winner for Event Platforms, vFairs has earned industry recognition for excellence.

See vFairs in action

Dear GTM Strategist,

Something has been puzzling me since November 2025. When we did a survey with my all-time favorite GTM buddy Kyle Poyar, “big product launches” were crowned as the next big thing for 2026.

I was like cool (orange iPhone just took over the world) - but what does that mean for our slightly less photogenic businesses where we can’t just have Steve Jobs deliver a keynote?

Does it mean you have to take over Domino’s sugar factory in Brooklyn for a day and buy a billboard on Times Square, like our partner Miro did?

Is it taking over LinkedIn like our friends at Attio did last week with their “Ask Attio” launch?

Or is it those secret Ahrefs or Clay launch events in Silicon Valley with a stellar speaker lineup that I am still FOMO-ing about because I couldn’t attend last year?

Is it all of these? But wait - I don’t exactly have the budget and people power to pull these off.

How can “average” businesses bring the power of “big product launches” to their relatively boring industry without breaking the bank?

Something clicked when I began to explore this topic … High Alpha Capital just crowned in-person events as the most powerful SaaS GTM channel in 2026 👑 and I was like - really? Then I saw this breakdown:

It made sense as I started to reflect more on it. Our ICP decision-makers are being bombarded with AI-generated outbound messages, cringe LinkedIn comments, and blogs that are written for AIs, not humans. Suddenly, seeing a genuine person face-to-face and consuming some proprietary insights is actually becoming mega-valuable and personally fulfilling.

But there is another “but”. I was never a social butterfly working a room at events, harvesting 200 business cards. As a self-declared introvert, I prefer to hang out with 2-3 people that I actually know, and honestly, the catering looks more tempting than approaching a stranger for small talk (even though event food usually sucks) …

Plus, organizing or even attending events is pricey. It’s not just the venues, staff, and booth prices. It’s the opportunity cost. People who go there cannot work normally for at least 3-5 days when you factor in international travel.

Knowing that we are onto something here, but also being pressured for the ROI of everything that we are doing, I said enough is enough.

I invited my long-time friend Aatir Abdul Rauf, VP of Marketing at vFairs, to teach us how to organize ROI-positive events that don’t suck. He is going to show us how to squeeze more value from event investments and exactly what tactics we should be borrowing from mega-brands like Amazon, Unilever, Microsoft, Nestle, Toyota, and many, many others.

Aatir, bring it on, brother - cannot wait to learn from you!

Redefining Events: What’s Actually Possible

Before joining vFairs 12 years ago, I viewed events as an overhyped money pit.

I thought flying a sales team to Vegas to man an $8,000 booth was a waste of money. Firing off another email campaign from the couch felt a lot safer.

But I learned the hard way that I was wrong.

Working in an event tech company gave me an appreciation for the infrastructure involved and allowed me to learn a few event chops along the way.

Last year, my team attended 5 industry events and hosted 10 intimate customer dinners. We generated $510K in new sales pipeline and an equivalent amount in retention and expansion revenue from existing customers.

But here’s what I keep hearing from marketing leaders: “We tried events. We did 52 webinars last year and got zero leads.” Or, “We spent $50K on a tradeshow booth, collected 200 business cards, and generated 3 qualified leads.”

The problem isn’t that events don’t work. The problem is that most teams are operating with an incredibly narrow definition of what an “event” even is.

When I say “events,” most people picture one thing: a live webinar. Maybe they’ll add “trade show booth” if they have a budget. That’s it. That’s their entire mental model.

But there’s an entire universe of event formats that most B2B teams have never explored. Each format serves a different GTM motion. Each one creates different outcomes.

Before we get into how to execute events profitably, we need to expand your definition of what’s possible.

Webinar vs Virtual Events: Know the Difference

Let’s start with what you already know: webinars.

Webinars are the most well-known event format. A live or pre-recorded online presentation, typically 30-60 minutes, focused on education or thought leadership. One speaker (or a small panel) presents to a passive audience who watches and maybe asks questions in a chat box.

Attendees consume content but rarely interact with each other. There’s no networking, no exploration, no community building. You get leads, but you don’t get relationships.

Most teams stop here. But there’s a massive leap between a webinar and a virtual event.

A webinar is a single presentation in a conference room. A virtual event is the entire conference center. You can do so much more than a video conference. Think: multi-session online experiences with keynotes, breakout sessions, networking lounges, exhibit halls with sponsor booths, gamification with leaderboards, chatrooms for attendee-to-attendee interaction, and on-demand content libraries.

T-Mobile used vFairs platform to host a virtual employee conference for 9,000 attendees with interactive booths, gamification, and chatrooms. The event created internal alignment on a product launch and generated on-demand training content used for months afterward.

Virtual events work when you need attendee-to-attendee networking, sponsor activation, multi-track agendas, or immersive brand experiences. Webinars work when you just need to deliver content efficiently.

Speak at In-Person Event

Instead of paying $15K for a booth where you’re one of 200 vendors, what if you were on stage in front of 500 people?

When you’re on stage, you’re positioned as the expert, not a vendor. Attendees seek you out after your session. You collect leads without a booth.

We weren’t into founder branding before, but last year our CEO spoke at events like Event Tech Live and SaaStock. He got a flurry of LinkedIn DMs right after, and a few materialized into either deals or referrals.

How to get speaking slots: Submit early (6-9 months before the event). Pitch a topic that solves attendee problems, not a product demo. Offer workshop formats or hands-on sessions. Partner with a customer to co-present a case study.

Sponsor Table Talks and Roundtables at Industry Events

Trade shows are noisy. Booths are transactional. But roundtables are intimate, focused discussions with 8-15 people who opted in because they care about the topic.

You get mindshare with exactly your ICP in a setting where they’re engaged and asking questions. You’re facilitating, not pitching. You capture high-intent leads who voluntarily joined your table.

How to make it work: Choose a topic your ICP cares about deeply. Facilitate the discussion, don’t dominate it. Capture context during the session (who said what, what challenges they’re facing). Follow up within 24 hours with resources that address their specific pain points.

Host Your Own Virtual or In-Person Conference

Why exhibit at someone else’s event when you can host your own?

You control the attendee experience, own the sponsor relationships, and position yourself as the hub of the industry. You monetize through sponsorships while building community.

Cadence Design Systems hosted CadenceLIVE Silicon Valley with 2,100 registrations and 1,400+ in-person attendees. They used a mobile app with AI matchmaking, session tracking, and real-time sponsor lead capture. Sponsors saw immediate ROI dashboards and renewed for the following year. Customer NPS post-event increased by 18 points. Expansion pipeline generated: $2.3M.

There’s a full spectrum of events, each serving a specific business outcome. Here are more event types you can consider based on your business goals.

Hosting Intimate Customer Dinners & Meetups

This is one of the highest-ROI formats if you execute it well.

You’re creating peer networks, not selling. You’re facilitating valuable conversations. Attendees leave with relationships beyond just your company.

We hosted around 10 dinners in Australia, the United States, and England last year. This is by far the best way to deepen relationships. We were able to nab multi-year contracts and also talk about expansion by getting referrals from one subsidiary of a company to another.

Curate the guest list carefully. Mix existing customers (60%) with high-value prospects (40%). Avoid anyone who won’t engage or who you wouldn’t want in the room.

Organize Customer Awards, Hackathons & Contest

Plan an awards program that recognizes customers for exceptional use of your product, innovative implementations, or outstanding results.

We host the Eventeer Awards every other year to recognize the best events that people have created using our platform. Customers submit entries explaining their event strategy, execution, and results. Winners are announced at a virtual ceremony.

Key considerations:

• Create clear award categories tied to business outcomes

• Capture nominations with a form on your event website

• Decide winners through public voting or a panel of judges (voting creates more buzz)

• Showcase finalists and winners in your marketing (with permission)

• Turn submissions into a content library

• Recognize winners publicly (ceremony, social media, swag)

Customers love recognition. They share why their event/campaign/project was successful. You capture rich stories that become case studies, testimonials, and social proof. You create community and friendly competition among customers. Prospects see what’s possible.

Beyond traditional awards, contests, and hackathons, create hands-on engagement. Challenge participants to build something using your product in a limited timeframe. The competitive format drives deeper product adoption. Participants learn advanced features as they build portfolio pieces. You gain fresh use cases and creative applications you hadn’t considered. Winners become advocates who’ve publicly demonstrated expertise with your platform.

Event Selection Framework

Most teams pick one event format and try to force it to deliver every outcome. That’s why they fail. A webinar won’t close an enterprise deal. A customer dinner won’t generate 500 MQLs. You need different formats for different jobs.

Build a portfolio. Mix formats based on your GTM priorities. Start with 1-2 formats, measure what works, then expand.

Here’s how to think about it:

How to Select the Right Events (And Avoid Getting Burned)

ICP Density Check

Before you commit budget, answer this question: Will a high concentration of your ICP actually be there? You can verify this by doing the following:

Attend as a participant first: If you’re uncertain about a new event, attend as a participant before you commit to a booth. Walk the floor. Observe the crowd. Talk to attendees. Then decide if it’s worth exhibiting next year. We did this with several events we wanted to add to our portfolio.

Ask your top 5 customers: “Where do you go to learn about [topic]? What events do you attend every year?” If three customers mention the same event, that’s a signal.

Review the attendee list (if available): Many event organizers share attendee demographics with sponsors. Request this before you commit. Look for job titles, company sizes, and industries that match your ICP.

Check LinkedIn activity: Search the event hashtag on LinkedIn 2-3 weeks before the event. Are your target accounts posting about attending? Are they organizing side events or meetups? If the community is active, your ICP is engaged.

Talk to past exhibitors: Find companies that exhibited last year and ask them honestly: “Was it worth it? Would you do it again?” The best intel comes from people who’ve already spent the money.

Assess Venue & Logistics

Even if your ICP is there, logistics can kill your results.

Look at the floor plan: If exhibitor booths are tucked in a basement away from the main sessions, coffee stations, and food, your foot traffic will be zero. Prime locations near high-traffic areas are worth paying extra for.

Check the schedule: If all the keynotes and main sessions run back-to-back with no breaks, attendees won’t have time to visit booths. You want built-in networking breaks.

Evaluate competing attractions: If there are 200 other booths and 50 of them are your direct competitors, you need a strong differentiation strategy or you’ll blend into the noise.

Assess satellite event feasibility: Are there good venues nearby for dinners or breakfasts? Can you realistically host a satellite event, or is the conference in the middle of nowhere?

Check Timing & Commitment

Does the timing align with your GTM calendar? If you’re launching a product in Q2, an event in Q4 won’t help. If you’re trying to close deals by the end of the quarter, an event 3 weeks before quarter-end gives you no time for follow-up.

Can you commit to pre-event promotion? If you’re booking the event 2 weeks out, you won’t have time to build pre-event momentum. You need at least 60 days for proper promotion and meeting scheduling.

Do you have the team capacity? If your best AEs are slammed closing deals, they can’t staff a booth effectively. If your product team is heads-down on a launch, they can’t support a customer conference. Timing matters.

Red Flags to Avoid

Walk away if:

• The event organizer won’t share attendee demographics or past attendance numbers

• Past exhibitors give lukewarm reviews or can’t cite specific ROI

• The event is brand new with no track record (unless you’re getting a steep discount as an early sponsor)

• The booth cost is more than 20% of your total events budget (you’re putting all eggs in one basket)

• You can’t pre-schedule at least 15-20 meetings with target accounts attending

Events are expensive bets. Be selective. It’s better to skip an event than to waste budget on the wrong one.

15 Plays for Building a Profitable Event Program

Build The Foundation Before You Execute Anything

Play 1: Set Clear Goals & Success Metrics

You can’t optimize what you don’t measure. Start by asking: What GTM motion are we trying to advance?

• Pipeline generation? Set a dollar target.

• Deal acceleration? Track how many opportunities moved stages.

• Customer retention? Measure expansion ARR or renewal conversations.

• Brand positioning? Track share of voice or speaking slots secured.

Write this down. Share it with your team. Get leadership to sign off. This number becomes your North Star.

Play 2: Get Internal Alignment

Events fail when Marketing plans them in isolation and expects Sales to execute. You need cross-functional alignment before you book the venue.

Involve Sales early. Ask which accounts they want to meet, which deals need acceleration, and which reps should attend. Sales has a relationship context that Marketing doesn’t.

If you’re exhibiting, request access to the exhibitor portal, where you can review the attendee list. Look for target accounts. Discuss strategy: Which accounts should we prioritize? Who should own each relationship? What meetings can we pre-schedule?

Loop in Product if you’re hosting a customer event or launching something new. Product needs to prep demo environments, handle technical questions, and gather feedback.

Assign clear owners. For example, Marketing owns the pre-event promotion. The event coordinator owns on-site logistics. Sales rep owns post-event follow-up. Hold a kickoff meeting 60 days before the event. Walk through goals, roles, and the campaign timeline.

Play 3: Build Your Event Portfolio Mix

You need a portfolio approach. Different event formats serve different GTM motions and different stages of the buyer journey.

A balanced portfolio might include:

• Monthly webinars for top-of-funnel thought leadership (100-500 people)

• Quarterly intimate dinners for relationship building with high-value accounts (10-20 people)

• 1-2 industry conferences per year where you exhibit or speak

• Annual customer summit, if you have a strong customer base

There’s no magic number. Some teams run 5 events a year. Others run 50. Find the right mix that compounds over time. Each event should feed the next. Past attendees become your database. Session recordings become gated content. Learnings from one event inform the next.

Share your event calendar in advance. People plan around predictability, and attendance grows when they know what to expect and when.

The Playbook for Exhibiting at Industry Events

Play 4: Build Pre-Event Momentum

The event starts 60 days before anyone walks through the door.

4 weeks out: Email your ICP list who registered. “We’ll be at booth #412. Want to grab coffee?”

2 weeks out: LinkedIn DMs to target accounts. “Saw you’re going to [Event]. We’re hosting a dinner Tuesday night—would love to have you.”

1 week out: Confirm all scheduled meetings. Send calendar invites with booth number and contact info.

Day before: Final reminder to confirmed meetings.

The best conversations happen before you arrive.

Play 5: Create Satellite Experiences

The real magic happens in the experiences you build around the main event: customer dinners with 15-20 VIPs at a sought-after venue, executive breakfasts with small group discussions, product previews for high-value accounts, and networking events co-hosted with complementary vendors.

At vFairs, we cross-matched an event attendee list with our CRM to see who, among our customers and prospects, was attending an industry event situated in Las Vegas.

We sent all of them email invitations to dinner 2 weeks prior and got 15 people to show up. Our sales reps were able to talk about expansion conversations and ideas along the way that would never have happened on Zoom.

These intimate settings enable real conversations that a noisy expo hall never will. And the work doesn’t stop at planning and executing; here are a few metrics you can track to assess the success of the event.

Track these in a spreadsheet or CRM. Analyze what works. Double down on high-performing formats and guest mixes.

I have a step-by-step guide for hosting a customer dinner. You can check it here if you’re interested.

Play 6: Staff Strategically

Send people who can have intelligent conversations about the problem you solve and the outcomes you deliver. This usually means a mix of senior AEs, Solutions Engineers, and at least one executive for high-value meetings.

If you’re running a major booth, consider training a dedicated team. Compensate them for booked meetings, not badge scans.

Create a briefing document: event goals, target accounts to prioritize, core messaging, schedule, and FAQ. Hold a team briefing 48 hours before. Walk through the document. Role-play conversations.

Play 7: Stand Out On-Site

Make it easy for people to engage. Think:

• Live product demos that solve a real problem in 3 minutes.

• Interactive experiences like assessments or ROI calculators.

• Branded giveaways that are actually useful.

• Theater presentations every 30 minutes with a clear CTA.

Play 8: Capture Context, Not Just Contact Info

Sales needs context: “Sarah stopped by the booth, watched the enterprise demo, asked about integrations, and mentioned she’s evaluating three vendors.”

If you’re using badge scanners or lead capture tools, make sure the data flows to your CRM with notes about what each person cared about. If you’re old school with business cards, scrub the data each evening and add context notes while it’s fresh.

Play 9: Follow Up Fast

Aim for follow-up within 24 hours. Segment by engagement level:

• High-intent leads (met with you, asked detailed questions, attended your dinner): Personal video from the rep they met.

• Medium-intent leads (stopped by booth, showed interest): Personalized email referencing what they cared about.

• Low-intent leads (badge scan only): Standard nurture sequence.

The Playbook for Hosting Your Own Events

When you’re the organizer of customer dinners, user conferences, regional roadshows, product launches, or virtual summits, you control the experience, which also means you own the risk.

Play 10: Choose Your Format Based on GTM Motion

Match format to what you’re trying to accomplish:

• ABM + Deal Acceleration: Intimate dinners (8-12 execs), exclusive previews, roundtables with industry experts.

• Demand Gen: Virtual conferences, webinar series, regional roadshows.

• Customer Retention: Annual user conference, VIP experiences, executive briefings.

• Thought Leadership: Hosted panels, invite-only summits.

Example: Common Room runs 40+ events a year by mixing formats: hosted dinners (1/month), sponsored dinners (2/month), and ancillary events around massive shows like Dreamforce to draft off their gravity without booth cost.

Play 11: Build Pre-Event Momentum

Treat event promotion like a product launch. 60-day runway:

• Week 1-2: Early-bird announcement to your VIP list (customers, close prospects, partners). Week 3-4: Expand to a broader audience with value messaging.

• Week 5-6: Reveal speakers and agenda highlights to build FOMO.

• Week 7-8: Create urgency as the date approaches.

Segment messaging by persona and account tier. A CEO gets a different email than an end user. Retarget people who visited your landing page but didn’t register—they were interested, they just needed a nudge.

Play 12: Deploy Event Infrastructure

Here’s where having proper infrastructure becomes critical.

When you’re hosting, you’re managing multiple stakeholder journeys simultaneously: attendees need seamless registration and personalized experiences, sponsors need self-service portals and real-time lead data, speakers need frictionless onboarding, and your internal team needs coordination tools.

At minimum, you need:

• Event Website & Registration: Capture pre-event intent, handle payments, segment attendees.

• Badge Printing: Professionalize arrival and secure the venue.

• Mobile App: Guide attendees, push notifications, enable networking.

• Session Tracking: Know which topics resonated and who attended what. Lead Capture (for sponsors): Real-time data, not a CSV exported three days later. When sponsors see ROI dashboards immediately, they renew.

Teams hosting events regularly use platforms that unify these functions. One system means attendee data flows consistently, templates are reusable, and each event takes less time to set up. The infrastructure compounds.

Play 13: Execute Flawlessly On-Site

Your attendees chose your event over everything else competing for their time.

• Smooth arrival: Fast check-in, clear signage, welcoming staff. First impressions matter.

• Relevant content: Sessions that solve real problems. If every session is a demo, people stop attending sessions.

• Structured networking: Most networking doesn’t happen organically. Facilitate it with AI-powered matchmaking that pairs people with similar interests, topic-based roundtables with clear discussion prompts, or speed networking with structured rotation.

• Executive access: Make it easy to meet your leadership team through office hours, fireside chats, or small group Q&As.

• Content capture: Record sessions for on-demand access and post-event promotion. Every presentation becomes gated content you can promote for months.

Play 14: Capture Behavioral Signals

When you host your own event, you have access to behavioral data that tells you what people actually care about.

Track:

• Registration behavior (when they signed up, which emails drove action)

• Session attendance (which topics resonated)

• Networking activity (who connected with whom)

• Q&A participation (what questions people asked)

• Resource downloads (which content they consumed)

This data should flow to your CRM so Sales knows exactly how to prioritize follow-up.

Someone who attended three technical sessions and asked about pricing gets a different follow-up than someone who only attended the keynote.

Play 15: Follow Up Based on Behavior

Session attendees get recap emails with slides and recordings from sessions they specifically attended. No-shows get access to on-demand content and an invitation to the next event. VIP attendees get a personal thank-you from executives and exclusive follow-up opportunities. Sponsors get ROI dashboards showing leads generated and engagement metrics.

Make every follow-up feel relevant and personal.

The companies seeing real ROI from events didn’t start by doing 50 events a year. They started with one or two, figured out what worked, built repeatable infrastructure around it, and grew from there.

The extent of your event program will vary based on your market, your resources, and what’s working. Some teams run 5 events a year profitably. Others run 50. The goal is building something that compounds—where each event feeds the next, past attendees become your database, and learnings stack over time.

Having the right infrastructure makes this possible. Whether that’s event software, templates, or well-documented processes, you need systems that eliminate manual work, capture behavioral signals, and let you scale without burning out your team.

Wow, wow, wow - wasn’t that awesome?

I am sure you can grasp at least 5 action points for your next event from this.

I was so inspired by this article that I wanted to bring what I learned to life immediately.

Some other updates:

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On Feb 19th, 2026, we will dive into how to launch a GTM campaign that actually delivers value instead of getting you blocked. Come join us, ask questions, and steal the insights you need to build an outbound or account-based campaign that converts 4x better than the industry average.

Register here. Jordan and I cannot wait to hang out with you!

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Dear GTM Strategist!

“Let’s A/B test it.” It is one of the most expensive phrases in GTM. Usually, it translates to: “I have an opinion, and I want to waste 4 weeks of engineering time to prove I am right.”

While most marketers are busy arguing over button colors or hero headlines, the top 1% of B2B companies are playing a different game.

They aren’t guessing. They are running thousands of experiments to find the exact elements that drive revenue.

But here is the problem. You likely don’t have the traffic of Amazon or the engineering resources of Adobe. You cannot statistically validate every hunch.

So, should you just guess? No. You should use GTM Intelligence.

This is why I love the approach of Casey Hill (CMO, DoWhatWorks). He doesn’t rely on “best practices” or opinions. He relies on surveillance.

His team tracks public data across millions of sites. They watch what companies like GoDaddy, Webflow, and Cisco test. And they wait.

If a variation survives for 3+ months? That is a winner. If it disappears? It failed.

This is “Survival of the Fittest” data. It is the closest thing we have to a cheat sheet for high-converting layouts without burning your own budget to find them.

I invited Casey to share the top 3 patterns emerging from this massive dataset for 2026.

What you’ll learn:

• The 3 “Sanity Checks” you must pass before you run a single experiment (most companies fail #1).

• Why “AI Magic” is losing and what specific messaging is actually converting buyers right now.

• The “Cost Signaling” shift: Why easy social proof (stars/logos) is dead and what is replacing it.

CRO should not be about opinions. It should be about engineering context that converts.

Now, let’s look at the data with Casey.

In today’s piece, we are going to talk about some of the emerging trends and key areas to focus your A/B testing in 2026.

But before we do, there are three key checks you want to look at before you begin testing.

1. Match traffic source to positioning / messaging. At DoWhatWorks, we drive a lot of our business from content and social channels, and as such, knowledge of exactly what our product does when leads hit the site is relatively low (versus say a retargeting ad pushed to free trial users). As such, we need to not only explain how the tech works and show lots of examples, but the visuals of showing the software platform are key to avoid misunderstandings (ie, we are not an agency).

2. With our clients, we like to advise the use of what we call a UVS (unified variable set). What it means is you start with a focused objective (say, reduce complexity on your pricing page) and every change you make should fold back up to that objective. So you might reduce the number of line items in plans, you might make sure there is just one primary pricing progression metric, you might simplify the visual real estate of the page by removing anything above the core plans, etc. But with this in mind, you wouldn’t just add a quiz below because it might be helpful, if it is not specifically focused on reducing complexity.

3. Are you testing something that will be meaningful? I use a litmus test that I call the “meaningful test” framework.

I define this principally as a test that does one of two things…

1. Do you introduce new information?

2. Do you introduce new direct value?

Let’s start with what is not a meaningful test…

❌ Testing button text of “Book a Demo” vs. “Get Demo”

❌ Testing a headline of “AI-Powered insights that make your team superheroes” vs. “Product. Engineering. Marketing. All aligned thanks to AI-powered insights”

❌ Testing a subheader of “Free forever” vs. “Free version.”

On the flip side, here is reassurance subtext from Kit that checks the “introduce new information” bucket.

They tested subtext that…

1. Clarifies how long the trial is

2. Clarifies that no credit card is required

3. Lets you know they offer free migrations

That is a great example of adding new information in your test.

Mercury testing into adding specific numbers into their subheaders is another great example of introducing new information…

Then, with Sage, you see an example of adding new direct value with an experiment…

They tested into adding a bar at the top of their site where you can add your industry and company size, and then you get redirected to a homepage specifically tailored to that industry and those pain points (I tried it for SaaS, and it was great).

Now, of course, you can run copy tests that don’t check one of the two boxes above, and they can still be successful. But this is a great way to improve your hit rate.

Now, let’s look at a few trends for 2026…

1) AI has a generic problem. Specificity in capabilities is the answer.

There is a major problem across AI companies selling agentic products.

They are unbelievably vague in their messaging.

I curated a list of 30 top companies selling agents.

Here are the headlines…

Company A: “We are a frontier research and deployment company providing enterprises with a powerful, cost-efficient, and easy-to-adopt technology to integrate agentic AI directly into their core workflows and processes.”

Company B: “Deploy, orchestrate, and govern fleets of specialized AI agents that work alongside your team”

Company C: “Ops teams can build and manage an entire AI workforce in one powerful visual platform.”

Company D: “[company] is the simplest way for businesses to create, manage, and share agents.”

It took me until company eight on the list that I got one that actually gave me any real information about what it did.

Company H: “Docket’s AI agent handles qualification, discovery, and booking while you sleep. Teams see 30%+ more qualified pipeline from the same traffic.”

I understand these brands want the optics of addressing a large TAM, but when you can go to ten sites, and the headlines are almost identical, this isn’t providing any clarity or insight that is helping prospects decide on your product.

What does the data say 💡: I looked at DoWhatWork’s database of hundreds of A/B tests around AI messaging. Tests from brands like OpenAI, IBM, Hootsuite, GoDaddy, Notion, and beyond. Consistently, vague AI positioning loses, and more tangible, capability oriented language wins.

Here is a test from a GoDaddy sales page that perfectly illustrates this.

The takeaway is clear → AI messaging that is clear and explanatory to the capability it solves for (i.e. our support agents provide 24/7 ticket coverage in 42 languages) dramatically outperforms vague or sweeping messaging.

2) Persona-based personalization is winning

In the past few months, we have seen many of the top SaaS/Fintech brands like MongoDB and Sage) test into personalization options at the top of their homepages.

For MongoDB, they have an “experience” selector at the top that’s tied to their two main ICPs: Developers and Business Leaders.

When you choose one, it completely changes the sections, the copy, and even the CTAs.

For example: in the hero, if you’re a developer, you can jump straight into the documentation. But for business leaders, that hero CTA becomes Pricing instead.

Sage, a popular fintech company, also has a similar implementation, but they customize the page based on industry/size vs. role.

Either way, this is the future. Provide customized and relevant information without intrusive pop-ups or making folks jump through multi-step forms.

Most websites bury the “who this is for” affinity, or the “how it works for XYZ persona” section way down the page (I’m guilty of this myself).

What does the DoWhatWorks data say? 💡Looking at hundreds of tests around personalization and persona-focused positioning, I consistently see these variants win (over outcome-focused copy and many other approaches).

Especially for enterprise brands selling to complex buying committees with very different needs (developers, legal, marketing, CFO, etc.), this makes the experience exponentially more relevant for the prospect.

Imagine the gap between what a developer wants to see and what a VP of marketing/sales/finance would want to see. Night and day.

3) The bar for what builds trust with social proof just went up.

Going through our test database at DoWhatWorks, I noticed something interesting.

Why are logo bars + quote testimonials losing, but case studies + video testimonials are winning on websites?

I recently sat down with Ethan Decker, biologist turned marketer, who talked through the science behind some findings around social proof.

He explained that “cost signaling” might be at play here.

The idea comes from nature, where an animal like an elk spends a lot of energy and resources to build impressive antlers. It’s a signal that can’t be easily copied by a less genetically fit elk.

In B2B, we often see that plain logo bars, star ratings, and quote testimonials lose in A/B testing.

One reason might be that these signals are easy to fake. Anyone can put down any logo on their site, or pull a quote from an imaginary source (or a past customer from 7 years ago).

Meanwhile, if you have a video testimonial of an executive at Orangetheory (like Webflow does) talking about your product and the benefits, that gives you a lot more credibility. Ramp does the same thing.

Or Clay has logos from OpenAI and Google linked to case studies. Now the logos can be validated.

Bottom line is: The easier it is to fake, the less credibility it carries.

How to apply this (even if you aren’t Adobe)

You might be thinking: “I don’t have millions of visitors. Can I still test this?”

1. Consistency > Volume

You don’t need massive traffic to get signals. You need consistency. If you have 1,000 visitors a month coming from a consistent source (e.g., SEO), you can run valid tests. If you have 10,000 visitors but they come from random viral spikes one month and paid ads the next, your data will be noisy. Focus on your stable channels.

2. Don’t forget mobile

B2B buyers are on their phones, too. When we analyze tests at DoWhatWorks, we look at both desktop and mobile versions. The good news? The patterns are typically mirrored (though not always). However, for every trend shared in this article, the mobile versions were tested and produced the exact same results. So yes, these specific strategies work on the small screen too.

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Dear GTM Strategist,

When I published the 2025 State of B2B GTM report with Kyle Poyar from Growth Unhinged last year, 53% of GTM leaders said they’re seeing little to no impact from AI adoption.

So we’ve spent the last month talking to 30 top GTM leaders about how they’re using AI to get results.

Not the stuff they’re thinking about using. Not what’s on their roadmap. Not pilot projects or overcomplicated workflows. What’s actually deployed, running, and delivering results right now.

This group is absolutely crushing it.

They are generating millions in pipeline, tripling their meeting booking rates, and automating what used to take teams of people.

That’s why we have now prepared the 2026 State of AI for GTM Report. We asked all leaders to share their best AI workflows with us - the workflows that can be replicated and applied to almost any business.

They are on different levels of complexity, from simple yet powerful ChatGPT prompts to setups with multiple tools.

But none of them requires any technical knowledge or coding skills to set up, and they work with the AI tools you use daily: like ChatGPT, Claude, or Gemini.

Today, I want to share with you some workflows you can copy right now.

The Have and Have-Not Era of AI in GTM

Here’s the thing that surprised me most: the gap isn’t about budget or team size.

Early-stage companies and late-stage enterprises show basically the same adoption patterns. 47% have no AI agents in production. 32% have 1-3 agents. Only 2% have more than 21 agents running.

The difference is clarity of use case and willingness to experiment.

The leaders seeing big returns aren’t waiting for perfect vendor solutions. They’re building workflows themselves using general-purpose LLMs (91% are using ChatGPT, Claude, or Gemini) combined with affordable tools like Clay, Zapier, and n8n.

Meanwhile, the 53% seeing no impact? They’re either:

• Waiting for their current vendors to “add AI”

• Playing with ChatGPT for one-off tasks, starting from a blank page every time

• Paralyzed by all the options and technical jargon

Here are some workflows you can try right away, one from each category in the report.

Content Creation: From Transcripts to Thoughtful Content

Most content teams are drowning. You’ve got SMEs who hate writing, tight deadlines, and pressure to produce more with less. AI can help - if you use it right.

AI works best when you feed it great source material. Garbage in, garbage out still applies. But primary sources in, quality content out? That’s where magic happens.

Workflow Example: AI Content Idea Generator from Call Transcripts

Workflow by Maja Voje (GTM Strategist)

Tool: your favorite LLM

One of my favorite workflows came from my own experimentation (yes, I dogfood everything). Here’s how it works:

Step 1: Drop a call transcript into ChatGPT/Claude/LLM of your choice. These can be customer calls, expert interviews, internal strategy sessions - any conversation with high-signal ideas.

Step 2: Ask it to analyze key themes that could become posts, blogs, or other content:

Your task: Extract 10 distinct, high-signal themes or insights expressed by [Name] that could be turned into strong LinkedIn thought leadership posts. For each theme, provide:

1. Theme title (short, sharp, scroll-stopping)
2. Core insight (1-2 sentences explaining the idea in plain English)
3. Why this resonates on LinkedIn (tie it to a real pain, misconception, or trend)
4. Suggested post angle (e.g. contrarian take, personal story, framework, warning, tactical breakdown)

Constraints:
- Focus on applied, opinionated insights, not generic summaries
- Prioritize ideas that spark discussion, disagreement, or "that's so true" reactions
- Write for [target audience, e.g. B2B founders / GTM leaders / operators]
- Avoid fluff, quotes without context, or surface-level themes

Transcript: [paste full transcript here]

Why this works: You’re not asking AI to generate ideas from scratch. You’re asking it to extract and package the good ideas that already exist in your conversations. The constraint-heavy prompt keeps output quality high.

Real result: I use this weekly. A 30-minute conversation becomes 8-10 content ideas with clear angles. What used to take hours of manual review now takes 90 seconds.

Workflow Example: AI Knowledge Graph for AEO

Workflow by Dima Durah (Record Scratch Productions)

Tool: ChatGPT Atlas

Dima solved a modern SEO/AEO problem: how do you make your website discoverable to both AI agents and search crawlers without duplicating work or using outdated SEO tactics?

Her solution turns any live website into a machine-readable knowledge graph that serves as a single source of truth for agentic AI grounding, human exploration, and crawler discovery.

The workflow:

1. Load the website in ChatGPT Atlas and ensure full page context is available.

2. Use Atlas to extract all semantically meaningful entities and relationships, outputting a self-contained interactive HTML file with embedded KG JSON.

3. Export the KG JSON using the “Export JSON” button to download the raw graph (nodes + edges) as structured data.

4. Feed the KG JSON back into ChatGPT using a conversion prompt to generate:

   1. llms.txt for LLM/agent grounding and discovery;

   2. robots.txt for crawler directives and discoverability hints.

5. Deploy llms.txt and robots.txt to your site root (or as plain-text routes). Optionally host the interactive KG HTML as a public artifact or internal tool.

Get detailed prompts and example outputs here.

The result: A single workflow that makes your site natively understandable to AI systems while maintaining traditional SEO discoverability - no manual duplication required.

Workflow Example: AI Content Strategist with Context Engineering

Workflow by Amos Bar-Joseph (Swan)

Tool: Claude

Amos generates over 1M monthly impressions on LinkedIn consistently and millions in monthly pipeline. His secret? Treating Claude not as a content generator, but as a collaborator through context engineering.

His system runs on three layers:

Layer 1: Conversations – He works inside a dedicated Claude Project so every conversation compounds the context without cross-contamination from unrelated threads.

Layer 2: Reusable Knowledge – He builds reusable “skills” (content pillars, brand voice, example posts) so Claude pulls only what’s relevant to each task. The more skills Claude has, the better it gets.

Layer 3: Instructions – His instructions are optimized for Human+AI collaboration:

• Force Claude to think creatively before responding

• Follow an opinionated collaborative workflow

• Extract the right human feedback at the right time

Instead of: “Write a LinkedIn post about [topic]”, Amos uses: Context-aware instructions that:

1. Reference specific past conversations and successful posts

2. Pull from relevant skills (voice guidelines, content pillars)

3. Guide a collaborative workflow where AI drafts, he refines, AI learns

Get full instructions here.

The result: Consistent 1M+ monthly impressions and millions in pipeline - but only because the system amplifies an original point of view. As he notes: “Without something original to say, the best architecture in the world just produces polished noise.”

Marketing: Research and Strategy at Speed

Product marketing and growth teams live in research mode. Customer interviews, competitive analysis, campaign data, persona development - it never ends.

The AI workflows getting traction here aren’t replacing human judgment. They’re accelerating the research phase so marketers can spend more time on strategy and less on data collection.

Workflow Example: AI Digital Twin for Customer Research

Workflow by Kieran Flanagan (HubSpot)

Tool: Claude

Kieran had a frustration: customer research takes too long, so marketers use it sparingly. But what if you could have customer research on tap and ask for opinions before work even launches?

After reading research from PyMC Labs and Colgate-Palmolive, he realized AI can act as a customer’s digital twin and give accurate feedback to help iterate faster.

His digital twin runs on three layers:

Layer 1: Real Customer Data – He feeds Claude actual signals: sales call transcripts, G2 reviews, CRM objection notes. This grounds the twin in real buyer language, not hallucinations.

Layer 2: Behavioral Insights – He asks Claude to surface patterns: motivations, emotional drivers, objections, buying triggers. This creates accuracy because he’s mapping why they buy or walk away.

Layer 3: Anchor Statements – Instead of asking Claude to rate something 1-5 (which defaults to vague “3s”), he creates five reference statements in customer language, from “this sounds too complex, not worth switching” to “this is exactly what we’ve been looking for.”

The workflow:

1. Feed Claude real customer data (transcripts, reviews, objection notes)

2. Ask Claude to extract behavioral patterns and buying triggers

3. Create 5 anchor statements in actual customer language representing the spectrum from “hard no” to “perfect fit”

4. When testing a campaign, have Claude write a natural response, then identify which anchor statement it’s closest to

5. You get both the score AND the reasoning

He loads everything into a Claude Project as a persistent digital twin he can test any campaign against.

The result: On-tap customer research that accelerates iteration. The caveat: “This system gives you accuracy on purchase intent, but only if you feed it real customer data. Synthetic data in, synthetic insights out.”

Get the full tutorial via Kieran’s newsletter.

Prospecting: Personalization Without the Manual Work

Outbound is broken. Spray-and-pray doesn’t work. But hyper-personalized 1:1 research doesn’t scale.

The teams seeing results are using AI to automate the research and personalization that used to require armies of BDRs. They’re not sending AI-generated spam. They’re using AI to find the right people, enrich them with relevant data, and craft contextual outreach.

Workflow Example: AI Outbound Micro-Campaigns

Workflow by Mike Ryan (Crescendo)

Tools: HubSpot, Instantly, Nooks, Clay, HeyReach, OutboundSync

Mike’s team faced the classic outbound challenge: how do you run personalized, multi-channel campaigns at scale without burning out your BDR team?

They built a human-in-the-loop AI system that automates prospecting, research, and sequencing across three distinct funnels: ABM, outbound, and inbound-to-outbound. The AI engine autonomously identifies, enriches, and engages prospects, then hands off to humans at key conversion points.

The workflow runs through five connected stages:

Stage 1: Prospect Identification
Marketing and sales upload or tag accounts based on ICP, ABM signals, or social metadata. Clay enriches each record with phone, email, LinkedIn, tech stack, and outsourcing data. The system refreshes accounts weekly, giving BDRs and marketing a new set of prioritized targets based on the most recent buying signals.

Stage 2: Automated Research and Sequencing
The system auto-generates email, LinkedIn, and call sequences that are sent via Instantly, HeyReach, and Nooks. Replies automatically stop the sequence.

Stage 3: Prospect Prioritization and BDR Enablement
HubSpot prioritizes and assigns tasks to BDRs based on enrichment data, engagement signals, and ICP fit. AI call scripts are generated directly on each contact record. Each BDR has a personalized Clay table, filtered by ICP fit and campaign relevance.

Stage 4: Engagement and Handoff
BDRs engage prospects using hyper-personalized, AI-generated messaging and Gong-recorded discovery calls. No-shows automatically re-enter Clay for resequencing. Qualified opportunities pass to sales automatically.

Stage 5: Closed-Loop Feedback
Replies sync back to HubSpot and Slack via OutboundSync. Marketing logs win/loss data and attaches Gong recordings to discovery calls. Non-responses trigger resequencing through Clay.

The tech stack: HubSpot (CRM), Clay (enrichment & workflows), Instantly (email), HeyReach (LinkedIn), Nooks (calls), OutboundSync (sync), Gong (call intelligence), Slack (routing).

The results:

• Millions in pipeline with 200%+ quarter-over-quarter growth

• AI engine creates ~25% of overall pipeline

• 50%+ open rates on AI-generated emails

The key: The system runs autonomously but keeps humans in the loop at conversion points. AI handles the research and sequencing grunt work; BDRs focus on high-value conversations.

Sales: Preparation and Analysis Without the Grunt Work

Sales teams spend absurd amounts of time on non-selling activities. Meeting prep. Call notes. Deal analysis. Following up on action items.

The workflows getting traction automate the grunt work while keeping humans in the actual selling moments.

Workflow Example: AI Re-Engagement for Closed Lost Deals

Workflow by Elaine Zelby (Tofu)

Tools: HubSpot, Tofu, Amplemarket, LinkedIn, Sybill

Elaine had a core GTM insight: closed lost deals are much easier to re-engage than getting a brand new logo aware, educated, interested, and signed. So she automated the entire re-engagement process.

The setup uses Sybill (AI call recorder) connected to HubSpot, which automatically fills 12 custom fields based on sales calls - following their SPICED methodology plus tech stack details.

When a deal moves to closed lost, it’s automatically added to a HubSpot workflow with a 90-day wait period, then gets sent to a Tofu campaign that:

1. Ingests all the CRM fields Sybill created (pain points, tech stack, closed lost reason)

2. Runs an additional AI research agent with this prompt:

Have they had any new marketing leadership changes in the past 3 months? What personas and industries do they sell to? What types of marketing content and campaigns are they currently running? Have they made any major announcements, hosted or had a major presence at any events in the past 3 months? Have they made any recent changes to their GTM motion?

3. Combines the research with CRM data to create personalized content for each contact:

   • 4 marketing emails

   • 4 sales emails

   • 2 LinkedIn DMs

   • 1 personalized 1:1 landing page

The landing pages export to Webflow, emails with personalization tokens go to HubSpot, sales emails and LinkedIn DMs flow into Amplemarket sequences, and the account gets added to a LinkedIn Campaign Manager audience for targeted ads.

The result: Fully automated, deeply personalized re-engagement that runs on autopilot while maintaining the quality of manual outreach.

Workflow Example: AI Sales Analyst

Workflow by Jonathan Kvarfordt (Momentum)

Tools: NotebookLM, Momentum

Most marketers treat call transcripts like data graveyards: recorded, stored, never analyzed. After analyzing hundreds of customer conversations at Momentum, Jonathan learned what actually works is better instruction architecture.

His NotebookLM system runs on three layers using 3 main documents:

Layer 1: Master Control – A single document NotebookLM reads FIRST that cascades instructions to specialized sub-documents. This creates a reusable framework that transforms raw transcripts into strategic intelligence without re-prompting every time.

Layer 2: Brand Standards – He encodes brand voice, forbidden phrases, hex codes, and pronunciation rules (because nothing kills credibility like AI mispronouncing “Momentum.io”). This ensures every output - whether podcast, deck, or report - sounds and looks like it came from their team.

Layer 3: Analysis Protocol – Rather than asking “what are the insights?”, he gives NotebookLM a forensic framework: analyze language patterns (what words CUSTOMERS use vs what WE use), objection architecture (price vs feature vs timing), buying triggers, moments of confusion, and competitive intelligence. Every insight requires transcript citations and exact quotes - no marketing-speak paraphrasing allowed.

The workflow:

1. Upload the instruction stack FIRST:

INSTRUCTION CASCADE:
1. Apply Brand Voice Guidelines from "Source A - Brand Standards"
2. Follow Output Formatting from "Source B - Format Rules"
3. Execute Analysis Framework from "Source C - Analysis Protocol"
PRONUNCIATION GUIDE (for Audio Overviews):
- [Your company name] = "[Exactly how to say it]"
- [Your product] = "[Phonetic spelling]"
- [Industry acronyms] = "[Spell out or full phrase]"
ANALYSIS CONTEXT:
You are analyzing real customer/sales conversations to extract authentic
voice insights for marketing strategy.
CORE PRINCIPLES:
- Use customer language, not marketing language
- Cite specific transcripts + timestamps for every claim
- Quantify patterns (mentioned in X of Y transcripts)
- Separate what was said from what it means strategically
- Flag contradictions and outliers
- No hallucinations - if uncertain, say so
OUTPUT RULES:
- Audio: 8-10 min, conversational, 2-3 examples per insight
- Decks: Bold insight headlines, one pull quote per slide, brand colors
- Reports: Markdown format, executive summary + detailed sections

2. Add 20-30 transcripts stratified by outcome (won/lost/pipeline or active pipeline conversations OR active customer conversations)

3. Generate audio overviews for the team, slide decks for executives, or markdown reports for strategy sessions

4. All outputs completed in 2-3 hours vs 15+ hours of manual analysis

Don’t delete notebooks. Next quarter, add new transcripts to the SAME notebook. Now you’re tracking pattern shifts over time - which objections decreased, which new concerns emerged, how customer language evolved. Your analysis compounds instead of resetting.

The result: Strategic intelligence from call transcripts in 2-3 hours instead of 15+. Teams get audio overviews, executives get slide decks, and strategists get markdown reports - all from the same workflow.

How to Actually Get Started

If you’re in the 53% seeing little impact, here’s my advice:

1. Start with one clear workflow, not “AI strategy”

Don’t try to transform your entire GTM motion at once. Pick one painful, repetitive process and fix it. Meeting prep? Content ideation? Lead enrichment? Start there.

2. Use what you already have

Most of these workflows work with free plans of ChatGPT or Claude plus affordable tools like Clay, Zapier, or n8n. You don’t need enterprise AI contracts or engineering resources.

3. Feed AI great inputs

The single biggest determinant of output quality is input quality. Call transcripts, customer conversations, existing high-performing content - these are gold. Random prompts to “write a blog post about AI”? Garbage.

4. Constrain the output heavily

The more specific your prompt, the better the output. Define format, length, tone, what to include, what to avoid. Vague prompts = vague output.

5. Keep humans in the loop

Every successful workflow in this report has human review at critical points. AI does the research, synthesis, or first draft. Humans make strategic decisions and ensure quality.

The Bottom Line

We’re in a weird transition period. AI is simultaneously overhyped (it won’t replace your entire GTM team) and underutilized (most teams barely scratch the surface of what’s possible today).

The winners aren’t waiting for perfect solutions. They’re experimenting, iterating, and building practical workflows with off-the-shelf tools.

The gap between the 24% seeing big impact and the 53% seeing no impact isn’t about access to technology. It’s about willingness to try things, tolerance for imperfection, and focus on practical use cases over abstract strategy.

Want to dive deeper? Kyle and I documented all 40+ workflows in the full report, including:

• Step-by-step implementation instructions

• Actual prompts you can copy

• Tool recommendations and cost estimates

• Results metrics from real companies

Get the full 60-page PDF “2026 State of AI for GTM Report” in Kyle Poyar’s latest newsletter.

Get the report

Huge thanks to all the experts who contributed to the report: Alex Shartsis (Skyp), Amos Bar-Joseph (Swan), Andrea Kayal (Help Scout), Ashley Cheng (Usersnap), Brendan Short (The Signal), Dave Rigotti (Inflection), Dima Durah (Record Scratch Productions), Elaine Zelby (Tofu), Elena Luneva (Elenetic), Gail Axelrod (Jellyfish), Fivos Aresti (Workflows.io), Francesca Krihely-Price (dbt Labs), Hamish Grant (SafetyCulture), Jeff Beckham (Gem), Jesus Requena (Sanity), Joey Maddox (Verisoul), Jonathan Kvarfordt (Momentum), Josh Grant (Webflow), Justin Norris (360Learning), Kieran Flanagan (HubSpot), Liam Gandelsman (Galileo), Matteo Tittarelli (Genesys Growth), Mike Ryan (Crescendo), Natalie Taylor (Capsule), Raj Sarkar (Cloudbees), Rishikesh Ranjan (StreamAlive), Ryan McCready (Quo), Tobi Idowu (Coast), and Torsten Walbaum (Operator’s Handbook).

If you try any of these workflows, I’d love to hear how it goes. Reply to this email or tag me on LinkedIn.

Go build something.

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 Clay.

Clay connects 100+ data sources into one platform where you can enrich and orchestrate GTM workflows without switching tools.

I prepared a free playbook with 7 Clay plays for ABM you can deploy immediately: from identifying high-intent accounts to auto-triggering sequences when they match your ICP. No theory, just the exact Clay workflows that convert. Get it here (no email required):

Get the playbook

Dear GTM Strategist!

Today, we’ll dive into a topic that I was actively avoiding for 2.5 years - ABM. Why?

While generic “spray and pray” outreach is taking its last breaths, people on social media and newsletters misrepresent ABM (Account-Based Marketing) hardcore.

Yes - theoretically, it makes sense to “do multiple touchpoints” if you are dealing with a small list of potential buyers that will buy $100K ARR products from you. But it is misrepresented as “running LinkedIn ads”, “liking someone's posts”, and my all-time favorite: “organizing webinars.”

You see, these are old-school B2B marketing tactics that we have been using for nearly 20 years - nothing super intelligent about it. Where ABM magic ✨ really happens is in the background: intelligence, proprietary signals, system, orchestrations of touchpoints, and speed of hyper-personal execution.

This is why Kyle Poyar and I prefer to call it ABX - account-based everything, because it does overlap with sales and GTM engineering IRL.

What I am saying is: “Please don’t just run LinkedIn ads and call it ABM” :)

To make this area a lot more tactical for you, I created a list of 7 ABM plays in Clay (for selecting target accounts by challenge type, for multi-threading, advanced personalization, and much more) and invited Katya Tarapovskaia, who has generated millions in revenue by running ABM campaigns for Snowflake, Twilio, and Mastercard, and now runs YouStellar, an AI automation agency. She’ll teach how ABM really works beyond pretty LinkedIn infographics.

What you’ll learn:

• Why contact-level intent beats account-level intent — and how top teams push data coverage from ~40% to 85% without platform lock-in

• How composable ABM stacks cut costs by 60–70% while increasing win rates by 15%+ and speeding up deal cycles

• A real AI-orchestrated ABM workflow that saves 40 hours per rep per month and turns intent into pipeline the same day

ABM should be about precision over volume, signal over noise, and owning your GTM engine.

Now, let’s hear it from Katya.

Real ABM in 2026 is different. It’s contact-level. It’s composable.

The teams winning aren’t touching more accounts, they’re identifying which specific people inside target accounts are showing buying intent, reaching those individuals with hyper-relevant messaging, and orchestrating sales follow-up with intelligence about their personality and decision-making style.

In 2026, Intent is no longer account-level. It’s contact-level. And it requires AI at every layer, orchestrated across best-of-breed tools you control.

What ABM Actually Is?

ABM is a go-to-market strategy where Marketing, Sales, and Success align on a named set of accounts and the specific decision-makers within them. You’re not optimising for more leads. You’re optimising for account velocity by getting the right message to the right person at the right time.

The three ABM traps that slow down programs:

1. “Display ads to a CSV“ is just targeted demand gen, not ABM. Real ABM means knowing which specific contacts are showing intent signals and reaching them across channels (ads, email, sales) with messages grounded in their role and buying signals.

2. “One playbook for all” doesn’t work. Enterprise, mid-market, and SMB require tiered ABM (high-touch, mid-touch, scalable) with different buying committee maps and contact-level personalisation for each.

3. “More air cover for SDRs” misses the real value. ABM’s superpower is prioritisation, knowing which accounts and which people within those accounts to pursue, when, and with what narrative. It stops wasted outreach.

The Composable GTM Orchestration Stack & Plays

Here’s the fundamental shift: Traditional ABM platforms bundle everything (data, intelligence, outreach, ads, automation). You’re locked in, paying platform markups, and constrained by their roadmap.

Composable ABM orchestration flips the model:

1. Choose best-of-breed tools for each function (enrichment, intelligence, outreach, ads, automation)

2. Connect them through an orchestration layer (Clay, Zapier, Make, n8n) that makes them work together

3. Pipe everything into your CRM (HubSpot/Attio/Salesforce) as your single source of truth

4. Own direct relationships with data providers and AI tools (BYOK - Bring Your Own Keys)

The outcome: Better data coverage (85% vs 40%), lower costs (60-70% savings), complete flexibility, and true ownership of your revenue engine.

Here’s my 6-Pillar ABM Orchestration Framework powering modern GTM teams:

Pillar 1: Data Enrichment

The problem: Single data providers give 40-50% coverage. Half of your target accounts are enriched; the other half are missing critical information. You can’t personalise what you don’t know.

The solution: Waterfall enrichment across multiple sources, orchestrated by Clay:

Apollo.io (speed + breadth, 275M contacts) → LeadMagic (accuracy + depth, 99% on direct dials) → Hunter.io, Clearbit, or 75+ other sources (specialised signals) → stops when found.

The outcome: Coverage 40% → 85% | Cost per enrichment: $0.06-0.10 (direct API) vs $0.17 (platform credits) | Save $700-1,100/month at 10K enrichments

Pillar 2: Account Intelligence

The problem: Two contacts with the same title might require opposite approaches. One wants data-driven ROI proof; the other wants references and partnership stories.

The solution: Layer multiple intelligence sources:

Humantic AI analyses LinkedIn profiles on a Contact-level + Agent Miia by Humantic AI researches any Account in minutes → predicts DISC personality types, communication preferences, decision-making styles + Account Intelligence | HubSpot + LLM integration surfaces patterns from your CRM history | Attention AI transcribes sales calls, flags objections, and surfaces deal intelligence

The outcome: 15% higher win rates | 30% faster deal cycles | 40 hours saved per rep monthly on call notes

Pillar 3: Personalised Outreach

The problem: Generic outreach yields a 2-3% response rate. True personalisation doesn’t scale manually.

The solution: AI generates personalised first lines at scale, coordinated across channels:

Clay enriches account data → GPT-4 (via your OpenAI API key) writes personalised first lines referencing specific triggers (funding, job changes, tech adoption) → La Growth Machine sends coordinated email + LinkedIn + Twitter sequences → Warmly identifies when they visit your site and personalises the experience → Clay as an orchestration platform ensures message consistency across touchpoints.

The outcome: 72% higher engagement vs generic | 50%+ lift in website conversion | 4x account reach without adding headcount

Pillar 4: Contact-level Targeting

The problem: Generic ads waste budget. You need specific accounts to see messages tailored to their industry, stage, and pain point.

The solution: ABM ads platforms with dynamic creative and autonomous optimisation:

Export target account list from HubSpot → Influ2 targets decision-makers on LinkedIn → Metadata.io auto-optimises budgets across LinkedIn, Google, Facebook → ZenABM shows different creatives based on account attributes and stage and measure results and optimise

The outcome: 50%+ engagement lift with personalized ads | 3x ROAS improvement | 90% reduction in manual campaign management time

Pillar 5: AI Automation (Workflow Orchestration)

The problem: Each tool creates manual work. Copying data between systems. Triggering sequences manually. It doesn’t scale.

The solution: Automation platforms that connect your entire stack into unified workflows:

Zapier (simple, 6,000+ integrations) | Make (powerful logic, visual builder) | n8n (open-source, full customization) + Clay as a Central Data Orchestration PLatform

Example intent-to-pipeline workflow (zero manual steps):

Attention AI flags high-intent phrase in sales call → Zapier triggers Clay workflow → Clay enriches account across Apollo + LeadMagic → Humantic AI analyses buyer personality + Account Insights → GPT-4 generates personalised follow-up email → La Growth Machine sends multi-channel sequence → Metadata.io launches targeted ads → Warmly syncs website activity → HubSpot creates deal + assigns to rep → Slack alerts rep with full context.

The Outcome: 40 hours saved per sales rep monthly | 95% reduction in data entry errors | Same-day response to intent signals (vs 3-5 days manual)

Pillar 6: Cost Optimisation

The problem: Platform credit systems create unpredictable costs. Scale = surprise bills. You’re paying platform markup on everything.

The solution: Bring Your Own Keys (BYOK) to data and AI. Own direct relationships.

Subscribe directly to LeadMagic, Apollo, Hunter (not through Clay credits) | Use your own OpenAI API key (not platform AI credits) | Own Instantly.ai or Smartlead (not platform email credits)

The outcome: $2K/month for a composable stack supporting $1M+ pipeline | Full cost transparency | BYOK prevents vendor lock-in price hikes

One ABM Play to Test it Out Today

Play: Competitive takeout with Clay-powered list building

This play targets accounts that use key competitors or where you previously lost, but where conditions have likely changed. It is ideal for pipeline creation in defined verticals.

How it runs:

​Source accounts from CRM:

1. Past closed-lost in CRM tagged with competitor.

2. Net-new accounts identified via tech-detection sources and fed through Clay.

Enrich with Clay:

• Pull decision-makers and influencers per account.

• Append intent-like proxies (e.g., job postings, role changes indicating new mandate).

AI workflow:

1. Cluster accounts by likely “why change now?” story (e.g., cost pressure, missing features, performance issues).

2. Drafts outbound and paid angles per cluster instead of “one message to all competitor users”.

The impact is a realignment of pipeline creation around realistic conversion opportunities rather than a long tail of “someday” accounts.

Why Composable AI+ABM Orchestration Is Winning

Key factors:

• Flexibility: Swap components as better tools emerge

• Cost control: BYOK prevents vendor lock-in price increases

• Data ownership: Your enriched data works across multiple use cases

• Speed to value: 2-4 weeks to launch

• Innovation: You control your stack evolution

What works is execution discipline + architectural thinking: alignment across marketing, sales, and RevOps on buying committee mapping, contact-level signal orchestration, composable tooling, and personality-informed personalisation.

That’s where revenue lives in 2026.

Thanks, Katya, for this hands-on guide to ABM in 2026.

If you want to learn directly from Katya, check out a free 4-week ABM Bootcamp (starting Jan 26th) by ZenABM. It includes guest lectures from other top experts I highly recommend (Ali Yildirim, Patrick Spychalski, Tas Bober…). Apply here.

Last but not least: I’m hiring! We’re looking for a designer (part-time) and a content manager (part-time or full-time) to join the GTM Strategist team. Check out more about the positions - or share with anyone who'd be perfect for the role.

Until 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.

This post is supported by Schematic — monetization infrastructure for modern SaaS and AI companies.

Schematic helps product and growth teams turn usage into revenue without rewriting application code every time pricing or packaging changes. Define plans, entitlements, limits, credits, trials, and exceptions once — then iterate fast as you learn.

Teams use Schematic to improve trial-to-paid conversion, unlock expansion, and avoid revenue leakage as products, pricing, and GTM evolve.

If you care about shipping growth experiments without billing becoming the bottleneck, Schematic is worth a look.

Try Schematic free

Dear GTM Strategist!

Every founder regularly asks themselves if they should raise their prices.

They also have board members, investors and advisors ask the same questions.

Ultimately, it’s a question of missed opportunity and lost value.

How much money are we leaving on the table?

With AI models embedded in products, pricing has never been more interesting but challenging. This was my exact conversation with a well-known startup founder (let’s save his face):

Maja: “But I have no idea what I’m getting for those 5000 credits.”

Him: “I know it is difficult to understand, but our 10 top users were burning like 80% of our AI usage fees so we had to turn to credits.”

Maja: “I get that, it sucks, but as a potential buyer, I still don’t know what package will cover my use cases and what value am I getting for those credits.”

Him: “Well, that depends on what you want to do, features burn credits differently.”

Maja: “Okay, I understand. Can you at least try to tell me how much it would cost me if I want to do X action with 2000 accounts? I love you and your product, but it is not my job as a buyer to do the pricing math. It is your job as a founder/seller.”

This isn't just my experience. While experts have been raving about outcome-based and usage pricing, the reality is that most B2B buyers want some predictability - and blaming them for being “old school” will not help us move more products.

What else do we want as founders? Predictable revenue.

Hybrid pricing has been grossly underrepresented in the AI star wars of searching for the latest and greatest. But it is a perfect bridge between where we are and where we wanna go.

In this article, I want to bring you some valuable tools to help you make these decisions:

• The “wirecutter” guide to modern pricing models with examples

• How to conduct willingness to pay research - aka “pricing therapy” interview with 8 questions to ask

• Anatomy of a great pricing page

If you are as puzzled and frustrated by “credits” in pricing as I am, and you value predictable costs as a buyer, I promise you’ll love this one.

This is a practical summary from Fynn Glover’s book, You’re Leaving Money on the Table, that I have hand-crafted for you. You can download the book for free - it takes <1h to read, and it’s packed with useful insights and models that will help you get to the pricing model that makes both you and your customer happy.

Download it here for free

It features insights from top pricing and business experts, including:

• Gaurav Vohra, Growth Leader at Superhuman, Advisor at Clay, Replit & others

• Kyle Poyar, Founder, Growth Unhinged

• Madhavan Ramanujam, Author of Monetizing Innovation

• Shar Dara, Head of Billing at Vercel

• Tido Carriero, Cursor, ex-founder & CEO, Koala, and fmr. VP, Segment

The “Wirecutter” Guide to Modern Pricing Models

“It was simpler to price SaaS before AI for one structural reason: you were selling applications with deterministic workflows and predictably high 80-90% gross margins. This made it safe to sell flat-rate, seat-based pricing,” explains Fynn in his book.

What happens when you’re selling probabilistic workflows, buy AI credits from other vendors, and have unpredictable 50-60% gross margins? Since most companies can’t price on “outcomes” alone due to the attribution problem, they end up with hybrid pricing models.

I am prioritizing this section because I know exactly how most of us will tackle pricing:

“Oh, cool - a company X does Z - they are great - should we follow that example? :)”

I am the same - show me the end goal to motivate me to do the heavy lifting.

So before really going into pricing research, which is non-negotiably great for you to do, let’s see what feasible pricing models are for you to consider.

These models are the best available proxy, based on what the product can do, what the customer perceives, and the value the vendor can objectively attribute to its product/service/agent.

This explosion of new models makes it harder than ever to know which one to choose.

Your job is to pick the least wrong one for your product and then iterate continuously as your product matures, your competition matures, and as you learn from your customers and salespeople.

If you want to get a detailed 4-step guide on how to choose the right pricing model for you and many more assets, you can download the You’re Leaving Money on the Table book.

Now for the hard part: validation. Those models mean nothing if your customers don't understand or value them. In the next section, you will learn how to test before you launch.

8 Steps to Discover Willingness to Pay, aka Pricing Therapy

Fynn Glover, CEO of Schematic, believes, and I agree with him, that being able to talk to customers about willingness to pay is arguably the most critical tool in a founder’s toolbelt for finding & accelerating Product-Market Fit (PMF).

Conducting PMF conversations should never stop. They should occur when you’re pre-product, after you’ve won initial customers, and then on an ongoing basis as you extend and deepen your PMF.

Over the last 2.5 years, Fynn has done countless WTP interviews with customers. But he’s also trained hundreds of founders on WTP, so they are equipped with the vocabulary of pricing and packaging and are confident in their ability to position and sell their pricing.

Here is a repeatable flow of questions you could ask customers, prospects, or design partners in under an hour:

Make sure the delivery is conversational, not static. This is what you need to align:

1. Clarity: Do you understand what value you create and how to express it?

2. Confidence: Can you deliver your price narrative with confidence, poise, and persuasion?

3. Consistency: Does every team inside your company tell the same story?

If those aren’t aligned, the model doesn’t matter. You’ll lose deals, underprice, or discount out of insecurity.

And before we go-go for this week, I want to leave you with one more super useful framework from the “You’re Leaving Money on the Table” book, which is…

Anatomy of a Great Pricing Page

The final step is to communicate your new model to the world. Your pricing page is your GTM strategy, made public. While specific designs change, the best pricing pages all share four key components:

1. A clear, re-stated value prop: The page must start with a confident headline that reminds the user what problem this product solves for them.

2. Clear tiers & price metric: The user must be able to self-identify which tier is for them and what they are likely to pay over time.

3. A good FAQ: This is where you handle all the “what if” anxieties. “What counts as an ‘action’?” “What happens if I go over?” “Can I roll over credits?” Answer these proactively so they don’t have to ask sales.

4. Overwhelming social proof: This is your final chance to de-risk the purchase. Use logos, testimonials, and case studies to prove that people just like them are already getting massive value from this.

Something like this:

Hooray … we made it 🥁 Thanks for sticking with me - pricing is not easy, but it is the only GTM element that directly makes us money - others spend it 😀 so we should be paying close attention to it and definitely avoid the “set it and forget it” pricing tactics.

Here are the next steps to take to make sure you apply what you learned today:

• Grab a free version of the book - <1h to read - min. 4 tangible takeaways 💪 or send the link to a colleague who is thinking about launching a new AI product in Q1 so that they will not end up leaving a bunch of money on the table. They’ll be grateful.

• Check my previous article on pricing.

✅ 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.

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Dear GTM Strategist,

If you still assign points to “leads” that open your newsletter and register for your webinars in the age of AI-first GTM, you are still lead scoring like it is 2006.

The symptoms of not having or having an outdated lead scoring problem are:

• Calendars packed with shit lead to demos 🧟 that make you question “what is the meaning of it all” 5 minutes into the meeting

• Constant quarreling between marketing and sales, why “hot leads” that downloaded the latest e-book about 2026 AI Trends are not responding to emails, and

• Waking up in the middle of the night in cold sweat, wondering where your next deal is coming from.

Lead scoring is rarely top of mind for most people - most would treat it as “set it and forget it test” - but it is more relevant than ever.

How so?

GTM got harder. Teams are drowning in data, signals, and noise, making it extremely hard to make sense of it all, while being constantly pressured to do more with less, faster than ever.

AI search modes have made customer journeys blurrier and even led us to question: Is something a human or an AI touchpoint?

Meanwhile, barriers for competitors have gotten even lower and customers pick who deserves their attention.

And in this world, your ability to focus on the best opportunities and do prioritization well has become mission-critical.

And lead scoring is a part of the solution - so in this edition, I’ll take you on a journey across all levels:

1. What is lead scoring, and what is not

2. We'll create a lead scoring system that captures fit, behavior, timing, and revenue potential

3. We’ll do some lead scoring workflows in Attio so you can see the system in action

Lead scoring helps us decide where to invest GTM effort to most likely yield strong results.

Note: This guide focuses on inbound lead scoring - evaluating prospects who have already engaged with your brand through website visits, content downloads, or product trials. If you're doing outbound (scoring accounts before first contact), you'll need to prioritize fit and external company signals over behavioral data.

What Lead Scoring Is (nobody said it that simply) 🤠

I love to think about it as “working backwards” from your ideal clients - those who buy fast, stay long with you, and expand profitably.

Imagine your perfect buyer. Better yet - show me their LinkedIn profile. Let’s suppose his name is Bob. He’s been a customer for a year.

Now, look at the digital breadcrumbs Bob left before he signed the contract - as well as firmographics:

• When did he first visit the website?

• Which webinars did he attend?

• How many times did he visit the pricing page?

• How big is his company (in terms of employees or revenue)?

Lead scoring is simply the process of finding “Bob” that are already hiding in your CRM - and spotting them early on.

Instead of treating every lead as equal, look for those specific signals that Bob gave off. When a new lead matches Bob’s patterns, their score goes up.

You want to attract more “Bobs” to your business - now let’s figure out what signals will enable you to find Bobs early on and focus your efforts on making sure that they have the best possible experience with your company.

It is a very simplified logic, but this is how every founder I work with can understand that lead scoring is not a “coz I told you so” chore, but a helpful tool to help teams make better decisions about where they spend their time and where to put more $$$.

How it looks in practice:

What else do the teams I work with like?

Math and causation/correlation riddles 🧩

So you can use lead score calculations to attribute “Bobness” on a spectrum from a shit lead that you would be wasting time with (pardon my French - non ICP) to a full-fledged perfect customer that deserves all your loving care and attention.

Very nice! Now that we understand the core logic, all we have to figure out is how to tweak this equation to build a powerful Bob detection tool. 🔎

What criteria to use for scoring: AI-Proof Lead Scoring Systems

For lead scoring in 2026, we need to move past tracking clicks and downloads. That level of measurement stopped being useful years ago.

With AI, it’s now much easier to spot trends and find new patterns to identify our best future customers, and eventually transform your lead scoring system into a dynamic model, not a static calculation.

For an effective lead scoring system, you need to add multiple layers of signals - here’s are the most common examples:

1. Contextual Fit (The Foundation)

Contextual fit is the foundation. Classic firmographics like company size, industry, and geography still matter, but they’re just the baseline.

What GTM teams really want to know is whether an account makes sense right now. That includes things like technical compatibility and existing toolstack - signals that tell you whether they can even use your product.

One of the most revealing signals? Current solution status. Companies using outdated competitors are prime displacement opportunities. Companies with no visible solution are greenfield opportunities. Companies that just implemented a strong competitor? Probably not worth your time right now.

Modern CRMs like Attio do a fantastic job by automatically enriching basic lead data with this information, so you can immediately proceed with lead scoring. For technology data, you can use Attio’s AI research agents.

Key signals to track:

• Company size (employees/revenue)

• Industry and geography

• Technology stack compatibility

• Adjacent tool usage (signals they’re in your ecosystem)

• Technology adoption patterns (early adopter vs laggard)

These signals don’t make a lead a high priority on their own, but they confirm the account belongs in your universe.

2. Behavioral Momentum (The Engine)

Momentum is where prioritization actually happens. The biggest mistake in traditional lead scoring is treating every action as equal. A single login or a click doesn’t mean much.

What momentum actually looks like:

• Single pricing page visit = 5 points

• Three pricing page visits in 48 hours = 25 points (active evaluation mode)

• Watched 80% of product demo + pricing visit + case study download = 40 points (serious buyer behavior)

Teams look for depth of usage, activation of sticky features, and repeated engagement over time.

Website engagement signals:

• Pricing page deep engagement (5+ minutes or multiple visits)

• Case study downloads in their industry

• “Product vs Competitor” comparison page visits

• ROI calculator usage

• Multiple unique visitors from same company (buying committee activation)

Product usage signals (for trial/freemium):

• Daily active usage vs one-time login

• Feature adoption beyond basics (3+ features used)

• Integration attempts during trial

• Crossing usage limits or thresholds

Content consumption signals:

• 50%+ open rate on email sequences

• Multi-stakeholder opens (different people from same company)

• Webinar attendance and engagement

• Internal email forwarding (they’re sharing your content)

The pattern you’re looking for is frequency + depth, not just volume. Someone who visits your pricing page three times in two days is showing more intent than someone who downloaded five e-books over six months.

3. Intent and Timing (The Trigger)

This is where most teams get lead scoring wrong. They focus only on what prospects do on their website and miss the external signals that indicate a company is ready to buy right now.

Company trigger events create buying windows - moments when budget opens up, new leaders want to prove impact, or operational pain hits a breaking point.

Growth & Funding Signals:

• Recent funding rounds (0-18 months): Series A/B/C indicates budget and urgency. Companies that just raised are in expansion mode and actively buying tools.

• Rapid hiring surge (30%+ team growth): Scaling fast creates process breakdowns and urgent need for new solutions.

• New office locations or geographic expansion: Multi-location complexity = new tool requirements.

• Product launch announcements: New initiatives require new tooling and infrastructure.

Leadership & People Signals:

• New C-level or VP hires in target functions: New leaders have 90-day windows to prove impact. They’re actively looking to make changes and show results.

• Job postings for roles that use your solution: Hiring indicates growth in that function and signals they’re investing in capabilities you support.

• Promotions into leadership roles: Internal promotions also create momentum for change and new tool adoption.

• Champion job changes: Your power users at other companies moving to new orgs (track these people - they’ll buy again).

Technology & Competition Signals:

• Technology stack changes: When companies add complementary tools, they’re in active buying mode.

• Competitive tool removal or contract changes: Displacement opportunity windows when competitors are being evaluated or removed.

• Competitor renewal dates: If you can find these, it’s your best timing window to reach out.

Market Events:

• IPO preparation or going public: Creates compliance, scalability, and reporting needs.

• M&A activity (acquiring or being acquired): Integration creates tool consolidation pressure.

• Regulatory changes affecting their industry: Creates compliance tool needs.

Why these signals matter: They answer the question “Is this the right moment?” A perfect-fit company with great engagement might not buy for six months. But a perfect-fit company that just raised a Series B and hired a new VP of your target function? That’s a now opportunity.

4. Revenue Potential (The Prioritization Layer)

Here’s the layer most teams miss entirely: predicted deal size.

Without this, all “good fit + high engagement” leads look equal. But a $200K opportunity deserves different resources than a $20K opportunity.

Why this matters:

• Account executives need to know which deals to prioritize

• Marketing needs to know where to invest ABM budget

• Your team can’t allocate effort properly when every lead just shows “Score: 85/100”

How to calculate revenue potential:

For seat-based pricing (most B2B SaaS): Estimated Annual Value = (Target Team Size × Monthly Price × 12)

Example: B2B Sales Tool at $100/user/month

• 150-person GTM team = 150 × $100 × 12 = $180K predicted ACV

• 75-person GTM team = 75 × $100 × 12 = $90K predicted ACV

• 25-person GTM team = 25 × $100 × 12 = $30K predicted ACV

Add growth multipliers for higher accuracy:

• Recent funding (Series A+): ×1.2 (they’ll grow the team)

• Fast hiring velocity (50%+ YoY): ×1.15

• Multiple office locations: ×1.1

Complete example:

• Base: 100-person GTM team × $100/month × 12 = $120K

• Recently raised Series B: ×1.2

• Fast growth signals: ×1.15

• Final predicted value: $166K ACV

Your sales team should see “$166K opportunity” in the CRM, not just “high value score.” This makes prioritization decisions obvious.

Bring it all together: Dynamic AI-Powered Scoring System

Instead of baking this formula into your CRM (or head in some cases), and considering it done, think about it as a dynamic system that constantly evolves. Why? Best in class GTM teams do not use a single score “Bobness”, they use multiple scoring systems that need to be revised as your market and GTM changes. They are not chasing perfection from the first iteration; they let it learn and enhance it with AI, pattern recognition, and machine learning.

Create v01 and iterate on it to become more accurate, but don’t settle on some lazy thinking like “founders from the US with more than 2 million revenue that like your LinkedIn post” - you can do so much better by integrating more intelligence into the mix that will help you make better decisions.

The four-layer system in action

Layer 1 (Fit): Are they in your ICP?

Layer 2 (Behavior): Are they engaging with your brand or product?

Layer 3 (Triggers): Is now the right time to buy?

Layer 4 (Value): How much revenue potential?

Example prioritization matrix:

• Champions (High Fit + High Behavior + Active Trigger + High Value): $180K ACV, engaged daily, just raised Series B → Your A+ accounts, get AE involvement immediately

• Hot Timing (High Fit + Medium Behavior + Active Trigger + Medium Value): $80K ACV, moderate engagement, new VP hired → Strike while timing is hot

• Nurture (High Fit + Low Behavior + No Trigger + Medium Value): $90K ACV, low engagement, no trigger events → Keep warming until trigger appears

But as always, the hard part isn’t defining signals.

It’s making them visible, adaptable, and actionable.

That’s why teams increasingly rely on flexible CRM systems like Attio with built-in lead scoring:

• multiple attributes instead of rigid lead objects

• views tailored to different GTM roles

• scoring logic that can evolve without re-architecture

• AI agent that researches the web to bring more data for lead scoring

My team created this short tutorial on how to create lead scoring workflow in Attio that will help you implement this system in your sales process using their workflows - no coding.

With the Segment integration, you can also bring product usage data into Attio and incorporate product-related, behavioral signals into lead scoring.

The bottom line: Lead scoring in 2026 isn’t about counting clicks. It’s about layering fit, behavior, timing, and value into a system that tells your team exactly where to focus their energy.

Build it, test it, iterate on it. Your calendar (and your revenue targets) will thank you.

Cool - hope you enjoyed this one.

Hit a heart if you did ♥️, ask questions in comments, and send it to a team member who should implement this to make it truly actionable.

And if you like what we talk about here, there is SO MUCH more actionable content, templates, and workshops available in my GTM Strategist solutions.

Happy lead scoring 🤘

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.

I hope you had a lovely holiday break, crafted clever plans for 2026, and cannot wait to execute on them.

Before you roll up your sleeves and work hard on making them happen - let me ask you a question that might change everything:

“Did you bake in enough leverage for your GTM?”

While makers of great products are working hard to produce more social media posts, send out more emails and pour more budget into ads, GTM strategists who understand one simple but incredibly powerful principle - “go where your ICP already is” - consistently achieve much better results.

As products are getting easier than ever to build (and copy), speed to market and exclusive access to high-quality distribution channels (paid, owned, or earned) have become the new gold.

But it does not stop there. Creating distribution leverage across marketing and sales explains only a small fraction of growth equations of some of the fast-growing companies in 2025, such as Lovable ($200M ARR in 8 months), Clay ($100M revenue ARR and $3.2B valuation), and Cursor (from $300M to $500M ARR in 2 months).

Not only did they build sticky products with great UX that supported habit creation and instant wow factors and clear ROI for their users. They have also built strong GTM ecosystems that add leverage across the customer journey, unlock new value propositions, open new revenue streams - and ultimately create new jobs and business opportunities.

When Kyle Poyar and I did the State of B2B GTM survey report, ecosystem marketing emerged as one of the next big things. I first heard about ecosystem marketing from Emily Kramer, and I cannot forget it since.

But after analysing the mega success of AI-native fast growers, one thing became crystal clear: we should not treat it as a “marketing tactic”. The principle of the ecosystem is deeply embedded in the business model design of these organizations and covers all stages of the customer journey.

GTM ecosystems are a function of business model design - and when done right, they become an almost unbeatable strategic moat for the company.

So how do best-in-class companies build their GTM ecosystem and, more importantly, how can you start building yours?

In this post, we’ll dive into:

• Definition of the GTM ecosystem, its stakeholders, and modes of value creation

• I have developed a GTM Ecosystem model that will help you build yours

• We’ll analyze a hands-on example of the GTM ecosystem by Clay to map the relationships between different components of the ecosystem

Let’s start mapping 🗺️

GTM Ecosystems: Growth by Design

A GTM ecosystem is a repeatable go-to-market system where multiple independent actors are economically and operationally aligned to collectively create, distribute, and capture value across the customer journey; in ways a company could not replicate alone.

It is not a channel.
It is not a campaign.
It is not a list of partners.

It is a growth system closely connected with value multiplication and creation of new business models that can impact organizational design.

Tactically, we can envision it as a map system where independent actors (products, partners, creators, platforms, communities) are economically and operationally aligned to co-create demand, accelerate adoption, and expand revenue.

Here are 4 characteristics of a GTM ecosystem:

1. Growth driven by external actors, not internal headcount

2. Distribution embedded into product usage

3. Monetisation expands beyond the core product

4. Ecosystems function as a strategic moat, not marketing tactics

I visualised it as a radial map, with the product at the centre of value creation and rings representing different stages of the customer journey.

• The Product Core = core of value creation

• Activation Ring = usage & stickiness

• Distribution Ring = demand generation and capture

• Expansion Ring = monetisation & expansion

• Growth Loops = compounding motion between the rings

Within these rings are ecosystem actors (platforms, partners, communities, programs, and technologies) that leverage the value creation of the ecosystem and expand TAM (total available market) beyond the product core.

If your GTM only grows when your team works harder, ships more campaigns, adds more channels, or increases spend, it’s not a GTM ecosystem. It’s a treadmill with diminishing returns.

GTM ecosystems flip the equation: growth stops being something you push and starts being something the system pulls by multiple stakeholders in the ecosystem.

The table below shows the difference between a GTM that scales by more of your input and a GTM ecosystem that scales by design when value is created for the customers and incentives are aligned.

Let’s make this discussion more practical now.

AI native companies are giving us a masterclass on how to scale beyond all traditional GTM motions using a powerful GTM ecosystem approach. What can we learn from them?

How Clay scales beyond traditional GTM motions

The fastest-scaling AI-native companies are not winning because they merely executed GTM better. They win because they design GTM ecosystems that compound leverage.

To illustrate the usefulness of the GTM Ecosystem model, I will analyze Clay’s ecosystem. Clay builds “a new economy” in the outer ring with Claygencies and GTM engineering. The goal of the analysis is to spark some ideas on how your product can start forming a GTM ecosystem or how your company can integrate into existing GTM ecosystems that are relevant for your target market and create new opportunities or revenue streams.

THE PRODUCT CORE

• Aha Moment: “I can orchestrate my entire GTM motion from a table.”

• Value Metric: GTM workflows automated per team/accounts activated.

ACTIVATION RING: WORKFLOW & USAGE

Strategic role: Clay becomes the control plane for GTM execution.

• Integrations (Tools): 150 integrations with data providers, CRMs, dev tools automation tools, marketing, social media and outreach tools that enhance product experience beyond core capabilities enabling new opportunities and revenue streams for partners

• Templates & Components: Pre-built Clay tables for prospecting, scoring, enrichment, and outbound are available in their Clay University section + integration partners share their playbooks