The real bottleneck is everything around the model: your context, your workflows, who can access what, what gets updated and what rots in a corner. An org problem, not a model problem.

Kieran Flanagan, put it plainly on Momentum: “model capabilities are moving really really rapidly, but integration into how companies work is moving really really slowly.”

So here’s my conviction. The next work surface for modern marketing teams won’t be a new tab in a SaaS tool, and it won’t be another dashboard. It’ll be Claude (or Codex, or an equivalent). Engineering already made that move. Marketing is next.

The market has noticed. Notion, Clay, Profound, DOJO: they all sell you a “system” now. Four different words for the same idea, the AI marketing OS.

(Be honest: when your competitor, your CRM, and your note-taking app all call themselves an OS, the word stops meaning much.)

Before playing critic, we built our own OS.

To do it, we mapped every marketing workflow we run at Bulldozer. Webinar production, the Momentum podcast, ICP (Ideal Customer Profile) and jobs-to-be-done pulled from sales calls, SEO/GEO, LinkedIn ads management, the performance dashboard... Thirty-two in total.

We didn’t rebuild all thirty-two. We ranked them by frequency × revenue impact, kept the top three, and rebuilt those properly: documented, AI in the loop, one measurable output each.

Eight weeks later, monthly pipeline went from €1.7M to €2.4M. That’s +41%, with no new hire.

I’m not sharing this to show off or wave a trophy (I already posted the number in May). I’m sharing it for what came next, and for what it cost to get there.

What the landing pages leave out: building an OS is mostly writing down what you never wrote down.

Most of the companies we see getting started are rebuilding the internal wiki they never took the time to create. The ICP that lived in one person’s head. The positioning that changed twice and got documented zero times. The data infrastructure nobody had time to lay down.

That work is worth doing. It’s how you onboard people faster, and how agents finally get real material to run on. BUT be honest about what it is: documentation. It doesn’t make your team smarter. Writing a “skill” for your job doesn’t deepen the expertise behind the job.

That’s where most AI OS projects stall. The wiki isn’t refreshed. It ages in place. Six months in, half of it is wrong and the agents are running on stale context. In short, you used AI to build... a filing cabinet.

Context has two layers:

• Everyone has the first: who your ICP is, what you sell, how you position.

• The second is the hard one: domain expertise. Not “here’s our brand voice,” but “in this situation you make this call, in that one you make the opposite.”

That second layer is exactly why companies hired agencies for thirty years. A good agency has seen your situation fifty times, across fifty clients. It knows which lever to pull and when, before you’ve finished describing the problem. That judgment doesn’t come out of a “skill” you cloned off GitHub last week.

The gap between a wiki and an OS is that judgment layer, kept alive. That’s exactly the direction we’re taking at Bulldozer (and rolling out with clients over the past few weeks): running the change with a team until AI is genuinely productive everywhere, instead of shipping a template and leaving.

If you want to build one, here’s the order I’d recommend.

1. Split context into two layers. Personal knowledge (your notes, your methods) lives in .md files in Obsidian, private. Company knowledge (ICP, objections, positioning, proof) lives in Notion, shared with the team. One partner reads both. Two tools, because two jobs.

2. Add a hierarchy of context files. One master file for who you are and how you work, then one per folder explaining what’s inside and how to read it. When an agent works in a folder, it reads the local file first. Right context, never too much, never expired.

3. Turn your recurring prompts into named “skills.” A skill is like a recipe. When I say I’m making a “tiramisu,” that naturally includes eggs, sugar, mascarpone... in a precise order with precise quantities. With skills, you stop rewriting the same instructions and build a library of recipes instead. Our “Monday” routine chains several skills and turns two hours of content production into twenty minutes.

4. Schedule routines to keep context alive. A monthly task that rereads the month’s sales calls, pulls new objections verbatim, and proposes updates to Notion and Obsidian before anything rots. A six-month-old context is a dead context.

5. Build feedback loops. An agent that scans what you shipped, pulls the performance data, and updates the system based on what worked. Without it, you’re guessing.

6. Dictate your context, don’t type it. Tools like WisprFlow let you talk into any field. Two minutes of voice after a prospect call carry more nuance than the three lines you’d type between meetings. Roughly 3x more context in the same time.

And build it inside a cloud or agent environment, Claude Code or Codex, not a chat window. That’s where the work surface is moving.

Every “AI OS” on the market sells the same promise: plug it in, the system runs itself.

None of them ship the two things that matter: your domain expertise, and someone to keep it alive.

You can keep buying this kind of tool and hope it’s plug and play (spoiler: it isn’t). Or you can start building the connection layer yourself, one context file at a time, and own the judgment inside it.

The tool was never the question. The context was. We can help you work on it. Reach out if you’d like to talk :)

Let’s grow 👊

— Jordan

Selma Chauvin est CRO d'Agorapulse, solution de gestion des réseaux sociaux. Elle a rejoint l'entreprise côté marketing avant d'être promue à la direction du revenu il y a 18 mois. Dans son scope : le marketing, les RevOps, les équipes commerciales et le partner management. Elle est l'une des rares CRO à avoir grandi du côté marketing plutôt que sales.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Quand une boîte cherche un CRO, elle embauche presque toujours quelqu’un qui vient des sales. Selma Chauvin, ancienne CMO apporte un nouveau de point de vue.

Dans cet épisode, elle explique comment elle a restructuré toute l’organisation Revenue d’Agorapulse et généré 40 % de croissance en partant d’un principe simple : le vrai problème était en amont.

Voici ce que j’en retiens :

1) Un CRO issu des sales sur un problème de pipe met juste plus de pression sur le court terme.
Si tu mets un sales aux commandes du revenu quand le pipe est vide, il va chercher des leads mais il ne va pas construire le système qui les génère. Il va pousser sur les résultats immédiats, ignorer la qualité, et reproduire en boucle le même débat avec le marketing. La seule façon de régler un problème de pipe, c’est d’avoir quelqu’un qui maîtrise la construction du funnel de bout en bout.

2) Les BDR avaient foiré deux fois, pour la mauvaise raison.
Avant l’arrivée de Selma, Agorapulse avait tenté deux fois de créer une équipe BDR. Les deux fois, ça avait planté. L’attente était irréaliste : on leur donnait une liste de comptes et on leur demandait de faire de la prospection à froid depuis zéro. Ça fait dix ans que ça ne marche plus. Selma a reconstruit l’équipe autour d’un principe différent : les BDR traitent d’abord les leads marketing entrants, et apprennent progressivement à faire de la prospection intelligente.

3) L’organisation n’est plus structurée par canaux, mais par étapes du funnel.
Fini le modèle classique avec un head of SEO, un head of paid, un head of content qui ne se parlent pas. Selma a créé trois pôles opérationnels responsables chacun d’une étape : brand et content pour générer de l’audience, demand gen et PMM pour la conversion en MQL, BDR pour transformer les MQL en rendez-vous. Chaque équipe a un objectif de volume à livrer à l’équipe suivante, et des taux de conversion à respecter. Si les taux se dégradent, c’est leur problème.

4) Chaque membre de l’équipe doit générer 20 à 30 % de son résultat lui-même.
Les objectifs sont calibrés de façon à ce que la chaîne inbound soit suffisante pour tenir mais que chacun ait besoin de générer sa propre partie pour atteindre son quota. Résultat : quand un commercial vient se plaindre du volume de leads, la première question est : où en est ta propre prospection ? Ce mécanisme a mis fin à la quasi-totalité des débats sur le pipe.

5) La localisation coûtait cher et rapportait peu. Agorapulse est passée à tout en anglais.
Le constat était brutal : une équipe dédiée à l’Allemagne (contenu, field, BDR senior, un AE) pour 2 000 € de MRR par mois. L’équipe ne se payait pas. Selma a choisi d’assumer d’être moins bien positionnée sur les marchés locaux plutôt que de dilapider du budget sur des opérations non rentables. Désormais, tout part en anglais. Ce qui fonctionne est localisé. Le reste touche des leads qui coûtent dix fois moins cher.

6) La démo automatisée arrive pour les deals transactionnels.
Mettre un commercial sur un deal à 200 € par mois est une erreur économique et humaine. Le commercial s’énerve, part, et le coût d’acquisition explose. Agorapulse déploie un outil de démo IA qui se promène dans la plateforme, clique, guide le prospect sans vidéo ni screenshot à maintenir. Les points de friction déclenchent une reprise en main humaine. L’objectif n’est pas de supprimer le contact : c’est de le réserver aux moments où il a de la valeur.

Où retrouver Selma : https://www.linkedin.com/in/selma-chauvin

Listen now

Enjoy,

— Jordan

For fifteen years, getting customers meant one thing: going after the people already looking. Someone types “payroll software” into Google, lands on your ad or your page, you capture the lead. Done.

Comfortable, measurable, reassuring for the board. All good.

The catch is that everyone learned to do that, on the same keywords, the same audiences, the same buyers, and the pie isn’t growing.

And AI just took another slice. People now ask ChatGPT and get their answer without clicking. The active demand (the one you were going to convert) is shrinking as you read this.

And by the way, you stopped clicking on Google links a few months ago too.

You rank higher, and you lose customers

One number, from a Bulldozer client (a B2B software company).

In two years, their site climbed from page 2 to the top of page 1 on Google. On paper, a win.

Except at the same position, their click-through rate went from 18% to 3%. Let me repeat that: the same rank now brings six times fewer visitors than it did two years ago.

The site didn’t drop in Google’s rankings. The click disappeared, swallowed by the AI answers at the top of the page, and by ChatGPT.

The whole promise of 2010s SEO, collapsing.

Same fight on paid, where leads cost more and more. From our calls: a cost per lead that went from €20 to €58 on Meta, €74 on Google. A retail client whose qualified leads dropped fourfold (quoting the CMO: “the audience is exhausted”). A founder doing the math: his lead costs €30 organically, €150 in paid to replace it.

Multiply your budget by five to get back what free used to give you. That’s the real bill for depending on capture.

How to dig your own grave

The same movie plays out, in this order.

First, the bidding war. Everyone piles onto the same keywords, increasingly onto their own brand name. One client tripled to quadrupled their “brand” budget just to stay visible when someone types their name (and to keep showing nice paid CPLs). They pay to defend ground they owned for free a few months ago.

Then, the doubling down. When a channel stops delivering, the reflex is to widen the audience. One client took monthly spend from €3,000 to €6,500. The result: “the more we spend, the more we reach off-target people.” You pay more and more for prospects who care less and less.

Finally, the panic. This is the one that traps you. A marketing lead told us about her backstage: her CEO writing every day, “we put in more budget, why aren’t there more leads?” “Total panic, all hands on deck, we have to fill an air pocket.” Under pressure, you cut what pays slowly (content, brand) to fund what shows up immediately (paid). You dig the hole you’re trying to fill.

That’s the spiral: cost rises, the board panics, you pour more into the most expensive channel, cost rises again. You never get out the top.

The way out: go back to creating demand

Let me start with what “creating demand” means to me, because it gets interpreted every which way.

It’s making people want you before they go looking, so you stop depending only on those already searching. Concretely:

First, change what you watch. Track brand search instead: the number of people typing your name into Google, ChatGPT, wherever. That’s the real thermometer of the demand you create. As long as you only steer paid cost-per-lead, you’re steering capture, not creation.

Then, reallocate. Take a slice of the budget that just rebuys the same audiences and put it into content that says something about you, to people who don’t know you yet. Not generic content AI recopies and summarizes in two lines. A point of view, your own numbers, a stance you own.

What should guide the effort: being there before the search. Your buyers spend their days on LinkedIn, YouTube, podcasts, communities, long before they open Google. When HubSpot lost 15 to 40 million blog visits in a year, their answer wasn’t to pour more into search. They went and occupied those spaces.

(Worth a listen: my episodes with Kieran Flanagan, SVP Marketing at HubSpot, and Kipp Bodnar, its CMO, who walk through the decisions behind it.)

Put faces forward. The founder and the experts speaking in their own name build more attachment than any banner. It’s slow, it demands a point of view, and it’s one of the few channels that doesn’t get repriced at auction.

Hold the line. Demand creation pays in months, not days. That’s exactly why people abandon it at the first air pocket. And exactly why it stays an edge: almost nobody has the patience to keep it up.

Bid more, or create more

You have two options.

Keep paying ever more for an ever-smaller slice of people already searching.

Or go back to creating demand, so people come looking for you.

The first is comfortable. It gives you a number Monday morning. It’s also the one that locks you in the spiral.

A founder hesitated to start: “what if we’re not even good at capture yet?” My answer: in a lot of markets, there simply isn’t enough demand left to capture.

We get into the detail in a free webinar.

Let’s grow 👊

— Jordan

Kipp Bodnar is CMO of HubSpot, a role he has held for over a decade. He co-authored the inbound marketing methodology that shaped a generation of B2B marketers, and has since co-written a new book with Kieran Flanagan called Loop, a framework for marketing in the post-AI world.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Kipp Bodnar helped build the inbound marketing playbook. Then he watched AI dismantle it in real time, from inside one of the companies most publicly exposed to that disruption.

In this episode, we covered what HubSpot did when the traffic collapsed, how to think about marketing in a world where the funnel no longer holds, and what separates good CMOs from great ones when everything is changing at once.

Here’s what I took away.

1) The old playbook is a liability, not a foundation.
Most marketing teams are paying break-even return on ad spend just to keep volume going. The game is not optimized. It is stuck. Kipp's position: if you walk into your board in 2026 with the same plan you had last year, you are showing them exactly how you intend to fail.

2) Loop replaces the funnel. Four steps: Express, Tailor, Amplify, Evolve.
Express: start with your own differentiated point of view, not what you think buyers want to hear. Tailor: personalize that message to the individual, not the segment. Amplify: distribute across the channels where your buyers actually spend time, including creators and answer engines. Evolve: learn fast and rebuild the next cycle on top of what actually worked.

3) Differentiation has moved from tactics back to story.
For 20 years, you could win on better meta ads or cheaper CPCs. AI is closing that arbitrage permanently. The test Kipp inherited from HubSpot co-founder Brian Halligan: could a yoga studio say this? If any company could put their logo on your message, you're just adding to the noise.

4) SEO is not dead. But it is now a Venn diagram with AEO.
HubSpot’s shift was not to abandon search, but to layer answer engine optimization on top of it. The mechanics are different: where Google rewarded authority and backlinks, AI answer engines build consensus. LinkedIn, YouTube, and Reddit are the three platforms cited most frequently in AI-generated answers right now. If you are not present across those three, you are invisible to the models your buyers are using for discovery.

5) Creator ROI is not a cost-per-lead calculation.
Most companies measuring creator partnerships on cost per lead are undervaluing them by design. The equation Kipp uses factors in lead cost, estimated AI visibility impact, and brand lift. The reason: a creator with a stranglehold level of trust over an audience delivers something that a Google ad cannot. Pricing it the same way is a category error. When you cannot find on-screen talent internally, there are three paths: acquire a creator's channel, pay an internal expert at market rate to create, or develop a partner who already has partial credibility in your space

6) The best CMOs run their team like Sequoia
Kipp's framework for what separates good from great: good CMOs optimize paid campaigns, run email, do product marketing. Great CMOs find two or three asymmetric bets unique to their company and go all in. Table stakes still get done. But the real leverage comes from the bets with outsized potential return, not the low-variance plays that everyone else is also making. The other thing great CMOs understand: the CEO is always the CMO.

Where to find Kipp: https://www.linkedin.com/in/kippbodnar/

Listen now

Enjoy,

— Jordan

I was the fourth AI talk of the day. The first three were about humanity. Mine was about your org chart.

I’ll start with the part where the cutters are right. A senior content strategist costs 90 grand a year. An enterprise Claude seat costs $200 a month. Four CMOs in ten are cutting labor costs this year, and on a 12-month view they win: the margin goes up, the board claps.

But a business doesn’t expire in 12 months. Your 2030 seniors are your 2026 juniors, and there is no other supply.

Cut the bottom of the ladder today, and in five years you bid for senior talent against every rival who made the same cut.

This already shows in payroll data. Stanford read 50 million American records: since ChatGPT launched, workers aged 22 to 25 in the most AI-exposed jobs are down 13%, while older workers in the same jobs are up 9%.

A leaner team is the wrong target. You want the one that comes out of the shock stronger, because it kept hiring and training juniors while everyone else cut. Nassim Taleb has a word for it: anti-fragile.

It doesn’t happen by accident. You build it, and here’s how.

Stop organizing marketing by channel

I run a marketing agency and most of you run marketing, so I’ll build this where I know it best. The shape holds in legal, finance, sales.

Open your org chart. Content. PMM. Design. Paid. SEO. Ops. Every box is a channel or a craft. That structure was built when production was scarce: you needed a team per format because making things was slow and expensive.

Production is now cheap. Claude writes the brief, the ten variations, and the report on the variations. So the anti-fragile org I recommend, the one we run at Bulldozer, rebuilds around three teams.

1️⃣ Engine.

Execution and AI. Distribution at scale, agents, the workflows that turn one idea into two hundred assets. Paid, ops, martech and most of production collapse here, and it runs like an engineering team: skills, QA loops, shipping cadence. Its junior is the GTM Engineer.

2️⃣ Creativity.

The idea, the angle, the brand, the point of view no model reaches on its own. When everyone ships at volume, the only edge left is the thing worth shipping. Taste lives here.

3️⃣ Relationship.

Events, dinners, communities, partners, the customers who buy because they trust you. The more the market drowns in AI output, the more a handshake is worth. No agent earns trust.

One thing sits across all three, never inside one: quality. I don’t believe in a “quality team” any more than a “good-decisions team”. Every box needs someone who owns the bar.

Run the test I give every leader: pull four sentences from your last reporting, slip in one written by AI, read them to your team. Half pick wrong. That trained eye is now a job inside each of the three.

The boxes re-sort. Paid and ops go to Engine; brand, content and positioning go to Creativity; field, community and customer marketing go to Relationship. Nobody keeps a team because the channel existed in 2019.

Take my own team. I’m a control freak. I re-edit everyone’s work, especially in marketing, because the quality bar is the one thing I won’t hand off. For years that meant me, personally, behind every draft my content manager Charlotte shipped, the exact same way I sat behind everyone’s.

Today Claude takes that same nitpicking with zero ego in the room, as many rounds as I want. And Charlotte moved to Relationship: events, dinners, customer rooms, the work no agent will ever touch.

The bar didn’t move into a box. It stayed on me, where it belongs.

Growth makes the firing question disappear

“Claude replaced my marketing team.” “Claude replaced my SDRs.” You’ve read the posts. Here’s the part nobody posts: Anthropic, the company that makes Claude, is one of the most aggressive hirers on the market right now, with 447 open roles while everyone else freezes.

The reason is growth. Revenue went from $1B to $30B in 15 months, and Dario Amodei said it on stage: they planned for 10x a year and saw 80. When you grow 80x, you stop asking who to fire and start asking how fast you can hire.

The firing debate is a symptom of flat growth, not of AI. Cut costs with no topline plan, and AI just buys you a cheaper way to shrink.

The market is already staffing the anti-fragile org. “GTM Engineer” went from 63 postings worldwide in 2024 to 3,342 today, median junior comp $110,000, no CS degree required. The same role is surfacing everywhere: AI Legal Analyst at Latham, Quant ML Engineer at Goldman, AI Research Engineer at McKinsey. Same generation, different jobs.

The juniors being hired are not the juniors being fired.

And juniors capture the AI gain while seniors barely move. Stanford measured AI across 5,172 support agents: novices gained 35%, top experts close to zero. The junior inside your Engine produces like a 2022 mid-level after three months.

Replace, re-sort, raise, grow

The playbook. Four moves, in order.

1. Replace the work you supervise at 100%. Every brief you re-edit, every report you proof. Hand it to Claude with skills that capture your standards: your banned words, your examples of great. The content system I broke down two weeks ago is this move, executed. Fire no one yet.

2. Re-sort the humans into the three teams. Engine, Creativity, Relationship. Map every freed-up person to one. Default is pivot. The exit conversation, if there is one, comes last.

3. Raise the bar. Ten times the output means ten times the rejections. Standards become the bottleneck, so reward rigor in onboarding the way you used to reward volume.

4. Set growth, then hire AI-native juniors. 20% year over year as the floor. Then open the GTM Engineer req and staff the bench you’ll need in 2030.

Be the leader who’s hiring

Two playbooks share step one: replace the supervised work.

The fragile one then fires the humans, books the cost win, and cracks in year seven.

The anti-fragile one re-sorts the team, keeps planting juniors, and grows.

By 2030 the first is bidding for seniors in a market it helped empty. Korn Ferry already priced that auction: $11,164 a year of premium per skilled worker.

The people being hired are not the people being fired. Be the leader who’s hiring.

(Your CFO already ran the cost tab. Send them this one.)

Let’s grow 👊

— Jordan

Dave Kellogg is Executive in Residence at Balderton Capital, the London-based VC that closed $1.3 billion in new funds in 2024. Before Balderton, he spent nearly a decade as CEO of Host Analytics and before that held senior marketing roles at BusinessObjects and Salesforce, among others. He writes at kellblog.com, one of the most analytically rigorous marketing and strategy blogs in B2B, and has been doing so for over 15 years.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Dave Kellogg started his career watching a technically superior product lose to Oracle, year after year. That experience imprinted one thing permanently: the best product doesn’t win. Everything he has built since then follows from that.

In this episode, we covered the full arc: how marketing’s job has changed over 30 years, why traditional SaaS companies are becoming zombies, and why the CMO role is being structurally dismantled at companies that have simply run out of patience.

Here’s what I took away.

1) Marketing’s job has changed five times in 30 years. The next version is “go get customers who drive outcomes.” From boxes of leads at trade shows, to qualified leads, to opportunities, to weighted pipeline, to deals that renew and expand. Each shift added one more layer of accountability. The next one, driven by outcome-based pricing, is go get customers who actually consume and generate results. The CMOs who don’t see that coming are still optimizing for metrics that stopped mattering two transitions ago.

2) Marketing exists to make sales easier. That’s it. Dave heard this from a VP at Oracle early in his career and it has guided everything since. His view: the CMOs who resist it are the ones who secretly want sales to be their pawns, a model that works in consumer goods and nowhere else in B2B. Embrace it and you become a full-funnel operator with a medical bag full of tools. Resist it and you spend your career fighting a losing political battle.

3) The three fastest ways to get fired as a CMO. First: refusing to be accountable for a pipeline target. Second: dismissing attribution as too imperfect to bother with. Dave’s position is clear — attribution is imperfect, but the CEO, CFO, and board will not stop asking. Your job is to provide it and manage the misinterpretation. Third: treating win rate as a sales problem. If only 5% of stage-two opportunities are closing, that is a marketing problem too.

4) Traditional SaaS companies are becoming zombies. There is no middle ground anymore. You either have a genuine AI product with an AI growth rate to match, or you are a profitable financial investment. The companies stuck between those two things, moderate growth, no compelling AI story, break-even economics, are the zombies. And the old trick of selling a future AI story in slides no longer works. The growth rate is the proof now.

5) Trust is gained in drops and lost in buckets. Dave’s response to AI-generated content flooding every channel: don’t be in the slop business. His AI slop detector is simple: his brain stops when he is reading something. The em-dashes give it away. The solution is not a content framework. It is a hard filter on everything you publish and the discipline to never over-claim. One credibility failure can erase a year of trust-building.

6) The CMO role is being dismantled. Function by function. Dave is seeing it happen more frequently at companies that have burned through multiple CMOs. Product marketing moves under the product team. Demand generation moves under the CRO. Brand and communications go directly to the CEO. What is left is sometimes called a CMO, but the job has been hollowed out. Dave is not recommending this. He is describing it. His diagnosis: CEOs cannot find the full-funnel athlete the role requires, and after the third or fourth failure, they stop trying.

Where to find Dave: https://www.linkedin.com/in/kelloggdave/

Listen now

Enjoy,

— Jordan

You’re reading the machine

This newsletter was produced by the system it describes.

So was every LinkedIn post, every tweet, every SEO brief we shipped this week.

One person. One weekly routine. We call it /week.

It opens on a Monday, and by lunch a full week of content exists: personal and company LinkedIn posts (one a day), this newsletter, SEO pages, the visuals, the tweets. And when there’s a podcast episode, a webinar, or a physical event in the pipeline, the full sequence that comes with it: emails, reminders, announcements.

The part the hype skips: when I produce with AI, the ideas, the angles, the direction stay mine. Claude doesn’t replace my thinking. It challenges it, it organizes it, it keeps the quality bar consistent across every format.

The output sounds like me for a reason. I wrote more than 1,000 LinkedIn posts and hundreds of newsletters by hand before any of this existed. The model didn’t hand me a voice. It learned mine. People still recognize my tone, because it is my tone.

The input is human. The final read is human.

The machine makes the work faster, not the thinking.

Now the architecture, because that’s the whole point.

We didn’t write one giant prompt. We split the content operation into dozens of small skills and slash commands, each with one precise job:

• one writes the newsletter

• one turns a transcript into posts

• one drafts the signature post for a new client

• one handles SEO briefs

32 skills, 29 commands, at last count.

On top of them sits one meta command. When we run /week, Claude doesn’t wait for instructions. It runs the interview:

• Is there a newsletter this week?

• A podcast episode? If yes, the transcript?

• A new client to announce? A webinar to push?

It asks, we answer, it routes each job to the right skill. The system is a conversation, not a button. That part took the longest to get right, and it’s the part nobody shows you.

One input feeds many formats. A podcast becomes a newsletter, the newsletter becomes LinkedIn posts, a post becomes tweets.

Text is the easy part. Most of it gets drafted, checked against our brand and anti-slop rules, and queued.

Visuals plug in at specific points only: the newsletter, the LinkedIn infographics, the webinar thumbnails. Everywhere else the post is text, and we don’t force an image where it doesn’t belong.

Yes, a human still reads everything before it ships. And this human is me ✌️

The shift for a CMO: the question stopped being “how many marketers do I have.” It became “what system do they run.”

Two times the output, one fifth of the time

Before the system, we produced about half of what we produce today, and a single week of it took two and a half days.

Now that same week takes half a day, and we ship twice the volume.

Stack the two together and you land on a tenfold jump in throughput: twice the output, in a fifth of the time 😵‍💫

The honest caveat: that 10x is on production, not on judgment. The system writes faster. It doesn’t decide what’s worth saying. That still sits with a human.

What actually breaks when you wire AI into content

Three things we learned the hard way.

1️⃣ Sequencing is the real bottleneck.

Writing was never the hard part. The model drafts fine.

One piece feeds several formats, and the order is everything. The newsletter sets the week’s thesis. The podcast deepens it. LinkedIn carries it. X sharpens it.

Get the order wrong and the whole week turns into rework. We iterated for months before the cascade settled. Now that order is baked into the meta command: the newsletter first, then the podcast, then the rest.

2️⃣ Visuals are still the weak link.

Watch a Gemini or OpenAI demo and you’d bet images are the first thing to fall into place. In production it’s the opposite.

Today our visual layer is a mix: some made with Nano Banana (Gemini), some built directly in responsive HTML, which lets us play with formats. Outside templated assets like webinar thumbnails, it’s still shaky.

The demos won’t tell you that. We will.

3️⃣ The human role moves up, not out.

When production becomes a system, the person on content stops drafting and starts directing: owning the cascade, supervising tone, choosing the angle.

Our content manager just moved into field marketing. The work that compresses into a system went to the system. The work that doesn’t compress, judgment and taste and relationships, went to the human.

How to build your own content machine

1. Treat content as a system, not a pile of tasks. One input, every format.

2. Split the operation into sub-tasks. Each one becomes a named command you can run, fix, and improve on its own. We covered the method for splitting work into sub-tasks in our previous edition, so we won’t repeat it here.

3. Put a sparring partner on top. A meta command that interviews you and routes the work beats a folder of clever prompts.

4. Design the cascade before you grow volume. The edge is in the sequence, not the tool.

5. The stack: Claude Code, 32 skills, 29 slash commands, a memory file, and about a dozen MCP connections (HubSpot, Notion, Buffer, Webflow, Ahrefs, GA4, Search Console, Slack, Gmail, Canva).

6. Guard quality. Every output passes an anti-slop filter before it ships. We keep the list of what counts as slop public, here: Wikipedia: Signs of AI writing. Speed is worthless if the copy reads like a robot.

7. Keep a human on visuals. Templatize what works, hand-build the rest. Don’t pretend it’s solved.

Build the system or staff the backlog

You can keep hiring to produce more content. Or you can build the system, ship ten times the throughput, and move your best people to the work a system can’t do.

One of those choices compounds. The other just grows the payroll.

Let’s grow 👊

— Jordan

Bruno Estrella is Head of Marketing at Clay. Before joining in late 2023, he spent five years at Webflow leading the growth team responsible for self-serve acquisition. At Clay, he oversees everything from community programs and executive content to billboard campaigns and international expansion. He joined when the company was 18 people. It is now 400.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Clay didn’t grow because it outspent the competition. It grew because it built a machine where the community does the marketing, the executives become the content, and frequency does the rest.

In this episode, Bruno Estrella broke down exactly how that machine works and what it actually takes to build it.

Here’s what I took away.

1) Creators want brand association, no money. Clay’s community flywheel doesn’t run on sponsored posts. When Bruno’s team approached creators and agency owners about posting about Clay, they discovered that what people actually wanted was to be publicly associated with a product that felt innovative and credible. That’s what Clay built programs around: experts, partners, educators. Not cheques.

2) One persona, one executive, one writer. Clay’s internal content system is entirely mapped to audience segments. One executive per persona, each paired with a dedicated writer they meet with weekly. Bruno for marketing leaders. Varun, the CEO, for founders. Their head of GTM engineering for ops profiles. The writer captures their thinking. The executive owns the voice. Everything funnels into LinkedIn.

3) Right channel, right people, high frequency. All three are required. Bruno’s observation: you can have the right channel and the right voices, but if the frequency drops, people forget you within three months. The obsession at Clay isn’t reach. It’s consistency. Frequency is what creates the feeling that Clay is everywhere.

4) Qualified engagements, not vanity metrics. Clay measures its LinkedIn presence through a custom metric: qualified engagements. They track every post mentioning Clay, then cross-reference the people engaging with those posts against their named account lists. The question isn’t how many likes. It’s whether the right people are in the audience.

5) Clay invented the GTM Engineer role. Then watched the market adopt it. Bruno and his team noticed that the best go-to-market operators at companies like Ramp and Rippling were neither traditional growth marketers nor classic ops people. They were something in between: highly technical, able to build revenue systems, fluent in APIs and automation. Clay named that profile, created content around it, and waited to see if people would self-identify. They did. Companies started opening roles for it. That’s how you know a category is real.

6) Early stage: brute force execution. Later stage: conviction over attribution. When Bruno joined Clay from Webflow, he tried to import the bi-weekly sprint planning and weekly metrics reviews he’d used at a much larger company. It didn’t fit. At 18 people moving fast, what mattered was shipping, not measuring. Now at 400 people, Clay has the cadence. But Bruno’s obsession remains the same: don’t let the metrics culture kill the speed. Some of the most important bets, like international expansion through community, can’t be modeled cleanly. You have to have conviction.

7) Seven figures on billboards. The goal was legitimacy. When Bruno joined Clay, the product was being associated with the grey-area growth hacking tools of a previous era. Billboards weren’t a reach play. They were a credibility signal. When people in your ICP see you on a billboard in San Francisco during Dreamforce, they stop wondering if you’re a legitimate company. Clay concentrated spend in a tight geographic window, mapped named accounts to specific billboard locations, and tracked results through discovery calls. Recruiting also improved significantly as a side effect.

Where to find Bruno: https://www.linkedin.com/in/brunoestrella/

Work with Clay: https://www.clay.com/

Listen now

Enjoy,

— Jordan

Engineering measures lines of code shipped. Sales measures pipeline per rep. Customer support counts tickets resolved. Customer success watches account usage. Each one can correlate “% of team using AI” with a real output number, and decide whether to push further or pull back.

Marketing has… LinkedIn posts?!

This is not a small problem. Very soon, every CMO will have a quarterly board slide titled “AI productivity,” and every CFO will expect a precise number. Except today, that number doesn’t exist! And the consequences on your budget are going to hurt…

Kieran was honest: he hasn’t cracked it at HubSpot either.
So I spent two weeks cogitating, mapping what we do at Bulldozer, and rebuilding the metric from first principles. Here is where I landed.

Two companies. Same story.

Klarna, Q1 2023. About 7,000 employees. ~$1B annual revenue. Revenue per employee: about $140,000.

Klarna, 2026. About 3,000 employees. ~$3B annual revenue. Revenue per employee: about $1M.

RPE multiplied by seven in three years 😵‍💫
Average salary moved from $126K to $203K over the same window.
Same Klarna. More output. Fewer, better paid people.

Shopify ran the same play differently. Headcount peaked at 11,600 in 2022, dropped to 8,100 by end of 2024. Annualized revenue: $10.7B. Revenue per employee: $1.32M.

Tobi Lütke’s April 2025 memo to his team made it explicit: prove to me that AI can’t do the job you want to hire for, before asking for more headcount!

We’re watching the same mechanic at our scale at Bulldozer. The marketing team has not moved for two months. Same five people:

• Charlotte on content

• Paul on growth

• Sylvain (SDR intern)

• Enguerrand and me

In that window, monthly generated pipeline went from €1.7M to €2.4M. Up 41% in 8 weeks. No new hire.

The point is not “fire your team.” The point is that Revenue per Employee (RPE: annual revenue ÷ total headcount, your CFO’s favourite ratio) is not an abstract horizon. It’s a number you can move within a quarter. Provided you have two other metrics underneath that predict it.

Three patterns we see across 50 marketing teams

1️⃣ Adoption theatre.

A Slack channel called #ai-workflows and self-reported “hours saved this week.” The numbers look great. They map to nothing measurable downstream. Pipeline didn’t move. Brand search didn’t move.
Spoiler: the CFO doesn’t care, because a vanity metric isn’t actionable.

Be honest — you have this Slack channel too.

2️⃣ Adoption rate is not rebuild rate.

Adopting Claude is free and instant. Three clicks. Anyone can do it.
Rebuilding a workflow is something else: you map the inputs, you define the expected output, you decide where the AI sits in the loop, you test it on 5 to 10 real cases, you measure quality drift, you iterate prompts for 4 to 12 weeks until it ships.

Adoption is a checkbox. Rebuild is engineering work applied to marketing.

The two metrics often correlate negatively in month one, because real rebuild is slower than fake adoption.

3️⃣ Outcome metric measured too early.

“Revenue per Employee” drops in the first 6 months of an AI program. Always.
You’re paying for tooling, paying for prompt iteration time, your team is shipping more outputs but the quality is worse before it gets better.
CMOs who report RPE to the board at month 3 panic and cut the program.

CMOs who report “rebuild rate” at month 1 to 3, then Pipeline per Employee at month 4 to 6, then Revenue per Employee at month 7 to 9, get to ship the full program.

Sequence matters more than the metrics themselves.

The lesson is mechanical: these three metrics are not independent. They are chained in time. Mis-sequence them and you self-sabotage.

Where to plug each metric (and when)

📆 Month 1-2: map your marketing workflows. Make the list exhaustive. At Bulldozer ours runs:

• webinar (announce, reminders, replay)

• Momentum podcast production (transcript to derived content)

• ICP and JTBD generation from sales call transcripts

• SEO and GEO (audit and production)

The total count is the denominator of your “rebuild rate.” Tool: Notion or a flat doc, it doesn’t matter. What matters is that nothing is missing.

📆 Month 2-3: rebuild your top three.

Not all at once. Pick three workflows ranked by frequency × value. Rebuild means documented, AI-in-the-loop, measurable output quality.
Track weekly: workflows rebuilt ÷ workflows mapped. That is your rebuild rate.

A 5-person marketing team should expect to rebuild 3 to 5 workflows in the first week.

📆 Month 3+: track pipeline per marketing FTE per month.

The formula is simple: new pipeline generated this month ÷ your number of marketing FTEs.

At Bulldozer right now: €2.4M ÷ 5 = €480K per FTE per month. Up from €340K eight weeks ago.

If your “rebuild rate” is climbing but pipeline per FTE is flat, you’re rebuilding the wrong workflows. Tool: HubSpot for pipeline tracking, Sheets or Looker for the FTE math.

📆 Month 6+: RPE goes to the board.

This is the metric your CFO and CEO are already watching. You don’t need to argue for it. You have been feeding it for six months without saying so.
If “rebuild rate,” pipeline per FTE, and the other GTM functions all moved in parallel, RPE moves in 2 to 3 quarters.

The rule is non-negotiable: never measure a layer without the one before it.

• RPE without pipeline per FTE is a macro number you can’t steer.

• Pipeline per FTE without “rebuild rate” is an output without a lever.

• “Rebuild rate” alone is just process work.

BUT the three together form a coherent system.

Two choices, no middle ground

You walk out of your next board meeting with these three metrics on your AI productivity slide. Or your marketing team stays the only GTM function invisible in the report.

(Your CFO already has a metric for AI productivity. Yours is the one missing.)

The rebuild starts this week, or it starts next quarter.

Let’s grow 👊
— Jordan

April Dunford is a positioning consultant and the author of Obviously Awesome, one of the most widely read books on product positioning in B2B. She spent years as a VP Marketing and CMO at a series of enterprise technology companies before going independent. She has since worked with over 200 B2B companies on positioning, from early-stage startups to companies approaching $100M in revenue. In 2026, she released a fully updated second edition of Obviously Awesome, seven years after the original.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most companies treat positioning as a one-time exercise. They do it at Series A, put it on the homepage, and move on. April Dunford has spent the last two decades cleaning up what happens next.

In this episode, we went deep on when to reposition, why deals go dark, and what most marketing teams get fundamentally wrong about competitive threats.

Here’s what I took away.

1) Your first wave of customers exists to invalidate your thesis, not confirm it. Every product launches with assumptions about who the buyer is, what the competition looks like, and what value the product delivers. Those assumptions are almost never right. April’s advice: keep positioning loose at the start so the market can pull you toward where you actually win. Tighten it only once the first wave has shown you where you were wrong.

2) Marketing teams change positioning because they’re bored, not because they should. April’s observation from running marketing teams: the team gets tired of the homepage at exactly the moment it starts working. Customers aren’t staring at it every day. The last thing you want is to change something that’s gaining traction just because the people closest to it have seen it too many times.

3) Don’t position against ghosts. In early 2025, April ran a positioning workshop with a company in the ITSM space. Their head of product insisted they needed to position against vibe coding tools. April’s question to the head of sales: has any customer ever mentioned it? Not once. Her position: if it’s not showing up in your deals, you don’t have to position against it. Following up a year later, it still wasn’t showing up. Monitor it. Don’t react to a threat your customers haven’t discovered yet.

4) 40 to 60% of B2B deals end in no decision. That’s a positioning problem. The data comes from a study of 2 million sales calls. In the majority of no-decision cases, the cause was indecision, not a preference for a competitor. Either all the solutions looked the same, or the risk felt too high. Both are positioning failures. The fix is sharper differentiation, clearer competitive framing, and a deliberate strategy to de-risk the purchase.

5) “Too expensive” usually means “too risky.” When customers say the price is too high, they often mean the commitment feels too large. April’s approach: break the deal into phases rather than drop the price. Let the customer prove the relationship before they have to bet everything on it.

6) Investor positioning and customer positioning are not the same document. Investors are evaluating a 10-year bet. Customers are solving a problem they have today. When a founding team comes out of a fundraising sprint and starts leaking investor language into the sales pitch, it confuses buyers who don’t share that time horizon and don’t care about the same risks.

7) Category creation is mostly magical thinking. Almost every company that has IPO’d in recent years competes in an existing category. April’s view: inventing a category name doesn’t mean you win it. The job of a market category is to orient the customer at a macro level. Existing categories do that work for free. New ones require you to explain the category before you can even begin the sales conversation.

Where to find April: https://www.linkedin.com/in/aprildunford/

Obviously Awesome: How to Nail Product Positioning so Customers Get it, Buy it, Love it (2026) : https://a.co/d/0eMZX1K9

Listen now

Enjoy,

— Jordan

Across 10+ B2B clients between 2024 and 2026, on 11x, Topo and Artisan, with 10,000+ voice calls placed:

Zero. Pipeline. Closed.

That’s not “below benchmark.” That’s not “needs more time.” That’s the bill.

The Information reported in February 2025 that 11x customers were churning inside 90 days of six-figure contracts. Topo quietly stopped calling itself an “AI SDR” mid-2025. The receipts came in. They’re expensive.

The 30x discount that lied

The triangle is short. On a Bulldozer-grade cold email setup — premium ICP, tight list, hand-written copy — we book meetings at roughly 2-3% of emails sent. Add a human SDR on follow-up to the same list and the meeting rate doubles to ~5%. Run the same 10,000 contacts through an AI voice SDR and you book… well, we did, repeatedly.

Zero pipeline.

Not 0.4%. Not 0.04%. Zero.

Math nerds will appreciate that “infinite worse” is mathematically correct when the denominator is zero.

So the apparent ROI was only ever the apparent half. Artisan’s entry pricing sits around $1,500/mo. 11x mid-market deals run $36K-$120K ARR. The Bridge Group benchmark for a fully loaded human SDR is $120K-$150K in the US, €75K-€90K in France. On the spreadsheet, AI looked 30x cheaper.

What the spreadsheet didn’t carry was everything else. Domain warming damage that bled into the email channel for weeks. Brand carryover damage on tier-1 accounts where a target’s CMO heard our pitch from a robot and remembered. The roughly 400 meetings we would have booked the human way on the same volume of contacts. Never booked.

Vendors quote calls placed. They quote meetings booked. They don’t quote meetings held times stage-2 conversion times closed-won attribution. That column is missing from every AI SDR dashboard for a reason.

Why every voice deployment broke

Three patterns. Same three, every time.

Pattern 1 — Reply quality. Prospects spot the voice in the first five seconds. They don’t say “stop calling.” They quietly add the brand to a mental blacklist and move on. The damage is invisible until the next quarter when the same accounts won’t take a real call from a real rep. As John Short framed it: outbound is humans having a conversation. Removing the human doesn’t compress the conversation, it eliminates it.

Pattern 2 — The deliverability tax, voice edition. Carriers spam-flag the number. The number burns. The next number burns faster. Carrier reputation systems are now sophisticated enough to fingerprint AI voice patterns across providers, which means burning numbers at one vendor follows you to the next. And the spillover lands on the email channel too — a deliverability score earned back over six weeks of careful warming gets torched in two weeks of AI calling.

Pattern 3 — Buyer-side AI is real. Seller-side AI doesn’t fit yet. Dave Gerhardt put this cleanly on Momentum: “Until my AI agent is doing research for me and buying on my behalf and is exclusively talking to your AI agent, humans are still involved in the buying process.” Today, the buying journey still routes through a human. Replacing the seller in that conversation means showing up to a meeting that never happens.

Some of the AI demos are good. So is a chess prodigy at eight. You still don’t put him in the CFO seat.

Where AI still earns a budget

The voice failed. The wider stack didn’t. Six moves we kept after 18 months.

1. Hand AI the research. Keep the message human. Clay or equivalent for enrichment, signal aggregation, list construction. Humans write the copy. Tools that pulled their weight across our deployments: Clay, lemlist, LinkedIn Sales Navigator.

2. AI on prep, drafts, and variants — never on first contact. GPT and Claude generate three versions of a sequence before lunch. A senior SDR picks the one that sounds like them, edits, ships. Five tested variants per hour beats one perfect one per day.

3. Three months of heavy human supervision. Minimum. Treat the AI tool like onboarding a promising intern. Roughly 80% of outputs need editing in month one, 50% in month two, 30% in month three. If you cannot afford the editor’s hours, you cannot afford the AI.

If “three months of human babysitting” wasn’t in the demo, that’s because it wasn’t in the deck.

4. Voice stays human until further notice. No exceptions on tier-1 or tier-2 accounts. Maybe — maybe — AI on tier-4 long-tail follow-up after six months of fine-tuning on your specific call recordings. Probably not, this year.

5. Track the metric that closes deals. Not calls placed. Not meetings booked. Meetings held × stage-2 conversion × closed-won attribution. AI dashboards omit the part of the funnel where the meeting doesn’t happen, and that’s the part of the funnel where the truth lives.

6. Read the vendor pivots. When Topo stopped calling itself an “AI SDR” and started calling itself an “AI research agent” mid-2025, that wasn’t a marketing tweak. That was the market correcting the autonomous-outbound narrative in real time. Listen.

Two choices for 2026

Option one: keep the seat. Pay $1,500-$10,000/month for the AI voice SDR. Watch the domain reputation collapse across the next 18 months. Read another LinkedIn post about how AI is replacing your team.

Option two: pay the humans. Use AI on the work that doesn’t need a voice — research, drafts, variants. Three months of supervision, then maybe a fourth. Wait for the tools to actually mature.

One option saves you €90K this year. The other saves you three years of pipeline.

Pick. (And if you pick the first one, please don’t put me on your call list 🙃)

Let’s grow 👊

— Jordan

Kieran Flanagan is SVP Marketing at HubSpot, where he has spent the last decade leading growth across the go-to-market organization. Before HubSpot, he was VP Growth at Salesforce. He started his career as a software engineer, a background that shapes directly how he thinks about AI, systems, and what marketing teams will look like in three years. He also co-hosts Marketing Against the Grain on YouTube with Kip Bodnar, and writes on Substack at kieranflanagan.io.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Most marketing leaders are obsessing over AI adoption. Who’s using it, how often, which tools. But that’s the wrong conversation.

In this episode, Kieran Flanagan opened his screen and walked me through the content system he built himself in Claude Code. A SVP Marketing, live in his terminal, showing his actual workflow. Beyond that, we went deep on what AI is doing to marketing organizations structurally, operationally, and strategically. It was one of the most concrete conversations I’ve had on this topic.

Here’s what I took away.

1) The most dangerous employee in your org has high AI agency and low domain expertise. They ship fast. They ship a lot. And almost none of it is good. Kieran's view: we're in the messy middle, where AI has handed everyone superpowers, but without real craft and taste behind the output, it just generates more noise. The best people in any organization right now are the ones who combine genuine domain expertise with high AI agency.

2) AI is flattening the marketing org, but not for the reasons most people think. Marketing is structurally unlike any other go-to-market function. Sales, support, CS, and engineering all have relatively uniform roles. Marketing has always been a collection of niche specialists who largely can't do each other's jobs. AI changes that. The org gets flatter, middle management thins out and the individual contributors who remain are generalists who can run the full loop on their own, no handoffs required.

3) Measuring AI adoption in marketing is a genuinely unsolved problem. In engineering, you correlate AI usage with shipped code. In sales, it's pipeline per rep. In marketing, there's no clean equivalent. Kieran's answer : show and tells. A dedicated Slack channel where the team shares what they're building. And leaders who are AI native enough themselves to know when something is actually working and when it isn't.

4) Data quality is where AI projects live or die. The personalization workflow Kieran built at HubSpot to book meetings took three to six months to get right. Because the data wasn't clean enough. His minimum viable definition: whatever raises your AI output above the average in your specific industry. No more, no less.

5) Most B2B brands will eventually have an AI mascot. If your executives won't show their faces publicly and you don't have a creator in-house, Kieran's prediction is that synthetic AI avatars become the face of B2B brands on personality-led channels. It's already happening in B2C. A blind study he cited found that people, when they don't know it's AI, actually prefer AI-generated content because it outperforms the average human output.

Where to find Kieran: https://www.linkedin.com/in/kieranjflanagan/

His YouTube channel : https://www.youtube.com/@MATGpod

His Substack : kieranflanagan.io

Listen now

Enjoy,

— Jordan

Monday morning, you open your CRM and more than half the pipeline sits under “direct” or “organic”. Deals you can’t trace, can’t explain, can’t defend at the next QBR.

This is the scene we see with a lot of CMO we sit down with, and the sequence that follows rarely varies. The CMO freezes, can’t justify the spend, and ends up retreating to “brand” as the catch-all explanation. The CFO doesn’t buy it, budget comes under pressure, and a quarter later someone else is writing the marketing plan (feels familiar?!).

When I asked Dave Gerhardt on the podcast if he works on attribution, his answer was “Fuck no.” Fair enough — Dave runs a media company, sells to his own audience, and owns his P&L. If you report to a board, though, “Fuck no” is not a line you can take to the CFO on a Wednesday 😅

Truth is, the dark funnel stopped being a measurement problem a while back; it’s now a behavior problem. Most of the 70% is now measurable — if you know where to look. The 72% number we’ll open with is where that starts.

The 72% Gap

Two CMOs, same market, same budget.

1️⃣ The first one opens her CRM on Monday, sees paid search at the top of the list with 28% of trackable pipeline, and doubles the paid budget for Q2. The numbers say so — case closed.

2️⃣ The second one does something much smaller. She adds a single field to every form: “How did you hear about us?” Three months later, she compares the two data sets line by line. Her CRM still says paid search, but her buyers are telling a different story — “your CEO’s LinkedIn post,” “the podcast episode with [expert],” “a peer mentioned you in a Slack group.”

At Bulldozer, 72% of our inbound leads report discovering us through LinkedIn or a podcast, and our CRM calls that exact same traffic “direct” or “organic.” The gap between what the system records and what the buyer actually remembers is massive, and we’re not an outlier — Refine Labs pegs it at 62% of closed-won revenue misattributed, HockeyStack sees 55-65% of pipeline stuck in the dark, and Dreamdata found 75% non-attribution on deals above $100K. For enterprise cycles, three-quarters of the pipeline shows up as a ghost.

Then the math gets worse. Alice de Courcy, who took Cognism from €3M to near €100M ARR, ran the comparison side by side and found 25 demo requests close one deal, while 100 content leads close one deal — at half the average contract value. The pipeline your CRM sees isn’t just the wrong pipeline; it’s also the cheaper one. You’re paying to measure the deals that are worth the least.

The Three Dark Zones

The dark funnel isn’t one thing — it’s three zones, and each one needs a different play.

Zone 1 — Measurable (20%).

Clicks, form fills, demo requests. Classic attribution works fine here, and it’s genuinely useful for tactical paid decisions, campaign tests, and creative selection. What it’s not useful for is justifying your total budget — you’d be explaining one-fifth of reality and calling it the full story.

Zone 2 — Observable (30%).

This is the zone most teams are blind in by choice, because it produces signals rather than leads — anonymous visits on the pricing page, scroll depth on comparison content, repeat LinkedIn engagement from the same account, downloads with no conversion attached…

This zone is the core of what we do at Bulldozer in ABX: we capture signals through Clay for real-time enrichment, score accounts and lead-level intent, and trigger outreach when the time is right. What’s hard is that you stop hunting leads to convert and start identifying accounts that entered an active research phase six to nine months before they’ll ever speak to sales.

By the time they reach out, you’re already on the shortlist, and 95% of the time the vendor on the shortlist on day one is the vendor that wins.

Zone 3 — Dark Social (50%).

Slack conversations, peer DMs, mentions in private communities, recommendations over coffee at a conference — historically impossible to track. This is where the narrative used to stop with a shrug: “accept that you won’t see it.”

That stopped being true in 2026. Zone 3 picked up a new dominant contributor this year — AI search — and it’s the first piece of the dark social you can actually measure.

73% of B2B buyers now use LLMs during their purchase research, but only 11% of domains appear in both ChatGPT and Perplexity (Averi.ai, 2026) — each model has its own citation graph.

More striking: only 10% of AI citations overlap with page-one Google results, which means 90% of AI visibility is completely untouched by classic SEO. Content older than 30 days gets 3.2x fewer citations, and AI-referred visitors convert 3-5x better than traditional organic (Source : Seer Interactive) — with Claude-referred traffic closing at 16.8% and ChatGPT-referred at 15.9%.

One counter-intuitive note worth holding in your head: being cited is not the same as being chosen. 40% of buyers remember a brand as “one of several options,” and only 26% remember one as the primary recommendation. LLM visibility is necessary, not sufficient — brand still does the closing.

The 90-Day Visibility Sprint

Three people, no new budget, twelve weeks. Here’s the sequence we run with clients.

Strategic moves (1-3).

1. Report by zone, not by channel. Build a three-column dashboard: Influence (what’s growing future demand), Demand (what’s converting now), Revenue (what’s closing). Kieran Flanagan, has shared the cleanest version I’ve seen — Grow Influence puts LLM Share of Voice as a top-line metric alongside brand search and brand impressions, Grow Demand tracks leads and meetings, and Grow Revenue tracks deal velocity, close rate, and touches to close. The CFO reads column two, the board reads column one, and you cover both without lying about attribution.

2. Reallocate time (not just budget). 20% on measurable (paid, tactical), 30% on observable (ABX, intent capture, account plays), 50% on dark (brand, content that circulates, AI visibility). Most teams invert this ratio and then wonder why they plateau.

3. Make Share of Voice in LLMs a board metric. Not a marketing KPI — a revenue metric tracked monthly and correlated with inbound demos at a 2-3 month lag. What we’ve seen with our clients: SoV up 20% tends to produce inbound demos up 25-35% one quarter later. So that’s a pipeline predictor :)

Operational moves (4-6).

1. Self-reported attribution, this week. Add “How did you hear about us?” on every form, export quarterly, and compare to CRM line by line. The gap you find is your “observable dark funnel”.

2. AI visibility audit. Pick 20-30 queries your ICP actually types, test them across ChatGPT, Claude, Perplexity, and Gemini, and score each: 3 points for first mention, 2 for top three, 1 for mentioned, 0 for absent. Tools our clients use: Profound, Otterly.ai, Getmint.

3. AI-ready content playbook. Refresh anything older than 30 days, front-load answers (44% of citations come from the first 30% of the page), add original stats (+40% visibility lift), and distribute where LLMs actually pull from — LinkedIn captures 62% of B2B software citations, then Reddit and G2.

Advanced moves (7-8).

1. Correlation tracking. SoV LLM → pipeline, 2-3 month lag, monthly cadence. You now have a leading indicator the CFO will sign off on.

2. New board dashboard. Retire “which channel generated this deal.” Replace with “Is our total influence growing?” — five lines: SoV LLM, brand search volume, inbound demos, sales cycle length, win rate vs. top three competitors.

Your Call

Two CMOs. Same market, same budget, same team.

Twelve months from now, both of them stand in front of their board. The first explains 30% of pipeline with attribution data and hand-waves the other 70%. The second presents a framework that covers 100% — measured where it can be, flagged where it can’t, blurry but accounted for.

Only one of them still has a budget a year later.

Which one are you?

Let’s grow 👊

— Jordan

About Dave

Dave Gerhardt is the founder of Exit Five, a membership community for B2B marketing professionals. He previously served as CMO at Drift and Privy, where he built his reputation writing about marketing on LinkedIn and running a podcast on the side. He started what became Exit Five as a $10/month Patreon while still a CMO and reached $10,000 in monthly recurring revenue before ever going full time on it.

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Every company wants a community. Very few are willing to do what it actually takes to build one.

In this episode, I sat down with Dave Gerhardt to understand what separates Exit Five from the graveyard of dead Slack groups and what most B2B marketers get fundamentally wrong about storytelling, content, and building in public.

Here’s what I took away.

1) Most communities die because they're not the product. The companies building dead Slack groups aren't really building communities. They're building nurture sequences with a community skin. Dave's position: when a vendor owns the community, there's always an ulterior motive. Exit Five works because the membership is the business. There is no SaaS tool waiting at the end of the journey.

2) Charging money is a forcing function, not a cash grab. The two complaints Dave received from his Arizona CMO event came from the two people who had free tickets. Pricing signals commitment. It also filters out the people who would dilute the quality of the room.

3) The founder has to do the unscalable work first. Dave has written roughly 8,000 posts inside Exit Five and left 40,000 comments. He personally policed poorly written posts, tagged members with relevant expertise when a question went unanswered, and publicly praised the content worth celebrating.

4) Storytelling isn't a framework you can download. You can't prompt your way to taste. Dave's view: the marketers who are genuinely good at brand and storytelling have wide interests outside of marketing (music, food, design) and they're curious enough to steal from places nobody else is looking.

5) Using AI well is still slow work. Dave uses Claude to write his newsletter. It takes him two hours. He feeds it the transcript, past writing for reference, context on the guest, and asks it to come back with questions before it writes a single word. The output is better but it is not faster. AI slop is what happens when the subject matter expert leaves the room.

Where to find Dave: https://www.linkedin.com/in/davegerhardt/

Join Exit Five: https://exitfive.com/

Listen now

Enjoy,

— Jordan

Though 68% of enterprise ABM programs still fail to generate expected results, and only 26% of teams call their program “truly successful” (N.Rich, 2025).

The common explanation: bad execution, wrong tools, not enough budget. After running 40+ ABM and ABX programs at Bulldozer, we see something different. The failure is almost never in the execution layer. It is in the targeting layer. Specifically: fuzzy ICP.

Today, Much of what companies call ABM is actually sales executions using marketing channels. And the reason it happens is that teams skip the ICP work and jump straight to the tech.

A Tale of Two Account Lists

We ran two ABM programs in the same 6-month window. Same agency. Same playbook. The results had nothing in common.

Company A sells food-waste software. Their CMO walked into our first call and said something most marketing leaders cannot: “There are exactly 450 integrated retailers in the world who could buy this. Here are their names.” Four buyer personas. No debate. No territory math. Just a list that fit on two pages.

Three weeks in, qualified leads started coming. By month six: 25+ MQLs across three markets, cost per lead at €186 in their best region, and a LinkedIn CTR that peaked at 6%. Their stack was Lemlist, HubSpot, and LinkedIn Ads. Nothing fancy.

Company B is a $100M+ SaaS. They came to us with 20,000 accounts loaded in Clay. Six segments, built by RevOps to carve equal sales territories. The VP Growth told us in our second meeting: “Those six segments? I don’t believe in them.” 🥲
The scoring model was probabilistic, multi-dimensional, inherited from their self-serve funnel. We flagged it early: “You are applying self-serve logic to ABM.”

Three months later, leads were piling up. Sales was processing half of them, two days late.

Same agency. Same methods. Company A spent a fraction of the budget. Had 1/40th the accounts. Used simpler tools. The difference: Company A knew exactly who to talk to. Company B was still figuring it out.

External data backs this up: companies with a documented ICP see 68% higher account win rates (TOPO/Gartner).

The Three Traps That Kill ABM Before It Starts

Three patterns repeat across the 40+ programs we have run.

🩸 The Territory Trap.

Most ABM target lists come from RevOps. They are built for equal sales coverage, not for conversion probability. One client had an ICP defined as “250 to 5,000 employees.” Our growth lead looked at it and said: “That looks like an addressable market, not an ICP.” They had a €3,500 monthly budget for 3,500 accounts. One euro per account per month. Looks like a mailing list to me…

🩸 The Stack Delusion.

Teams buy Clay, 6sense, and Demandbase before they can answer “who are our 50 best-fit accounts?”. The Snowflake ABM team wrote about this: “Companies buy into the promise that ABM can be done via technology as an all-in-one, without realizing they have to power that tech with their own strategies.”
Vendors sell ABM like it is a button you press. ABM is a strategy, not software.

🩸 The Activation Gap.

Targeting can be right and the program still fails when sales cannot process the output. 56% of opportunities handed to sales never close (Forrester, 2024). A former Head of Marketing at a $1B+ analytics company told us about rolling ABM out to 60 salespeople: “Maybe 6 or 7 were actually motivated. If you do it at too large a scale, it is the best way to waste money.” They later cut from 2,000 accounts to 500. That is when it started working.

I like Allan Dib’s analogy: a 100-watt lightbulb lights a room; a 100-watt laser cuts steel. Same energy, different focus. The companies spending less on ABM with a tight ICP outperform those spending more with a wide net.

Five Moves to Fix the Foundation

1. Run the ICP regression before buying any tool.

Pull your closed-won deals from the last 24 months. Segment by ACV, sales cycle length, expansion rate, win rate. Kellogg (soon on Momentum 👀) calls this the shift from “ICP as aspiration” to “ICP as regression.” At Bulldozer, we analyzed 1,495 deals and found a 0.84 correlation between initial budget above €25K and high customer lifetime value. Your ICP is in your deal data. Not in your CRM segments.

2. Apply the Geoffrey Moore test.

“Big enough to matter, small enough to lead, good fit with your crown jewels.”
If your target account list has more than 500 accounts, run the math: monthly ABM budget divided by number of accounts. Below €50 per account? You are spreading too thin. Company A had 450. The D-Day logic: concentrate force on one beachhead. Do not spread across the coast.

3. Score Activation Readiness, not just Account Fit.

Most scoring stops at firmographics and intent. We use four layers:
+ Fit (firmographic + technographic, static)
+ Context (hiring signals, funding rounds, stack changes, semi-dynamic)
+ Intent (pricing page visits, content engagement, ad clicks, dynamic)
+ Activation Readiness (can sales contact within 1 hour? is there a feedback loop? operational).

That fourth layer is what everyone skips. From our data: even if context is strong and there is no engagement, the account is not ready. Marketing needs to keep working. And the inverse: a perfect-fit account where sales takes 2 days to respond is a burned lead.

4. Report pipeline by ICP tier in every QBR.

Kellogg flags the “false focus” trap: most ARR sits outside your top ICP tiers and nobody notices. Make ICP tier a required CRM field. Review quarterly. If more than 40% of pipeline comes from outside Tier 1-2, your ABM program is running on the wrong list.

5. Run the €1 test.

Monthly ABM budget divided by number of target accounts. If the result is under €50, you do not have enough budget for that list size. Shrink the list or increase the budget. No middle ground.

450 Accounts or 20,000?

You can keep optimizing the stack. Better scoring, more intent signals, fancier dashboards. Or you can answer one question first: do you actually know who your best customers are?

Company A knew. 450 accounts. Leads in 3 weeks.
Company B did not. 20,000 accounts. Three months of expensive silence.

The most expensive ABM program is the one targeting the wrong accounts.

Let’s grow 👊

— Jordan

Alice de Courcy is the former CMO of Cognism. She joined when the company was just below £3M in revenue and left as it was approaching £100M ARR nearly seven years later. Over that time, she built a marketing organization of 28 people operating across European markets and the US. She has since written two books on the CMO role and now works as a fractional CMO.

🎙️ Listen on YouTube, Apple Podcast, Spotify

She joined Cognism as Head of Marketing, not CMO. She earned the title, year after year, by treating each year as if she needed to re-interview for the role. That’s the mindset this entire conversation is built around.

Here’s what I took away.

1) There’s an epidemic of CMOs who won’t execute. CEOs are scared to hire marketing leaders because they expect a positioning document, not a growth engine. Alice’s view is direct: when marketers ask to be “more strategic,” they’re often asking to stop doing the work. Execution never stops, regardless of seniority.

2) Gated content is a math problem. It took 25 demo requests versus 100 gated content leads to close a single deal. The ACV on content leads was also half the size of demo leads. When she modeled that and showed it to the C-suite, the conversation shifted immediately.

3) Builders and managers are not the same hire. Bringing in a senior marketer from Salesforce sounds like a shortcut. But someone who spent 10 years delivering a playbook they didn’t build has never built one. Alice’s position: she wouldn’t make that hire for a build phase. The only exception is someone who has already done a build phase somewhere in between.

4) Internal marketing is the actual job. Always having the numbers. Communicating wins proactively before being asked. Repeating until it lands. That’s what built trust with the CEO and the board, not the strategy deck.

5) The content engine has four layers. Authority content, owning the industry narrative. Product-linked content, connecting topics to the offer. Social proof validation from outside voices. And distribution. Without distribution, the other three don’t exist.

Where to find Alice: https://www.linkedin.com/in/alice-de-courcy-5205516a/

The diary of a first-time CMO: https://amzn.eu/d/0h0vDLCC
The diary of a first-time CMO: Volume 2 : https://amzn.eu/d/04Dn3lUa

Listen now

Enjoy,

— Jordan

Why commission a portrait when a camera captures a face in seconds? What actually happened was the opposite. Fine art markets exploded. The handmade became precious. Mechanical reproduction made crafted work more valuable, not less.

The same dynamic is playing out right now in B2B marketing.

Everyone has the same tools now

When I look across the marketing teams we work with at Bulldozer, there’s a clear pattern: everyone has access to the same AI stack. Claude, ChatGPT, Midjourney…. Same models. Same prompts. Same outputs. Same posts. Same decks.

71% of consumers distrust brands that rely heavily on AI-generated communication (Edelman, 2024). And it’s not because they are anti-AI. It’s because they are pro-craft! They’re rejecting generic. And when your tools are the same as your competitors’ tools, the only differentiator left is what you bring to the prompt.

You and your competitors can spin up a blog post in 45 seconds. You can produce 200 social assets a week. Speed is no longer a differentiator.

What differentiates is how you use those tools. And how you use them is determined by one thing: the quality of the system you built around them.

Craft ≠ artisanal = system.

Let me be precise about what I mean, because this gets misread.

Craft is not about slowing down or going analog. It’s about having a documented, transferable, teachable system for what ‘good’ looks like for your brand specifically.

Your visual identity: not just a logo and a hex code. The specific weight of your font, the exact spacing on your layouts, the way your brand uses negative space, the image style that feels like you and not like a stock library.

Your motion language: how your UI animations feel, whether transitions are snappy or breathe, what energy your brand communicates when things move.

Your tone of voice: the words you never use, the rhythm of your sentences, the level of formality with your audience, the topics you own versus the ones you leave to others.

None of this comes preloaded in ChatGPT.

When you give a well-configured AI agent a detailed brand playbook — colors, voice, examples, explicit do’s and don’ts — the output is sharper, faster, and distinctly yours. When you give the same AI nothing, you get something that looks like everyone else.

When a client comes to us with a documented brand system, we can deploy AI across content, ads, and email sequences and stay on-brand at 10x the volume. A client without one generates volume and loses identity simultaneously.

The bottleneck is (and has always been) judgment.

Three things your brand system must codify

• Visual architecture: not just a logo and a hex code. Your full design system — grid, spacing, hierarchy, image style, layout logic. The more specific, the more useful.

• Voice guidelines: not ‘conversational and expert.’ Specific. What words do you use? What analogies work? What topics do you avoid? Provide examples, not adjectives.

• Agent configuration files: every AI tool you use — whether it’s an LLM for copy, an image model, or an AI analyst — needs a brief. Written. Explicit. Documented. Think of these markdown files as onboarding documents for a creative partner who has never met your brand.

Four moves to make next month

1. Run a brand audit first. Before evaluating new tools, document what you have. Try to have your brand system on 3 pages max.

2. Build your agent brief. A markdown file covering: who you are, who your audience is, what you sell, your tone, examples of good and bad content, words you never say. This is the operating document your team’s entire AI workflow will run on.

3. Set quality gates. AI speed is only valuable if there’s a review step for brand fidelity. Build that gate into your workflow.

4. Treat guidelines as a living product. Update them quarterly. Add new examples, remove outdated ones. Your guidelines are the operating system. Don’t let them go stale.

When everything can be generated, the only thing that matters is whether people recognize it as yours.

What you want is to produce dinstinctive content — at speed.

Taste was never going anywhere.
It just got more expensive to ignore.

Let’s grow 👊

— Jordan

Eli Schwartz is an SEO strategist and the author of Product-Led SEO, one of the most influential books in the field. His last full-time role was at SurveyMonkey, where he led SEO before going independent. He has since spent seven years consulting for companies including Tinder and Mixpanel. He also runs a newsletter followed by nearly +15,000 readers.

🎙️ Listen on YouTube, Apple Podcast, Spotify

Every few years, a new technology appears and the same people declare that SEO is finally dead. TikTok. Voice search. Now AI. Each time, they’re wrong, just early enough to sell you something new.

In this episode, I sat down with Eli Schwartz to figure out what’s actually changed and what hasn’t.

Here’s what I took away.

1) GEO is not a separate investment. It’s just SEO in 2026. Breaking out “AI visibility” from SEO is the same mistake as having a dedicated mobile SEO person alongside your SEO person. Eli’s position: the team you’re already paying for should know how to do this. If they don’t, that’s the problem.

2) AI took over top of funnel. SEO owns everything below. When someone describes symptoms or an open-ended problem, the LLM answers. When they know what they’re looking for, they go to Google. The funnel didn’t disappear, it got redistributed.

3) Citations are just backlinks with a new name. The same manipulation that killed low-quality link building will kill low-quality citation building. What gets cited is what’s already a real brand, not what spammed its way onto a list.

4) Brand before SEO. Always. If you don’t have a brand yet, paid search first to validate search-market fit. Then brand. Then SEO. Spending on SEO before people are even searching for you is wasted money.

5) SEO belongs in product, not marketing. When SEO sits in marketing, it writes tickets. When it sits in product, it ships. Eli’s most successful engagement, at Tinder, worked because he had direct access to engineering.

Where to find Eli : https://www.linkedin.com/in/schwartze/

Product-Led-SEO : https://a.co/d/04cqmiRN

Listen now

Enjoy,

— Jordan

🎙️ Listen on YouTube, Apple Podcast, Spotify

In this extract, Adrian Rosenkranz (CRO at Webflow) shares the data that is redefining the role of the website in B2B marketing:

→ +125% increase in AI bot crawling on Webflow sites. The website is no longer built for humans alone

→ 6x conversion rate from visitors coming through LLMs and why it changes everything about the experience you need to deliver

→ “Own the answer”: the 4-pillar framework (authority, measurement, content, technical setup) to exist in AI-generated answers

→ Is the website dead? What would need to be true for that to happen and why we’re far from it

→ The Wikipedia effect: when information alone isn’t enough, it’s the experience that brings people back

→ Why AI will kill websites optimized only for SEO and unlock creativity for the rest

Where to find Adrian : https://www.linkedin.com/in/adrianrosenkranz/

Listen

Enjoy,

— Jordan

Most of the conversation around it focuses on which jobs AI will replace. But looking at it as a marketer, I had a different question: which channels are structurally out of reach for AI saturation — at least for now? Where is the uncrowded space?

That’s what this edition is about.

In 1983, American Express mailed 25 million letters in a single year. Handwritten salutations. Individual names. Personalized offers. Response rates: consistently above 3%.

Forty years later, that same 3% feels almost mythological. Today’s average B2B cold email gets a 2% open rate on a good day, and a 0.1% reply rate if you’re lucky. LinkedIn InMail? 15-18% open rate when it worked. Now closer to 8%.

B2B CPL across paid channels has inflated 40-70% since 2021 depending on your vertical. LinkedIn CPCs are up 30%+ year-over-year. Google Ads CPCs for competitive SaaS terms regularly clear €20 per click. Email deliverability is under permanent pressure — domain reputation, inbox placement, GDPR friction.

Your 2026 marketing plan probably has more LinkedIn budget, more email sequences, more retargeting layers. So does your competitor’s.

You’re fighting over the same 3-5% of buyers who are actively in-market right now. The other 95%? You’re not reaching them. Or more precisely: you’re reaching them on channels so saturated they don’t see you.

So let’s talk about five channels. All underused. All working.

Direct mail

The DMA’s most recent B2B benchmarks put direct mail response rates at 4.4%. Email averages 0.12%. Direct mail is 36x more likely to get a response.

Physical mail gets opened. When a CMO at a €50M ARR SaaS company receives a physical envelope with their name (not “Marketing Leader at [Company]”!) it gets opened!

There’s no spam filter. No Promotions tab. No unsubscribe button.

At Bulldozer, we’ve run direct mail sequences for three enterprise B2B clients (deals above €50K ACV) over the last year. Same pattern across all three: accounts cold for 60+ days on every digital channel responded within a week of a physical touchpoint. A pattern break in a sea of identical digital touches.

───

📍 The right list: accounts in your CRM for 90+ days with 6+ digital touches and zero response. They’ve seen your emails. The letter is the interrupt.

📍 What to send: one page, signed by your CEO, SDR or AE. Nothing promotional in the first touchpoint. Open a conversation, not a pitch.

📍 One caveat worth naming: direct mail is a precision play, not a volume play. The economics hold at 50-100 accounts. At 5,000, you’re back in the same crowded game.

Gifting and dimensional mail

One step further on the physical spectrum. A book relevant to your prospect’s current challenge. A curated package tied to something specific about their business. Platforms like Sendoso, JeudiMerci (FR), Gyfti (FR) and Reachdesk handle fulfillment and CRM tracking.

Most B2B marketers haven’t touched them. The reason: it feels like too much effort. That’s exactly why it converts. An account that receives a €40 relevant gift from your company doesn’t forget it.

───

📍 Right trigger: a deal gone cold after 4+ weeks of digital touches. The message should reference something specific about their situation — their Q3 results, a recent initiative, a competitor move. Specificity is what makes it land.

Handwritten cards

Genuinely rare in B2B. A VP Sales who sends 10 handwritten notes per week (nothing promotional) generates replies that email sequences don’t. Volume ceiling: 40-50 per month at quality. Scale with Handwrytten or Manuscry (FR) to hold quality at 100+ per month.

Executive dinners and private events

A dinner for 12 CMOs. A private roundtable working through a problem they’re actively navigating. An in-person workshop with real content (not a sales pitch repackaged as education…).

Physical presence with a curated audience is where pipeline builds quietly.

───

📍The math: 12 people in a room, 3 become clients at €80K ACV = €240K pipeline from a €5K dinner. Webinars went digital and crowded. This format hasn’t.

Voicemail drops (and what AI changes)

Ringless voicemail (RVM) delivers a pre-recorded message directly into someone’s voicemail without the phone ringing. No disruption. No cold call awkwardness.

Industry average open rate: 96% 😅

The use case: 20-50 key accounts where you want a decision-maker to hear a human voice. Under 30 seconds. Something like: “I wanted to reach you personally. We’ve been working with three companies in your space and I’ve seen something specific to your situation. Worth 15 minutes?”

At €0.05-0.10 per send, the cost per touchpoint is a fraction of SDR time spent on cold calls that go to voicemail anyway.

Now add voice cloning. ElevenLabs or Bland.ai can clone your CEO’s voice from a 3-minute audio sample. You record once. Your team delivers 500 personalized voicemails with your CEO’s actual voice — each opening with the prospect’s name and one company-specific observation. The CEO’s 20 minutes per week of outbound voice becomes 500 touches without losing the human signal.

The companies running this today are ≈12 months ahead of when it becomes standard practice.

───

📍Legal note: in most EU markets, RVM sits in ambiguous territory under ePrivacy. In France, outbound calls to B2B numbers are generally permitted under legitimate interest. Validate with legal before running at volume. In North America, standard practice.

SMS: build the list now, use it later

98% open rate. Average read time: under 3 minutes.
Email’s best case: 21% open rate, read 6+ hours later.

At Bulldozer: SMS reminder sent 2 hours before a webinar vs email-only control group. Show-up rate increased 28%. Same content, different channel.

SMS has a hard prerequisite in Europe: explicit opt-in. Every conversion touchpoint — demo confirmation, event registration, content download — is an opportunity to collect it. 500 clean permission-based contacts delivers more than 10,000 cold addresses.

───

📍 Use it for: event reminders, deal-stage nudges, re-engagement after email silence.

Five moves to start this month

1. Digital dead-end audit.

Pull your CRM. Find accounts touched 6+ times in the last 90 days with zero engagement. Top 50 by ICP fit. Top 10 get dimensional mail — a relevant book or curated package. The other 40 get a one-page letter from your CEO with one observation specific to their business.

2. Set up a gifting trigger.

Pick the deal stage where you lose the most accounts. Configure: deal goes 4 weeks without activity → €30-50 relevant gift with a handwritten note. Measure meetings rebooked within 30 days against your current no-action baseline.

3. Voice clone test on 20 accounts.

Record your CEO for 3 minutes. Clone the voice with ElevenLabs (free tier gets you started). VP Sales writes 3 scripts — one per core ICP persona. Drop on accounts dark for 45+ days. Measure any re-engagement signal at J+7: email reply, LinkedIn connection, SDR callback.

4. One handwritten note per day.

Your VP Sales, one note per business day to a target account. One observation, nothing promotional. Run it for 6 weeks and track reply rate.

5. Build one SMS opt-in touchpoint.

Add optional SMS opt-in at your highest-intent conversion moment with a clear value statement. Start building the list now. You’ll use it every quarter.

One thing you can’t skip: compliance 🫠

Physical mail to B2B company addresses: permitted under legitimate interest in most EU markets. Validate on your specific scenario.

Gifting: no regulatory issue generally, but enterprise companies have internal policies. Check before sending anything over €50.

Voice cloning + voicemail: same rules as outbound calls in your market. Get legal to sign off before running at volume.

SMS: explicit opt-in required in Europe. No exceptions.

The best growth channel isn't the newest one. It's the uncrowded one.

These channels work because they require effort and judgment. Handwritten notes don’t automate. A curated executive dinner doesn’t scale to 10,000. That friction is the feature. The same automation flooding email and LinkedIn hasn’t reached these formats. When it does, something else will open up.

Let’s grow 👊

— Jordan