Presenting partner: Viktor

Viktor is an AI employee that lives in Slack and Microsoft Teams. Connects to 3,200+ tools. Does the work, not just answers questions. Hire Viktor for your team.

GTM partners:

Ahrefs Brand Radar - Make AI recommend your brand.

Attio - The agentic CRM built for modern GTM teams.

Clay - Build systems to grow revenue.

Hyperagent - Agents that ship real, powerful work. Sign up for a paid account and receive $100 in credits.

Miro - The collaborative workspace where GTM teams and agents converge to build together.

Dear GTM Strategist,

You work hard to promote your product … very hard.

Now imagine a world where the product promotes itself, and every new sale doesn’t have to come from you grinding the funnel or jumping on one more sales call …

Meet referrals - the closest thing in PLG to “making money while you sleep,” and a way to create leverage in your PLG or hybrid GTM motion. Heck, you can even consider them for services.

People have always referred stuff they like to their peers. Guess how I found my hairdresser and the gym I go to. 😀 That’s natural. That’s borrowed trust.

If your product/service/offer has sweet product-market fit and is loved by its clients, you can either live with the fact that referrals will always happen anyway - or you can actively leverage them into a powerful GTM motion that drives growth at marginal cost compared to everything else you’re doing.

Now, the tricky part: plenty of B2B companies build the program before the product has earned it, see nothing happen, and conclude that referrals don’t work.

So I went looking for how you tell the difference. I collected the best referral ideas from product and growth legends in the Cello Referral Atlas, a nine-chapter guide to B2B referrals with each chapter written alongside the operator credited on it. Elena Verna, Head of Growth at Lovable, makes the case for why word of mouth is the channel to bet on now. And Stefan Bader, interviewed by Kyle Poyar, walks through how to tell whether your product has earned a referral program yet.

Today we’ll go through:

• the 3 conditions that decide referral fit, and the “death valley” outside them

• what a good program’s numbers look like, from 10 million referral users

• the 5 build steps, from where the button goes to who pays the tax

If you’ve been sleeping on referrals or taking them for granted, I bid you good morning with this one 😀

1. Growth is broken. Trust is the fix.

Author: Elena Verna, Head of Growth at Lovable. Her chapter is adapted from a post on her newsletter.

Elena’s argument is that the classic acquisition channels are all getting worse at the same time, and she doesn’t expect them to come back.

Her specifics:

• SEO. AI made the content it depends on nearly free to produce, and the answers now show up in ChatGPT, Claude and Perplexity before anyone clicks a link.

• Paid search. The pool of searches is fixed, so bidding on them costs more every year. You are also bidding against AI tools that solve the same problem for $20 a month.

• Corporate social. Platforms have stopped paying out external traffic. You can still get attention there. You can’t turn it into a click off-platform.

What is working, in her reading, runs on trust: founders and employees posting as themselves, creators who stake their reputation on you, communities where users answer each other, and a product good enough to carry the brand by itself. All four feed word of mouth, which she calls both the strongest trust signal there is and the hardest one to fake. Elena puts it plainly:

“Features are easy to copy. Trust isn’t.”

She pushes the same idea into retention. Every product answers “why you?” one of three ways: cheaper, faster, or better outcomes. Most SaaS was built on the first two, and AI now beats both for about $20 a month. So customers stay with the products and teams they believe will keep getting better.

The part that stuck with me: every product she says she trusts (Lovable, Miro, Granola, Oura, Stripe, Superhuman, Substack, ChatGPT among them) she found through a referral.

3 more lines from her “what this means operationally” section:

• Let the builders do the talking. Your CEO, your engineers, your designers. People don’t want the corporate account’s point of view.

• Ship daily if you can. Users signed up for one version and keep getting a better one. It feels like a free upgrade, and it builds loyalty without you asking.

• Accept less polish. A polished ad puts distance between the brand and the people watching it.

If next year’s growth plan is mostly more paid budget, read her chapter first. The 4 channels she lists cost less and don’t get pricier every year.

2. User referrals only work if you earn them first

Authors: Stefan Bader, co-founder of Cello, with Akash Bajwa, principal at Earlybird. Adapted from Stefan's interview with Kyle Poyar for Growth Unhinged.

Of the referral channels in the Atlas, user referrals convert best. Stefan’s point is that most companies still can’t run one.

It only works if people already recommend you for free. The program’s job is to make that easier. A reward can’t start a habit that isn’t there, and a bigger reward won’t either.

So the useful question is whether your product qualifies. Three conditions decide it:

• High product lifetime value.

• Fast time to value.

• High product engagement.

Top performers clear all three. Two is usually enough. Outside those ranges is what the chapter calls the referral program death valley. The chapter is specific about what that means: fewer than roughly 500 monthly active users, an ACV low enough that a meaningful reward eats the margin, and a sales cycle long enough that the referrer waits months to see anything.

Launch there and the program doesn’t die. It limps. A few signups a year, a steady drain on engineering and marketing time, and no way to tell whether the channel would ever have worked.

And here are the two words. Kyle’s chapter doesn’t soften them:

“The honest answer in that case is not yet.”

The fix is in the product, not the program. Get more users active, cut time to value, or raise ACV, then come back when two of the three hold. If you need reach before that work is done, affiliates are the program that runs without an engaged user base.

Why it’s worth earning:

• Referred users stick. Wharton research (Schmitt, Skiera and Van den Bulte, Journal of Marketing, 2011) found referred users churn 18% less and carry a 16% higher lifetime value. The per-head margin advantage fades by around month 29, so the durable win is retention.

• You’re undercounting it already. A growing share of B2B recommendations happens in Slack groups, LinkedIn DMs and WhatsApp threads. The chapter calls this dark social. Standard analytics can’t see it, so most GTM models undervalue the channel.

• The reward has to matter to a person. Discounts and in-product credit are weak when the company foots the bill. And your users need a network worth sharing: the pool of people who want an AI writing tool is far larger than the pool who need an HR system.

What good looks like: tl;dv got a first version live within a day, on about 4 hours of developer time. On Cello’s platform data (n = 10 million B2B SaaS referral users), best-in-class programs convert 64.6% of referred visits to purchases on free trial products and 48.3% on freemium.

The metric to plan against is your word-of-mouth coefficient: how many new users each existing user brings in organically. At 1 or above, growth compounds without spend. Most B2B products sit well below 1, so the realistic goal is raising it. The Referral ROI Calculator in the Atlas resources sizes it from your access model, ACV and MAUs before you commit anything.

3. The build, once you clear the test

Here are five build steps from Stefan Bader that will help you avoid common failure points and launch a high-performing referral program:

1. Put it inside the product. The referral flow and the reward tracking both live in the product, next to the features people already use. Ask for identity and payout details only after the user has earned something. Asking upfront cuts participation.

2. Work backward from ACV to the reward. Direct cash beats discounts, because nobody is motivated by savings on a product their company pays for. Set a reward cap (a one-off for a qualified signup or demo, plus a percentage of the referred customer’s revenue) and lead with the cap when you market it. “Earn up to $3,000 per referred user” is the version people share. Cello’s own survey of 100 SaaS users found most B2B programs under-reward: even for low-ACV tools, people expected $105 to $345 per referral.

3. Do the sharing work for them. Swipe copy, a landing page with an FAQ, and examples of what a good referral message looks like. Gusto scans contacts and suggests who to refer based on email domain.

4. Launch it more than once. Staged rollout to power users first, then email, in-app, social and a link from the login page. After that, nudge at the moments people are primed: NPS of 9 or above, a closed support ticket, a hit milestone, a monthly reminder.

5. Don’t run payouts from a spreadsheet. KYC falls on you even with a compliant payment gateway, tax treatment differs by country, and in the US you owe a year-end tax form to any non-corporate referrer who earns $2,000 or more.

What both chapters have in common

Put them side by side and they make one argument from two directions. Elena’s channels only work if the product is worth talking about. Stefan’s three conditions are the same idea, measured. A referral program removes friction from advocacy that already exists.

Where I stand: if two of Stefan’s three conditions hold for you, build it now and keep the first version small. tl;dv’s 4 hours is the right ambition for version 1. If they don’t hold, spend the quarter on time to value instead. Full disclosure: Cello sells referral software, and I’d still rather you launch nothing than launch into the death valley and conclude referrals don’t work.

The full Atlas goes deeper on each of those steps across 7 more chapters, plus affiliate and influencer programs, the reference tables and the calculators.

It’s free, ungated, and you can download the whole thing as one PDF.

Which products do you trust, and did a person send you to them? And if you just ran the fit test in your head, which of the three conditions are you missing?

Start your referral program now

Talk soon,

Maja

P.S. Kyle Poyar and I are organizing a BIG State of B2B GTM Summit on October 14th. We'll present the results of our survey - which tactics, channels and tools are hottest for 2027, and what’s working best in 2026. We'll have 12 amazing speakers (big reveal next week - some of the hottest names in GTM).

All we’re missing is you. Join us for a free community event: Save your seat here.

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

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

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

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

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

Presenting partner: Viktor

Viktor is an AI employee that lives in Slack and Microsoft Teams. Connects to 3,200+ tools. Does the work, not just answers questions. Hire Viktor for your team.

GTM partners:

Ahrefs Brand Radar - Make AI recommend your brand.

Attio - The agentic CRM built for modern GTM teams.

Clay - Build systems to grow revenue.

Hyperagent - Agents that ship real, powerful work.

Miro - The collaborative workspace where GTM teams and agents converge to build together.

Dear GTM Strategist,

Grok Bot is here, and it is the most interesting piece of technology I have tested this year.

I was in the private beta in August, before any of this hit your feed. By now, the bots are wired into the live parts of our GTM stack, including our hero setup (Granola + Clay + Attio), and the best part is they run after you shut down the laptop for the day.

This month, the SpaceXAI team is launching like crazy, and the whole world is watching. Their “open company building” this week was a masterclass on its own: SpaceXAI and Grok Bot are dominating social feeds with practically no paid budget (building in public).

The launch video for Grok Bot had more than 55 million views (!) and millions wathced the live building sessions this week. If you study only one launch this quarter, I’d choose this one.

The bots themselves are unlike anything we have tested before: extremely intuitive, easy to pick up, and so horizontal that our imagination went wild. Here is what I actually use them for today:

• Account research

• Curating news for our content calendar

• RevOps grunt work

• Sourcing new real estate investment opportunities

In this edition, I want to take you into the nitty-gritty of it:

• The launch playbook behind Grok Bot, and why it is dominating feeds with almost no spend

• Curated GTM (marketing and revops) bot examples

• Our internal Grok bot stack, the one we run at Growth Lab today

I hope you squeeze as much value out of it as we have, get many useful templates for instant runs, and get plenty of new inspiration for your next builds and launches.

Ladies, gentlemen, and nerds of all AI religions: Let’s Grok!

What is Grok Bot - and why does it matter for GTM

Grok Bot is a product that took the GTM and AI space by storm. It was launched a month ago. I was happy to be one of the beta testers, and it really means a next step in how we use AI.

I like to compare it with open-source systems like OpenClaw and Hermes, but they require just too much technical setup for an average user, while Grok Bot is just easy to use. They even simplified some parts of the interface that are still quite geeky in other general AI tools - but more on that later.

But let’s meet the bot and what its key features are:

• It’s always on. Unlike other AI agents, it sets up the routines that run without you needing to keep your computer running.

• It runs a virtual computer. That’s the key difference from Claude/ChatGPT, and what makes it similar to OpenClaw. But in OpenClaw you had to set up an additional computer (most commonly a Mac Mini) yourself, while Grok Bot just spins up a virtual Linux cloud computer with a browser and filesystem where you log in to your apps.

• It has shared memory. It builds the context from all the conversations and gets sharper over time, so you don’t have to start every chat with a clean context.

• It connects beyond MCP and API. You can take over the virtual computer, log into the websites and apps you use, and let the bots work there.

• It’s very visual. If you need to teach it a repetitive task, you just record your screen.

• It works across devices. Continue the work between desktop and your phone.

• It’s a fleet of bots. One of the most magical features is how bots collaborate. The best use case is to create bots as specialists with clear jobs. They run in parallel and you can message every bot like you would message a co-worker.

In a nutshell: Grok Bot brings many out-of-the-box features that had to be specially engineered before. Building context for your work in Claude Code requires lots of manual work and folder structuring.

Best practices for GTM

Grok Bot is great for personal productivity, but it also opens tons of great GTM use cases. The concept is similar: you connect it with your apps and explain (or show) it the workflows; it can do lots of heavy lifting for you.

Connect your tools. You can use connectors in a similar way as you are used to with Claude or ChatGPT.

If some tool doesn’t have a connector, just use the virtual machine to log in. For example, I logged my Grok Bot into Booking.com, and it shortlists the best places for my trips - saving me lots of time and tab-hopping and FOMO from not checking the website daily myself to find our dream vacation house in Thailand.

Here are some other best practices I have set up:

Account Research

This bot has a detailed prompt that researches opportunities that land in my inbox.

The key part is the signal: Grok Bot has access to my inbox, so it will automatically run the research when I get a new collab inquiry. If there is a very hot opportunity, it will ping me to react fast and already propose an action - so I don’t need to monitor my inbox all the time.

So at the right signal, I get a full briefing and background, including the proposed action (like the suggested reply or offer).

If your work is mostly on outbound, you can set up a similar routine based on signals that are relevant to you. It can check Clay to find the best contacts, update your CRM, and send you updates to Slack when needed.

Competition Monitor

I created a bot that constantly monitors some websites and alerts me only when there are major pricing or offering changes.

I created it with a simple prompt:

Set up a competition monitoring agent that will daily check the websites of competitors that I will mention and only let me know if you observe pricing changes, new offerings, and so on.

AEO Monitor

An AEO bot that continually runs routines for maximizing your mentions in AI tools.

Give it a daily routine to connect it with an AEO data provider (like Ahrefs Brand Radar) and run daily checks of the top prompts your buyers. It should monitor your position across ChatGPT, Gemini, Perplexity, and Google AI Overview and tell you if anything changes.

It can proactively suggest improvements for your website and new topics, or even write the title changes and just ask for your approval.

Outbound Prospecting

I also asked Krista Letz, who runs GTM for SpaceXAI, about her best use cases and tips for Grok Bot, and she shared this outbound prospecting template with me.

This bot builds a prospecting plan, does deep research, and will actually watch webinars and listen to podcasts on your behalf, so your messaging is personalized. Then it drafts email and LinkedIn notes in your voice, and you review the drafts in the morning.

Commitment Tracker

A commitment-tracking bot can watch your Slack and email for promises you made or what others owe you, log them, and track them until they are delivered.

For more, the SpaceXAI team has now launched a marketplace where you can get the templates and playbooks for your bots. You just copy it like a prompt and it will create a new bot (and ask you some questions/permissions if needed).

Fair advice: Grok Bot is new and still in beta, so not every connection or integration will work smoothly yet. From time to time, you might run into something that needs another approach, but these are the proven cases.

How to manage the bots

Good news: you can create a bot to manage the bots! The name that best fits it is Chief of Staff. It will coordinate the bots for you, autonomously fix what’s needed, and only get back to you for key decisions. It can run morning or evening standard routines, collect status, and only escalate when it needs a human yes or no.

Here are the best practices for your bot fleet:

• One bot = one stable job. Narrow, specialized jobs work better than “general helpers”. Each bot should have its clear goal, resources, approval line, or schedule.

• Keep the conversation flowing. Each bot has its own conversation, so it builds the memory and routines. Meanwhile, tools, skills, and cloud computer are shared between the bots. So you only log in to tools once (if needed - if no official connector exists), and all the bots can access them. This means that tasks can be passed between bots.

• Bots can DM each other. For example, a signal from one bot can nudge another one to start the workflow. You can even create a group chat with up to 6 bots.

Here’s an example of how I briefed two content-related bots to hand tasks between each other:

Another example is to create a Token Officer bot. Instruct it to watch how the other bots burn tokens and optimize their work.

Some other best practices:

• Always try the official connector/MCPs first. If they don’t exist or are too limiting, you can log into almost any app via Grok Bot’s virtual computer.

• Scope every specialist bot narrowly - scale it later.

• Be specific with prompts - every goal should have a clear goal. Think of the process, not a one-time job.

• If you have your skills already set up in Claude, you can just copy/paste them and the bots will learn from them.

A masterclass in product launch

Apart from Grok Bot just being an awesome product, I also admire how the team approached both product building and the product launch. Roman Ugarte recently shared the story on Lenny’s Podcast. What stayed with me most:

• Relentless customer focus. The team personally onboarded almost 300 beta customers and checked in on their usage and feedback while building the product (full disclosure: I was also in the program). They didn’t go just for tech/pro users and early adopters, but also onboarded a diverse set of mainstream users they want to go for, like someone who runs a coffee shop. This brought real-life insights.

• Design choices. To be customer-focused, the team made a conscious decision to strip out many of the add-ons that became persistent in AI tools. You know what I mean:

To appeal to a mass audience, maybe not everyone needs to know which skills were loaded and which functions were called. Grok Bot just works and doesn’t bore you with the details of what runs in the background.

Grok Bot is not just hype. It’s how GTM tools evolve and enable us to do more.

It’s amazing to see the workflows that were created in the first month. We’re still early, so I’m excited to see what’s next. Share your favorite workflows with me!

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

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

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

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

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

Viktor is an AI employee that lives in Slack and Microsoft Teams. Connects to 3,200+ tools. Does the work, not just answers questions. Hire Viktor for your team.

GTM partners:

Ahrefs Brand Radar - Make AI recommend your brand.

Attio - The agentic CRM built for modern GTM teams.

Clay - Build systems to grow revenue.

Hyperagent - Agents that ship real, powerful work. Sign up for a paid account and receive $100 in credits.

Miro - The collaborative workspace where GTM teams and agents converge to build together.

Dear GTM Strategist,

Anze here - I’m running most of the GTM and AI setup at GTM Strategist.

Maja asked me to work on even more hands-on content, so we are starting a new format. Our goal is to give you the latest and greatest from the best GTM and AI practitioners. In video episodes, we’ll host people from the cutting edge of the industry, working on real-life implementations.

You might remember The GTM Repository from earlier this year, one of the best-performing guest pieces we have run: a markdown blueprint for giving Claude Code a permanent GTM brain, with the repo attached so you could copy it.

Nico Druelle wrote it. He is building state-of-the-art, large-scale GTM systems at companies like Canva, Linear, Descript, and WorkOS through The Revenue Architects, his GTM engineering studio.

Nico has been using Clay since the early days, so when Clay shipped a CLI and Workflows, he quickly adopted these features and now relies on this system to build at scale.

I asked him to show me what building Clay workflows directly from the AI agent looks like.

He shared his screen for 45 minutes and built the whole campaign in front of me.

What you’ll learn:

• How you can work with Clay without even opening Clay

• How to structure the repo before you let an agent build anything

• How a Clay Workflow looks and how to inspect the runs

• Where the scoring logic lives now

• How to test a workflow so the agent does the QA for you

Enjoy the episode!

TL;DR:

The setup: we sat in the chair of Lovable’s head of growth and ran a displacement play. Around 100 accounts were scraped from competitor tooling (Vercel, Replit, Squarespace), and we built one workflow to decide which of them deserve an email next.

→ CLI means command line interface. It used to be developer-only because you had to know the commands. You don’t need to know them anymore; your coding agent (Claude Code, Codex…) picks them. You don’t need a technical background - you just log in through the terminal once, then talk to it in plain English.

→ Clay drew a line between what agents build and what people build. Workflows, Functions, Claygents, Audiences are all reachable from the CLI. Workflows are a completely new way of using Clay. Tables remain the classic Clay interface, but you cannot build a table from the command line, and that is deliberate. Tables were designed for a human to look at. A workflow is a graph, and graphs are the kind of thing agents get right.

→ Our build took Codex 15 to 20 minutes. The agent also took initiative on its own. It added funding data as an intent signal without being told, which happened to be a good call. Not every liberty will be, so you still review what got built (or give it stricter instructions when necessary).

→ Everything lands back in Audiences, Clay’s canonical record layer. Companies, people, what was enriched, and also what was skipped. Run a different angle next month and you read from those records instead of paying for the same data twice.

→ When starting with Workflows, think of the objective first and give your agent enough context. Then it should figure out the workflow by itself. Context doesn’t have to be structured: Talk at the agent for ten minutes if needed. Have it plan the build before it builds. Then push three accounts through with written success criteria and let the agent check its own output against them.

Maja here. I need to pick your GTM brain.

Kyle Poyar and I are bringing back the State of B2B GTM Report.

Last year our findings reached over 1,000,000 people and shaped how a lot of teams picked their tools and channels. This year we want it sharper, and that only works if practitioners tell us the truth about what’s working.

5-10 minutes. What’s working in your GTM right now, and what isn’t.

Take the survey

Report drops mid-October, in time for your 2027 planning.

Thanks to Nico for coming to our new “hands-on” format and sharing his screen directly with us.

For more Clay Workflows plays, check out this guide we launched last month.

What is the first workflow you would hand to an agent?

And let us know in the comments how you like this new format and what you would like to see being built.

Anze & Maja

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

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

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

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

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

Viktor is an AI employee that lives in Slack and Microsoft Teams. Connects to 3,200+ tools. Does the work, not just answers questions. Hire Viktor for your team.

GTM partners:

Ahrefs Brand Radar - Make AI recommend your brand.

Attio - The agentic CRM built for modern GTM teams.

Clay - Build systems to grow revenue.

Hyperagent - Agents that ship real, powerful work. Sign up for a paid account and receive $100 in credits.

Miro - The collaborative workspace where GTM teams and agents converge to build together.

Dear GTM Strategist,

Most CRM advice comes from people who sold you the implementation and then moved on to the next client. They are not there in month four when the scoring model turns out to be wrong.

Kyle Doherty runs RevOps at Attio - an agentic CRM that is used by companies like Parallel, Turbopuffer, Wordsmith, and Granola - and more than 30,000 others.

That means he configures Attio for Attio’s own revenue team. He lives with every decision he makes. If the routing is bad, his AEs tell him on Monday morning.

I asked Kyle to walk me through his setup and GTM stack. What stayed with me was how simple most of it is - and how he adds AI and agents on top of it: reading calls, updating records, explaining why deals were lost.

What you will learn:

• How to turn prompts into shared team infrastructure, with the full “Update Deal” prompt to copy

• The custom agent that reads every closed-lost deal and writes back why you lost

• Why reps run their own Claude skills through MCP instead of one workflow everyone follows

• Two ways to store signals in a CRM

• Why Attio segments before it scores, and what their entire routing logic amounts to

• Kyle’s full GTM stack - including the integration he built himself

Over to Kyle.

1) Shared prompts as team infrastructure

Keeping a CRM current used to be the most cumbersome task. That is exactly where AI now comes in.

Ask Attio is an effective AI assistant, accessible anywhere in Attio. What makes it so useful is a library of prompts anyone on the team can build, save and share directly in the CRM.

So good prompts are not just personal productivity hacks - a shared prompt becomes a team infrastructure.

The plays our AEs lean on most:

• Update Deal is a favorite one - they are generally used after calls or at the end of the day and look at the call(s) and email(s) and suggest updates. Most AEs have their own versions tailored to how they want it.

• Prep for call - many AEs have their tailored version of these as well, but generally they assemble a single pre-call brief by pulling the account together from everywhere it lives: CRM records and deal history, recent emails and calls, product usage and trial status, support conversations, and fresh web research on the company. It adapts to the moment, giving a light intro brief for a first meeting and a fuller one for a returning call, with a recap of their stated pain and timeline, gaps in qualification, and a proposed agenda. The rep opens one doc instead of eight tabs.

• Prep all today’s external meetings - these types are similar but help AEs prep for the day instead of a single call.

• Draft specific emails - revenue team members have these from everything from hand raiser follow-up emails to trial-ending emails to handoff emails for SEs and CSMs. It’s not just salespeople that use these but the entire revenue team.

Here’s an example of the “Update Deal” prompt:

**Context**

You are updating a deal record based on a specific sales call. You have access to the call transcript, any notes linked to the meeting, the deal record to update, and associated people and company records.

**Task**

Analyze the call and determine whether any deal record updates should be made, including:

- Deal stage: should it move forward, stay the same, or move backwards. If the deal stage is being updated to Lost, suggest an update for a loss reason attribute.

- Estimated close date: does it need to change based on timeline signals

- Deal value: does scope, seat count, or budget indicate a change

- Next steps: what actions were agreed, and who owns them

- Associated people or stakeholders: any new people records that should be added as contacts or decision makers

- Current solution: what is the prospect currently using as a solution

- Competitors: what are the other solutions the prospect is evaluating alongside our product

- Any other attributes on the deal record that might be relevant to the conversation

**Constraints**

Use the following output structure:

- Be concise and actionable, avoid long paragraphs

- If information is missing, write “Unknown”

- If there are no relevant call recording and deal record in context, ask the user to confirm what the relevant call recording and deal record is before proceeding

- If there are multiple deals in context, ask the user to confirm which deal is the relevant one

Output two parts:

- Recommended updates to the deal record: a record update suggestion for the deal

- Evidence from the call: For each update, include a short one sentence justification on what was said and by whom. Format each attribute update like this: Field: {name}, Current value: {current value if set}, Proposed update: {new value proposed}, Reason: {short reason for the proposal}. Split each attribute evidence into a new subsection, and use bullet lists to separate the details within a single attribute.

Many reps create and share their own prompts. We want to empower them to be creative within our sales process and adapt to what leads and customers need, instead of forcing “one prompt to fit everyone”.

Pro tip: In Ask Attio, click “Browse prompts” for a selection of proven and effective prompts you can immediately use.

2) Custom agents

Most teams treat a lost deal as a closed task. The stage updates, the rep moves to the next opportunity, and nobody looks at the deal again.

I have built a custom agent in Attio that looks at closed-lost deals and analyzes the associated emails, calls, and everything that is already in the CRM. The agent then determines why the deal was lost and updates the record: it populates attributes such as closed-lost details, summary, category, and confidence score (how confident the agent is in its findings).

Two things we do with this agent:

• Trend analysis. After some time, you can see whether losses are clustering on price, timing, a missing capability, or a competitor, and how that shifts quarter to quarter. This is great feedback for product, marketing, and other teams.

• Re-engagement campaigns. A deal lost on timing will lead to a completely different follow-up than a deal lost on a feature that is in the development pipeline. With a category and a confidence score on every record, you can build a list for each and write to it properly.

This is just one example of how we use custom agents to analyze CRM data. Attio agents are incredibly powerful and reliable because they operate off of Attio's Universal Context (the actual CRM data, calls, emails, etc.) and thus don't hallucinate.

Another example is a custom agent that evaluates whether a deal has sales qualification criteria (based on the CHAMP framework).

3) MCP: Same data, different interface

Our AEs and CS folks also work through the MCP connector, running their own Claude skills customized for how they work or which segment they cover.

That is the part I would highlight for anyone building this internally. You do not need one canonical AI workflow that everybody follows. A rep covering marketplaces needs different context than one covering VCs, and a CS person post-sale needs something different again. Same underlying data, different skill on top.

So if reps just want to make a quick update or a check, they don’t need to open CRM: they just tell Claude and it does the lookup or updates Attio for them. (Plus, Attio’s MCP is actually purpose-built, not just a re-dressed API, which results in low token consumption.)

Maja has previously shared this infographic of the best ways to use Attio MCP that can give you more ideas:

4) Capturing and storing the signals

Everyone can produce a list of signals worth tracking.

The hard thing is landing them somewhere that produces an action. Only a signal you can act on is intelligence.

If a signal arrives in a place nobody looks, it does not matter how good the signal was. Most teams drown in signal volume and then conclude signals do not work.

Here are two ways that can help you tackle signals in Attio - depending on the problem you are solving.

Store signals as attributes on the object. Fine for simple, low-volume cases. It breaks the moment you have something like revealed web visitors, where one company generates a stream of events. You want to see the journey. An attribute gives you a single field that keeps getting overwritten.

Build a custom signal object. More flexible, but it gets messy fast. Every signal type carries different data, so you end up with an object holding 30 attributes where only two of them apply to any given record.

If you go the attribute route, the fastest way to get the data in is a ready-made app. The Clay integration below is a good example of what that looks like in practice.

5) Segmentation before lead scoring

People get lost in building scoring models.

I understand the pull. A weighted model feels like maturity. But scoring is a prioritization tool, nothing more, and the model you build on day one runs entirely on assumptions you have not tested.

We started with the simplest possible version: sales qualified, yes or no.

That was tied to a goal. We optimize for learning speed. A strict score early would have filtered out companies we still needed to see, and we would never have found out they were good. We only layered in tiering once we had attributes we could defend, because we had watched them play out.

What happens after the yes or no

Not sales qualified: they go into normal PLG sequences in Customer.io. No rep touches them.

Sales qualified: they enter a sales assist motion. Routing is location first. Our UK team covers Europe, our US team covers the Americas, South Pacific, and East Asia. Then some light segmentation decides whether it goes to senior AEs or the full pool.

That is the entire routing logic. No vertical ownership, no round robin with exceptions.

The reason is that we want our AEs to be athletes. Give them good data and let them decide where their hour goes. We work with them afterward to learn which attributes actually mattered, and that loop improves the system faster than any routing rule would.

Why segmentation matters more than the score

We sell a horizontal product, which makes a single score cutting across every customer type close to meaningless.

The clearest example I can give: a 20-person VC firm is a very attractive lead for us. A 20-person tech startup is an okay lead. A 150-person tech company is attractive again.

Two of those have identical headcount and completely different value. VCs use the product differently than AI startups, who use it differently than marketplaces, who use it differently than fintechs. Different behavior, different seat counts, different reasons to expand.

You can run simple scores that cut across segments. Once you go deeper than that, you have to score within them.

Agents give us better attributes than enrichment alone

For certain company and people attributes, agents get us better data than waterfall enrichment does. This is not either/or. We still use Clay. But for some fields, an agent wins, because it pulls real-time data and because you can shape the output to exactly what you need.

Industry is a typical example. There are two usual ways to get industry:

• Waterfall enrichment pulls from several providers who each define industry differently, so it’s very hard to normalize.

• Self-reported LinkedIn industry often isn’t how you’d want to classify a company, so it's not great either. For example, a SaaS company selling into healthcare will list itself as being in the healthcare industry, but for our purposes they’re a vertical SaaS company in the healthcare vertical.

Instead, we chain two agents. The first researches the company against the things we actually care about. The second classifies it strictly against our own taxonomy, with no freedom to invent a category.

The output is a custom industry value that is genuinely useful for segmentation and prioritization, because it was built against our definitions rather than a data provider’s.

Code blocks instead of nested logic

Attio workflows had long chains of if and switch statements. They worked, but nobody could read them. To change one branch, you first had to hold the full tree in your head.

We now replace those chains with one code block. The workflow becomes short, and a new person can follow it.

There is a second benefit to that: You can write unit tests for a code block in an AI coding tool like Claude Code. Run many tests against the logic before you put it in the workflow. Then do the manual test in Attio at the end. This is faster, and the quality is better.

6) What we connect Attio to

Most of our stack connects through the App store. There are a lot of integrations there, and for the majority of tools, that is the whole setup: find the app, connect it, done.

When there is no app for something, you are not stuck. The Attio SDK makes it straightforward to build your own, and that is a real difference from most CRMs, where a missing integration means a Zapier chain or a ticket to engineering.

That is how Lemlist ended up in our instance.

When I joined Attio in January, we needed to get Lemlist activities into Attio, but there wasn’t a native integration. So I vibe-coded an Attio SDK app and installed it in our instance to allow us to listen for Lemlist activities such as email sent, email reply, LinkedIn invitation sent, etc., and then associate these activities with people and their related companies and deals.

Here is what our stack looks like today:

Signals in

• Segment: product and website events land on the company record, so a rep sees usage before the call

• Market signals: funding news, job postings, and social engagement, so we know when something changes at an account

• Luma: event signups and attendance, which we track on event lists

• Attio call recorder: calls and transcripts on the deal, which the closed-lost agent reads

The system of record

• Objects: people, companies, deals, workspaces, users, and custom objects. We model the business the way it actually works instead of forcing it into someone else’s shape, which is why we get a real 360 view of a lead or a customer

• AI layer: Ask Attio, custom agents, and workflows running across all of it

Work out

• Attio sequencer: inbound follow-up

• Lemlist: outbound sequences, with activity synced back through the SDK app I built

• Slack: alerts and handoffs to the rep who owns the account

• Claude via MCP: each rep runs their own skills against the same data

Data layer

• Clay: enrichment and research on new companies and people

• Fivetran: pulls source systems into BigQuery

• BigQuery: our warehouse

• Polytomic: pushes computed attributes from the warehouse back onto Attio records

• Omni: AI analytics on top of the warehouse

Thanks, Kyle, for these hands-on insights!

If you are looking for an AI-native CRM, one that is built for how work gets done in 2026, I seriously recommend that you check out Attio.

It’s easy to set up, but if you have a development background (or your colleagues do), you will also be amazed by how easy it is to build on top of it and customize it. You can find great use cases on Attio’s Engineering blog.

And if you’re eager to learn more, explore their latest features or dive into the background of their recent out-of-home campaign.

Until next week!

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

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

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

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

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

This newsletter is kindly supported by Attio, the agentic CRM built for modern GTM teams - one that builds pipeline, advances deals, and grows accounts around the clock.

5,000+ fast-moving companies rely on Attio’s flexible data model, modern design, and builder-friendly setup to shape their CRM around how they work - not the other way around. It’s built for founders, revenue ops, and GTM teams who need a system that adapts as fast as we do.

So if 2026 is the year you finally break up with your legacy CRM or start fresh on a new project, this is the perfect moment to take Attio for a spin.

Try Attio for free

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This newsletter is sponsored by Cello - referrals on autopilot.

Partnerships are one of the most profitable GTM motions, and most teams are still sleeping on it. Usually because building a program from scratch means a week of research before the first email.

Cello built a Claude skill that does it in a few minutes. Give Partner Radar your website URL and it returns a segmented partnership strategy plus 20-25 qualified partners with LinkedIn profiles, audience size, and a personalized hook for each.

Get the Partner Radar skill

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

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

Me: “Great. Who are we selling to?”

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

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

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

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

People usua…

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

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

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

Try Miro MCP

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

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

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

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

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

Human-crafted. Community-run.

Get the skills

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This newsletter is kindly supported by Attio, the agentic CRM built for modern GTM teams - one that builds pipeline, advances deals, and grows accounts around the clock.

5,000+ fast-moving companies rely on Attio’s flexible data model, modern design, and builder-friendly setup to shape their CRM around how they work - not the other way around. It’s built for founders, revenue ops, and GTM teams who need a system that adapts as fast as we do.

So if 2026 is the year you finally break up with your legacy CRM or start fresh on a new project, this is the perfect moment to take Attio for a spin.

Try Attio for free

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This newsletter is sponsored by Cello - referrals on autopilot.

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

If you want to know what that math looks like for your product specifically, Cello built a free ROI calculator. Plug in your MAUs and contract value - it outputs projected referral ARR, LTV:CAC, and payout economics over 5 years.

Calculate your referral ROI

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

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

Join us live

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

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

Save your spot

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

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This newsletter is supported by Swan AI.

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

The architecture matters more than the agents.

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This newsletter is sponsored by 1mind.

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

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

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

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

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

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

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

Register here

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This newsletter is supported by Attio, the AI CRM.

Attio just shipped the biggest update in 2026: completely new Workflows that don’t just automate your work. They do it. Workflows became AI agents.

For years, CRMs reminded you to do the work: update the record, chase the deal, send the follow-up. Attio flipped it. You build an agent once - your best rep’s playbook - and it runs across every account: enrichment, scoring, outbound, briefings, follow-ups. Same standard every time.

And if you can describe the play, Attio builds the workflow for you. Tell it your goal. Review. Run. Close the deal.

See what you can build

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

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

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

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This post is sponsored by Ahrefs - a leading SEO & marketing intelligence platform that helps you accelerate the growth of organic traffic to your website.

Ahrefs just launched Agent A - an AI agent that builds and runs your marketing tools, reports, and automations based on your Ahrefs data.

Take it for a spin:

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Supported by 1mind → AI Superhumans for revenue teams

Superhumans (1mind’s Agentic Conversational AI Agents) integrate seamlessly into existing workflows to qualify buyers, book meetings, deliver pitches, give demos, handle objections, uncover pain points, build value models, and onboard customers. They delight buyers, drive operational efficiency, and scale beyond human limitations focusing on increasing revenue, shortening sales cycles, and increasing ACV.

Founded by Amanda Kahlow, former founder and CEO of 6sense and a trailblazer in account-based marketing (ABM) who has now created AILG (AI Led Growth). 1mind has raised $40 million from Battery Ventures, Primary Ventures, Wing Venture Capital, Harmonic Growth Partners, Operator Collective and Success Venture Partners, and prominent executives from Monday, ZoomInfo and Databricks.

See the Superhumans in action

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This newsletter is sponsored by Miro.

Miro’s MCP server connects your boards to the AI tools you already use: Claude, Cursor, Lovable, Replit, ChatGPT, and more. This turns your collaborative workspace into a live context that AI agents can read, research, act on, and build from.

Imagine sending your prototype, which you agreed on with your team in Miro, directly to Claude Code to build it in minutes — that’s a reality with Miro’s MCP server (and much more).

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