Charles Tenot

CEO @lemlist & lempire. I share what I learn as the CEO of a profitable SaaS growing to $40M to $100M+. Follow me to learn from the journey.

I love Tech, Sales, Marketing and Leadership. 

I share my learnings and thinking on LinkedIn, don't hesitate to follow & interact.

COO / General Manager

Growing lempire from 20M$ to 100M$ while generating 35%+ EBITDA

lempire products: lemlist, lemwarm, Taplio, Tweet Hunter, lemcal - more to come

I have no idea what my kids' life will look like with all this AI. 
But one thing I'm sure of: they need to understand that you can build things that other people use.

So I started vibe coding apps with them.

Last week, we built a cookie spot rating app to find the best spots in Paris.

This weekend, we made a Harry Potter sorting hat quiz (the "Sorting Hat" test).

Each one took about 30 minutes. 
And they're better than most apps you'd find online for the same thing.

I don't know what jobs will exist in 10 years. 
I don't know what skills will matter most.

But kids who grow up thinking "I can build this" will be in a better place than kids who just think "I can use this."

PS: sorry the test is in French

CEO @ lemlist & lempire

I just watched our PM build a complete feature in 2 hours.

No dev involved.

He used Claude Code to create a working mockup, test all the edge cases, then generated the PRD from the code.

This is how our entire Product team works now.

Here's the process:
→ They download the full codebase locally.
→ They use Claude Code with custom skills we built for coding best practices.
→ They vibe code their features.
→ Build the interface.
→ Test the flows.
→ Catch edge cases nobody thought of.
→ Then they use AI to generate the PRD from the working prototype.

What they deliver to Engineering:
→ A fully functional mockup that devs can test
→ A detailed PRD with user stories already mapped out

This reduces conception cycle by over 50%.

Better product decisions because they can interact with the actual feature.
Fewer bugs because edge cases are caught early.
Less confusion between Product and Tech.

Most Product teams are still writing specs and hoping devs understand.
We're shipping working prototypes before the first planning meeting.

This is what all Product teams will do in the future.

Curious to hear from other Product teams: how are you using AI in your process?

Help needed: if you are looking for a Senior Finance profile, my life partner, Violaine, is available now.

After spending 3 years building a network of kindergarten for children with neuro developmental disorders, she is now available for something new.

Why I recommend her, even though I'm clearly biased:

→ Worked with top Private Equity firms globally
Always recommended afterwards

→ Hybrid experience with Senior Finance roles (M&A, Reporting) and 3 years of entrepreneurship where she ran notably finance for their 50 people business.
She can be both strategic & hands-on, she's not the "ivory tower" CFO.

 → Amazing analytical skills, she notably worked on complex problems in manufacturing, much more complex than SaaS.

 → Great business sense, easy-going, high work ethics

What she's looking for:
→ CFO or Finance Director-level
→ Interim or part-time
→ Open to full time with flexible work location
→ Start date from March 9th

Don't hesitate to like or comment to help me get more reach, thanks a lot 🙏

→ Phone: +33 6 69 25 57 26
→ Email: vsaintromas@gmail.com

As a CEO, I'm almost never a decision maker, yet very few salespeople understand this.

I still get cold called, emailed, 10x per day.

But even when the approach is solid, the best I can do is forward it to someone on my team. 

And most of the time, I won't even do this. 
Just because I don't want to make my team think I have evaluated the solution and I recommend it, and ultimately I'm not the one who makes decisions for teams.

Teams make their own decisions.

The CEO role is about creating alignment, building the best team, and challenge them when needed. 

But I'm not the one who will say "we should buy this."

If your outbound strategy is targeting CEOs, it probably works up to a certain point. 
But past 100 employees, it might not be the best approach, unless you are actually selling to CEOs, but I can count on my hand the number of things that I decide to buy myself.

The person who feels the pain, evaluates the tools, and makes the call is almost never the CEO.  It's the C-Level, the VP or Head Of. 

My advice: don't waste your time on the wrong target.
Map your buying process, find the person who feels the pain, evaluates the options, and owns the budget.
Then spend your time preparing a great outreach for them.

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I read Cognism "Top 21 GTM Tools" blog post.

Clay wasn't in it. 
lemlist wasn't in it. 
Apollo wasn't in it. 
Salesloft wasn't in it. 
Outreach wasn't in it.
Gong wasn't in it.

The biggest names in GTM. 

All missing from a "top GTM tools" list.

That's not an accident.

When a company publishes a "best tools" list, they're not trying to help you. 

They're running a content play.

The rules are simple: 
→ Include tools that don't compete with you 
→ Include tools that make you look good by association 
→ Never mention the ones that could actually take your customers

So here's the irony.

If you're ON a competitor's "top tools" list, it means they don't see you as a threat.

If you're NOT on it, it means you're the one they're worried about.

Next time you read a "best tools" listicle, don't look at who's on it.
Look at who's missing. ✌️

Yesterday we ran a webinar about how ElevenLabs runs Go-To-Market at 330M$ ARR. 

Over 1,500 people registered. If you missed it and want the recording just drop a comment and I'll DM it.

Here's my human summary of what Jonathan Chemouny shared after 1 year there.

About ElevenLabs GTM:

→ 330M ARR, +300M in 2 years. Just valued at 11B.
→ Only 400 people. Close to 1M ARR per employee.
→ GTM team is about 100 people.
→ Org is SDR + AE but they call everyone "GTM." 
Limited titles, limited hierarchy.
→ They maximize distributed ownership per country and region. 
They hire founder-like profiles who run their region like CEOs.
→ Top performers come from Finance, even Product backgrounds (👋 Oscar Lanzendorf)
→ Their sales close an average of 20X their OTE 
(source: Carles Reina podcast at 20VC, link in comment)
→ They grew outbound from 5% to 46% of revenue in one year. 
Outbound lets them better target ideal customers and segments.
→ Average deal size ranges from 30K/year to millions for Enterprise.
→ Outbound sales cycle is slightly longer than inbound but average deal size is higher.
→ They receive thousands of inbound leads daily and built an AI agent that answers prospects 24/7.
→ Everyone does outbound, even if you're hired as a potential GM-level.

What enables their incredible success:
→ Very high hiring bar. 4 interviews, harsh panels.
→ They hire for smarts, not experience. 
They don't want people who come with playbooks. 
They want people who can build playbooks.
→ They use AI a lot but will never run things 100% AI. 
Humans think of a creative way to approach an account and craft the message. AI handles things like voice notes or AI-generated video.
→ They meet prospects and customers in-person a lot. 
Jon believes humans should do things AI can't, like building real relationships.
→ LinkedIn and WhatsApp are their primary outbound channels. Quality before quantity.
→ They don't monitor activity metrics. 
They track the number of meetings done with prospects.

And of course, they have an insanely strong product.

I hope you find this valuable. 

If you have a company or GTM person we should invite next, don't hesitate to share.

---

Bonus: we created an agent that was fed on Jon's knowledge and our webinar prep document, I'll share the link with the recording.

cc thanks Rémi Kokabi, Domitille de Saint-Exupéry Rafael Ornia Mitchell

This guy has been in Sales for 20+ years. He helped build Intercom, Lattice, and now ElevenLabs. 

Next week, he'll be sharing his tactics to book meetings in 2026, and how ElevenLabs has been growing by +300M$ ARR in 2 years.

Here are few things I can tell you as a teaser:

→ How they increased the share of outbound from 5% to 30% in 12 months, and how their around 100 reps use lemlist

→ ElevenLabs is hiring "GTM" people. No title, no hierarchy, background mostly from VC or former founders

→ They give a lot of ownership to their sales team, who act almost as local CEOs working in pairs with Product to adapt to local markets

→ They mostly rely on full-cycle Sales handling both Inbound & Outbound. 
He'll explain why they decided not to specialize

→ Jon still does a lot of outbound himself, he built a few very creative tactics notably using AI in his messages. He will share a full breakdown of those that work best

This is certainly the most exciting Webinar we've run in months for anyone on a Sales team, so don't tell me you can't find 45 mins to get all these learnings. 

Link in the comments

And big thanks Jonathan Chemouny for unpacking so much value 🙏
cc Stan Massueras 💙

Intercom

Lattice

ElevenLabs

lemlist

GTM Europe at ElevenLabs

GTM Europe @ElevenLabs

You can beat 99% of companies by doing a few things well:

→ Set one clear vision and don't change it every 3 months 
→ Say "no" to everything that doesn't contribute to the strategy 
→ Remove low performers after weeks, not years 
→ Use your own product 
→ Talk to customers 
→ Keep reporting light 
→ Lead every meeting from the customer's standpoint

It looks simple written down like this.

But most companies don't have an execution problem. 
They just keep adding layers, processes, and distractions on top of it.

The companies that win aren't doing anything secret. 
They're just not getting in their own way.

I asked someone at ElevenLabs what makes their culture different. 
Their answer helped me understand how they went from 30M to 330M ARR in less than 2 years.

Yanek Brinkmann, one of their GTM team, told me:

"It's the closest you can get to doing your own business while still being in a job."

I've always believed that someone who works like they're running their own business has 10X more impact than someone who's just doing their job.

The difference is ownership. Real accountability. 

The kind you only get from people who actually care.

You'll get more from someone less experienced with insane grit than from someone senior who's just collecting a paycheck.

ElevenLabs understood this. 

And they still operate like that at 400 people.

Imagine 100 GTM people with a founder mindset.
→ Clear ownership on their region. 
→ Working directly with Product to adapt when needed. 
→ Flat org. No titles.

Their reps close 20X their OTE. The industry average struggles to hit 5X.

But this only works if you actually distribute ownership and responsibility. 
Not just talk about it.

I'm convinced this is how world-class companies are built now. 
And they will crush big groups with pyramidal organizations.

➡️ Later today, we're sharing more about ElevenLabs's playbook with Jonathan Chemouny. 

Already 1,350 people registered. You can still take your spot, it's free value.
No recording. We want people live and contributing.

Link in comment.

Really happy to see lemlist ranked #1 software in France on G2.

Not because of the ranking itself but because of what it took to get here.

The last few months have been the strongest innovation pace we've ever had. 

And all of it comes from listening to our 20,000+ customers every single day.

Sales is still one of the hardest jobs in most companies. 
No product has made it easy yet. Including ours.

But that's exactly why we keep building.
Let's keep going.

Big up to the team ❤️

I have data-backed proof that spray & pray doesn't work.

It comes from 350M+ emails and 4.5M LinkedIn messages sent with lemlist over the past months.

This chart represents our 20,000+ customers.

From pre-revenue founders to companies with 300M$+ in ARR and 100+ sales reps.

How to read it:
→ Horizontal axis: our 10 deciles of customers, from the 10% with the worst results to the 10% with the best
→ Each bar: the volume of messages sent
→ Vertical axis (line): the average reply rate

What we see:
There's a clear negative correlation between volume and reply rate.
The fewer messages teams send, the higher their reply rate.

In detail:
→ The first group shows that the bottom 10% aren't sending enough messages, or just lack adoption. They'll probably churn unless they use lemcoach.
→ Then across the other 9 deciles, each one has lower volume and a higher reply rate than the previous one.

If this doesn't show you that it's better to do less but better, I don't know what will.

Spoiler: it's not the spray & pray "AI SDR Agent" that will solve this.

Every Tech company board is the same: "We need to go Upmarket"

The reason is usually financial. 

Going upmarket increases "Enterprise Value", because is usually means higher retention.
And of course, investors want to increase enterprise value.

But going upmarket at the expense of losing your historical market isn't always the right call.

You might lower churn. 

But you can lose growth and end up struggling.

This is why at lemlist, we spent over 9 months debating if we should go upmarket or not. 

Because serving small customers was always important to us for many reasons:
→ They trusted us first
→ More customers = bigger brand
→ Small companies can become big ones with time
→ Serving emerging businesses drives real purpose 
→ Startups are usually the innovators, they push us to keep improving the product

We finally decided to build lemlist for sales teams and go upmarket, but we decided to do it slowly, not as a pivot, but as a continuous progress of the platform. 

And it paid out because today we have customers from 1 to 150 users.
We have grown by +70% in 2025 while being profitable, and across all segments.

After seeing from the inside 4 different companies go upmarket, I wanted to share my learnings:
Wednesday with Lucas Bédout from Hyperline, we are organising a meet-up in Paris.

Here's a preview of what we'll talk about:
→ Why it took us 9 months to decide between going upmarket or continuing to build a suite for founders 
→ The stages we went through, mistakes we made, and what we did right 
→ Key people we hired (hey Yann Guilleux) 
→ How we run inbound & outbound, and how we use our own product 
→ How we grew the sales team from 1 person (hey Tal Baker-Phillips) to 30+, across 3 areas and 2 roles (AE & Inside Sales) 
→ How we signed our first 100K+customers, coming from 1K$ARPA 3 years befre
→ How we monitor productivity & measure performance 
→ What we built in the product to maximize conversion & score leads 
→ How we organized RevOps & Tools 
→ Our Sales Quota, Payback and how we maintain <3 months payback while adding Sales headcount

Bonus: how we increased ARPA by +30% in one month by changing the wording of one question in our product onboarding 🔥 

Free event. Paris. March 11.
Link in comments.

cc: thanks Antoine Leprince for the organization 🙏

Slack, Figma, Notion, Dropbox, Datadog. They all started PLG. 
None of them stayed PLG. Here's why:

PLG is great for small customers. 

Low CAC, high scalability, self-serve magic. 

But as your product improves, you start attracting bigger companies. 
And enterprise buyers don't convert on PLG products. 
They need help. They need sales.

This is where PLG + Sales-Assisted comes in. 

And I'm convinced it's the ultimate state for SaaS:
→ PLG for small customers. Low CAC, fully self-serve, scales beautifully.
→ Sales-assisted for larger customers. You can segment by geo, industry, company size and organize your team around it.

This combination maximizes conversion across every customer segment.

Since we added a sales motion at lemlist, our growth has never been stronger. 

We grew +70% last year. PLG + Sales-Assisted together.

But here's the hard part:

In PLG, everything is streamlined: credit card payment, automated provisioning and payment retries.

But when sales gets involved, you start dealing with:
→ Custom quotes
→ Custom terms and legal discussions
→ Discounts and approval workflows
→ Custom billing terms and payment methods

You go from a scalable model to a non-scalable model overnight. 

And that's where most companies struggle.

The good news is that with the right tech, you can now build scalable systems even for sales-assisted motions.

On March 11, I'm co-hosting an event in Paris with Lucas Bédout (CEO at Hyperline) and the Business Operations Network to dig into exactly this. 

How to build a hybrid revenue engine that scales.

Free for BON members. 

Link to register in comments.

cc Victoria Dalleau 🙏

Nothing prevents you from sending 10 follow-ups. 
And yes, you'll get more replies.  But it's doing invisible harm to your outreach.

If you look at reply rates by number of follow-ups, the pattern is clear. 
Each follow-up increases your chance of getting a reply. Even a little.

But the incremental gain drops fast. 
After 4 follow-ups, you're barely moving the needle.

And here's what most people miss.

Every extra follow-up increases the chance your leads mark you as spam. 

Not by replying. 
By hitting that little spam button because they're annoyed.

When that happens, it hits your domain and email reputation. 
Your emails start landing in spam more often. 
Not just for that lead. For everyone.

This is the classic trap where short-term gains quietly destroy long-term results.

More follow-ups today = Fewer emails delivered tomorrow.

As a CEO, when people agree with me too easily, I don't take it as a compliment. I take it as something to question.

Things I don't expect from people in the team:

→ Telling I'm right
→ Liking my LinkedIn posts
→ Being overly positive on my ideas and initiatives
→ Nodding in meetings
→ Sugarcoating bad news
→ Using my notes / inputs as is without re-working them
... and certainly not telling me I'm smart, or giving any compliment

Here's what I actually value:
→ Saying what you think
→ Thinking on your own before defaulting to the group
→ Coming to me when you disagree or don't understand, especially when it's uncomfortable
→ Bringing problems early and as they are
→ Having a point of view and defending it

I recently listened to a podcast from an experienced CEO who said his biggest mistake was surrounding himself with people who gave him compliments but weren't truly competent at their jobs.

The best people I've worked with were never those who constantly agreed with me, they were the one who made me change my mind.

We’re organizing a webinar with of the brains behind Ramp’s GTM systems, now building Perplexity’s Enterprise growth engine. 

Nathan spent 3.5 years at Ramp building the business systems behind one of the most successful sales-led companies in the world.

Now he's Head of Enterprise Ops at Perplexity, building the GTM machine of an AI company. From the inside.

I'm really excited about this one. Nate is an absolute GTM master, and we're very lucky to have him.

There are hundreds of LinkedIn posts telling you to comment "Claude" to get an AI config that runs your GTM on autopilot. 
We've tried many of them and the results are always disappointing.

Over the past months, I've interviewed 50+ companies to understand how they actually use AI for sales. 
The reality is far from what you see on LinkedIn, most sales teams are still doing things manually, with a ChatGPT tab open on the side.

Nate is different: he actually built AI-powered GTM systems that people use every day, and that deliver real ROI. 

Here's a preview of what he shared with us:
→ Reps used to spend 30 min prepping each account. 
Now they trigger deep research in natural language and get a full brief in minutes.

→ Outreach used to be {first_name} + generic template. 
Now it references real strategic context. 
Earnings calls, hiring signals, tech stack changes.

→ RevOps used to own every playbook. 
Now the rep is the operator. No bottlenecks. No stale processes.

→ And some things they tried but decided not to release, usually because AI can prep you, but AI can't be you 👀 

He's going live with us to walk through the exact workflows. 
Not theory, but how things actually runs in production.

If you lead Sales, RevOps, or GTM, this one's worth your time.

Link in comments.

VIP hosts: Pierre Touzeau and Rémi Kokabi  🔥

We have 2 data analysts at lemlist, but one is paid in tokens and never sleeps. 
It's's called Squilly. 

Squilly is our AI data analyst. 

It runs queries, builds dashboards, and answers data questions from anyone on the team. 24/7.

You don't have to wait that someone has time to handle your query.

What's good with Squilly is that he qualifies your need before answering.
As you can see in the video, it always asks what exactly I'm looking to know.

You can see that it shows you the query & the cost too, pretty useful.

Squilly now handles a significant share of our data requests.
It's pretty cool because it manages simple, low value requests and our Data team can actually focus on more complex tasks. 

Employees have answer faster as it removes the ticketing logic.

It's the same pattern I already shared with AI managing 56% of our support tickets, AI does a great job to manage most simple tasks, leaving human with more value-adding things to do. 

Big up to the Data team 👏

This week, a bot fixed its first production bug at lemlist. 

Here's what happened:

A user double-clicked a delete button. That sent two requests at the same time.  The API returned a 409 conflict error.

Our bot detected it in the logs, investigated the root cause, traced the code path, wrote the fix, and submitted a pull request with the right reviewers assigned.

Total human involvement: reviewing and merging the PR.

In the meantime, we also had 3 features shipped to production by... Product Managers.

A year ago, we never thought this would happen so fast.

I was talking to a friend, a developer with 20+ years of experience.
He told me his workflow has completely changed.

Before, he spent most of his time in "flow." Going from one coding step to the next.
Now, he spends more time thinking about the overall architecture. 
Then he works with AI to develop the different parts. Then he reviews the code. Makes sure the logic is right.


And yet, we're still hiring developers.

Because our bet is that the job isn't disappearing. It's shifting. 
And good developers will always be a scarce resource required to build great products.

You could argue AI will improve so fast it won't need developers at all.

My gut says you'll always need people who understand systems deeply enough to work with AI.

But we'll see. What's your take?

Sending a LinkedIn message in a sequence increases email reply rate by 38%. 
And no, that's not a typo. I'm talking about email replies. 

LinkedIn reply rates are averaging 15%, vs. 1-3% for emails (up to 15X higher)

But here's what we found across 35M leads:

When you add LinkedIn steps to an email sequence, your emails start getting more replies too.

The prospect sees your name on LinkedIn. 
Maybe they check your profile.
Maybe they read your message.

Then your email lands, and you're not a stranger anymore.

That familiarity is worth +38% on your email reply rate.
This data is coming from 35M leads that we analyzed at lemlist.

This is pure facts that multi-channel is the right way to do outbound today, and why we build lemlist to include as many channels as possible.

---

6 examples why short-term growth almost always comes at the expense of long-term growth:

→ Hiring the least worst candidate because you need someone now vs. waiting for the best even if it takes 6 more months. 
Impact compounds. An A-player generates 100X more value than a B-player.

→ Sending 10 follow-ups to squeeze out more replies vs. stopping at 4. 
Each extra follow-up barely moves the needle but increases spam reports. 
Your domain reputation takes the hit and deliverability drops for everyone.

→ Blasting your entire TAM in one month to hit pipeline targets vs. working accounts in waves. 
You get a short spike, then you've burned your brand with every account.

→ Cutting product investment to boost short-term margins or hire more Sales. Customers don't leave immediately. 
They leave 12 months later when a competitor ships what you didn't.

→ Discounting to close a deal before quarter end vs. holding your price. 
You hit this quarter's number, but you've trained the buyer to expect discounts. Next renewal, they push harder. 
They talk to their friends. 
And your average deal size erodes across the whole pipeline.

→ Not promoting a great rep with manager potential because they're bringing in short-term revenue. 
They'll leave 6 months later. And you'll end up hiring someone less good.

Every one of these looks like the right call in the moment. The cost just shows up later.

That said, there are moments where you need to move fast and accept some debt. 

The interesting part is knowing when you're making a conscious trade-off vs. when you're just not seeing the long-term cost.

Founder | B2B GTM | Scale without headcount by mapping your playbooks into an AI revenue engine

Be honest - how many Linkedin posts have you seen this year saying that XYZ tech/playbook/widget is absolutely essential to your GTM this year?

Probably a lot

We now have thousands of sales and marketing technologies on the market

And all of them are pitching their solution as best in class

With playbooks, agencies, influencers to sell them

And yes, we have preferred tech vendors

Here's the difference

We don't throw you in canned playbook, we actually look at your Go to Market model before making recommendations

Let me tell you a funny story

I talked with an SDR team that was booking 80 meetings a month with parallel dialers and 10k emails per SDR per month

But 60% of their meetings got disqualified

Why?

They didn't qualify target accounts before doing outreach

Tech and playbooks don't save you deploy the wrong one at the wrong time

So what do we do different?

When you work with us, we start with an assessment

We look at your funnel metrics, your best customers, your messaging/copy, and get input from your team on detailed attributes that make up a great customer

That you CAN'T find in an off the shelf tool

After that we use our knowledge of best practices and tooling to identify the biggest opportunities to unlock more revenue per headcount in your team

This could look like

> Customer analysis
> Serviceable Addressable Market Mapping (because TAM is where you lose deals not win them)
> Target Account Lists
> Automated Contact Prospecting & Enrichment
> Pipeline Validation (we build out a script, test it to book meetings for you, help you take the meetings through completed discovery with next steps, then give you a completed call framework, email sequence, and discovery framework)
> Outbound Sales Team Coaching (Phone first)
> Sales Process Optimization
> Managed Cold Emails & Cold Calling
> Inbound Lead Flow Optimization
> AI Business Case Building (which we used to open a $300k+ opp at UPS)
> Implementation of Nooks, Clay, Evergrowth, and other sales tooling

Yes, we do that under one roof

Yes, it was very hard to find a team good enough to do all of this in one place

If you want to learn more, book a time here: https://calendly.com/b2bcatalyst/your-gtm-system-audit-linkedin
> Cold Email Infrastructure

Scott Martinis

GTM engineers & AI platforms are trying to figure out GTM context engineering by reinventing product marketing from first principles. Most will fail.

Context - I am seeing a ton of really smart people like Nico Druelle, Charlie Saunders, Alex Fine🌲, 🏍benyamin ‎ and others start to work on the problem of how to create centralized context libraries to iterate on messaging and targeting.

I am also seeing many platforms try to do the same thing.

The operators I included above will probably succeed. 

The platforms and GTM engineers that follow in their footsteps will largely fail.

Why?

Simply put - GTM context engineering is effectively reinventing a complete and fleshed out disciple (product marketing) from scratch.

Given INSANE amount of training volume, ML modeling (Karpathy released a new library to help automate this) CAN effectively invent the necessary cognitive architecture to help generalize those patterns.

Outreach could probably do this if they really wanted to. Gong as well.

But that's a waste of time, and anyone with less training data will probably fail horribly.

Think about what ACTUALLY happened to optimize messaging in high performing GTM orgs years back:

Product marketers would run meeting analysis, win/loss interviews, customer interviews, and talk with analysts.

They would then consolidate this into strategic narratives, positioning frameworks, competitive battlecards, ICP/segment/persona definitions, and value frameworks for major audiences.

If you want to do context engineering, you could try to reinvent this discipline from first principles.

But you're going to demonstrably slower than people who just... 

Interview good product marketers 

And figure out how to automate the product marketing analysis pipeline.

The marketers I've been most recently impressed with on this front are Emily Pick, Julia Hugg, Trinity Nguyen 💎

Octave, of course, is way, way ahead of the pack here.

Instead of trying to figure this out all by themselves, GTM engineers and revops leaders should take the shortcut and take the time to learn product marketing from elite practitioners instead

This was why all the AI SDRs failed by the way. 

It's not the lack of data foundations that broke them as many revops people supposed (though that doesn't help)

It was actually the lack of understanding the GTM strategy is quite complex, and guessing and checking without proper PMM foundations is not fast enough to get ROI on automated outbound.

What does this mean?

Well, the actual foundation of your GTM strategy is not revops OR GTM engineering, it's REALLY good product marketing

Both of those functions rely on good PMM to build good data models and automated workflows... though they both relying to some degree on well thought out enablement playbooks to know what to include in the data model and what to automate as well.

TLDR: model your context engineering on PMM. Will save 6-12 months.

I make AI work for complex B2B GTM through pipeline impact, not data projects | CEO, B2B Catalyst

I analyze your outbound data before telling you what’s broken | CEO, B2B Catalyst

So it turns out you can CSV dump from every GTM tool into your GitHub repo then Claude Code can write you data lake scripts in like... 20 minutes?

And if you dump in cold call transcripts from Gong or Chorus then you can basically rewrite any messaging playbook your org has in a few hours

Working next on ways to extract use cases from sales calls at scale, map in depth what messaging wins and loses

Then take the use cases and scrape online presence of won deals to extract custom signals

Tag won and lost deals with them

And build custom signal models predicting win rate and ACV

Future is pretty wild

Next up:

- Automated coaching briefs across the org
- Down funnel marketing campaign attribution
- Automated and targeting iterations for marketing campaigns and sequences
- Weekly or monthly auto playbook updates broke. down by segment
- Weekly or monthly value framework updates, battle cards, competitor battle cards (possibly automated)

Of course the more normal use cases like 

TAM scraping, signal sourcing, account prioritization, contact sourcing, qualification, classification 

TitanX phone intent

AI outbound personalization on emails calls LinkedIn

AI content generation and amplification 

AEO

AI inbound and outbound SDRs

Personalized ABM content

Personalized meeting prep

ETC

Candidly those feel a bit more pedestrian and table stakes to me though in 2026

Still... very exciting possibilities for GTM orgs

The key I think is knowing where to start

Which is usually your biggest bottleneck 

(DM me "context lake" if you want this in your org)

Every GTM intelligence tool is racing to answer the same question: who should we target?

Intent data. Buying signals. De-anonymization. Account scoring.

The targeting layer is getting genuinely good. 

And it's about to be commoditized. 

The same way signal aggregation became table stakes after Clay and 6sense turned it into infrastructure.

There's a deeper problem.

These tools are optimized for finding people who want to buy tools like them. They surface intent signals and firmographic matches.

They don't find the prospects where YOU have a differentiated advantage at closing.

That's a different ICP layer. Not "who looks like our customers" but "who has a problem we solve better than anyone else."

Most teams build messaging off the same shallow foundation. Company value props plus signals. "They just raised a Series B, mention growth." "They posted a sales role, mention scaling."

Relevant first line. Generic pitch.

The signal tells you WHEN to reach out. Your value prop tells the buyer what YOU think matters. 

Neither one tells you what the BUYER thinks matters.

The thing that converts is explaining why your solution beats the buyer's current state in a way they actually agree with.

That's differentiation. And it's missing from almost every discussion on signals & ABM I've seen. 

A VP of Ops at a 200-person logistics company. What's her job-to-be-done? What objection does she raise first?

That's not in your intent data. It's in your call recordings. It's in the patterns your best reps know but nobody's formalized.

Pull your last 30 closed-won deals. Listen to the first calls. What did the buyer say they cared about? Group them.

You'll find 3 to 4 patterns. Those are your real use cases.

Each one should shape both WHO you target and WHAT you say. Not a template with a different first line. A different reason to meet, built on why your solution changes their current state.

The targeting will keep getting smarter. The message won't get better until someone studies the buyer.

If you want help building this layer, DM me.

I rebuilt my company's website through an MCP inside Claude Code. Told my AI co-worker what to change. It read the Webflow data directly, made the edits, and published.

Mostly. I still had to open Webflow for some formatting changes the MCP couldn't handle. But 80% of the work happened without touching the UI.

That alone changed how I think about platforms.

MCP is Model Context Protocol. It lets AI talk directly to a platform's data and take actions.

Every legacy platform is walking the same path right now.

Step 1: Platform. You log in, click buttons, build reports, configure sequences. Standard SaaS.

Step 2: The AI can READ the platform's data. Pull reports, see pipeline, check analytics. You still log in to do anything.

Step 3: The AI can ACT. Create records, update fields, trigger workflows. You log in less.

Step 4: The AI owns the domain. It decides what to do, does it, reports back. You make decisions. You rarely touch the platform.

I'm somewhere between step 3 and 4 with Webflow. It's not perfect. But the direction is clear.

Now think about Salesforce, HubSpot, Outreach, Salesloft.

Once an AI can read your CRM, update records, pull analytics, manage sequences, and trigger workflows.. what's the UI for?

Salesforce has Einstein. HubSpot has Breeze. Outreach has "AI everywhere."

Bolting a chat window onto a legacy platform is step 2.5 at best. And the companies building step 4 aren't starting from step 1. They're starting fresh.

Pick the GTM platform you use most. Map it against the 4 steps. Where the AI feature actually sits vs. what the marketing claims will tell you everything you need to know about that vendor's roadmap.

I've spent 8 years building outbound systems for B2B companies. Teams of just the founder to 50 SDRs.

Over the years I realized success isn't nearly as random as it looks. 

There's a specific hierarchy of constraints, and if you don't solve them in the right order, your outbound fails.

The problem is that methodology usually takes months of manual consulting work.

Recently I compressed it with AI.

I fed 21 sales call transcripts, 585 cold call records, every script, deck, and playbook a company had into the system.

It found a two-layer messaging problem nobody had diagnosed.

Wrong product frame combined with wrong awareness level. 

That alone explained why cold calls weren't converting.

They had 6 script iterations that accumulated over months of gut-feel revision. None of them were working. 

The system identified which proof points were actually landing on calls, and zeroed in on a new approach built from their own data.

It took an evening.

I'm publishing the full methodology next week, prompt templates, benchmarks included. Free.

But first, I want to run it for 3 more companies. For free.

What I need: your last 30 days of email sends and replies, cold call recordings, and discovery calls. That's it. 15-20 minutes of exports.

Signal enrichments on your companies and contacts are nice to have, but not needed.

What you get back: a constraint map, messaging diagnostic, rep coaching briefs, and talk tracks revision suggestions. In days.

DM me "sprint" if you want in. I'm taking 3.

taplio

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Scott Martinis

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