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

Maximus Greenwald

ceo @ warmly.ai | revenue ops obsessed | ex-google | follow to watch me try to build a sales tech unicorn

Follow me for researched insights around Demand Gen, Sales & Marketing problems and how they can be solved with AI, grit & Purple Cow Marketing 🟣🐮.

Go-To-Market & Marketing for B2B will change forever with AI. From talking to 100s of CMOs & CROs from our customer base, we will soon see legendary, small demand-generating marketing teams who create massive pipeline with full-funnel orchestrations & automation. I'm deeply passionate about building AI Marketing & Sales Agents to aid this evolving landscape. 

Founders - Read my founder newsletter "Going CLEAR" to get the behind-the-scenes on growing our AI Marketing startup, Warmly. https://www.linkedin.com/newsletters/7174798574000439296/

GTM Teams - How to build a repeatable sales motion from 0 to 250+ paying customers:
> https://www.growthunhinged.com/p/the-path-from-0-to-100-paying-customers
> https://www.growthunhinged.com/p/a60761fe-4af2-4a29-9ca7-7b479565f31a

Personal interests: triathlons, telemark skiing, chess, rugby & freestyle rap (amateur but having fun)

Warmly,
Max

Cofounder & CEO

maximus greenwald

It took 21 days shy of 4 years but we landed our first 7-figure deal this month.

Here's a good reminder on why everything is harder & takes longer than you think:

I took this picture - Feb 1, 2022 with my leadership team at Warmly.ai at the San Diego Zoo.

It was to commemorate our #1 startup goal: take down an elephant.

Better known as: sell to a Fortune 500 enterprise customer.

Specifically a 7-figure deal.

We were at $35k ARR and wanted a 7-figure deal.

Fat chance!

But...

23 months later we had done the hardest part: sign a 6-figure deal ($950,000) deal.

81 meetings, 3 in-person, 21 stakeholders & 4 (!) times realizing the original decision maker wasn't the real decision maker and needing to get to the Final Boss.

But it wasn't 7-figures :( 

It became my Moby Dick. Er, Moby Elephant.

2 years later (21 days ago) and almost 4 years since we took this picture - finall signed! The mythical elephant, the mythical 7-figure deal.

ps. Now off to get 10 more!

ceo of warmly.ai, the #1 GTM brain for agents and humans | sharing behind-the-scenes marketing insights & trends | ex-Google & Sequoia scout

My cofounder Alan Zhao makes his 9 reports work overnight. Is he cruel? No - his reports are all AI Agents for Marketing & Sales. 

Today - he's releasing his 9-agent guide that's made him a 1-man marketing team producing $1,000,000+ in qualified pipeline/month. 

⭐️ Comment "Agent Guide" & I'll DM it to you.

What does Alan do? At our startup, Alan sits at the intersection of Marketing, Customer Success & Product focusing on eating our own dogfood, using AI GTM Agents, & increasing our own pipeline.

In order to run his 9 reports to maximum efficiency he firsts pulls together a GTM Brain of sorts including all of our:

- Intent signals (info from Warmly, on who visited, which pages, how long, when)
- CRM data (deals, contacts, lifecycle stage, last activity)
- Slack conversations (what are reps saying about accounts?)
- Call recordings (what did prospects actually say?)
- Enrichment data (info from Warmly, on company size, tech stack, ICP fit)
- Ad impressions (who's seen which campaigns?)
- Outreach history (who already got a message, and when?)

Then he commands his agents to split our TAM into:
- those in-market now (--> send to reps immediately every morning)
- those not yet in-market.

With the latter he'll give off agent tasks to serve them:

- LinkedIn ads (like a video about our Marketing Ops Agent)
- Retargeting (stay top of mind)
- Content (blog posts like this one)
- Automated nurture (email sequences, but thoughtful)

The goal is simple: keep them aware and engaged until they ARE in-market.

Some of his favorite marketing agents:

1/ Lead List Builder
Queries the context store for today's high-intent accounts. Filters by ICP tier, checks outreach history, identifies buying committee. Outputs a prioritized list for our SDRs with full context on each account.

2/ LinkedIn Audience Manager
Takes the buying committee contacts from high-intent accounts and adds them to our LinkedIn ad audiences. They'll start seeing our ads within 24-48 hours.

3/ Attribution Analyst
For deals about to close, it builds the complete buyer journey. Scours CRM notes, call recordings, Slack notifications, chat messages. Answers the question everyone always asks: "Did our ads influence this? When did they first engage? What content did they consume?"

4/ Content Writer
Takes everything he's learning from running GTM and helps him write about it. It has access to the same context store, so it can pull real examples.

ps. Comment "Agent Guide" & I'll DM it to you!

Most founders spend years asking one question: “Can I make this work?”
The better question is: “Can I distribute this?”

Episode 6 of Founder Brand - "Content Flywheel" with Dave Gerhardt is live (full ep in comments). This one forced me to face a few uncomfortable truths about building in public.

Even a 1 of 1 product doesn’t win on its own anymore.
Everyone’s bullshit meter is high. We’ve all been told everything is faster, better, & different.

That’s why product market fit isn’t enough.
You need product market message fit.

If you can’t scale belief in what you built, it doesn’t matter how good it is.
This is where the content flywheel actually matters.

Not because founders need to post more.
But because once you train your brain, almost everything becomes marketing.

- An internal sales meeting
- A hiring conversation
- A pitch that landed
- A joke a rep made on a call
- A lesson that showed up in Slack

Most founders keep this trapped inside the company.

The best ones turn it outward.

Create something once. Get leverage from it everywhere.


Dave in the episode talked about how one conversation can turn into:

- Multiple LinkedIn posts
- A newsletter theme
- A future episode

The biggest bottleneck isn’t editing or ideas especially in the age of AI.

It’s hitting publish.

Most founders say, “I’m not ready yet.”
At some point, you have to commit for a year and see what compounds.


Warmly,
Max

PS. I’m on a quest to tell my story as a founder. Follow along as I build my founder brand.

PPS. To unlock our next chapter (Series B), we need to scale from $5M to $10M ARR so we can continue to help GTM teams build pipeline. With Warmly, marketers know exactly who to target and sales rep only work the hottest leads.

I have received 5,000+ sales emails in the last few years. I’ve only responded to three, ever... 😳 

4998 of them weren’t good IMHO.

Here’s one of them attached that I responded to with a play-by-play analysis on *why* I responded 👇

1️⃣ “Max > 6sense ” - as his subject line. He knew this was a competitor we ran up against. Granted we do extremely well here, he had my attention now. FWIW, he probably could have inserted any competitor's name and it would have done the trick.

2️⃣ Ok. So he piqued my interest. What’s the next line I can see on my phone after the eye-grabbing subject line? FYI free tip. Most executives read emails on the go and the iPhone app has a different preview length so you must be concise. 

His email then reads ”X is probably not as friendly of a solution but they offer a number of integrations.” Again, he speaks two facts here. The first is something we often hear from customers and the second is one that relates his assumed pain for us and his value proposition: integrations. 

3️⃣ He then after points out our competitor’s # of integrations, asks what is the “business impact” against win rates which is a critical business metric we are focused on. Damn. This guy is good. He doesn’t just ask for a demo. He makes me think about the negative business impact of not talking to him. He’s got me hooked 👏

4️⃣ To add icing on the cake, he references similar companies to Warmly like PeopleDataLabs and Hubspot. Logos he knew I would recognize AND respect. Used them as validation and social proof. A critical step in a sales email.

5️⃣ Ends with a simple, sweet, CTA, with a PS to make it feel even more personalized. 

Did I respond to this cold email? You bet. 

I also added him on LinkedIn and put him on my ‘must recruit later’ list which trust me, I will hire him.

At the end of the day. Sellers have to recognize this cold heart truth. 

Executive buyers are in Zooms all day long. Back-to-back meetings. 

I barely have a chance to respond to important emails, internal Slacks, or even family texts (sorry mom). Plus my assistant moves cold emails to my cold email folder for quarterly review.

What makes you think you’ve earned a response to your automated sequence if you’re going to hit me cold? 

Now go check whether you have any warm leads to spend extra time on instead of hoping that I’m bored and scanning through my cold email folder

Impact > Activity 

Warmly,
Max

A word for everyone out there doing something really hard this year: 

Make sure you're not holding the handle bars too tight, it makes the bumps hurt more.

GTM teams are using LLMs but won't reach 10x efficiency without state representation via a context graph. 👇

You can add better prompts.
You can add more agents.
You can add more CRM integrations.
And still get bad decisions.

This is because your GTM model never had a coherent view of the complex reality of everything your prospect did in the 6 months prior to buying.

We start with signals:
A pricing page visit is a fact.
An intent spike is a fact.
A job change is a fact.

But LLMs do not reason over facts in isolation.
They reason over relationships (connecting signals together).
Without an explicit state model, every GTM agent is operating under partial observability.

What’s missing is a context graph.

That graph is made up of nodes (people, accounts, roles, deals, competitors, content) and edges (who reports to who, who is on the buying committee, how many times you've talked to them, who are the objections, when are they in market).

Time, as opposed to static state, is the first-class dimension.
The question “should we act now?” is not something you can retrieve from a vanilla CRM.

Instead you need a path traversal with constraints. 
I'm going to engage this lead because they:

Visited pricing
after competitor comparison
while hiring SDRs
after champion churn
before budget reset

If the model cannot walk that graph, it cannot make a decision.
No amount of prompt engineering fixes missing structure.
This is where most agent systems break.
Each agent reasons locally.
None share a global state.
There is no coordination layer enforcing coherence.
Humans can't do this alone (this is why AI is fun).

But again important to be clear: LLMs are not decision engines. They are policy evaluators. They take a state snapshot plus constraints and choose an action.

If the snapshot is wrong, the output will be wrong with high confidence.

GTM teams will need to rely on a context graph that is:
 - globally readable
 - temporally aware
 - entity consistent
 - queryable in one hop
 - writable by agents

By 2030 we predict at Warmly:
- Models will commoditize.
- Prompts will standardize.

The real moat in GTM will be who owns the best state model of buyer reality.

So if you are still modeling GTM as events instead of relationships, you are building agents that cannot reason.

If you'd like to see the future of that context graph for GTM - we'd love to talk to you about our TAM Agent & vision for the GTM brain!

Here's something most people miss: building AI agents for GTM is fundamentally harder than building agents for coding or support.

Here's why:
--
Coding agents work because code is deterministic. 
A test passes or it doesn't.
Support agents work because knowledge bases are static. 
The answer to "how do I reset my password" doesn't change week to week.
GTM is different. 
What worked yesterday stops working tomorrow.

Example:
--
You connect Clay to Outreach. 
You wire up your intent data to your sequences.
You build the orchestration workflow everyone on LinkedIn told you to build.
You hit play.
And nothing changes.

I've started calling this the event horizon. 

The point where you've done everything the playbooks tell you to do, and you still can't see what's next.

The tools are connected. The data is flowing. But you're still manually deciding who to reach out to, what to say, and when to say it.

The workflows automated the keystrokes. 
They didn't automate the judgment.

Each account's context is unique. The "right" decision requires synthesizing signals that change hourly. 

And there's no ground truth. Only outcomes you won't see for months.

This is why GTM is 6 to 12 months behind the coding agent frontier.
But it's also why the opportunity is massive.

The teams figuring this out aren't just connecting more tools. 
They're building context graphs like Warmly,. 
Not just data points, but connected understanding of everything happening with an account that agents can actually reason over.

Not "someone visited your pricing page."
More like: 
- they visited pricing after reading a competitor comparison
- after their VP of Sales engaged with content in your category
- while their company is hiring three SDRs
- six months into a contract with your competitor.

That's context. And almost nobody has it structured in a way that matters.

The window to build this is about six months. After that, the playbook will be obvious and everyone will have agents running outreach :) 

Right now, the edge goes to teams treating this as their primary initiative instead of a side experiment.

Who on your team is thinking about this full time? 
Alan Zhao and I would love to chat with them

Going Clear Day 743: Today I'm sharing our CAC Payback Per "Channel" - though with all my heart I hate isolating out particular channels.  I'm curious where folks think I should place my marketing $$ in 2026?

Quick soapbox: 
-----
B2B marketing is an omni-channel problem. You need to work prospects all-over and when they're ready they will engage. Yes some channels will be more effective than others.

But almost never does a lead come inbound only because of just 1 channel.
------

Anyways back to our analysis. Here's what we found:

- some channels are getting more efficient
- some channels are staying steady. 
- some channels are a few are still high-risk bets.

Here’s the breakdown:
1/ Content & Social: fastest payback - still ~3 months 
[hard to "spend" on / scale]

2/ SEO / AEO: compounding predictably - ~3 months

3/ SDRs: big improvement - from 14 → 10 months
[SDRs have a big hidden influence on Content/Social]

4/ Paid Ads: also improving - from 16 → 10 months
[some ads are for defense but I still factor that in]

5/ Conferences: experiment is ok, long payback & surprisingly we only got our Micro Tier deals here
[we also learn a lot when we go to conferences]

5/ Outbound Marketing: experiment is ok, long payback, small deals only so far
[lots to optimize here that takes longer than a quarter]

We’re keeping the approach simple:
→ 75% of our effort goes to what we know works
 → 25% stays open for new bets (because nothing scales forever)

The goal isn't to “find the silver bullet.” It's to double down on efficient, repeatable motions... while always testing for the next one and recognizing that you need 5+ channels working to growing pipeline in 2026.

How are you balancing proven plays vs new bets heading into 2026?

I've raised over $22M in VC Funding with this Data Room Folder Structure (do NOT forget the README):

Background: 

I'm a first time founder and had to spend a lot of time learning how to fundraise. Been turned down by 100+ investors, and am lucky to have 35+ investors (funds, angels, etc).

We've raised a Pre-seed from Accelerators (YC, Techstars, On Deck), a Seed, Seed Extension, Series A and Series A Extension.

This structure is what seems to work the best.

Folder Structure:

README: Coach investors on how to understand your business and in what order. Messy diligence folders mean they give up sooner.

0/ Deck

1/ Origin Story of Warmly - Pivots, cofounder background & transparent updates (85+ updates over 6 years now)

Why: Engender human empathy. I'm human, our founding is a fun, thrilling story. Investors like metrics but they invest if they love the founder. It also shows I'm coachable

2/ GTM Tech Overview - Primer on our space & overview on intent data

Why: Frame investors' thinking. They are horizontal and I am vertical in my area. Show them I'm an expert, don't tell them

3/ Product Overview & Details - videos & slidedecks explaining the problem we solve, several product roadmaps and 3rd party evals on the quality of our data

Note: Importantly is the file format. Some like videos, some 1-pagers, some slide decks

4/ Customers - Customer file, segmentation, retention & churn analysis, example customer collateral

5/ Business - Team overview, comparable exits/funding, competition, KPIs, historical P&L, financial projections, past board decks

Note for 4 & 5: Before Series B, you only need (a) some hero stories of happy customers, (b) overview stats and (c) light financial projections. At Series B you'll need to switch from just storytelling to metrics.

In the early stages, don't listen to VC investors who demand all this hardcore quantitative analysis. They say they want that but thats not how the underlying reasons they actually invest.

6/ GTM - Pipeline forecast, overview of top channels, sales rep performance, sales efficiency overview

Note: this is important for Series A & beyond if you're B2B

7/ Legal - Incorporation, Summary Cap Table & Other Docs

ps. I do a "Pre-Commit Data Room" and a "Post-Commit Data Room". What is in the Post-Commit Data Room? All the more sensitive stuff. Make investors commit to joining your round if they want access. Can't give the goods too early!

To every seller out there trying to close out the fiscal year today, I salute you 🫡

To every buyer out there waffling on a contract they know they'll sign eventually - be a kind soul & sign today! 🙏

ps. Signing Saturday Jan 31 still counts ;)

There's a new objection that SAAS/AI vendors are facing in 2026: why can't I build this in house with OpenAI/Claude?

Here's our story: Last week we had our renewal for our Customer Success platform, which sits on top of our Hubspot instance. 

It's $15k/year and provides health scores and alerts for CSMs.

But one of our CSM's Keanen Maas and our head of product Carina Boo said we could probably vibe code our use cases in a few hours. 

Using Claude Code + Google Sheets + some MCPs, they built a 99% replacement... so we churned.

The old build vs buy equation isn't an equation if building it is (a) not hard to maintain and (b) can be built by non-technical teams.

For SAAS/AI Vendors out there here are 3 ways to try to mitigate this objection:

1/ We'll build faster: we use Claude Code too and we can code more value than you will for your static 1-time replacement

2/ We have unique data: our solution has something you can't scrape from the web or leverage from your proprietary data. You need us to accomplish the task

3/ Custom experiences with professional services: we'll build you a custom-flavor of our generic software that perfectly fits your use cases (maybe via a Forward-Deployed Engineer).

What else am I missing?

The next wave of AI is dynamic reasoning in environments where the answer key is always changing (& the battlefield will be won in 2026-2027).

Consider GTM (Warmly,'s space):
- Your ICP shifts as your product evolves
- Competitive positioning changes quarterly
- Buyer personas vary by segment and market condition
- What worked last quarter may not work this quarter

Every deal is different, and every company's GTM motion is unique

Bluntly put: the world changes.

The "right" answer depends on context that didn't exist six months ago. The model needs to learn continuously from outcomes, not just retrieve from documents.

The companies that win this wave in 2026 will build something fundamentally different: not AI that retrieves answers, but AI that develops judgment through continuous interaction with dynamic environments.

Let's take Palantir as a case study:

Before LLMs, Palantir competed with Snowflake and Databricks on data infrastructure. The market saw them as enterprise data platforms, expensive, complex, government-focused.

Post-LLMs, Palantir no longer believes they have any competitors.

Why? Because they made a different architectural bet.

Snowflake and Databricks optimized for SQL and query throughput: get raw data into tables, run fast analytical reads, ship dashboards and models on top. They built infrastructure for answering questions about data.

Palantir built an ontology, a world model where data is represented as objects, relationships, and properties. 

Nouns, verbs, adjectives. Named entities, typed relationships, constraints. 

Not tables and joins, but the way humans actually think about their domain.

When LLMs arrived, this ontology became the perfect interface. Models don't want a trillion rows. 

They want a structured, language-shaped substrate: something you can linearize into a coherent prompt, traverse, and act on.

The results speak for themselves with Palantir: 
- 30%+ year-over-year growth accelerating
- 50%+ growth in U.S. Commercial

One of the fastest-growing enterprise software stocks.

The market is recognizing something important: ontology beats query optimization when AI is the consumer of your data. 

It allows AI to reason over your business the way humans actually think about it—not as tables and joins, but as entities, relationships, and meaning.

Palantir proved the thesis. Now the question is: who builds the ontology for each vertical?

For government intelligence and defense, Palantir won. For GTM, the seat is empty for now but we want that to be Warmly,

Stay tuned to learn how we're building a GTM context graph

8 of my customers got $0 in Closed/Won from Warmly.ai's Marketing Agents in September. But 8 of them made more than $390,000+ in Closed/Won.

Warmly's Marketing Agents deliver ROI in 2 main ways: Sourcing Warm Leads and Influencing Warm Leads.

And our ROI Dashboards can tell customers where we've added value (or not). 

Below I show the aggregate reports across our customer best including the "best" performers and "worst" performers for September 2025.

When I looked at the aggregate reporting it was interesting to see how varied the results were. Some gave us clues to which ICP does better with our agents, or which customers were more engaged.

Partly it's just the reality of pipe gen: some channels give you ROI one month, then don't the next. Thats why an omni-channel approach to GTM is critical 

(and why we don't pretend to take 100% of the Closed/Won credit for leads we found you unlike 6sense).

Even for those 8 customers who have not seen a Closed/Won deal from their September usage of Warmly - pretty awesome to see that 6 of the 8 generated $40k+ in Pipeline from that month.

ps. how do our agents actually work?

- Our TAM Agent generates warm leads for you by using Signal Data. Then it scores those leads and engages them TOFU for you with Ads, Emails & DMs.

- Our Inbound Agent influences warm leads for you by increasing website conversion. Once TAM Agent drives them to the website, Inbound Agent leverages AI chatbots & pop-ups to get booked meetings. 


*Yes, thank you, I see that my AI image going from spreadsheet to graphic messed up a few of the multiplication for ROI. I promise the $$ columns are real!

What actually happens on your website while your team is asleep? For most companies, the answer is nothing. But a huge portion of traffic happens outside working hours - often 30–50%.

That means dozens (sometimes hundreds) of visitors show up every night with intent, questions, or confusion… and no one is there to help them.

- Someone trying to understand a feature
- Someone evaluating your product
- Someone ready to book a demo

All gone cold by the morning.

And the worst part? These aren’t “nice to have” conversations. These are warm leads.

For years, companies have tried solving this with basic chatbots, but honestly, most are either too dumb to be useful or too time-consuming to build.

Excited about our new AI Studio - a way to build fully autonomous AI agents that act exactly like a member of your team & can be created in under 3 minutes.

It’s as easy as:
1/ Upload your additional content (we auto-pull your website content)
2/ Configure tone & rules
3/ Done

Your agent learns everything about your product, and you can deploy it instantly.

Plus you can build multiple agents for different use cases

The outcome?

- Fewer missed conversations
- Lower support headcount pressure
- Faster response times
- Higher demo conversion
- A more human, helpful website - 24/7

If you’re already a Warmly customer, reach out to your CSM to try it.

If you’re not, now’s the time to experience what it feels to have a full time Inbound SDR.

I exported my LinkedIn network from 2019 (when I started Warmly) & 2026 (today) to crunch what changed. There were 3 big shifts that mirror my own shift from Founder --> CEO:

Shift 1: founders building anything --> GTM professionals in my space

Shift 2: junior employees --> c-suite

Shift 3: sales (where we started) --> marketing (where we're going).... though all this is muddied by the merging of sales/marketing in GTM :) 

Background: The data from 2019 was admittedly small (~1,200 connections).

The majority were:
- Individual contributors
- Early career friends
- Managers building their first teams

Titles were mostly: Founder, Sales, Account Executive, Manager, Early-stage operators

My network reflected where I was: learning, experimenting, & building reps.

Fast forward 2025 (26,500+ connections) and the standouts are
~16% Director / Head-of GTM
~12% Marketing & Growth

More execs, more builders, more people solving the same GTM problems from different angles.

I realized as the problems I worked on became more specific, the people around me evolved too.

You'll notice my highest percentage of connections is still Founder/CEO. 

First - I haven't lost my desire to learn/grow from others around
Second - I've made a lot of friends / and connect with folks who want to follow my journey as I (start) to be able to share lessons/learnings instead of just consume them

For others out there - if you exported your network from 5–6 years ago, what would it say about who you were building toward back then?

Your startup feels less like a startup when:
- you re-use your Annual Kick-Off presentation template 3 years in a row ✅

- you manage to get everyone on to just 1 project tracking tool down from 6 (Linear) ✅

- you start to care about margin ✅

- someone who used to work with you and went to their next career chapter comes back to work with you a second time ✅

... what am I missing?

"Prideful Targeting" is the first Deadly Sin of AI Marketing --> sticking to just accounts, not people.

Welcome to a new series by Warmly, - 7 Deadly Sins of AI Marketing. Today is #1.

These are mistakes we've made ourselves, or keep seeing teams make across GTM.

The first is not recognizing that AI has allowed us to "Yes, And" an ABM list. 
Yes - get the accounts.
And - get the people behind the accounts.

You ever chase an account for 6 months… and then find out the buyer left 3 months ago?

That’s the frustrating part of an ABM list in the world before AI.

ABM tells us we’re doing the right thing - targeting ideal accounts, personalizing by industry, aligning with sales.

But as we know - accounts don’t buy software. People do (until the robots take over).

And people change jobs, priorities, and vendors constantly.

If your GTM still targets companies - ignoring the people inside them - it’s already misaligned with reality.

That’s Sin #1 of AI Marketing: Prideful Targeting

Believing your account list is smarter than the market.

Person-Based Marketing with tools like Warmly, flips the model:
✅ Tracks real buyer signals - who’s researching, who’s switching roles, who’s ready now
✅ Uses AI to score people based on fit and timing - not just job titles
✅ Activates across email, ads, site, and outbound - when buyers are actually in motion

ABM gave us structure.
PBM (Person Based Marketing) gives us precision.

You don’t need to just chase companies anymore.
You need to meet buyers where they already are.

#deadlysinsofAImarketing

The 3rd Deadly Sin of AI Marketing is Gluttonous Pipeline --> saying its qualified when you know its not.

Last week we covered the first 2 of 7 Deadly Sins of AI Marketing: Prideful Targeting and Sloth-y TAM. 

These are mistakes we've made ourselves, or keep seeing teams make across GTM.

Ever run a campaign, book 30 meetings… and close one deal?

Definitely not a pipeline problem but a gluttony one.

This is the third deadly sin of AI Marketing: Gluttonous Pipelines

More leads. More accounts. More activity.
We chase more when what we really need is better.

Most marketing teams today still operate like it’s 2018:
- Pull a massive TAM list
- Run ads, outbound, sequences to all of it
- Celebrate engagement like it’s revenue

Meanwhile, the sales team is buried under cold leads and half-fit personas they’ll never close.

Here’s what changed for us in 2025 (& now '26):
✅ We stopped chasing volume
✅ Started scoring every lead and account based on real-time intent
✅ Used AI to identify true ICP and buying committees
✅ Pushed our team to have 3x coverage of real pipe instead of 5x coverage of fake pipe

And now (thanks to advances in GTM AI) we're replacing spreadsheets with autonomous agents that do the heavy lifting:
→ Rank leads by fit and likelihood to buy
→ Build dynamic buying committee lists
→ Sync across channels automatically
→ Keep everything updated as new signals emerge
(all possible with our TAM Agent but possible to do in other GTM tools too!)

Important to ask your reps & your revops team: how much of your funnel is truly sellable right now?

#7deadlysins #aimarketing

GTM will move from a tech stack to a tech graph by 2027. 

Great advice from Alan Zhao on how to prep you organization:

Cofounder, Head of Product, Marketing, & AI @ Warmly.ai | Building the GTM Brain | #1 Context Graphs for GTM Agents & Humans

Ever feel like your GTM stack is “done”… but nothing actually feels easier?

All the tools are connected, workflows are running.
And yet you’re still manually deciding who to reach out to, what to say, and when.

I kept running into that wall, and it bothered me more than I expected.

So I wrote out how I think about what comes after workflows, based on what we’ve been building at Warmly - how agents should make decisions and why GTM is harder to automate than code.

If you’ve hit this point too, this will probably resonate!

We've reached the point where the playbooks stop. What happens when you've connected all the tools, wired all the data, and still don't know what's ne

Open article: The Agent Architecture for GTM: A Framework for What Comes After Workflows by Alan Zhao • 8 min read

The Agent Architecture for GTM: A Framework for What Comes After Workflows

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