The set-up

Most of my week ran on 2 clocks at once. On the surface it was a strong start to Q3, well ahead of pace, with H1 closed above plan on both new and existing business, new leaders starting, good energy in the office. Underneath, I was building next year’s re-acceleration plan and presenting it to a room of 30-plus execs and leaders, while covering an interim country manager seat on top of my regional director role. I was running on the leftovers of my energy before the summer break. Nobody sees that juggle directly. What they see is the output, and this week the output came down to 3 calls about focus, ownership, and who gets seen.

Here is what that taught me…

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Lesson 1: Focus beats new markets, even when the pressure comes from your CEO

My CEO has asked me more than once to go more aggressive into new markets we do not operate in yet. This week I stopped answering it with conviction and answered it with a revenue ladder. I put the last 6 quarters of closed-won on the table: only my 5 core markets sit in the top 5, and the markets I was being pushed toward do not crack the top 20. I had also asked my director of marketing and growth to pull the leading indicators, MQLs, brand awareness, any early sign a new market was warming. It was not there. So I held the plan to its 5 core markets, anchored on lifting net revenue retention toward 100-plus, not on planting a flag somewhere new.

Every 2 points of NRR is worth almost 2 months of new-business target to me. That is where the next leg lives, not in a market with no signal yet.

My take: the ladder did the talking, not my belief. I could have argued focus as a principle and lost the room to ambition. Instead I showed 6 quarters of revenue and let the data hold the line. Scaling toward the next leg, my growth comes from going deeper where I already win, and the data is what makes that defensible upward.

The European read: scaling here is a country-by-country question, not a map you colour in. Depth in 5 markets you actually understand beats a thin presence in 8 you do not, and the revenue ladder is what tells you which 5 to protect.

The rule: expansion is a data decision, not an ambition. If the leading indicators are not there, the market is not there yet.

You know it worked when: you can defend your focus to your board or your CEO with 6 quarters of revenue, not a slide of conviction.

The play: 5 steps to defend focus against the pressure to expand (2026 series). Build the revenue ladder, test the leading indicators, and run a readiness gate before you open a market. Get full access below ↓

Access this GTMcraft Play [HERE}

Lesson 2: When you are stretched too thin, say it, then hand the team the ownership

Covering 2 seats this week, I hit the wall most operators hide. I chose not to absorb it quietly. I told my mid-market reps directly: I am too stretched right now, so you need to take ownership. Then I put 6 placeholder 1:1 slots in my calendar and asked each rep to book their own time and show up with 1 specific ask. No specific ask, no meeting. It was the most honest thing I said all week, and it was also the most useful.

The version of me that pretends to be available in both seats is a bottleneck. The version that names the limit and hands over the booking turns my scarcity into their ownership.

My take: admitting the capacity gap out loud felt like exposure, and it was the right call. A stretched leader who fakes availability slows everyone down. One who says the limit and makes the reps drive the ask gets a team that owns its own pipeline instead of waiting on a calendar.

The European read: on a lean team spread across markets there is no spare manager sitting in every country. When you cover 2 seats, the only thing that scales is rep ownership, so make the ask explicit and make them the one who drives it.

The rule: hidden overload breaks the quarter. Name the capacity gap, then convert it into ownership with a specific-ask rule.

You know it worked when: your reps book their own time with a real ask, and the deals keep moving in the seat you cannot fully cover.

The play: 5 steps to run two GTM seats without losing a quarter (2026 series). Map the time you actually have, set 3 explicit expectation statements, and audit the gap weekly before it costs you a deal. Get full access below ↓

Access this GTMcraft Play [HERE]

Lesson 3: Your specialists are full team members, not on-call experts

This week I told my mid-market team that our solutions engineer, does not get looped into deal reviews only when a deal looks technical. She needs to sit in every deal review, every forecast call, and every team meeting. Same for the partner manager and the overlay payroll manager. We have a habit of isolating expert roles and paging them situationally. I want them as standing members, carrying context across every deal instead of parachuting into 1. And before everyone scatters for summer, I brought my leaders together to celebrate an H1 that closed above plan on both new and existing business, because the quiet contributors who made that number deserve to be seen, not just the closers.

Standing membership and recognition are the same idea from 2 angles: the people who make the number happen should be in the room and named on a rhythm, not called in by mood.

My take: the fastest way to waste a specialist’s edge is to treat them as a resource you page. Make your SE a standing member and she carries the thread across every deal, not only the 1 I flagged as technical. Recognition works the same way. On a rhythm, it reaches the quiet contributors. Left to mood, it defaults to the loudest closer.

The European read: a lean multi-market team wins on how well its few specialists are wired into every deal, not on how many it can afford to hire. Standing membership spreads 1 solutions engineer across markets better than any org chart, and it is the cheaper edge.

The rule: a specialist you page is a specialist you underuse. Put them in every cadence, and recognise the quiet contributors on a rhythm, not by mood.

You know it worked when: your specialists raise risks early because they were in the room from the start, and recognition reaches past your top 2 closers.

The play: 5 steps to run a recognition rhythm that does not play favorites (2026 series). Rotate named recognition weekly and make specific praise a standing system, so belonging and retention do not ride on the loudest performer. Get full access below ↓

Access this GTMcraft Play [HERE]

This week’s play

I held the focus in Lesson 1. This week’s inline play is the method behind it: how I build next year’s plan early enough to actually hit it, and why I start in July, not October.

Play: 5 steps to build next year’s plan early enough to hit it (2026 series)

Fixes: late-autumn planning that lets activation lag eat the first half of next year, so a program greenlit in Q4 does not show up in the number until H2.

Best for: operators at 2-10M+ ARR building next year’s number while running this one, where hiring, marketing, and product asks all take a quarter or 2 to activate across markets.

The 5 steps:

1. Map the activation lag: for every lever (hiring, marketing programs, partnerships, product), write how long from greenlight to first impact on the number, so you can see what has to start now.

2. Build the number top-down, then stress-test it bottom-up against 6 to 7 years of your own trend lines, so ambition meets what the data actually supports.

3. Draw 2 scenarios, a realistic case and an aggressive one, and mark which the trend data supports. Assume the aggressive case is not real until it earns it.

4. Cost the initiatives and headcount behind the realistic case, so the plan is an investment ask, not a wish.

5. Start now, in July, and co-build a rolling forecast with your leaders, so programs activate before H1 instead of after it.

Template: a 2-scenario planning sheet with 1 row per growth lever: the lever, its activation lag, the realistic contribution, the aggressive contribution, and the trend-line evidence behind each. Any lever with no evidence is a wish, not a plan.

Paste this into your AI:

Here is my last 6 to 7 years of revenue by quarter and my list of growth levers for next year [paste]. Map the activation lag for each lever, build a realistic and an aggressive scenario stress-tested against the trend lines, mark which scenario the data supports, and tell me which programs have to start this quarter to land in H1.

Full play, template and workbook inside GTMcraft. Reply to this email or DM me to get access.

One question for the room

Look at your best rep, the one already hitting the number. When did you last tell them to ask for more help, not less, because the extra question is the quality injection that separates a good quarter from a great one?

Reply or send me a DM.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

Applied AI: Write The Loop - Friday July 17, 2026

For a year the pitch was to prompt Claude better. This week the operators pulling ahead stopped prompting and started writing the loops that prompt it for them.

10 signals from this week’s Claude Signal, across 5 tracked creators. 3 problems. 1 playbook to steal.

• Loop = an agent that runs Goal, Act, Check, Repeat. It writes down what done looks like, takes a step, scores the result against that, and rewrites until it passes.

• Rubric = the short written standard the loop grades against, the 5 lines that separate your best output from your worst.

This week’s number 1 move

Stop writing the task. Start writing the check.

For a year the pitch was to prompt Claude better. This week the person who leads Claude Code, Boris Cherny, put the next step plainly: “I don’t prompt Claude anymore. I have loops running that prompt Claude. My job is to write loops.” Charlie Hills shows what that looks like on real GTM work. A loop has 4 beats: Goal, what done looks like, written first; Act, the model takes 1 step; Check, something scores the result against the goal; Repeat until it passes. The beat everyone skips is the check, and it is now the whole job. Charlie built a rubric from his 110 best and worst past editions, and a fresh agent scores every draft cold. This edition went from 51 to 95 before he read a word.

My take: this newsletter runs on a version of this. A loop scores the draft against a rubric before I read it, so my time goes to the judgment, not the first pass. Writing the check instead of the task is the real change in the job, and it is the cheapest edge a lean team has this year.

Do first: take 1 recurring output you already produce and write, in 5 lines, the rubric a stranger would use to score it 0 to 100. That rubric is the check for your first loop.

The rule: if you can write the rubric, the loop can score it. Read the checks, not the drafts.

Sources: Charlie Hills, “How to (actually) use Fable 5” (Jul 5) · Ruben Dominguez, “Claude Code Loops” (Jul 7)

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of GTMcraft OS.

Problem 1: Your recurring GTM output still runs through your own eyeballs

Every week you write the same shaped thing, a follow-up, a one-pager, a weekly digest, and you are the only quality gate it passes. That is the bottleneck. Charlie Hills removed himself from it by writing a check instead. He had an agent export his 110 past editions with their open rates, compared winners to flops, and turned the gap into a rubric out of 100. Now a fresh agent scores every draft, sends anything under 95 back with notes, and only surfaces the pass. It compounds, because every miss he catches by hand becomes a new rubric line the loop never misses again. Ruben Dominguez maps the same idea onto the 4 loop types in Claude Code: turn-based, goal-based, time-based, and proactive. The work you repeat is exactly the work a loop can score.

My take: I would rather write the rubric once than re-read every draft. On a lean team the loop is the QA hire I cannot make, and it gets stricter every week instead of tired.

The European read: a lean multi-market team has no second set of eyes to check every piece, in every market’s language. The loop scores your outbound and your digests against your own rubric, so quality does not drop just because you ran out of hours in one country.

The rule: if you can write the rubric, the loop can score it. Keep the judge separate from the writer.

You know it worked when: you read a score and a pass, not a first draft, and the drafts you do read are already at your bar.

The play: 5 steps to build a measurable loop that scores its own output (2026 series). Turn 1 recurring output into a Goal, Act, Check, Repeat loop that grades itself against your rubric. Get full access below ↓

Access this GTMcraft Play [HERE]

Sources: Charlie Hills, “How to (actually) use Fable 5” (Jul 5) · Ruben Dominguez, “Claude Code Loops” (Jul 7)

Problem 2: Your team runs on the most expensive model with no routing

Fable 5, Claude’s top tier, moved to pay-per-use on July 12. Ruben Hassid gives the frame a lean team needs. Picture the top model as a senior lawyer at 1,000 dollars an hour and the cheaper Sonnet as a capable intern at 100 who still follows what the senior says. You pay the senior to set the strategy, then the intern drafts against it consistently. The failure mode is a whole team on the top model with zero training and nobody tracking usage, billing the senior rate for mundane work. Hassid’s own habit is to ask the top model 1 or 2 hard turns to set direction, then drop to a cheaper tier for the rest. Charlie Hills runs the same split inside his loop, top model plans and Sonnet executes, and Maja Voje’s design test found Claude Code wins partly because it edits in place instead of regenerating, which burns fewer tokens.

My take: now that Fable moved to pay-per-use, paying the senior rate for routine drafting is a leak a lean budget feels fast. I ask the top model 1 hard turn to set direction, then route the rest to the cheap tier, and the bill stays sane.

The European read: a lean European budget cannot subsidize the whole team running every task on the top model. Route strategy to the top tier and execution to the cheap one, and you get the same output at a fraction of the spend, which matters more as intelligence gets metered.

The rule: top model sets the strategy, cheap model does the routine execution. Never pay the senior rate for intern work.

You know it worked when: your Claude bill drops and no output got worse, because only the strategy turns hit the top tier.

The play: 5 steps to cut your Claude bill 50 to 90% (AI-First series). Route strategy to the top model and execution to the cheap one, and track usage so the bill stops running away. Get full access below ↓

Access this GTMcraft AI Play [HERE]

Sources: Ruben Hassid, “Fable 5.” (Jul 8) · Maja Voje and Anze Voje, “AI Design Tools vs Human Designers” (Jul 10)

Problem 3: Your AI content is polished, and nobody could tell it is yours

Every AI has read the same internet, so everyone’s first draft sounds the same. Kieran Flanagan gave away a Claude Skill this week that acts as a pre-publish coach rather than a rewriter. It runs 2 gates. The Share Test, would your ideal reader forward this. The Onlyness Test, could only you have written it. It spars with you until your differentiated take shows up, instead of smoothing the draft into generic prose. Maja Voje’s team reached the same place from the design side: across 6 AI tools, Claude Code won for production because it carries your brand context, but a human still owns the final taste pass on anything meant to last. Ruben Dominguez names why it compounds, you cannot improve the model, so the moat is the system you build around it, your data, your memory, your standards.

My take: the taste pass is the one thing I will not hand over. Polished is the floor now, everyone has it. The operator take, the lived European read a competitor’s agent has never had, is the moat, and no model has my week.

The European read: across markets, the thing a competitor’s agent cannot copy is your lived operator take, in your voice, on the market you actually sell in. The taste gate protects the one part of the content that is genuinely yours as you scale it.

The rule: if a competitor’s agent could have written it, it is not done. Add a taste gate that asks, could only we have said this.

You know it worked when: your published content carries a take only you could have, and readers forward it because of it.

The play: 5 steps to ship AI content only you could have written (2026 series). Add a Share Test and an Onlyness Test to your content loop, so polished stops being the finish line. Get full access below ↓

Access this GTMcraft AI Play [HERE]

Sources: Kieran Flanagan, “I Built an AI Skill to Sharpen My Taste” (Jul 10) · Maja Voje and Anze Voje, “AI Design Tools vs Human Designers” (Jul 10)

Steal this move: wrap your highest-volume GTM output in a self-scoring loop

Play: 5 steps to build a measurable loop that scores its own output (2026 series)

Fixes: recurring GTM output that still passes through your own eyes as the only quality gate, so your time goes to re-reading instead of judgment.

Best for: a founder or operator at 0 to 10M+ ARR producing recurring output like outbound, competitor digests, or content by hand and checking it yourself.

Template: a 5-line rubric scored 0 to 100, plus a folder of 3 to 5 best-in-class past examples as the answer key the loop grades against.

Paste this into your AI:

Here is 1 recurring GTM output I produce and 3 to 5 of my best past examples [paste]. Draft the 5-line rubric a stranger would use to score it 0 to 100, then set up a loop that drafts, scores against the rubric, and rewrites anything under 95. Keep the judge separate from the writer, and end with a taste-gate question that asks whether only we could have written it.

Full play, template and workbook inside GTMcraft OS. Reply to this email or DM me to get access.

Also on the radar

• Ruben Dominguez, “Self-Evolving Agents” (Jul 10): the reason agents compound is a memory layer, which cuts tokens about 90% and latency about 91%. Give your top GTM agent a notes file it appends 1 lesson to after every run.

• Maja Voje and Anze Voje, “AI Design Tools vs Human Designers” (Jul 10): their 6-tool design bake-off landed on a clean split, Claude Code for production, humans for the final pass. Run your next on-brand asset in Claude Code with your brand guide loaded.

Why this matters now

For a year the whole game was better prompts. This week 3 separate creators described the same next step: you stop being the one who prompts and become the one who writes the check. This builds on The GTM Engine from July 5, where the move was to put recurring GTM work on a schedule. The loop is the next layer: the scheduled work now scores itself and improves without you. And it lands the same week intelligence got metered, so routing the cheap execution away from the top model is no longer optional.

For a lean European team scaling toward 10M: write the check, route the model, and keep the taste pass. The edge is not access to the model, it is the loop, the routing, and the take you build around it.

What is your number 1 takeaway this week? Reply or send me a DM.

Go deeper with GTMcraft OS

Scale on plays, not prompts.

You do not need another newsletter, podcast or LinkedIn post. You need the system under it.

• Read the plays. Every play, live and current, inside GTMcraft OS.

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• Get the markdown. Drop the raw play into your own stack or AI tool.

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• The full Plays database is over 100 plays and growing every week.

Curious to see a sneak peek of GTMcraft OS, DM me or reply on this email.

See you inside GTMcraft OS, your GTM co-operator. Not another community.

Koen

Your (human) GTM Agent

Applied GTM: Pay For Behavior - Wednesday July 15, 2026

People do what you pay them to do, not what your strategy deck says. That is the whole edition.

11 signals from 41 GTM newsletters and the signal databases. 3 problems. 1 playbook. Covers July 5 to 11. Read time ~7 min.

This week’s number 1 move

Treat comp as the operating system for your GTM.

Brian Le scaled Notion’s sales comp from 80 people to over 400, and his framing is worth stealing: comp is the operating system for go-to-market. Most teams build it last and blame it first. It is the thing that turns your strategy into what a rep actually does. Jason Lemkin made the same point from the opposite end this week. He is churning off a vendor he was a founding reference account for, and the reason is the incentive, not the product. A discount would have kept him, but no one there is comped to save the account, so no one picks up the phone.

My take: I just spent a week where comp and ownership decided everything, the rep who owns his number versus the one who waits on my calendar. Your comp plan is the quietest instruction you give a team, and they follow it far more closely than any kickoff speech.

Start here: open your own comp plan and, line by line, name the single behavior each component pays a rep to do. Any line that does not map to a real priority this half is a leak, quietly teaching your team to do the wrong thing.

The rule: your plan is not a spreadsheet you true-up at quarter-end. It is the machine that decides what the whole team does all quarter.

Sources: GTMnow, “The Compensation Blueprint for a High-Performing Sales Team” (Jul 10) · SaaStr, “Should You Discount to Save a Renewal” (Jul 8)

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Problem 1: You run comp on a spreadsheet, and it is quietly degrading

Brian Le ran Notion’s commissions by hand across stitched-together spreadsheets before he automated them, and he is blunt that every comp plan lives on 3 inputs: pay mix, quotas and pay curves, and governance. When one stops talking to the others, the plan degrades. You see it first in the pacing, deals bunching at the end of the quarter, then in the distribution. CJ Gustafson tells the finance half. At a company going from 10M to 150M ARR, commissions ran out of 1 Excel tab, and it worked fine right until they hired into the plan. A few hundred reps on custom deals turned that tab into a 6-figure true-up that blew up the quarterly projection. Solve the manual thing before it gets huge, because “that will be a good problem to have” is how you pay for the mistake twice.

My take: I read pacing and distribution across markets every quarter before I read the payout. When deals bunch in the last 2 weeks or everyone is over 100%, the plan is talking, not the team, and I diagnose by segment before I touch anything.

The European read: one comp plan has to work across several markets with different deal sizes and norms, so it degrades faster than a single-country plan. Read the pacing and distribution per market, and fix by segment, not with a blanket change that breaks a country that was fine.

Start here: pull your last 2 quarters of attainment by rep and check 2 things: whether deals bunch in the final 2 weeks, and whether everyone is over 100%. Either one means the plan is off, not the team.

The rule: if everyone hits quota, the quota is wrong. If no one does, the plan is broken. Read the pacing and the distribution before the payout.

You know it worked when: you can name the behavior every comp line pays for, and deals stop bunching at quarter-end.

The play: 5 steps to pay for the behavior you actually want (2026 series). Wire each comp component to a named behavior, then read pacing and distribution before the payout. Get full access below ↓

Acccess the GTMcraft Play [HERE]

Sources: GTMnow, “The Compensation Blueprint” (Jul 10) · Mostly Metrics, “Tactics for Budgeting at Hyperscale” (Jul 7)

Problem 2: No one is paid to save the base, so it walks

Jason Lemkin is churning off a vendor he championed for years, and his point is the incentive, not the product. Move your whole customer success team into sales, comp everyone on new logos and expansion, and the quiet work of pulling a wavering customer back belongs to no one. The number backs him up. Median private B2B net revenue retention has slipped to about 101%, barely above break-even, while companies above 120% trade at roughly a 63% premium. Hakan Ozturk shows where the save is actually decided, and it is at kickoff, not the renewal call. Most account plans get filled in once and never reopened, so the 500K renewal gets the same attention as the 20K one, and the exposure surfaces at T-14, the one week there is no room left to fix it.

My take: EB just hit 100% NRR for a full quarter on my team, and it happened because someone owns the base, not because the product got better. The renewal nobody is paid to save is the one you lose, and you lose it quietly.

The European read: in fragmented markets the base you already won in a country is your cheapest growth, cheaper than opening the next market. If no one in that country is comped to save it, it churns while you are busy chasing the new logo elsewhere.

Start here: list your 5 largest renewals in the next 90 days and, next to each, name the 1 person whose comp changes if it is saved. Every blank is unowned revenue.

The rule: if no one is paid to save the renewal, it is not being managed, it is just scheduled. Score the risk at kickoff, not at T-14.

You know it worked when: every top renewal has a named owner whose paycheck moves with it, and exposure shows up in week 1, not week 50.

The play: 5 steps to defend your NRR before renewal day (2026 series). Build a T-120 renewal board, score each account on hard signals, and trigger weekly saves before the base walks. Get full access below ↓

Access the GTMcraft Play [HERE]

Sources: SaaStr, “Should You Discount to Save a Renewal” (Jul 8) · The CS Cafe, “The Success Plan That Survives a CFO Question” (Jul 5)

Problem 3: A mis-hire and a slow ramp burn capacity you already bought

Brian Le’s sharpest line is that a rep who quits over a broken plan is a capacity problem, not a payroll line. You lose every dollar you spent ramping them, then pay it again on the backfill. Jason Lemkin shows how to spot a mis-hire fast: every VP has 1 number that is their job. Revenue per lead and velocity for a VP of Sales, qualified leads for marketing, net retention for CS. If that number does not move in 1 cycle, it is not going to. Nick Cegelski shows how to protect the ramp: onboard in the order industry, then product, then sales, because under stress reps fall back on what they learned first, and they need the prospect’s world before the pitch. Pair every classroom hour with a doing hour so reps build real pipeline during ramp.

My take: I just closed a hire on exactly this logic, hire on the 1 number, verify with real work, ramp on doing. A mis-hire on a lean team is not a payroll line, it is a capacity hole you feel immediately and pay for twice.

The European read: a lean team spread across markets feels a mis-hire the week it happens, because there is no bench to absorb it. Ramp in the order industry, then product, then sales, so a new rep in a new country reaches real pipeline faster and you stop paying twice for one seat.

Start here: take your most recent leadership or rep hire and write the single number that is their job. Then check whether it moved in their first full cycle. If it did not, you have your answer.

The rule: every hire has 1 number that is their job. If it has not moved in 1 cycle, it is a mis-hire, and every week you wait burns capacity you already bought.

You know it worked when: your last hire’s 1 number moved in cycle 1, and new reps build real pipeline during ramp, not after it.

The play: 5 steps to ramp a new rep to real pipeline (2026 series). Onboard industry then product then sales, pair every classroom hour with a doing hour, and hire on the 1 number. Get full access below ↓

Access the GTMcraft Play [Here]

Sources: SaaStr, “What Happens When You Hire the Wrong VP” (Jul 6) · 30MPC, “How to Build a World-Class Onboarding Plan” (Jul 7)

Steal this move: wire your comp to the behavior in 4 weeks

Play: 5 steps to pay for the behavior you actually want (2026 series)

Fixes: a comp plan that quietly rewards activity you did not actually want, so the team does the wrong thing on Monday.

Best for: operators at 0 to 10M+ ARR running 1 plan across several European markets, where a blanket change breaks a country that was working.

The 4-week move:

1. Week 1, audit the plan. Map every component to the 1 behavior it pays for. Flag any line that does not map to a real priority this half.

2. Week 2, read the degradation. Pull 2 quarters of attainment by segment and market. Check for end-of-quarter bunching and everyone over 100%.

3. Week 3, own the base. Name the person whose comp moves for each of your top renewals, and put a real incentive on the save.

4. Week 4, protect the ramp. Write the 1 number for every recent hire and check it moved in cycle 1. Fix onboarding to industry, product, sales.

Template: a 1-line-per-component table: component, the behavior it pays for, the priority it maps to this half, keep or fix. Any component with no real priority is a leak.

Paste this into your AI:

Here is my comp plan by component and 2 quarters of attainment by rep and segment [paste]. For each component, name the single behavior it pays for and whether it maps to a priority this half. Then flag end-of-quarter bunching and any segment where everyone is over 100%, and list the 3 changes that would most cleanly point the plan at the behavior I want.

Full play, template and workbook inside GTMcraft OS. Reply to this email or DM me to get access.

Also on the radar

• SaaStr, “5 Learnings from ServiceTitan at 1B ARR” (Jul 10): revenue grew 25% while sales and marketing spend rose 5.6%, and margin doubled. Put your revenue growth rate next to your sales-spend growth rate, and if spend is outpacing revenue, that is your leak.

• PricingSaaS, “Inside Docusign’s Enterprise Credit Model” (Jul 10): Docusign moved enterprise pricing off seats to a credit model, charging nothing for search. Check whether your biggest account would pay more or less on usage than on seats.

3 questions for the room

• Read your own comp plan line by line. Is there a single line paying for a behavior you do not actually want?

• List your 5 biggest renewals. For how many can you name the person whose paycheck moves if the account is saved?

• Name the 1 number that is the job for every person you hired this year. Any blanks?

What is your number 1 takeaway this week? Reply or send me a DM.

See you inside GTMcraft OS, your GTM co-operator. Not another community.

Koen

Your (human) GTM Agent

Week in the Trenches: Own The Room - Monday July 13, 2026

This week I closed a 6-week hiring marathon, told a rep to be more egoistic, and drove to a customer instead of dialing in, and all 3 came down to the same thing: who shows up and owns it.

From my week as a European GTM operator. 3 lessons. 1 play to steal. Read time ~5 min.

The set-up

Most of my week was invisible. On the surface it was a strong start to Q3, revenue goals already at 20% of the quarter, all 3 Benelux leadership seats filled, a customer trip to Ghent. Underneath, I was running 2 roles at once, my international director seat and the interim Benelux country manager seat, while quietly building the FY27 re-acceleration plan I present in Munich this week. Nobody on the team sees that juggle directly. What they see is the output, and the output this week came down to 3 calls about presence and ownership.

Here is what that taught me…

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Lesson 1: A great hire is a decision you make fast and verify slowly

We closed a hiring marathon this week that had run 6 weeks and cost 2 earlier finalists a no. Our new Benelux country sales manager starts September 1. Here is the part that matters: I knew inside the first 15 minutes of the case interview that I saw myself working with this person, a clear bar raiser who could lift the whole mid-market team. The other 8 conversations, the cross-functional interviews, the cultural round, the CRO interview and the founder interview with the CCO, the team meet-and-greet, were not me deciding. They were me making sure, and letting the team decide into it before the offer went out.

Conviction and rigor are not opposites. The 15 minutes gave me the conviction. The 8 conversations earned the team’s buy-in, so the hire lands with a bench that already met them.

My take: I ran the full marathon rather than settle, and I would do it again. The 15 minutes gave me the conviction. The 8 conversations are what made the hire the team’s, not just mine.

The European read: on a lean team spread across markets, one leadership hire lifts or drags the whole bench, so the bar has to clear your best performer, not just fill an open seat in one country.

Do first: for your next leadership hire, write down in one line whether you saw a bar raiser in the first 15 minutes. If you did not, no number of interviews will fix it.

Do this week: replace one interview with a live working case on a real problem, and score the candidate against your best current performer in that role.

Do this month: bring the team into the process before the offer, a meet-and-greet or informal input, so they own the hire with you.

The rule: if you need 8 interviews to decide, it is a no. Use them to be sure of a yes, and to let the team own the hire with you.

You know it worked when: the team met the finalist before the offer and is already pulling for them on day one.

The play: 5 steps to hire GTM bar-raisers on a real work sample (2026 series). A paid week-one proof task, a scorecard against your best performers, and the discipline to say no. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Lesson 2: Tell the underperformer to be more egoistic

The most honest conversation of my week was a mid-year review with a mid-market rep sitting at 75% in his first mid-market quarter. I did not soften it. I told him to become a little more egoistic: stop waiting for my time when you are stuck, ask for help directly, and take ownership of your own pipeline instead of pacing to the partner or the customer.

The concrete version was 3 things. Prioritize revenue-generating activity over everything else. Know your top 5 deals cold, full MEDDICC, by heart, not pulled from AI in the meeting. And be the orchestrator of each deal, the one who sets the pace, rather than the one who waits on it. Ownership of your pipeline is not optional, and no manager’s calendar is a substitute for it.

My take: giving a rep permission to be egoistic about their own number is some of the kindest coaching I know. Waiting on my calendar was never going to move his pipeline for him.

The European read: on a lean team spread across markets there is no manager with spare bandwidth in every country, so rep ownership is the only thing that scales. The number cannot wait on one person’s calendar.

Do first: in your next 1:1 with a struggling rep, ask them to walk their top 5 deals from memory on full MEDDICC. Where they cannot, that is the gap.

Do this week: agree the 2 or 3 revenue-generating activities that matter most, and cut a low-value commitment to protect them.

Do this month: set a self-sourced pipeline expectation for every rep, so owning the book is the standard, not the exception.

The rule: a rep who waits on your calendar does not own their pipeline. Give them permission to be egoistic about their own number.

You know it worked when: a rep asks you for help on a specific deal step instead of waiting, and can recite their top 5 without notes.

The play: 5 steps to make every AE own pipeline generation in their book (2026 series). Move deal and pipe ownership to the rep, so the number does not wait on a manager’s time. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Lesson 3: Show up in person, for the rooms that decide the number

My one word for the week was in-person. I drove to Ghent for a customer rather than taking the call remotely, and I keep coming back to the same read: the deals and relationships that move the number are still won in the room. But it is a spend, so I am deliberate about it. I bring a second person only when the right senior personas are on the customer side, so the extra seat has someone to talk to. And I weigh the whole contract value, not just the first-year ACV, because a lower ACV deal with a large multi-year commitment can easily justify the trip.

This is sharper in Europe than most places. Showing up in person means travel across markets, not a cab across town, so every visit has to earn its cost. The upside is real: presence closes what a video call leaves half-open, and it is an edge a fully remote competitor will not pay for.

My take: in person is a spend, so I am deliberate about it, but the rooms that decide the number are still won face to face, and a remote-only competitor will not pay for that.

The European read: showing up in person means travel across markets, not a cab across town, so every visit has to earn its cost. The ones that do are an edge a remote-first competitor skips.

Do first: for your next in-person visit, confirm 2 things before you book: are the senior personas in the room, and does the total contract value, not just the ACV, justify the travel.

Do this week: pick one deal you have been running remotely where the senior buyer is reachable, and get in the room.

Do this month: set a simple travel rule for the team, senior personas confirmed plus total contract value over a threshold, so visits are deliberate, not habitual.

The rule: in person is an investment, not a default. Spend it where the senior personas and the whole-contract value both say yes.

You know it worked when: your in-person visits close at a higher rate than your remote ones, and you can name why you went.

The play: 5 steps to run team selling that multi-threads every deal (2026 series). Bring the right second person into the room and thread the buying group, so a visit closes more than it costs. Get full access below ↓

Access the GTMcraft Play for free [HERE]

This week’s play

You hired the bar raiser. This week’s inline play is the next move: ramp them fast, so the seat you just filled starts paying.

Play: 5 steps to ramp a new rep to real pipeline (2026 series)

Fixes: a mis-hire and a slow ramp that burn the capacity you already bought, so you pay twice for one seat.

Best for: operators at 2-10M ARR onboarding a new rep or leader into a lean, multi-market team, where a slow ramp in one country stalls the whole number.

The 5 steps:

1. Write the 1 number that is the job before day one, so the hire and the manager both know what ramped means.

2. Onboard in the order industry, then product, then sales, because under stress reps fall back on what they learned first.

3. Pair every classroom hour with a doing hour, so reps build real pipeline during ramp, not after it.

4. Run 20-80 after onboarding: 1 skill taught in visceral detail each month, reinforced weekly with 4 real call or deal reviews.

5. Check the 1 number at the end of cycle 1. If it did not move, act, because every week you wait burns capacity you already bought.

Template: a ramp scorecard with the 1 number that is the job, the week-1 doing task, the skill taught this month, and a cycle-1 checkpoint banded moved or not moved.

Paste this into your AI:

Here is a role I am ramping and the 1 number that is its job [paste]. Draft a 30-day ramp plan ordered industry, then product, then sales, with a doing task paired to each classroom block, a 20-80 skill schedule for months 2 and 3, and a cycle-1 checkpoint that tells me whether the number moved.

Full play, template and workbook inside GTMcraft OS. Reply to this email or DM me to get access.

One question for the room

Look at your last hire, in your market: did you decide in the first 15 minutes and spend the rest making sure, or did you talk yourself into a maybe over 8 interviews?

Reply or send me a DM.

Go deeper with GTMcraft OS

Scale on plays, not prompts.

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• Read the plays. Every single play is free and live in the Operator Room on Notion. Click through from any edition.

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Curious to see inside GTMcraft OS, DM me or reply on this email.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

Applied AI: Build The Engine - Friday July 10, 2026

For a year the pitch was to prompt Claude better. The operators pulling ahead stopped prompting and started building systems that run their GTM work while they sleep.

12 signals from this week’s Claude Signal, across 5 tracked creators. 3 problems. 1 playbook to steal. Read time ~7 min.

• Routine = a Claude agent that runs on a schedule or a trigger, in the cloud, with nothing open on your laptop.

• Agent = software that reads your context and takes steps on its own, instead of a prompt you fire each time.

This week’s number 1 move

Put one recurring GTM job on a schedule instead of on your attention.

Maja Voje and Kyle Poyar surveyed 200 GTM operators for their Claude for GTM Pulse Report, and the highest-impact use case was GTM engines: systems where Claude reads your context, connects to your stack, and runs while you sleep. 67% said they had built workflows that were previously impossible, and only 27% had replaced a tool, so this adds capacity to the team rather than swapping a tool. Kieran Flanagan shows the smallest version: a Routine, an agent that runs on a schedule or a webhook (a signal one app sends another when something happens, like a form submission), in the cloud, with nothing open on your laptop. His Monday competitor digest pulls a week of competitor moves, checks them against his positioning, and drops the 3 that matter into Slack, every Monday, without him.

My take: I already run one. Every Friday an agent walks me through the same 5 questions about my operator week and hands me back a clean writeup, which is where this newsletter starts. It runs whether I am in the mood or not, and that is the point. A lean team does not need the full engine this week, it needs one job that stops depending on you remembering to do it.

Do first: pick the one GTM job you repeat every week, competitor scan, inbound triage, or content brief, and write in one line what good output looks like and where it should land, a Slack channel or a Gmail draft. That line is the spec for your first agent.

The rule: if a GTM task happens on a clock or a trigger, it is a Routine, not a thing you remember to do.

Sources: Kieran Flanagan, “The 4 New Claude Code Features for GTM Operators” (May 22) · Maja Voje and Kyle Poyar, “Claude for GTM Pulse Report” (Apr 24)

Hi, it is Koen Stam and welcome to GTMcraft OS: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Problem 1: You still run competitor intel, inbound, and content briefs by hand every week

This is the clearest GTM win in Claude right now, and almost nobody has set it up. Kieran Flanagan’s Routines turn recurring work into agents that run on a schedule, a webhook, or an API call. 3 map onto a lean team: a Monday competitor digest that pulls the last 7 days of competitor posts, product updates, and reviews, checks them against your positioning, and drops the 3 that matter into Slack at 6:30am; an inbound draft agent where a high-fit form submission fires a webhook, Claude reads the company and the role and any prior call, and leaves a first-touch email in your voice as a Gmail draft for a rep to send in 30 seconds; and a Friday content brief that reads your best posts and hands you 5 angles for next week. The survey backs it: 92% save time, 67% built workflows that were previously impossible.

My take: we are wiring exactly this into our Q3 plan, an AI layer under the mid-market pipeline motion, because the work is already repeatable and we are just still the ones running it. On a team with no ops hire, a Routine is the closest thing you get to an extra teammate, and it works across every market at once, not just the one you had time for.

The European read: the competitor scan and inbound triage that a big team gives to an ops person is the exact work a lean multi-market team never gets to. An agent runs it across all your countries on the same schedule, so coverage stops being a function of who had a spare hour.

Do first: list the recurring GTM jobs you do by hand each week and circle the one that is pure pattern, like the competitor scan or inbound triage.

Do this week: build it as a Routine on a schedule, connect Slack or Gmail, run it once, and tighten the prompt. Keep a human approval step on anything that goes to a customer.

Do this month: add a webhook-triggered Routine, like the inbound draft agent that fires off your high-fit lead form.

The rule: if a GTM task happens on a clock or a trigger, it is a Routine, not a thing you remember to do.

You know it worked when: a useful competitor digest or inbound draft shows up in Slack or Gmail without you starting it.

The play: 5 steps to put your GTM busywork on autopilot while you sleep (2026 series). Turn one recurring GTM job into an agent that runs on a schedule, without you. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Sources: Kieran Flanagan, “The 4 New Claude Code Features for GTM Operators” (May 22) · Maja Voje and Kyle Poyar, “Claude for GTM Pulse Report” (Apr 24)

Problem 2: Your reps and RevOps burn hours reading the CRM and call recordings

The other Kieran Flanagan play is Claude driving your real, logged-in browser. One instruction: pull every deal in stage 3 for more than 21 days, read the last 5 notes on each, and tell me which have a clear next step, which are waiting on us, and which need a manager this week. That is a RevOps morning as a single prompt. Same for calls: read the last 10 calls we lost on pricing, name the 3 most common objections, and show me where reps stumbled, and you have a coaching brief before the team review. Maja Voje and Kyle Poyar found the winners wire Claude into a stack, with Claude as the intelligence layer reading across the tools, and the reading and summarizing is the part to hand over.

My take: the reading is exactly the low-value time a lean team cannot spare. I would rather my reps spend their hours in the customer conversation and let Claude do the CRM read-through, especially when the calls are in 3 languages and nobody has time to review them all. Hand over the summary, keep the decision. The decision is still yours.

The European read: a multi-market team reads deals and calls across several languages, which is slow and gets skipped. Claude reads all of it and hands you the pattern, so a lean team gets the coaching brief a bigger single-market team would staff a person to produce.

Do first: pick the CRM or call read-through you do most, like the stuck-pipeline review or a lost-deal debrief, and write it out as one prompt.

Do this week: connect Claude to your logged-in CRM or call tool, run that prompt once, and check the output against the source before you trust it.

Do this month: run 2 read-through jobs on a schedule, for example a Monday stuck-deal review and a weekly lost-call debrief, and review them between meetings.

The rule: if the task is reading your own CRM or calls and summarizing, hand it to Claude and keep your time for the decision.

You know it worked when: your Monday pipeline review starts from a Claude-built table, not a manual scroll through the CRM.

The play: 5 steps to turn CRM and call reading into a 12-minute scan (2026 series). Hand the CRM and call read-through to Claude, keep your time for the decision. Get full access below ↓

Access the GTMcraft play for free [HERE]

Sources: Kieran Flanagan, “The 4 New Claude Code Features for GTM Operators” (May 22) · Maja Voje and Kyle Poyar, “Claude for GTM Pulse Report” (Apr 24)

Problem 3: Your GTM agents drift and cannot scale because they have no shared system

Agents are only as good as the context they run on, and this is where most GTM teams stall. Maja Voje and Kyle Poyar found that 93% of Claude power users treat detailed company context as the foundation, and the operators seeing real returns build context systems, a context file plus saved skills plus connectors, rather than better prompts. A context file is a plain document holding your ICP, positioning, and voice that the agent reads every run. A skill is a saved capability you call, so you do not rewrite the instructions each time. Ruben Hassid’s reversal this week is the how for a lean operator: stop running on loose files and folders, which leak old context and rot when nobody maintains them, and build skills you call and projects where one account’s context lives and stays put. Build the system once and every agent inherits it.

My take: this is the difference between 3 clever agents and a team that scales. We are standing up several agents at once, account search, partner intent, an AE layer, and if each runs on its own scattered prompts, they drift the moment I look away. One shared context file, in our voice, is what keeps them saying the same thing as we open the next market.

The European read: when you open a new country, a shared context system means your one positioning, in your voice, is applied on the first pass, instead of every rep re-teaching the agent what you sound like. The context is the thing that travels across markets, so a fix in one file cascades to every country at once.

Do first: write one context file for your product: your ICP, your positioning, your voice, and the 3 things Claude always gets wrong.

Do this week: turn your most-repeated GTM task into a saved skill and your top account into a project, and point your Routines at that context file so every run starts from your standard.

Do this month: give the team one shared set of skills, so a fix cascades instead of everyone rebuilding the same one.

The rule: if your agents run on prompts instead of a context system, they drift. Put the context in files they read every time.

You know it worked when: a new GTM task starts from your context system and sounds like your team on the first pass.

The play: 5 steps to turn your judgment into a context layer Claude reads (2026 series). Put your ICP, positioning, and voice in files every agent reads, so they stop drifting. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Sources: Maja Voje and Kyle Poyar, “Claude for GTM Pulse Report” (Apr 24) · Ruben Hassid, “I was wrong about Claude” (Jul 1)

Steal this move: put your first GTM job on a schedule in 4 weeks

Play: How to Become AI-First as Founder or Revenue Leader (AI-First series)

Fixes: recurring GTM work still done by hand, and AI used as one-off prompts instead of an operating system the whole team runs on.

Best for: a founder or operator at 0-10M ARR with a lean, multi-market team and no ops hire, ready to move one weekly GTM job off their attention and onto an agent.

• Week 1, Define: pick one weekly GTM job. Write what good output is and where it lands, a Slack channel or a Gmail draft. Draft one context file: ICP, positioning, voice.

• Week 2, Build: create the Routine on that schedule, or a webhook off your lead form. Paste the prompt and connect Slack, Gmail, or your CRM. Point it at the context file.

• Week 3, Validate: let it run for a week. Check each output, tighten the prompt, and add a self-critique step until you would ship the output without edits.

• Week 4, Operate: add a second Routine, share the skills with the team, and keep a human approval step on anything that goes external. Track agent output weekly, in one shared channel.

Paste this into your AI:

Here is a weekly GTM job I do by hand [describe it], and here is my ICP, positioning, and voice [paste]. Draft the agent spec: the exact steps, the input it reads, the output format, and where it should land. Then write the self-critique step it should run on its own output before handing it to me.

Access the GTMcraft Play for free [HERE]

Also on the radar

• Anthropic, “Redeploying Fable 5” (Jul 1): keep a human check on any agent whose output ships unread. Verify before you trust a Routine that runs without you.

• Maja Voje and Kyle Poyar, “Claude for GTM Pulse Report” (Apr 24): the command line beats connectors on reliability, 100% versus about 72%, at a fraction of the cost, so build your most important Routine on the most reliable path.

Why this matters now

The operators seeing real returns share one move: they turned recurring GTM work into agents and gave those agents a shared context system to run on. Ownership of your instructions is the floor. The engine is what you build on it.

For a lean European team scaling toward 10M: put one recurring job on a schedule, hand the reading to Claude, and keep your hours for the customer conversation and the decision.

What is your number 1 takeaway this week? Reply or send me a DM.

See you inside GTMcraft OS, your GTM co-operator. Not another community.

Koen

Your (human) GTM Agent

Applied GTM: Precision Over Volume - Wednesday July 8, 2026

Half the year is gone, and every operator worth reading last week made the same call from a different seat: in the second half, precision beats volume.

9 signals from 41 GTM newsletters and the signal databases. 3 problems. 1 playbook. Covers June 28 to July 4. Read time ~7 min.

This week’s number 1 move: Win H2 on precision, not on more volume.

2 posts made the same case from opposite ends. Nick Cegelski at 30MPC broke down how deals actually close at a deadline. You project-manage each one to a signature with a reverse work-back plan, and a deal you cannot build a believable plan for belongs to next quarter. Elias Torres, on GTMnow, framed the other half. Companies spend about 1 dollar on software for every 6 on labor, and at Drift 200 people still touched only 4 to 5% of the customer base. So he is chasing 1B dollars in revenue with under 100 people by pointing effort at the customers everyone else ignores. Same lesson from both seats: in the second half, the number comes from getting specific about the few deals, customers, and moves that pay.

My take: I just closed a quarter across several markets, and the pattern held every time. The deals that closed had a dated plan the buyer co-owned. The rest were effort with hope attached. Precision comes down to which 10 deals you actually work this week.

Start here: list every open deal in your Q3 pipeline and write the single next step and the date it has to happen for the deal to close this quarter. The deals where you cannot name a believable next step are not Q3 deals. That shorter list is your honest pipeline.

The rule: if you cannot name the next step and the date, it is not pipeline. It is hope with a number on it.

Sources: 30MPC, “How to Close Your Deals Before End of Quarter” (Jun 30) · GTMnow, “Inside How Agency is Building to 1B with Fewer Than 100 People” (Jul 2)

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Problem 1: Everyone bought the same list

Cannonball GTM went deep on logistics and found the 525,000-name FMCSA carrier directory picked clean and spammed to uselessness, because every competitor bought the same file and ran the same sequence. The edge moved to trigger data almost nobody mines: safety-score drops, insurance-lapse filings, equipment liens that signal a fleet expansion. Jordan Crawford on On the Edge makes the same case for every industry. When every team buys the same AI and sends the same clean, generic email, the win is pointing your targeting at public records that prove a real problem right now. His line to keep: let the machine find the pain, let the human write the message. None of this needs a new tool, only a signal you are not using yet.

My take: this bites harder in Europe than anywhere. In a single small country the buyable list is a few thousand names, and every competitor is emailing the same ones this week. The trigger, read off a national register or a public filing, is the only way to reach an account before the whole market does. And the register is different in every country, which is exactly why a generic list loses and a local signal wins.

The European read: the specific trigger travels across markets better than any list, because each country has its own public data and its own noise level. Build the signal once, localize the source per country, and you have an edge the account list can never give you.

Start here: take your current outbound list and ask one question. Could a competitor buy this exact list today. If yes, it is not an edge, and you need a trigger.

Do this week: pull your last 20 closed-won accounts, find the one public event that happened just before they bought, and build one small list of companies showing that same event this month, in the country you are working.

Do this month: stand up one trigger-based list as a standing weekly feed, not a one-off pull, so reps reach accounts as they enter the buying window.

The rule: if a competitor can buy your exact prospect list, it is not an edge. Target the trigger that proves the problem, not the title that fits the persona.

You know it worked when: a smaller, trigger-built list books more meetings than your last big generic send.

The play: 5 steps to build a prospect list your competitors cannot buy (2026 series). Swap the bought list for a trigger-built one your rivals cannot replicate. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Sources: Cannonball GTM, “Public Data Sources for the Logistics and Transportation Industries” (Jul 3) · On the Edge by Blueprint, “Programmatic GTM Playbooks” (Jul 1)

Problem 2: You staff for volume while the number comes from a leaner team

Elias Torres and Elena Verna attack the same reflex from 2 sides: adding people is not the same as adding output. Torres points at the waste directly. Companies spend roughly 1 dollar on software for every 6 on labor, and most of that labor chases new logos while the base they already won goes untouched. That is why he is building toward 1B dollars with a team under 100. Elena points at how hiring itself breaks now. Everyone has learned the vocabulary of AI competence, so a confident interview tells you almost nothing. The only honest signal is a work sample or a paid trial where the person does the job in front of you. Put them together: stop treating headcount as capacity.

My take: I lived the hiring half this week. We said no to 2 strong-sounding finalists because good was not the bar, and I would rather cover the seat myself than add someone who does not lift the team. On a lean European team you feel one wrong hire immediately, and you feel an untended customer base a quarter later. Both are the same mistake: confusing more people with more output.

The European read: a lean team spread across markets already runs at the edge of capacity, so the growth is usually in the base you already own in a country before it is in a new seat in the next one. Point the team you have at expansion and retention first, and hire on proof, not talk.

Start here: take your next open req and write the one-page proof task the person would do in week one. If you cannot describe the job as a concrete task, you are hiring a title.

Do this week: map where your team spends its hours against where your revenue comes from, and name the one activity that eats the most time and touches the fewest euros.

Do this month: before you approve the next hire, ask what a sharper use of the team you already have would do instead. Add the seat only if the answer is still a person.

The rule: headcount is not capacity. Hire on a work sample, and point the team you have at the revenue you are ignoring before you add a seat.

You know it worked when: your last hire was chosen on a work sample they completed, and your team’s hours line up with where the revenue actually is.

The play: 5 steps to add output without adding headcount (2026 series). Point the team you have at more of the number before you open a new seat. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Sources: GTMnow, “Inside How Agency is Building to 1B with Fewer Than 100 People” (Jul 2) · Elena’s Growth Scoop, “Please Stop the AI Confidence Theater” (Jul 2)

Problem 3: Your pipeline is padded with vanity

Maja Voje and Nick Cegelski go after the same lie from opposite ends of the funnel: the number that looks like progress but never converts. Maja lays out 5 ways to run LinkedIn for pipeline and is blunt that followers and impressions are not the goal, ICP-fit leads are, and 66% of her survey said LinkedIn is where the B2B money is, so the channel is real only if you measure the right thing. Cegelski makes the same point one stage down. A deal you cannot build a believable work-back plan for, with a real next step and a date, does not belong in this quarter’s pipeline. Counting it as forecast is hope with a number on it.

My take: this is the one I check first every Monday. Reach that never becomes an ICP-fit conversation, and deals with no dated next step, are the 2 numbers that make a bad quarter look survivable until the last week, when it is too late to fix. Strip both out early and you get an honest picture while you can still act on it.

The European read: across fragmented markets it is easy to mistake reach in a big country for demand in the country you actually sell in. Count ICP-fit conversations and dated deals per market, not aggregate impressions, or you will forecast a number that lives in a market you are not really winning.

Start here: open your pipeline and mark every deal where you cannot name the next step and the date it happens. Total it. That is the gap between your forecast and your real pipeline.

Do this week: take your top LinkedIn activity from the last month and check what it produced: followers and likes, or a conversation with someone in your ICP. Keep the one that started a real conversation.

Do this month: set one rule for pipeline entry, a named next step with a date, and one rule for channel spend, ICP conversations over impressions, so vanity stops inflating both ends.

The rule: if you cannot name the next step and the date, it is not pipeline. If reach never becomes an ICP-fit conversation, it is not demand.

You know it worked when: your forecast only holds deals with a dated next step, and your channel report counts ICP conversations, not impressions.

The play: 5 steps to strip the vanity out of your pipeline and forecast (2026 series). Cut the reach and the deals that never convert, so the forecast holds. Get full access below ↓

Access the GTMcraft Play for free [HERE]

Sources: GTM Strategist, “You Don’t Have to Become an Influencer to Win on LinkedIn” (Jul 3) · 30MPC, “How to Close Your Deals Before End of Quarter” (Jun 30)

Steal this move: rank accounts on signal, not size

Play: 5 steps to raise win rate by ranking accounts on signal, not size (2026 series)

Fixes: a target list ranked by company size, so reps chase big, inactive logos instead of the accounts actually in a buying window.

Best for: operators at 2-10M ARR running outbound across smaller European markets, where the addressable list per country is small and the win comes from reaching fit accounts on a live signal before the rest of the market does.

The 5 steps:

1. Define a strict fit gate and drop every account that fails it, however big the logo.

2. Select 5 to 8 high-impact buying signals you can read from public or product data.

3. Score signals weekly and apply recency decay, so a stale signal loses weight.

4. Map the reachable decision-makers for each account before you reach out.

5. Combine fit, signal, and influence into a tiered score, then assign owners and response times per tier.

Template: a scoring row per account, banded 1 to 5. 1 = fits but no live signal. 5 = fits, multiple fresh signals, decision-maker reachable this week. Work tier 5 and 4 first, refresh the scores weekly, back-test against closed-won monthly.

Paste this into your AI:

Here is my account list with firmographics and any signal data I have [paste]. Apply a fit gate first and drop non-fits. For the accounts that pass, score each 1 to 5 on: freshness of buying signal, number of signals, and whether a decision-maker is reachable. Output a table sorted highest first, so my top-tier accounts to work this week are on top.

Access the GTMcraft Play for free [HERE]

Also on the radar

• Mostly Metrics, “Why We Get Excited About Deferred Revenue” (Jun 30): deferred revenue is growth you already sold. One example starts the year about 30% pre-sold. Build a one-page deferred waterfall and bring the percent already contracted to your next forecast review.

• SaaStr, “Adobe Just Deferred a Big Annual Price Increase” (Jun 24): the first real crack in B2B pricing power since 2022. Check whether your renewal quotes still assume an increase your buyers will quietly refuse.

3 questions for the room

• Could a competitor buy your exact prospect list today? If yes, where is your real edge, per market?

• What percent of your team’s hours point at the revenue you already have, versus chasing new logos?

• Strip every deal with no dated next step out of your forecast. How much is left?

What is your number 1 takeaway this week? Reply or send me a DM.

Go deeper with GTMcraft

You do not need another newsletter, podcast or LinkedIn post. You need the system under it.

• Read the plays. Every play, live and current, in the Operator Room.

• Download for your archive. Take the PDF or workbook offline.

• Get the markdown. Drop the raw play into your own stack or AI tool.

• Build your own. Use the GTMcraft Notion databases (100,000+ signals) to spin up a play for your exact motion.

• The full Plays database is over 100 plays and growing every week.

Curious to see a sneak peek of GTMcraft, DM me or reply on this email.

See you inside GTMcraft OS, your GTM co-operator. Not another community.

Koen

Your (human) GTM Agent

Week in the Trenches: Fundamentals Win Quarters - Monday July 6, 2026

This week I closed one quarter in the dark and opened the next one on a beach, and both came down to the same thing: fundamentals, not more effort.

From my week as a European GTM operator. 3 lessons. 1 play to steal. Read time ~5 min.

The set-up

Last week was the end of Q2 and the first days of Q3, across the markets I run. 2 weeks out we were behind pace on mid-market, with the pipeline sitting there on paper but the late stages soft. In the same week I stepped back into the Benelux country manager seat I held myself 3 years from Sept 2022 to August 2025, as interim, on top of my international role, likely into October. So I closed one quarter from the trenches and opened the next one as a first-line leader again.

Here is what that taught me…

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement today.

13+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room.

Lesson 1: The late stage is where reps separate, and a mutual action plan is the separator

We were ghosted since Friday on a deal worth 100k plus. On Friday the CFO said he would sign. Then nothing, all of Tuesday, the last day of the quarter. Late in the evening one line arrived: the HR director will come back to you. The country manager, the rep, and I sat on Slack looking at each other. Then the buyer came back with one final question. We turned it around, and 30 minutes later the contract was signed.

That deal did not close because we chased harder. It closed because the rep knew the decision date, the decision process, every step left to signature, and was multi-threaded across the buying group. The deals with that clarity closed. The deals without it moved to next month. Coverage was never our problem. Late-stage control was.

Steal this: open every deal you expect to close this quarter and answer 3 questions with the buyer in the room: what is the decision date and the exact steps to signature, who signs and who can kill it and have you spoken to each, and does the buyer have the same plan written down that you do. If you cannot answer all 3, it is not a late-stage deal.

The rule: if the buyer does not co-own the plan, you do not control the close. You are waiting on it.

The play: The Mutual Action Plan GTM Playbook (2026 series). Turns late-stage control into a repeatable discipline. Get full access below ↓

5 steps to drive late-stage deals

Lesson 2: Reset the leaders before you touch the numbers

Day one of Q3, I did not open the pipeline or start the planning deck. I took my 4 Benelux leaders to Scheveningen for a full day on the beach. No laptops. No number review. A long walk and honest conversation about where the team’s energy, belief, and focus actually are.

We came back with 3 named owners for the quarter, one each: a more dynamic ICP built on real H1 signals, cross-functional collaboration and the office culture, and a mid-market pipeline-generation culture, which we named as the single biggest bottleneck for H2 and 2027. 3 owners, 3 problems worth solving, and nothing else on the list. The textbook says start a quarter with analysis. I reset the leaders first, because they carry the message to everyone else.

Steal this: before you build your Q3 plan, take your leadership group somewhere with no screens for half a day. Come back with a maximum of 3 named owners for the 3 problems that decide the quarter. Alignment first, spreadsheet second.

The rule: if your leaders are not aligned on the 3 things that matter, no plan survives contact with the team.

The play: 5 steps to reset the quarter with your leaders and 3 owned initiatives (2026 series). The exact move above as a repeatable play: a no-laptop leadership reset that ends with 3 named owners. Get full access below ↓

5 steps to reset the quarter

Lesson 3: Hold the bar, even when holding it costs you

Same week, we said no to 2 finalists, one for country manager, one for solutions engineering. Neither was bad. Good was simply not the bar. On a lean team, a bar raiser lifts the average and a mediocre hire drags it, so I would rather hold a seat open and step in myself as interim than settle to fill it.

That is the harder version of the decision. Holding the bar meant I took the country manager seat back on top of my other role, which is a real squeeze on my week. But a lean team scaling toward its next 10M cannot carry a hire who does not raise it. The way I test it: I put the person in a live case, a real conversation not a rehearsed pitch, and I ask myself if I see a bar raiser I would want to build with. If the answer is not yes, it is no.

Steal this: for your next hire, replace the pitch with a live working session on a real problem, and benchmark the candidate against your best current performer in that role. If they do not clear that bar, hold the seat and cover it, rather than settle.

The rule: on a lean team, one wrong hire costs more than an open seat. Hold the bar or carry the gap yourself.

The play: 5 steps to hire GTM bar-raisers on a real work sample (2026 series). A paid week-one proof task, a scorecard against your best performers, and the discipline to say no. Get full access below ↓

5 steps to hire GTM bar-raisers

This week’s play

Play: The Mutual Action Plan GTM Playbook (2026 series)

Fixes: deals that stall in the late stage because the buyer is not co-owning the path to signature.

Best for: operators at 2-10M ARR closing mid-market deals across several European markets at once, where each country’s procurement and decision process runs at its own speed and the only thing that travels is a plan both sides hold.

The 5 steps:

1. Map the buyer’s real internal decision stages, not your CRM stages.

2. Co-create the mutual action plan live on a call, with the buyer typing too.

3. Use one simple template so a rep can activate it in minutes, not build it from scratch.

4. Coach reps on the conversation, not the document. The skill is asking, not filling in.

5. Inspect the plan in every deal review, so it becomes how you forecast, not paperwork.

Template: score each open late-stage deal 1 to 5 on plan health. 1 = no dates, single-threaded, no buyer copy. 5 = dated steps to signature, multi-threaded, buyer co-owns it. Anything scoring 3 or below is not a committed deal this quarter.

Paste this into your AI:

Here are my open late-stage deals with the next steps and stakeholders I have on each [paste]. For each deal, score plan health 1 to 5 on: dated steps to signature, number of stakeholders engaged, and whether the buyer has a copy of the plan. List the deals scoring 3 or below and the single question I should ask the buyer this week to raise the score.

Full play, template and workbook inside GTMcraft
→ Reply to this email or DM me to get access.

One question for the room

Look at the deals you expect to close this quarter, in your market: how many would still be there if you asked the buyer to show you their copy of the plan?

Reply or send me a DM.

Go deeper with GTMcraft

You do not need another newsletter, podcast or LinkedIn post. You need the system under it.

• Read the plays. Every play, live and current, in the Operator Room.

• Download for your archive. Take the PDF or workbook offline.

• Get the markdown. Drop the raw play into your own stack or AI tool.

• Build your own. Use the GTMcraft Notion databases (100,000+ signals) to spin up a play for your exact motion.

• The full Plays database is over 100 plays and growing every week.

Curious to see a sneak peek of GTMcraft, DM me or reply on this email.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

This week’s number 1 move

Score every booked meeting on pain, before you score anything else.

Cannonball GTM’s reminder: only ~5% of any market is actively buying in a quarter (Prof. John Dawes, Ehrenberg-Bass). Companies switch ~every 5 years, so ~20%/year, ~5%/quarter in play. Cold untargeted outbound books at ~0.025%. Pain-based outbound at accounts with a real un-shopped problem runs 1–4%.

For a lean European team this is the whole game. Stop measuring activity, start scoring need.

Start here: score last month’s booked meetings for pain intensity 1–5. Below a 4 is not pipeline yet. That one column resets your forecast.

Source: Cannonball GTM, “The 5% problem,” May 22.

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. Translated into 100+ GTM plays for you to implement tomorrow.

14+ years over 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Scaling from 2-10M+ ARR multiple times. Same recipe. Different motions.

Now all part of the GTMcraft Operator Room. Interested? DM me.

This week’s number 1 move

Score every booked meeting on pain, before you score anything else.

The sharpest signal this week is Cannonball GTM’s reminder that only about 5% of any market is actively buying in a given quarter, a number from Professor John Dawes at the Ehrenberg-Bass Institute. Companies switch providers roughly every 5 years, so only 20% of your market is in play in a year and about 5% in a quarter. Cold, untargeted outbound books meetings at about 0.025%. Pain-based outbound, aimed at accounts with a real problem they have not started shopping for yet, runs 1 to 4%.

For a lean European team this is the whole game. You do not have the headcount to spray, and in a single country your in-play pool is small. So you stop measuring activity and start scoring need.

Start here: take last month’s booked meetings and score each one for pain intensity on a 1 to 5 scale. Anything below a 4 is not pipeline yet. That one column will reset your forecast.

Source: Cannonball GTM, "The 5% problem," May 22.

Problem 1: Your outbound is busy but not booking

Two posts this week land in the same place from different angles. Cannonball GTM argues the only outbound metric that matters is meeting booked rate, meetings divided by activities, and almost no benchmark publishes it. At or below 0.25% you are spraying, and the list is the cause, so do not touch the message. Tightly targeted lists under 50 contacts run far higher reply rates than big sends. Outbound Kitchen says the same thing about the stack: a funnel where every stage compounds beats more volume, and no AI SDR rescues a bad list.

Filtered for a lean team in 2026: none of this needs a new tool. It needs the numbers you already have and a smaller, better list.

The European read: in a fragmented market your beachhead country might hold a few hundred real accounts, not a few thousand. Spray and you burn the only market you can service this year. A sub-50 list built on one named pain is not a constraint here, it is the correct size for your stage.

Start here: calculate your meeting booked rate on last month’s outbound. At or below 0.25%, rebuild one sub-50 list in your home country around a single pain, and leave the copy alone.

The rule: at or below 0.25% fix the list, at or above 1% with flat replies fix the message.

The play: The Pain-Acuity Targeting Play. Builds the sub-50 list and scores it before a rep sends anything. Best for the 2-5M leg, land motion, single country. Inlined below.

Sources: Cannonball GTM, May 22; Outbound Kitchen, June 14.

Problem 2: You are reviewing the 20% of the funnel you can see

Jeff Ignacio at RevOps Impact makes the structural case: most of the buying process, 70 to 80% by Forrester’s data, happens before a buyer ever contacts you. The winning vendor is already on the day-one shortlist about 95% of the time. Your pipeline review sees the fraction that left a trace in the CRM and misses the rest, the brand and word-of-mouth work that decided the deal before a rep got involved.

Filtered for a lean team in 2026: you cannot buy your way onto that shortlist with budget. You earn it with a point of view and with happy customers who talk.

The European read: in one country, word of mouth compounds faster and travels further than in a market of 330 million. The community is smaller, the conferences are the same 200 faces, the buyers know each other. That is your cheapest channel, and it is invisible in your CRM. When you are deep in one market, reputation is pipeline.

Start here: pull your last 10 closed-won deals and read the AE notes for one thing, how did they first hear about you. Tag each as found-us, campaign, or outbound.

The rule: if your found-us deals win more and close faster than outbound-sourced, that gap is your case for going deeper in one market, not wider across many.

The play: The Found-Us Cohort Play. Surfaces the invisible 80% so you can fund it. Best for the 5-10M leg, expansion within a country. Get access to the play in GTMcraft.

Sources: RevOps Impact, June 8; April Dunford, Positioning, June 11.

Problem 3: Your account list is sorted by the wrong thing

Two posts attack the same lazy default, ranking accounts by company size. Jordan Crawford at Blueprint scored more than 1,000 accounts on their owner’s real influence, board seats, stages, podcasts, not revenue, and found the few worth a personal ask were almost never the biggest logos. The data was public; assembling it was the work nobody does. Full-Funnel’s breakdown of Backbase, a Dutch company, shows the prospect-side version: a focused account function that picks the next action per account from real signals beats a broad list worked by filter.

Filtered for a lean team in 2026: AI does the assembling now. The judgment about which signal matters is still yours.

The European read: in a multi-language market, the signal that matters includes things a size filter never sees. Which country the buyer actually operates in. Which language their team sells in. Whether they have local presence in your beachhead. Re-sort on those and you stop wasting a small team on accounts you cannot serve or speak to yet.

Start here: take your current top-20 target list and re-sort it by one real signal, active pain or local presence in your home country, instead of company size. The reorder is your real priority list.

The rule: if you cannot name the signal that puts an account in your top 20 this week, it does not belong there yet.

Sources: Blueprint, June 10; Full-Funnel B2B, June 12.

Steal this move: The Pain-Acuity Targeting Play

Play: The Pain-Acuity Targeting Play (v1)

Fixes: Your team sprays a small home market and books meetings with no need behind them. The pipeline looks full and forecasts like fiction.

Best for: The 2-5M leg of scale, land motion, single beachhead country. The point where your in-play pool is small and every meeting has to count.

The 5 steps:

1. Size the real pool. Take your home-country TAM and cut it to the roughly 5% likely in play this quarter. That is your honest top of funnel.

2. Pick one pain. Name the single problem you close fastest on. One pain, one list.

3. Build the sub-50. Find up to 50 accounts in your country showing that pain on public signal, not firmographic filter.

4. Score before you send. Band every account 1 to 5 on pain intensity and 1 to 5 on urgency. Work only the 4s and 5s this week.

5. Go deeper, not wider. For every 4-5 account, run a real multi-thread sequence in the buyer’s language. Leave the rest for nurture.

Template (the Pain-Acuity Rubric):

• Pain intensity 5: a crisis with a named consequence. Someone is accountable for fixing it now.

• Pain intensity 4: an active, recognised problem. Leadership is already discussing it.

• Pain intensity 3 or below: known friction they have normalised. Not pipeline yet.

• Urgency 5: an external deadline, a contract end, a board directive.

• Urgency 4: pressure that is getting worse and they know it.

• Band = lower of the 2 scores. Work 4-5 this week. Develop the mixed. Drop the 1-2.

Paste this into your AI:

Here is a list of target accounts in [country] with public signals [paste]. For each account, find evidence of [the one pain], then score pain intensity 1 to 5 and urgency 1 to 5 using the rubric above. Output a table sorted by band, highest first, with the one signal that justifies each score and the buyer’s likely working language.

Get access to the full GTMcraft play in the Operator Room. Reply or DM me for access.

Also on the radar

• GTMnow, “The Distribution Era,” May 29: as AI collapses the cost of building software, the product stops being the moat. Distribution, trust, and GTM execution become the edge. For a lean European team, that edge is depth in a market you own.

• PricingSaaS, June 12: monetization is being re-cut in public every week. Worth a quarterly check on your own packaging before a competitor forces it.

Questions to you as Founder or GTM Operator ↓

• What is your meeting booked rate on last month’s outbound, and is the list or the message the problem?

• How many of your last 10 wins first heard about you through a channel your CRM never recorded?

• If you re-sorted your top 20 by local signal instead of size, how many accounts would fall off?

What is your number 1 takeaway this week? Reply or send me a DM.

Go deeper with GTMcraft, the room for Founders & Operators

You do not need another newsletter, podcast or LinkedIn post. You need the system under it.

• Read the plays. Every play, live and current, in the Operator Room.

• Download for your archive. Take the PDF or workbook offline.

• Get the markdown. Drop the raw play into your own stack or AI tool.

• Build your own. Use the GTMcraft Notion databases (100,000+ signals) to spin up a play for your exact motion.

• The full Plays database is over 100 plays and growing every week.

Curious to see a sneakpeak of GTMcraft, DM me or reply on this email.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

Week in the Trenches as GTM operator: Trust The Process

The deals closed in the last days of the quarter. But the lessons were about what I set up weeks earlier and what I made myself stop doing in the final hours.

From my week as a GTM operator. 3 lessons. 1 play to steal. Read time ~5 min.

The set-up

35 degrees in the Netherlands, 2 days left in Q2. On Thursday I hosted 40+ HR professionals at a customer dinner while 4 deals moved on WhatsApp and Slack across 3 countries. One rep signed a 50k deal at the table.

55 quarters in sales, and I still catch myself thanking the wrong half of the team first. Reps get the applause. Deal desk, legal, and implementation are the half that makes the contract signable. Hold that thought; it runs under everything below.

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. 100+ playbooks structured. 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Same recipe. Now all part of the GTMcraft Operator Room.

Consider joining? (send me a DM for access).

Lesson 1: A digital sales room lifts win rate, but only if reps actually run it

I started async selling and digital sales rooms in Benelux 3.5 years ago. This quarter the data landed at our GTM leadership review. Across the international team, win rate up 43% and MRR win rate up 57% for deals run through the room. Spain was the outlier: win rate up 118%, MRR win rate up 388%. Same tool, same rollout, the UK was flat to slightly negative.

That gap is the whole lesson. When the same tool wins for one team and not another, the variable is execution on the room, not the tool. Rita in London proved it. She would not touch the room and was skeptical it did anything. I challenged her with the data, she started running it, and now she is an evangelist. Skeptic to evangelist, on evidence, not on a mandate.

Steal this: Pull your win rate split by reps who run your sales room well versus reps who do not. The spread is your execution gap, and it is coachable this week.

The rule: When a tool wins for one team and not another, fix the execution, not the tool.

Lesson 2: Predictability is leadership currency, so I put the forecast in public

I have been in this role since last summer, and June carried all of Q2’s new-business pressure. On June 18, 8 days out, we were at plan 50k, closed 11.8k, so 23.6%, with a 38.2k gap and 77.2k of pipeline. I posted to 140 people in our international Slack that we would hit plan. My words were that soon it was going to rain MRR and new logos.

That is a bet on predictability, made on purpose. Predictability earns investment, because saying what you will do and then doing it is what lets the business plan around you. The forecast held for 4 weeks even while the closed number lagged, and Gong deal health backed the call the whole way. Friday close: 27.7k for the month, 55.3% of target, with the quarter at 82.8%. The number was always going to land late. Saying so in public changed how the team worked the final week.

Steal this: Post your number where your team can see it, before it feels safe. The public commitment shifts the last week from hoping to closing.

The rule: Predictability is the currency. Say what you will do, then do it.

Lesson 3: In the last 48 hours, lead less

Here is what 55 quarters taught me about the close. The instinct is to push harder. It is the wrong instinct. By the final 48 hours, sales is a process and there is only so much you can still change. Showing up as a leader with questions and pressure only pulls reps off the deals.

So this quarter I did the opposite. Set the scene, coach to the process, be available for escalations, then get out of the way. The pipeline you built weeks ago is the pipeline that closes. Standing over it does not add a deal; it only adds noise.

Steal this: For your next close, block your calendar to be reachable for escalations and cancel every “where are we” check-in. Trust the pipeline you already built.

The rule: At the close, be available, not in the way.

This week’s playbook drop

Separate from the lessons above, here is what I built over the weekend. Every Saturday I run the GTM Signal across 40+ newsletters, and the strongest problems become plays. This week’s signal was Defend The Base: the revenue you already earned the right to is leaking. 3 new plays came out of it.

• The T-120 Renewal Motion Play. Work every renewal from 120 days out, on hard signals, not vibes. Outlined in full below.

• The Value-Unit Pricing Play. Put a value unit between your price and your cost, so a buyer cannot look up your cost and route around your markup.

• The AI Shortlist Play. Buyers shortlist you through AI before a rep ever hears about it. Own the answer before they ask.

Play: The T-120 Renewal Motion Play (v1)

Fixes: Renewals get decided 120 days out, but you show up at T-14. The base leaks while the CRM still reads green.

The 5 steps:

1. See the leak. Compute real net revenue retention by segment, not blended, and name the one segment bleeding the most.

2. Build the T-120 board. List every renewal in the next 120 days, on the auto-renewal date, one owner per account, sorted by revenue at risk.

3. Score risk on hard signals, not vibes. Band every account red, amber, or green on 3 forward signals only.

4. Trigger the action, not the score. Attach a named play to each band, and for every red book the exec value review this week.

5. Compound it. Turn each save into a repeatable motion and set a QBR cadence that proves value before the renewal window.

Template (the T-120 Renewal Risk Rubric):

• Signal 1, champion engagement: green if replied in 30 days, amber 30-60 days, red 60+ days or gone.

• Signal 2, usage trend last 60 days vs prior 60: green flat to up, amber down 1-20%, red down 20%+.

• Signal 3, exec sponsor: green in seat and engaged, amber in seat but quiet, red left or reorged out.

• Band = worst of the 3. Red, book the exec review this week. Amber, re-engage the champion. Green, line up the expansion ask.

Paste this into your AI:

Here is a CRM export of accounts renewing in the next 120 days with usage and activity data [paste]. For each account: compute the usage trend, find the last champion reply date, flag sponsor status, then assign a red, amber, or green band using the rubric above. Output a table sorted by ARR at risk, largest first, with the recommended action per row.

Want the full GTMcraft Play? DM me or reply on this email for access.

What else happened

Gave an internal candidate who did not advance a full written feedback doc, which meant switching from manager mode to assessor mode and naming things a 1:1 never would. Trumpet MCP with Claude is now live and auto-builds sales rooms from Gong call intel. The Benelux country sales leader hire is getting to a decision stage. That was my week (publicly shared, so you can hopefully learn from it).

One question for the room: when a play works for one team and not another, did you fix the tool or the execution? Reply and tell me which one you got wrong last.

Go deeper with GTMcraft, the room for Founders & Operators

You do not need another newsletter. You need the system under it.

• Read the plays. Every play, live and current, in the Operator Room.

• Download for your archive. Take the PDF or workbook offline.

• Get the markdown. Drop the raw play into your own stack or AI tool.

• Build your own. Use the GTMcraft Notion databases (100,000+ signals) to spin up a play for your exact motion.

• The full Plays database is past 100 and growing every week.

Curious to see a sneakpeak of GTMcraft, DM me or reply on this email.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

The 90-second version

• The problem: you rolled out AI, adoption looks great, and the system works exactly the way it did before.

• The frame: adoption is people going faster. Transformation is the system working differently. They are not the same number.

• The 3 lessons that close the gap: data is the ceiling, the hard part is how you operate, agents need oversight and context.

• The proof: each lesson sits on a top-down CRO talk, a bottom-up workflow a rep on my team built, and a named external operator with real numbers.

• The action: pick the one lesson your team is weakest on, run its Do-first step today, and paste the oversight prompt at the bottom.

Read time: 10 min

Skip this if you already operate this way

1. 90% of our GTM team used AI within months, and per-rep research dropped from 2 hours to 15 minutes. That is adoption. It is the easy part.

2. Transformation is the system working differently, and it needs 3 things data is the ceiling, the operating model is the hard part, agents need oversight and context.

3. Under each lesson sits a real workflow a rep on my team built bottom-up, plus a named external operator who proves the lesson is universal.

4. Each lesson ends with a tiered action ladder: Do first, Do this week, Do this month, and the signal that tells you it worked.

5. The agent-oversight prompt at the bottom is runnable today. No GTMcraft Operator Room membership needed.

2 numbers before we go in

90%. The share of our GTM team using AI within the first few months. The easy part.

2 hours to 15 minutes. The drop in daily research time per rep. Also the easy part. Neither number means the system changed.

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. 100+ playbooks structured. 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Same recipe. Now all part of the GTMcraft Operator Room.

Consider joining? (send me a DM for access).

Two things happened this quarter that only make sense next to each other.

My CRO, Philip Lacor, stood on a stage and laid out how Personio is moving from a traditional GTM to an agentic one. Top-down. The strategy, the operating model, the 3 hard lessons from doing it.

In the same quarter, I watched account executives and account managers on my own team screenshare systems they had built themselves. Bottom-up. No mandate, no central project. Just reps removing their own repetitive work.

The interesting part is not either layer on its own. It is that they matched. Every lesson Philip named from the top, someone on my team had already proved from the bottom. That is what transformation looks like when it is working. The strategy and the screens agree.

Here is the frame, worth stealing as-is. Adoption is individuals working faster. Transformation is the GTM system working differently. The 2 numbers above are adoption numbers. The system only changes when you get the next 3 things right.

Lesson 1: data is the ceiling

The top-down version.

You can buy the best models, the best agents, the slickest tooling, and none of it climbs higher than the quality of the data underneath. An agent reading messy Salesforce, stale ICP definitions, and an intent score nobody trusts will confidently produce garbage. Philip’s framing was blunt: treat the data layer as infrastructure, not a project. It is never finished. Deduplication, enrichment, scoring models, intent iterations. The work never stops because the ceiling moves every time the data improves.

Bottom-up proof:

one of my account managers built a portfolio dashboard that scores every account on risk and expansion by combining live Salesforce data with Gong call sentiment. It works because the underlying signals are clean: license gap, last contact, renewal proximity, adoption score, sentiment. When a rep can see all five in one view and trust them, prioritisation stops being a guess. The interface is not what makes it useful. The data feeding it is. The same dashboard on dirty data is a liability, because it routes reps toward the wrong accounts with total confidence.

External proof:

Yamini Rangan, HubSpot, at SaaStr. Across 2,500+ customers their support AI resolves an average of 52% of tickets, but top performers hit 70 to 80%. The gap between 80% and 40% resolution comes down to knowledge base quality and completeness. Same agent. Different data underneath.

• Do first (under 30 min): name the one data source your team distrusts most. The one that makes reps say “I do not believe that number.”

• Do this week: pull 20 records from that source and check them by hand against reality. Quantify the error rate so it stops being a feeling.

• Do this month: assign one owner to that source and treat it as infrastructure, with a recurring clean-up cadence. It is infrastructure, so it never closes as a project.

• You know this worked when: a rep pulls a number off that source in a deal review and nobody in the room questions whether it is real.

Go deeper: my breakdown “Before GTM Engineering, Fix Your Architecture” walks the full sequence, architecture before data before signals before execution. It lives in the Operator Room at gtmcraft.circle.so.

Lesson 2: the hard part is not AI, it is how you operate

The top-down version.

The technology is the easy part now. Anyone can spin up an assistant. The hard part is redesigning how the team works around it: who owns the agent, what the rules of engagement are, how a frontline experiment becomes a standard instead of dying as a one-off. Philip’s model is three tiers. Locked foundations the centre owns and governs. A structured program that ships within those standards. Permeable grassroots experimentation where reps try things, log them, and the best ideas escalate. Ideas flow up, standards flow down, one front door, no side channels. That is an operating model, and it is the actual thing that moves the needle. The tools are downstream of it.

Bottom-up proof:

An account executive on my team automated his entire morning inbox review. It used to take an hour: open Gmail, cross-check Salesforce, search Slack for context, write replies from scratch. Now a scheduled routine runs at 7:30am, reads 8 live data sources, drafts every reply in his own voice, flags what needs a decision, and lands a summary in Slack before he sits down. The build is impressive. What keeps it alive is operating discipline. He trains it in a dedicated Slack channel, a thumbs down and a note, and the next morning it has adjusted. That feedback loop is the operating model in miniature. The agent improves because he operates it like a new hire. The cleverness of the tech is beside the point.

External proof:

Yamini Rangan, HubSpot. When they went AI-first, HubSpot spent 4 months planning their 2023 roadmap, then in January 2023 said pivot, that entire roadmap is not what we are building, and shipped first AI features 2 months later. The decision was an operating decision made at the top. The tooling followed. Same shape as Philip’s three tiers, same shape as my AE’s feedback loop, just at company scale.

• Do first (under 30 min): pick one AI experiment a rep is already running quietly. Just name it and write down what it does.

• Do this week: give that experiment a home. Name one owner, write the single rule it must follow, and decide how it escalates if it works.

• Do this month: stand up the smallest version of three tiers. One locked standard the centre owns, one place experiments get logged, one path for the best ones to become the standard.

• You know this worked when: a rep’s side experiment becomes a team default without you personally carrying it there.

Go deeper: my AI-SDR operating-system playbook is built entirely on this idea, that the challenge is never deploying the agent, it is designing the operating model around it. In the Operator Room.

Lesson 3: agents and assistants need oversight and context

The top-down version.

An agent without oversight drifts. An agent without context guesses. Both fail quietly, which is worse than failing loudly. Philip was direct that this is where most of the real work lives: purpose-built context rather than reused decks, daily review like you would give a new rep, guardrails before scale, metrics defined upfront. The honest half of his lesson was the failure list. Light training leads to fast degradation. Too much or outdated context makes outputs worse. Parallel agents dilute quality. Scope creep kills focus. None of that is a technology problem. All of it is an oversight problem.

Bottom-up proof:

The same inbox agent from lesson 2 makes this concrete. It never sends anything automatically. It drafts, it flags, a human decides. That is oversight built in on purpose. Nothing about it is accidental. And it only drafts well because it is fed purpose-built context: this rep’s voice, his accounts, his product channels, his feedback from yesterday. Strip the context and it writes generic email. Strip the oversight and it sends the wrong thing at 7:30am with nobody watching. The system is good precisely because a human stayed in the loop and the context was built for the job.

External proof:

Jason Lemkin, SaaStr. As AI gets better, the remaining human work gets harder, not easier. SaaStr sent 4,495 AI emails with top response rates, but it required 90 minutes every morning training the AI and an hour every night reviewing performance. And the iteration cost is brutal: their AI SDR handling sponsor inquiries needed 47 iterations to stop being too aggressive on pricing. 47, not 4. On context, Rangan’s framing is the cleanest I have heard. When an employee joins, you give them your products, your ICP, your brand voice, your competitive differentiation. The same thing is happening with agents. An agent is a new hire.

• Do first (under 30 min): take one agent or assistant your team already uses and answer two questions. Who reviews its output, and how often. What context was it given, and was that context built for this job or borrowed from a deck.

• Do this week: if either answer is weak, fix the weaker one. Add a daily review owner, or rebuild the context file for the specific job.

• Do this month: write the context file properly, the way you would onboard a rep, and set a standing review cadence before you scale the agent to anyone else.

• You know this worked when: you can hand the agent to a second person and it holds quality without you in the loop.

Run this today. Paste it into a Claude Project to audit one agent or assistant for oversight and context before you scale it.

You are an AI operations reviewer for a B2B SaaS GTM team. Your job is
to stress-test one agent or assistant for the two things that make
agents fail quietly: missing oversight and missing context.

The agent I want to audit:
- What it does: [one or two sentences]
- Who uses it and how often: [fill in]
- What it is connected to (data sources, tools): [list]
- What context it was given: [paste it, or describe what you fed it]
- Who reviews its output today, and how often: [fill in, or "no one"]

Do this:
1. Oversight check. Tell me where this agent can fail without anyone
   noticing. Name the single highest-risk unreviewed output and what
   a bad version of it would cost.
2. Context check. Compare the context I gave it to what you would give
   a new hire in this role (ICP, voice, product, guardrails, what good
   looks like). Name exactly what is missing.
3. The fix. Give me a daily or weekly review cadence sized to the risk,
   and a rewritten context outline built for this specific job rather
   than borrowed from general material.
4. The scale gate. Tell me the one signal that means this agent is safe
   to give to a second person, and the one that means it is not ready.

Ask me for anything you need before you answer.

Go deeper: the full reality of running agents in production, including the iteration counts and the daily-review cadence, is broken down in the Operator Room alongside the AI-SDR playbook.

Steal this move

Close the adoption-to-transformation gap in 4 weeks. For you if you are 0 to 10M+ ARR and your AI rollout looks busy but the system has not actually changed.

• Week 1, raise the ceiling: name your least-trusted data source, hand-check 20 records, and assign it an owner. Data before agents.

• Week 2, build the operating model: take one quiet rep experiment, give it an owner, one rule, and an escalation path. That is your smallest three-tier system.

• Week 3, install oversight: run the prompt above on one live agent. Add the review cadence and the rebuilt context file it gives you.

• Week 4, connect the three: pick the one workflow where clean data, a clear owner, and human oversight all meet, and make it the reference example you point the rest of the team at.

Why this matters now

The teams winning with AI in 2026 are not the ones with the most tools. They are the ones who rebuilt the system underneath the tools. Clean data so the agents can see. An operating model so good ideas compound instead of dying as one-offs. Oversight and context so the agents act like trained hires instead of confident strangers. None of that is a purchase. Every bit of it is work you do over time, learning, breaking it, iterating, the same thing every operator winning with AI will tell you off stage. Adoption is a Tuesday. Transformation is a quarter. The reps on my team did not wait for the quarter. They started bottom-up, one removed job at a time, and the strategy from the top met them halfway.

Reply to this email with the one lesson your team is weakest on right now: data, operating model, or oversight. I read every reply, and the patterns tell me what to build next.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

The 90-second version

• Your AI bill is mostly re-read, not new work. Most setups resend the same context every call. Prompt caching cuts repeated input on Claude by up to 90%, and a full cleanup drops a typical bill 50 to 90%.

• The fix is Monday-sized: lean your context files, start fresh sessions instead of long threads, batch tasks into one message, and route cheap models for cheap work.

• The government switched off Anthropic’s most powerful model 3 days after launch. Hassid and Charlie Hills both had to rewrite this week’s guides from Fable 5 to Opus 4.8. If your workflow is wired to one model, that is a single point of failure.

• The how-to is now free. Hassid shipped a Skill that walks anyone A to Z, so knowing how stops being the job. Amodei says it plainly: execution commoditizes, judgment survives.

• This week’s build: cut your AI bill in 4 weeks. Lean one bloated context file this Sunday.

Read time: ~5 minutes.

Lane note: Saturday is GTM execution, Sunday is Claude. Here is the one move that matters most this week.

This week’s number #1 move

Your AI bill is mostly paying to re-read the same thing. Ruben Dominguez pulled the mechanic this week: you pay per token on the way in and the way out, and most setups resend the same instructions, the same context, and the same files on every single call, paying full freight thousands of times. Prompt caching alone trims that repeated input on Claude by up to 90%, and stacking the rest of the cleanup drops a typical bill by half or more, with output quality held steady. For a lean team watching a Pro or Max plan hit its limit by lunch, this is the cheapest win of the week, because none of it needs a better model. It needs less waste.

Start here:

Open your longest-running Claude session, the one you keep adding to, and start a fresh one for the next task. Every follow-up in a long thread re-reads the whole history and bills you for it again.

The rule:

If a session keeps growing, it keeps re-reading. Start fresh per task and keep the files it reads lean.

Sources:

• Ruben Dominguez, Jun 14 (https://www.the-ai-corner.com/p/llm-token-cost-optimization-playbook-2026)

Problem 1: You wired your workflow to one model, and this week one got switched off

Most setups quietly assume the model you use today will be there tomorrow. This week broke that assumption. Anthropic shipped Fable 5, its most powerful model, and the US government ordered it suspended three days after launch on a national-security order. Both creators publishing this week felt it. Ruben Hassid had a finished newsletter on the new model that he could not send. Charlie Hills had to bolt a correction onto the top of his guide, swapping every Fable 5 step to Opus 4.8. If your best workflow only runs on one model, or worse, lives inside one chat’s memory, you have a single point of failure you do not control.

Start here:

List the workflows you would lose if your main model vanished tomorrow. Star the one that would hurt most.

The rule:

If a workflow only exists inside one model’s memory, you do not own it, you rent it. Keep the instructions and context in files you control.

Sources:

• Charlie Hills, Jun 14 (https://charliehills.substack.com/p/claude-code-is-terrible-at-design)

• Ruben Hassid, Jun 14 (https://ruben.substack.com/p/i-replaced-myself)

Problem 2: The how-to is now free, so knowing how is no longer the job

Ruben Hassid, whose entire newsletter teaches people how to do things with AI, spent this week building a Skill that does his job for him. You type /how-to, describe what you want, and Claude walks you A to Z, even if you have no idea where to start. His point is sharp and a little uncomfortable: the how-to is the easy half, and it is now free. What he still gets paid for is the other half, the mess. The button that will not click. The two days he tested something that did not work so you never have to. Dominguez heard the same line from Amodei this week: as AI takes the execution, the edge moves to judgment and taste. So stop hoarding the how. Package the steps you repeat into a Skill, and spend the time you get back on the part the AI cannot do, deciding what is worth doing and whether the output is any good.

Start here:

Pick one task you explain to people or redo from scratch every week. Write the steps down the way you actually do them.

The rule:

If you can write the steps down, it is a Skill, not your job. Keep the judgment, automate the how.

Sources:

• Ruben Hassid, Jun 14 (https://ruben.substack.com/p/i-replaced-myself)

• Ruben Dominguez, Jun 18 (https://www.the-ai-corner.com/p/dario-amodei-circuit-documentary-10-takeaways-2026)

Problem 3: Your GTM motion still runs through you at every step

Most lean GTM has one bottleneck, and it is the founder. Ruben Dominguez took apart how Carles Reina, employee number four at ElevenLabs, built a go-to-market toward $500M in revenue, and the useful part is that the playbook is teachable. You can encode an operator’s rules into agents that run the motion: source, enrich, sequence, forecast, expand. The agents inherit the discipline that scaled revenue instead of generic best practice, and your people keep the part that actually closes, the judgment and the room. This lines up with what Amodei told Dominguez the same week: once AI takes execution, the edge moves to go-to-market craft. The mistake to avoid is bolting AI onto a broken funnel. Write down the rules you already run by, hand the repeatable steps to agents, and draw a clear line around the work that stays human.

Start here:

Take one GTM step you run by hand every week, like research, enrichment, or first-touch drafting, and write the rules you follow when you do it well.

The rule:

Automate the steps you can write rules for. Keep the close, the forecast call, and the relationship human.

Sources:

• Ruben Dominguez, Jun 20 (https://www.the-ai-corner.com/p/your-gtm-run-by-agents)

• Ruben Dominguez, Jun 18 (https://www.the-ai-corner.com/p/dario-amodei-circuit-documentary-10-takeaways-2026)

Also on the radar

• Ruben Dominguez, The AI Corner: the SaaSpocalypse is real but misread. $285B of software market cap vanished the day Cowork launched, yet total software grows while distribution shifts. Write the sentence “AI can do everything we do except ___” and make your ending specific. Link

• Charlie Hills, MarTech AI: before you ship anything Claude built, ask “what would the taste critic say?” On his own site it caught five ambient animations running at once that read as AI-made. Link

• Charlie Hills, MarTech AI: borrow premium design components without inheriting their look. Treat every pasted component as a structural donor: keep the skeleton, swap in your colors, fonts, and words. Link

• Ruben Dominguez, The AI Corner: prompt caching is the lever most teams skip. Most apps resend the same system prompt thousands of times a day at full price, when caching cuts that repeated input on Claude by up to 90%. Link

Steal this move: cut your AI bill in 4 weeks

For you if: founder or operator, 0 to 10M+ ARR, watching your Claude plan hit its limit faster each month or your API spend climb, with no engineer to optimize it for you. Four progressive weeks, Measure to Operate. The full checklist is on the card.

Why this matters now

The model is no longer the moat. This week made that concrete from three directions: it got cheaper to run, faster at replacing your execution, and fragile enough for a government to switch off in three days.

So your edge moves to what sits around the model. The waste you cut. The judgment you keep. The files you own that any model can read. The motion you encode so it runs without you.

This builds on Software Got Cheap from June 7. Cheap building raised the question of what to build. This week raises the question of what you keep when the model itself is not yours to count on. It also extends Taste Is the Moat from May 24, with a new line on it: the how-to is now a free Skill, so taste and judgment are what is left to sell.

For a lean team at 0 to 10M+ ARR, the move is the same in every lane. Spend less re-reading, depend less on one model, automate the steps you can write down, and keep the judgment for yourself.

What is your number one takeaway from this week’s GTMcraft Sunday signal?

Reply or send me a DM.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

The 90-second version

Most quarterly targets are a top-down wish, then a plan reverse-engineered to fit. This week three different corners of the GTM world said the same thing in their own language: stop managing the number by its easiest proxy and start building it from the drivers underneath. CJ Gustafson made the capacity case, Everstage’s Siva Rajamani made the comp case, and Pierre Herubel, Primer, Full-Funnel, and Outbound Kitchen made the demand and outbound versions. The pattern is the same every time. The visible metric is not the lever.

• Build the number bottoms-up, rep by rep, or you are forecasting fiction.

• Roughly 90% of comp plans pay for raw activity, not the outcome you need this quarter.

• Your demand gen only works on the 5% ready now. The other 95% decide later who they already trust.

• Prospects do not care which channel you pick. Relevance built from real account facts gets the reply.

• Watch the quiet leaks: renewals that turn last-minute, and pricing a competitor re-cuts while you sleep.

Read time: ~5 minutes.

Lane note: Saturday is GTM execution, Sunday is Claude. Here is the one move that matters most this week.

This week’s number #1 move

Build next quarter’s number bottoms-up, then pay for the behavior that hits it.

Two posts this week point at the same blind spot in how founders set the number. CJ Gustafson at Mostly Metrics walks through a sales capacity model built bottoms-up, rep by rep: ramp, real productivity, and attrition, not a top-down target divided by hope. On the GTMnow podcast, Everstage’s Siva Rajamani says he has seen more than 300 comp plans and roughly 90% make the same mistake. They reward raw activity and bookings volume instead of the specific outcome the business needs this quarter, whether that is margin, new logos, or multi-year deals.

Put them together and the move is simple. The target you commit to should fall out of real per-rep capacity, and the comp plan should pay for the one behavior that gets you there. Most founders do the reverse. They set an aspirational number in a board deck, then bolt on a comp plan that quietly pays for the wrong thing, and then wonder why the team is busy and the number still misses. The capacity model tells you whether the number is even possible with the people you have. The comp plan decides whether the people you have will run at the right thing. Those are the two dials. Almost everything else is noise.

Start here:

Take your current quarterly target and divide it by realistic per-rep productivity from your last two quarters of closed-won per ramped rep. If the rep count that implies is more than you have, the number is fiction, and the gap is your first conversation with your board or your CRO.

Sources:

• Mostly Metrics, June 18 (https://www.mostlymetrics.com/p/how-to-build-a-sales-capacity-model-using-claude-excel)

• GTMnow, June 16 (https://thegtmnewsletter.substack.com/p/gtm-194-sales-comp-plan-mistakes-siva-rajamani-everstage)

Problem 1: Your demand gen only sells to the 5% ready today

Three posts this week circle the same trap: treating every demand channel as a lead spigot aimed at people ready to buy now. Pierre Herubel’s 95/5 playbook is the spine. At any moment about 95% of your buyers are not in-market, so content that only chases the 5% with a form leaves the real game unplayed. Keith Putnam-Delaney of Primer, on GTMnow, makes the paid version: the new paid playbook is audience-first, building the right audience and earning attention, not blasting conversion ads at a cold list. Full-Funnel’s field-events playbook makes the in-person version, where the whole point is to engage target buyers and engineer the follow-up so sales is not ghosted the week after.

Across paid, content, and events, the winners build demand with the 95% so they are the obvious choice when those buyers finally enter the 5%. The teams that only run capture plays look efficient on a weekly dashboard and starve their pipeline 90 days out, because nobody was building trust with the people who were not ready yet.

Start here:

Open your last 5 pieces of content or campaigns and label each as for the 5% ready now or for the 95% not yet. If four or five are conversion plays, you are only fishing the small pond.

The rule:

If a campaign only works on buyers ready to purchase this quarter, that is lead capture rather than demand creation. Fund both, but never let the 5% plays crowd out the 95%.

Sources:

• GTMnow, June 18 (https://thegtmnewsletter.substack.com/p/gtm-195-b2b-paid-advertising-strategy-keith-putnam-delaney-primer)

• Full-Funnel B2B, June 19 (https://fullfunnel.substack.com/p/field-eventsconference-playbook)

• Pierre Herubel, June 17 (https://pierreherubel.substack.com/p/the-955-playbook-for-linkedin-growth)

Problem 2: You tune the channel when the reply comes from relevance

Two posts from Outbound Kitchen this week take apart the favorite outbound distraction: which channel, which tool. Elric Legloire is blunt. Your prospects do not care whether you reached them by email, phone, or LinkedIn. They reply when the message is relevant to them right now. The companion piece says your outbound stack has one job, and most teams build it for the wrong one, optimizing sending and volume instead of the relevance and timing that earn a reply.

The through-line is uncomfortable. Channel-hopping and tool-stacking are how teams avoid the harder work of being relevant. Adding LinkedIn to the sequence feels like progress. Buying a new sequencer feels like progress. Neither one makes a single message more relevant to the person reading it. Relevance comes from a real fact about the account, a trigger, a role change, a public announcement, not a cleverer subject line. That is slower to produce and it does not show up in a tool demo, which is exactly why most teams skip it.

Start here:

Pull your last 10 outbound messages and read the first line of each. Does it name something specifically true about that account right now, or could it be sent to anyone in your ICP? Count how many pass.

The rule:

If your opener could be sent to any account in your ICP, the channel will not save it. Rewrite for relevance before you touch the tool.

Sources:

• Outbound Kitchen, June 15 (https://newsletter.outbound.kitchen/p/stop-debating-email-vs-linkedin)

• Outbound Kitchen, June 14 (https://newsletter.outbound.kitchen/p/your-outbound-stack-has-one-job)

Problem 3: Your comp plan pays for the wrong behavior

The number 1 move names the pattern; this is where it bites your team day to day. Siva Rajamani’s read of 300-plus comp plans is that roughly 90% reward volume, because volume is easy to measure and easy to pay on. CJ Gustafson’s capacity work is the other half. When the model underneath is built rep by rep, you can see exactly which behavior closes the gap to the number, and that is what the plan should pay for.

The trap is a comp plan written once, then left alone while the business changes what it needs. A plan that paid for new logos last year may be quietly funding low-margin renewals this year, and the reps are doing exactly what you are paying them to do. You do not have a motivation problem or a talent problem. You have an instructions problem, and the instructions are the comp plan.

Start here:

Write the one sentence your comp plan rewards most, in plain words. Then write the one outcome the business needs this quarter. If they are not the same sentence, that is your leak.

The rule:

If the behavior your plan pays for most is not the outcome you need this quarter, your reps are not the problem. The plan is.

Sources:

• GTMnow, June 16 (https://thegtmnewsletter.substack.com/p/gtm-194-sales-comp-plan-mistakes-siva-rajamani-everstage)

• Mostly Metrics, June 18 (https://www.mostlymetrics.com/p/how-to-build-a-sales-capacity-model-using-claude-excel)

Also on the radar

• PricingSaaS, June 19: Linear re-cut its seat-based model deliberately while peers bolt on AI credits. Packaging is being re-engineered in public, so if you have not revisited yours this year, a competitor has. (https://newsletter.pricingsaas.com/p/how-linear-evolved-its-seat-based-model)

• CS Cafe, June 14: your best CSM keeps landing on the renewal email chain at the last minute, and the cause is structural, not effort. Become the person who proves value before the renewal, not during it. (https://www.thecscafe.com/p/customer-success-default-owner-system)

• Elena Verna, June 18: as build costs collapse, tiny markets that were never economic become viable, so expect more small focused competitors, not fewer. (https://www.elenaverna.com/p/the-mom-and-pop-saas-era-has-arrived)

• Cannonball GTM, June 15: a worked inventory of free public data for outbound shows the edge is assembling real account facts nobody else pulls, not buying another list. (https://cannonballgtm.substack.com/p/public-data-sources-for-the-construction)

Steal this move

Rebuild your number from the drivers up in four weeks. For you if you are 0 to 10M+ ARR and your quarterly target is a top-down guess your team quietly does not believe.

• Week 1, build the number: divide your target by real per-rep productivity to see how many ramped reps it actually needs. Fix the gap on paper.

• Week 2, re-point comp: name the one behavior your plan pays for most and re-aim one accelerator at the outcome you need this quarter.

• Week 3, feed the 95%: add one demand play for buyers who are not ready now, and pre-write the follow-up for your next event.

• Week 4, earn the reply: make names a real account fact a pass-or-fail check on every outbound first touch, independent of channel.

Why this matters now

The cost of building has collapsed, which means more competitors and less room for a number you cannot defend. The teams that win build from the drivers up: a target that falls out of real capacity, comp that pays for the outcome, demand that courts the 95% and not just the 5%, outbound that earns the reply with relevance. None of it needs a new tool, only the numbers and customers you already have. Pick one dial this week and turn it on purpose.

What is your number one takeaway from this week’s GTMcraft Saturday signal?

Reply or send me a DM.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

The 90-second version

• 90% of companies have staff pasting work into personal AI accounts. 57% have typed sensitive data in. On personal plans, Claude and ChatGPT train on it by default and can keep it 5 years.

• The fix takes two minutes: turn off model training in settings, default to a temporary chat for work, and never connect a work account to a personal AI.

• Building got cheap, so the question flipped from can you build it to should you. 42% of startups still die building something nobody wanted, and that share rises as building gets cheaper.

• Most operators run agent-grade work through a chat box. Three surfaces now: Chat for answers, Cowork for file work, Code for builds and loops. Charlie Hills frames the same ladder as three automation levels: start low, climb only when you must.

• This week’s build: stand up an AI data-hygiene baseline for your team in 4 weeks.

Read time: 5 min

Lane note: Saturday is GTM execution, Sunday is Claude. Read here the Saturday edition ↓

Here is the one move that matters most this week ↓

This week’s number 1 move

Lock down what your team feeds the AI before it costs you a customer or a lawsuit. Ruben Hassid pulled the numbers this week, and they are not comfortable. Staff at more than 90% of companies use personal chatbots for work, 57% have typed sensitive information in at least once, and 22% still reach for a personal account even when the company pays for a tool. On personal Free, Pro, and Max plans, Anthropic and OpenAI train on your chats by default and can keep them up to 5 years. Samsung banned ChatGPT after engineers leaked source code three times in 20 days, and in Europe pasting a customer name or email into a personal account can be an unlawful data transfer.

Start here: open Claude, go to Settings, then Privacy, and turn off “Help improve our AI models.” Two minutes. Do the same in ChatGPT under Data controls.

Source: Ruben Hassid, “Stop using your own Claude at work.” (Jun 10)

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. 100+ playbooks structured. 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Same recipe. Now all part of the GTMcraft Operator Room.

Consider joining? (send me a DM for access).

Problem 1: Your team is pasting company secrets into personal AI accounts

At 0 to 10M ARR you have no IT department policing this. The founder is the policy. Hassid’s numbers say the exposure is already live: most of your team is pasting work into personal accounts, and more than half have put sensitive data in. The defaults work against you, because on a personal plan the provider trains on those chats unless you turn it off, and retention runs years. The legal edge is public and growing. The US Defend Trade Secrets Act lets a company sue over leaked secrets, a secret stops being legally secret the moment you hand it to a third party under loose terms, and GDPR adds a second front in Europe.

Start here: turn off model training in every AI account you and your cofounder use, and confirm the toggle saved.

The rule: if you would not post it in the all-hands channel with your name on it, do not paste it into a personal AI. Anonymize it first, or use the company tool.

Source: Ruben Hassid, “Stop using your own Claude at work.” (Jun 10)

Problem 2: The bottleneck flipped from can you build it to should you

When building was expensive, can-you-build-it was the gate, and the people who could ship won. That gate is gone. Ruben Dominguez frames the new shape: the founder stops being the contributor who writes the code and runs the ops, and becomes the orchestrator of agents that carry the work out, with the edge moving from execution to judgment. Anthropic’s own Founder’s Playbook puts numbers on the speed, with validation cycles that took months now taking an afternoon. Here is the trap most people miss. The old CB Insights figure, 42% of startups dying because they built something nobody wanted, does not shrink when building gets cheap. It climbs, because the cost of building the wrong thing dropped too, so more wrong things get built.

Start here: take the last thing you shipped or were about to. Write one sentence on what evidence said someone wanted it before you built it. If you cannot, that is the gap.

The rule: no build starts without one line of evidence that someone wants it. If you cannot write that line, you are not ready to build it.

Source: Ruben Dominguez, “The one-person company stopped being a meme.” (Jun 11, free section)

Problem 3: You run agent-grade work through a chat box

Most operators do everything in one chat window, including work the chat window is the wrong tool for. Two creators map the way out this week. Ruben Dominguez lays out the three surfaces: Chat for a quick answer you copy out, Cowork for work on your own files where the AI reads a folder and writes the real spreadsheet back to your machine, and Code for builds and for loops that run without you. Charlie Hills draws the same ladder as three levels of automation: Level 1 is a Skill you fire from Chat on demand, Level 2 is that same Skill on a schedule in Cowork, Level 3 is Claude Code running unattended on Anthropic’s cloud with your laptop closed. His discipline is the part to steal: start at Level 1, climb only when the level you are on stops doing the job, and keep a human on the publish button at every level.

Start here: list your 3 most-used Claude tasks, label each one Chat, Cowork, or Code, and move the most painful one to its right surface this week.

The rule: match the work to the lowest surface that does the job. Quick answer is Chat, file work is Cowork, builds and loops are Code. Start one level lower than you think, and a human approves every publish.

Sources: Ruben Dominguez, “The one-person company stopped being a meme.” (Jun 11), Charlie Hills, “The 3 levels of Claude automation.” (Jun 7), Ruben Dominguez, “The head of Claude Code stopped prompting Claude.” (Jun 12, free section)

Also on the radar

• Ruben Hassid, How to AI: connectors are the most dangerous thing on a personal AI account. A connected AI can be hijacked by the content it reads, so never connect a work account to a personal AI. Link

• Ruben Dominguez, The AI Corner: Claude Code shipped a goal command (v2.1.139, May 11) that runs a loop until your condition is true, grading the work each turn. Most builders have not tried it once. Link

• Ruben Dominguez, The AI Corner: Anthropic published a 36-page Founder’s Playbook, with validation cycles that took months now taking an afternoon. Link

• Ruben Hassid, How to AI: vibecode a side project on the company’s AI and the company can own it. The line between work and personal AI is also an IP line. Link

Steal this move

Stand up an AI data-hygiene baseline in 4 weeks. For you if: founder or GTM operator, 0 to 10M+ ARR, lean team with no IT or security function, and staff running customer data, deal terms, or forecasts through AI accounts.

• Week 1, Define: Inventory who uses which AI account, personal or company, and what data each one touches. List every connected app across the team.

• Week 2, Build: Turn off model training on every account. Set the temporary chat as the default for work. Write the paste rule and the anonymize habit, and share both.

• Week 3, Validate: Disconnect every work account from every personal AI. Spot-check the last week of usage against the paste rule and fix the gaps.

• Week 4, Operate: Decide the company-tool question. Put a 15-minute connector and settings review on the calendar, monthly.

Why this matters now

The cost of AI dropped to near zero, so everyone is feeding it more: more of your pipeline, your customers, your deal terms. The input side is where the risk and the edge both moved.

This week one creator showed the risk, which is that your team is already pasting secrets into accounts that train on them. Two more showed the edge, which is that when building is cheap, the judgment about what to build and which surface to use becomes the scarce thing.

This builds on Software Got Cheap from June 7. Cheap building was the upside. This week is the bill: what you feed the machine, and whether you should have built the thing at all. It also extends Taste Is the Moat from May 24, now with a number on it, because as building gets cheaper the 42% who build the wrong thing grows rather than shrinks.

For a lean team at 0 to 10M+ ARR, the move is simple. Govern your inputs, sharpen your judgment, and match the work to the lowest surface that does the job. The typing is no longer the hard part.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

Full source list

Ruben Hassid, How to AI:

• Stop using your own Claude at work. (Jun 10)

Ruben Dominguez, The AI Corner:

• The one-person company stopped being a meme. (Jun 11) (free section used; full playbook behind the paywall, not reproduced)

• The head of Claude Code stopped prompting Claude. (Jun 12) (free section used; full playbook behind the paywall, not reproduced)

Charlie Hills, MarTech AI:

• The 3 levels of Claude automation. (Jun 7)

The 90-second version

• Buyers finish 70 to 80% of their evaluation before they talk to you, and the winning vendor is already on the shortlist 95% of the time. Earn the spot early or lose it quietly.

• In the AI era, prospects buy from the vendor with a clear point of view on where the market is heading, not the longest feature list.

• Your outbound is a measurement problem before it is a message problem. A meeting booked rate at or below 0.25% means the list is wrong, so stop rewriting subject lines.

• Your pipeline review only sees the 20% of buying that left a trace in the CRM. The other 80% is brand work you are not counting.

• Sort your accounts by revenue and you get the org chart you already have. Sort by real signal and you get a list competitors cannot buy.

Read time: 5 min

Lane note: Saturday is GTM execution, Sunday is Claude. Here is the one move that matters most this week.

This week’s number 1 move

Write down your point of view, then check if your reps can say it.

April Dunford’s piece this week is the sharpest version of a pattern showing up across the newsletters. Buyers face a wall of AI noise they cannot evaluate, so they look to vendors for one thing: a clear point of view on where the market is heading. The Gartner data she cites shows buyers use about 7 information sources, and when AI is one of them, 69% still turn to a rep to validate it. A point of view states your belief about the future of your category, rooted in the one thing you do better than anyone. Product vision and feature lists do not count. OpenAI bets on AI as a metered utility, ServiceNow bets on workflows and governance, each rooted in a strength that vendor already owns.

Start here: open your homepage and your last three outbound emails, and find the single sentence that states where you believe your market is heading. If it is not there, you do not have a point of view yet.

Source: April Dunford, Positioning, June 11

Problem 1: Your outbound is busy but not booking

Two posts this week say the same thing from different angles. Cannonball GTM argues the only outbound metric that matters is meeting booked rate, meetings divided by activities, and almost no benchmark report publishes it. At or below roughly 0.25% you are spraying and praying and the list is the cause, so do not touch the message. Well-targeted campaigns run 1 to 3%, and tightly targeted lists under 50 contacts average a 5.8% reply rate against 2.1% for big sends. Outbound Kitchen agrees: a funnel where every stage compounds beats more volume, and no AI SDR rescues a bad list.

Start here: calculate your meeting booked rate on last month’s outbound, meetings booked divided by total activities. At or below 0.25%, the list is the cause, not the subject line. The rule: at or below 0.25% fix the list, at or above 1% with flat replies fix the message.

Sources: Cannonball GTM, June 12; Outbound Kitchen, June 7

Problem 2: You are reviewing the 20% of the funnel you can see

Jeff Ignacio at RevOps Impact makes the structural case: companies miss bookings by roughly the same percentage their pipeline coverage is short, and the cause sits upstream of the CRM. Forrester data he cites says 70 to 80% of the buying process happens before first contact, the winning vendor is on the day-one shortlist 95% of the time, and 13 buyers inside the org plus 9 outside form opinions you never track. Your pipeline review sees the 20% that left a trace. Maja Voje’s piece rhymes: half of founders scale at the wrong time because they read the wrong numbers, so scaling is a measurement decision before a budget one.

Start here: pull your last 10 closed-won deals and read the AE notes for one field, how did they first hear about you, then tag each as found-us, campaign, or outbound. The rule: if your found-us cohort wins more and closes faster than outbound-sourced, that gap is your brand budget case in the CRO’s own format.

Sources: RevOps Impact, June 8; GTM Strategist, June 12

Problem 3: Your account list is sorted by the wrong thing

Two posts attack the same lazy default: ranking accounts by size. Jordan Crawford at Blueprint scored 1,145 customers by their owner’s real influence, board seats, conference stages, podcasts, not truck count, and found the 56 worth a personal ask were almost never the biggest logos. The data was public and free to read; assembling it was the work nobody does. Full-Funnel’s Backbase breakdown makes the prospect-side version: a focused account function that picks the next action per account from signals beats a broad list worked by filter.

Start here: take your current top-20 target list and re-sort it by one real signal, active pain or who is connected in your market, instead of company size. The reorder is your real priority list. The rule: if you cannot name the signal that puts an account in your top 20 this week, it does not belong there yet.

Sources: Blueprint, June 10; Full-Funnel B2B, June 12

Also on the radar

• CS Cafe, June 7: the renewals you cannot see are the ones that blow up. Become the one person who can prove value to the actual user, not the gatekeeper. (https://www.thecscafe.com/archive)

• PricingSaaS, June 12: pricing moves from Clay, Datadog, Front, and Monday.com show monetization is being re-cut in public every week. (https://newsletter.pricingsaas.com/archive)

• MKT1, June 9: a test of B2B billboards is a reminder that brand channels need a number before a budget. (https://newsletter.mkt1.co/archive)

• GTMnow, June 12: a 1.5B dollar investor on why funding merely good companies is a mistake. Great beats good, focus is the moat. (https://thegtmnewsletter.substack.com/archive)

Steal this move

Earn your way onto the day-one shortlist in four weeks. For you if you are 0 to 10M+ ARR and your pipeline review only shows the deals already in the system.

• Week 1, find your point of view: write the one sentence on where your market is heading and the choice it justifies. Test it on two reps and one customer.

• Week 2, fix the list before the message: calculate meeting booked rate, then rebuild one sub-50 list around a single named pain.

• Week 3, measure the upstream: add the already-knew-us field and pull the found-us cohort win rate from your last 12 months.

• Week 4, re-sort the targets: rank your top accounts by real signal and influence, not size, and brief reps on why each is on the list.

Why this matters now

The decision is moving earlier. Buyers shortlist before they raise a hand, they trust vendors with a clear point of view, and they evaluate through channels your CRM never sees. The teams that win in 2026 earn the spot upstream: a real point of view, lists built on pain not volume, measurement that counts awareness, accounts ranked by signal not size. None of this needs a new tool, only the numbers you already have.

See you inside GTMcraft’s Operator Room,

Koen

Your (human) GTM Agent

Skip this if you read Monday Tuesday

The GTMcraft system in 5 lines:

1. Most GTM organisations from 5M to 25M ARR have a systems problem, not a headcount problem

2. Processes first, systems second, people third. The order most founders have backwards.

3. Documentation is the foundation AI amplifies. Undocumented processes return noise, not speed

4. Phase 1 CREATE: build the coaching and documentation foundation before adding people

5. The two prompts below are the starting point. No community membership needed to run them

Read here the Tuesday edition ↓

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. 100+ playbooks structured. 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Same recipe. Now opening as a community.

2 numbers before we go in

• 10% of my current paycheck in 2016. That was the year that built the next 10.

• 100,000+ GTM signals indexed in 2026. That is what the same instinct looks like productized.

Berlin, 2016: Saturdays nobody asked for

I was 29 when I moved to Berlin for the international sales champion role. The title was generous. The actual job was simpler. Take what I was learning from Jacco van der Kooij through the Winning by Design Customer Centric Communication framework and the train-the-trainer program, and coach 6 country leaders and 60+ GTM colleagues in Amsterdam, Berlin, Madrid, Milan, and Paris on how to apply it.

The local sales leaders had the teams. They had the authority. They had the regions. I had a methodology I had just learned myself and a question I kept asking: how do I help these leaders lead better.

Monday to Friday was the assignment. Saturdays were different.

On Saturdays I built enablement content. Outbound playbooks for the BDR teams. Discovery checklists for the new AEs. Coaching frameworks for the country leaders themselves. Onboarding decks for new hires the local teams were about to bring in. None of it was on my objectives. None of it was reviewed by anyone above me. None of it was tracked.

I built it because the work needed it and because building it was the thing I wanted to do that day.

I made 10% of what I make today and I had no clear next step on the org chart.

The instinct that took 10 years to name

At the time I did not have a frame for what I was doing. I thought I was just being thorough. Or maybe ambitious in a quiet way.

10 years later the frame is obvious.

The instinct was to take the messy, lived experience of the country teams and turn it into systems other people could use. Playbook the thing. Document the thing. Build the checklist so the next rep does not have to figure it out from scratch. Coach the leader so the leader can coach the rep. Compound the work.

That instinct is not a career skill. It is the operator instinct. And it does not require authority to be useful. It requires the willingness to do work nobody asked for, in a format other people can use, for a payoff you cannot see yet.

The 6 country leaders did not promote me. They could not. I did not report to them. What they did was let me coach them, trust the methodology, and run the regional motion together. The result was visible. The region moved. ICs got better. Managers got sharper. Leadership saw it.

2 years later that role led to country manager for the Netherlands. 8 years after that, the director international seat I arrived in yesterday.

Nothing about it was linear. All of it was the same instinct.

The thing Winning by Design taught me that most founders still resist

In 2016, Jacco van der Kooij gave me a framework I have never stopped using. The core of it was not a sales methodology. It was a way of thinking about how growth works.

Most GTM organisations are built around people performance. You hire a superstar. The superstar drives the number. You hire more superstars. When the superstar leaves, the number drops and you hire again. The culture becomes the person, not the system.

Winning by Design’s Growth Architecture describes this precisely. SaaS-native organisations stack their model processes first, systems second, people third. The data is based on systems performance, not individual performance. The growth engine runs on the system. People tune it.

I saw this play out in real time across 6 country teams. The markets that were winning had reps executing a common process. The markets that were struggling had a superstar carrying the number alone. When the superstar moved on, the number went with them.

The Saturday work in 2016 was my version of building the system layer. Career frameworks. Onboarding programs by role. SPICED as the shared language from SDR to CSM. Peer coaching structures that let a rep in Amsterdam learn from a close in Milan. None of it was about individual talent. All of it was about making the talent that was already there compound.

That principle has not changed. What has changed is that in 2026, AI makes it non-negotiable.

An undocumented process fed into an AI system returns noise. A documented, structured process returns speed. The operators who have their systems in place are already seeing 30 to 40% output gains from the same headcount. The ones who are waiting to document first are watching those gains go to their competition.

There is no excuse left. Voice notes, structured prompts, AI drafting. The work of documentation is cheaper than it has ever been. The only thing that still requires the operator is the judgment to know what is worth documenting.

I co-lead the Winning by Design Growth Institute chapter for the Netherlands. It is the same conversation every time: founders who know they need to systematise, but have been building around their top performers for so long that the idea of process-first feels like a demotion. It is the opposite. Processes are what make your best people scalable.

What GTMcraft actually is in 2026

GTMcraft is the productized version of the 2016 instinct, rebuilt for the 2026 reality.

3 layers, all working toward 1 outcome: more ARR from what you already have.

Layer 1: Living dynamic GTM Playbooks.

7 GTM playbooks live now. Account prioritization, warm outbound, deal progress and closing, async selling, intent outbound, AI-SDR operating system, international scaling. Each one is a 28-asset suite: the markdown playbook, the master PDF, the 13-sheet workbook, the operating doc, the visuals, the phase guides. The same kind of documentation I was building on Saturdays in Berlin, now built to a tested standard and shipped as a complete operating system.

Layer 2: Group and 1-1 Coaching.

1-on-1 sessions with CEOs and GTM operators. Group coaching cohorts. Async selling cohort running now. The coaching is where the playbooks become decisions. A playbook on the shelf is a document. A playbook in a coaching session is a change.

Layer 3: Claude Projects, Cowork and agents.

Two Claude Projects sit at the core: the Sales Coaching System and the Playbook Builder. Both available to run today. The prompts are below.

All 3 layers point at the same outcome.

The 2026 bet: more ARR from what you already have

This is the part that took me 10 years to be willing to say out loud.

Most GTM organisations scaling from 5M to 25M ARR do not have a headcount problem. They have a systems problem.

• AEs who could close 20% more if the coaching was sharper.

• SDRs who could book 30% more if the outbound playbook was tighter.

• Customer teams who could retain 5 points higher if the renewal motion was documented.

• Managers who could move the region if they had a coaching framework that actually got used.

The instinct in 2016 Berlin was: build the system, coach the people, move the region without adding headcount. The bet in 2026 is the same.

More ARR from what you already have. Better systems. Sharper coaching. AI agents that scan the noise so the operator can focus on the decisions.

The founders and revenue leaders I work with are not asking how to hire 10 more reps. They are asking how to make the 8 they have ship more revenue.

GTMcraft is the platform I am building for that question.

Your Claude Projects for coaching and playbook building

Two prompts. Both runnable today inside Claude Projects. Both are the same work I was doing manually in Berlin in 2016. Now with an AI system holding the structure so the operator can focus on the judgment layer.

Prompt 1: Sales Coaching System

Surface: Claude Project (or connect to Gong, Leexi, or any call recording tool to run it as an agent)

What it does: Analyses call transcripts and delivers framework-grounded coaching feedback per coachee, tracking development over time. Runs SPICED or MEDDICCC depending on the organisation. The longer you use it, the sharper the output. It compounds per rep, same way coaching compounds in a real team.

Paste this as your Project instructions:

You are a structured B2B SaaS sales coaching system operated by a GTM coach serving multiple organisations.

Your job: analyse call transcripts and deliver actionable, framework-grounded coaching feedback that compounds over time per coachee.

When a transcript is uploaded, follow this intake sequence:
1. Ask for the coachee name. If known, auto-load their profile. If new, register: name, role (SDR/AE/AM/CSM/Manager), organisation, experience level (junior <1yr / mid 1-3yr / senior 3+yr), ACV band, coaching tone preference (direct / supportive / growth-oriented), sales motion (PLG-assist / SMB velocity / mid-market / enterprise).
2. Ask for call type: Cold Call / Qualification / Discovery / Demo / Follow-Up / Negotiation / Pricing / Closing.
3. Confirm the framework: SPICED or MEDDICCC.

Analysis output structure:
- Executive summary (3-4 sentences, overall impression)
- Framework element scores (1-5 per relevant element, rubric-based)
- Talk-to-listen ratio estimate + question quality assessment
- Top 3 strengths with transcript evidence
- Top 3 improvement areas with transcript moments and rewrite suggestions showing how to say it better
- Next-step quality assessment (was it earned, time-bound, owner assigned?)
- 3 actionable takeaways for the next call
- Reflection questions for the coachee

Coaching tone rules:
- Junior reps (<1yr): supportive, celebrate small wins, frame improvements gently
- Mid-level (1-3yr): growth-oriented, push on patterns, expect self-awareness
- Senior (3+yr): direct, strategic, challenge assumptions, focus on elite-level refinements

After every session, update the coachee development areas. After 3-5 sessions for the same coachee, generate a Development Trend Summary: what improved, what is stuck, recommended focus shift.

Never give generic feedback. Every observation must reference a specific transcript moment. Always provide rewrite suggestions showing how to say it better. Goal: the coachee walks away with 3 things they can do differently on their very next call.

What you get: transcript in, structured coaching report out. Every session builds on the last. After 5 sessions for the same rep, you see pattern trends.

What you still own: the judgment layer. Which patterns matter for this rep at this stage. When to push. When to build confidence first. The system holds the structure. You bring the coaching instinct.

One session looks like this in practice. You upload a 40-minute discovery call transcript. The system scores each SPICED element, flags that the rep accepted a surface-level answer on impact without layering a follow-up question, and gives you 3 rewrite options for the exact moment it broke down. The rep walks out of the debrief with 1 thing to try on the next call, not a list of 10.

Prompt 2: GTM Playbook Builder

Surface: Claude Project (or run as a scheduled Cowork session to check for new inputs and build automatically. Details inside the GTMcraft private community)

What it does: Takes your raw operator experience, field observations, and named data points, and structures them into an execution-ready GTM playbook. The same kind of documentation I was building on Saturday mornings in Berlin. Now it runs in 30 minutes instead of a Saturday.

Paste this as your Project instructions:

You are an operator-grade GTM playbook builder for B2B SaaS founders and revenue leaders scaling to $10M ARR.

Your job: take raw operator experience, call notes, and field observations and produce execution-ready playbooks that can be implemented in weeks, not quarters.

Every playbook follows a 3-phase structure:
- Phase 1 CREATE (foundation, Days 1-5): what gets built before anything runs
- Phase 2 OPERATIONALISE (daily execution, Days 6-14): what runs every day and week
- Phase 3 SCALE (compound, Week 3+): what extends the system across the org

When a playbook request is received, ask for:
1. Topic and operator problem (what execution problem this solves)
2. Target company stage (0-2M / 2-5M / 5-10M ARR)
3. Known data points or named examples from your own experience
4. What is working now versus what is breaking down

Output structure per playbook:
- TL;DR (4 bullets: problem / system / asset / action)
- Lessons from the field (first-person, lived operator experience)
- 12-step operating model across 3 phases (4 steps per phase)
- Per step: why it matters, how to execute it this week, what good looks like (4+ observable bullets), common trap with fix
- AI layer per step: what the tool does, where human judgment stays required
- Scaling guidance per step: founder-led / lean team / full GTM

Writing rules:
- Declarative statements only. Cause to effect framing. Specific timelines in days.
- Observable behaviors, not opinions.
- Honest gaps acknowledged when data is not fully there.
- Never use: em dashes, hype words (transform, unlock, supercharge, game-changer), contrast constructions (it is not X, it is Y).

Goal: the operator can implement Phase 1 by the end of this week, Phase 2 by the end of next week.

What you get: raw observations in, structured playbook out. The system holds the 12-step architecture. You bring the operator knowledge that makes it real.

What you still own: the anecdotes. The named moments. The specific deals and reps and outcomes that make a playbook credible versus generic. The AI holds the structure. You bring the truth.

Want to run this as an automated agent? The Cowork version checks for new inputs, triggers a build, and notifies you when the draft is ready. That workflow lives in the GTMcraft private community room.

Why I am building this on the side

The honest answer is that I did not start GTMcraft. The Saturday playbooks in 2016 were GTMcraft. I just did not have a name for it yet.

The director role at Personio is the day job. It is the seat where I learn the scale layer of the instinct, work with a senior team I deeply respect, and ship the work that pays the mortgage and funds the side build.

GTMcraft is the creator practice. It is the place where the same instinct that built Saturday playbooks now builds a productized system for other operators. It runs outside Personio. It is not a Personio deployment, not endorsed by Personio, not part of any Personio tech stack.

Both are real. Both compound the same instinct.

If you have ever felt the pull to build something on the side and wondered whether the side work counts: the work you do on Saturdays for the payoff you cannot yet see is the work that becomes your career in 10 years. Document it. Systemise it. Ship it to people who can use it. The instinct is the asset.

What is inside GTMcraft

If this story landed, the place I am building all of it is gtmcraft.circle.so.

7 GTM playbooks live now. The Monday/Wednesday/Friday operator rhythm. All GTM AI agents including the Sales Coaching System and the Playbook Builder above, plus the founder-side Head of Sales on Demand stack. Automated Cowork workflows for both agents. Monthly office hours. Peer matching with operators scaling 0 to 10M+ ARR.

The operator room is open. Operators in the current cohort are using the Playbook Builder weekly and shipping one documented playbook per month.

[DM me to learn more]

2 spots for 1-on-1 work

If the systems problem in your GTM feels urgent and you want to work on it directly, I have 2 spots open for 1-on-1 engagements.

Here is what that looks like in practice. Over the past year I have been working with a sub-5M ARR company. Started as coaching the CEO and one rep on process and methodology. Grew into becoming their Head of Sales on Demand: building the playbooks, deploying the AI agents, and running the strategic GTM decisions alongside their team.

That is the model. Coaching plus agents plus strategic advice, scoped to where your GTM is breaking right now.

If you are a founder or revenue leader under 10M ARR and the documentation and systems layer is the thing holding your team back, reply to this email with one line on where you are stuck. I read every reply.

One question before next week

The Saturday work in 2016 looked unproductive on paper. It was the most productive thing I have ever done.

Reply and tell me: what is your Saturday work right now? The thing nobody asked for, that you are doing anyway, that you suspect compounds.

I read every reply. The patterns in your replies become the next layer of GTMcraft.

See you Saturday in the GTM Signal,

Koen

Your (human) GTM Agent

Monday was a public holiday in the Netherlands. Normal rhythm resumes Wednesday.

2 numbers before we go in

30. The newsletters in my Chrome bookmarks folder. I opened every one manually, every week, for over a year.

31. The number I read by hand now. The agents do it. I review the output.

The bookmarks folder that started this

Hi, it is Koen Stam and welcome to GTMcraft: The Future GTM Operator. This newsletter is built from 100,000+ GTM signals collected from 100+ operators and founders, combined with 13+ years of my own lessons and failures from the trenches. I write at the intersection of go-to-market practice and AI-powered systems for founders scaling 0 to 10M+ ARR.

100,000+ GTM relevant signals from LinkedIn, Newsletters and Podcasts indexed. 100+ playbooks structured. 3 GTM operator jobs across 3 GTM motions (SMB, MM, ENT). Same recipe. Now opening as a community.

The two agents

Two editions. Two lanes. One format. Never mixed.

Saturday: GTM execution

The agent scans all 30 newsletters every Saturday morning. Lane rule: pure execution only. Pipeline, positioning, retention, org design, outbound, pricing. If a GTM principle survives without an AI tool attached, it stays Saturday. If an insight is inseparable from Claude, it routes to Sunday or gets dropped. Every publish date is verified against a 7-day window before anything gets included.

Output: 4 operator problems, each backed by 2+ verified sources, a hero move, tiered actions, and a 4-week steal-this-move play.

Sunday: Claude

The agent scans 3 dedicated Claude writers every Sunday morning: Ruben Hassid (How to AI), Charlie Hills (MarTech AI), Ruben Dominguez (The AI Corner). Lane rule: Claude-specific only. Workflows, prompts, specs, experiments that actually ran this week. Not AI news. Not feature announcements.

Output: same structure as Saturday. Problems, tiered actions, a 4-week play.

The seam rule is what makes both editions worth reading. A great GTM insight wrapped in a Claude workflow wants to appear in both. The rule says no. Strip the tool, keep the principle on Saturday. If the insight cannot survive without Claude, it runs Sunday only. The discipline of separation is what keeps each edition clean.

The 30+ sources behind Saturday

30+ newsletters. 6 categories. Scanned every week across a 7-day window.

The three steps at the bottom of that visual are the whole system. Scan: read all 30 across the window. Synthesize: group the noise into 3 to 5 real operator problems, each backed by multiple sources. Ship: you get the soundbites, tiered actions, and one steal-this-move playbook.

The categories are the editorial logic. GTM strategy and systems, sales and outbound, marketing and positioning, growth and product, AI and automation, founder and creator. Each cluster has 5 sources. No single cluster dominates a week. The signal has to show up across multiple categories before it becomes a problem block.

This Saturday: lean beats big

11 signals from 30 newsletters. 4 problems. 1 playbook. Here are the 3 that matter most:

• Problem 1: your GTM org is carrying weight your 2026 peers already cut Signal: ICONIQ 2026 benchmark across 150+ B2B companies. The median org is running 20-30% leaner, roughly 9x flatter, and producing nearly 2x net new revenue per rep versus two years ago. A $270K productivity gap separates top from bottom performers. Do first: calculate net new revenue per rep. Last 4 quarters of new ARR divided by fully loaded GTM headcount. You know this worked when: you can name your revenue-per-rep number from memory.

• Problem 2: your pipeline is fishing in the same 5% as everyone else Signal: Cannonball GTM put a number on it. Only about 5% of your market is actively buying in any quarter. The growth lives in the 15% with acute pain who have not started shopping yet. They bring need without budget or timeline and close at lower rates over 90 days. Do first: score your last 10 booked meetings on pain (1-5) and urgency (1-5). Anything scoring 1-2 on both is not pipeline. You know this worked when: your forecast separates need-4-5 deals and outbound stops getting killed at first review.

• Problem 3: your retention number is masking churn that now hits in Year 2 Signal: SaaStr on the shift. The accounts that used to churn in Year 3 are now going in Year 2 because alternatives are easier to adopt. Do first: pull gross logo retention next to net revenue retention. A wide gap means expansion is papering over churn. You know this worked when: you can see a low-engagement account two quarters before renewal, not two weeks.

This Sunday: write the spec before you prompt

8 signals from 3 creators. 3 problems. 1 playbook. Here are the 3 that matter most:

• Problem 1: your prompts describe the task. They never say what done looks like. Signal: Ruben Dominguez and Ruben Hassid both traced the same failure. Karpathy’s Autoresearch method works because the spec tells the agent exactly what better means. Without a spec, every output is version 1. The agent only improved because it knew what improvement looked like. Do first: rewrite one prompt you use 3+ times a week. Add one line: “Done looks like: [specific format, length, one success metric].” You know this worked when: a first draft clears your bar with light edits and you stop re-running the same prompt to get what you want.

• Problem 2: you run experiments one at a time, by hand. Signal: Charlie Hills on the small version of Karpathy’s loop. Eric Siu took the same loop to marketing and went from 30 experiments to 4,500 per year at a cost of $36,500. You do not need a scoreboard at that scale. You need a spec and a way to compare variants before you ship. Do first: pick one asset you ship weekly. Have Claude produce 5 variants and score each against your spec before you choose. You know this worked when: every shipped asset has a runner-up and a logged reason it lost.

• Problem 3: your judgment lives in your head, so nothing runs without you. Signal: the quiet detail in Karpathy’s setup. The human writes exactly one file - the spec - and the system runs the rules from there. Charlie Hills makes the fixes concrete. As long as your criteria live in your head, you grade every draft and nothing compounds while you sleep. Do first: write the 5 rules Claude should apply every time it judges your output. That file is your spec. You know this worked when: a teammate runs the loop and hits your bar without asking you.

The Saturday production prompt

Paste this into a Claude Project or Cowork session, add your source URLs, and it will scan the week, verify dates, identify operator problems, and format them with tiered actions.

You are GTMcraft Signal Editor (Saturday), producing the GTM
Execution edition: highest-signal go-to-market insights from
the past 7 days, organized by operator problems with tiered actions.

Lane rule: pure GTM execution only. Pipeline, outbound, pricing,
positioning, retention, sales leadership, PLG, demand gen,
forecasting, hiring, org design. No AI tactics, no Claude tactics.

Seam rule: if a GTM principle survives without the tool, keep
the principle and strip the tool. If inseparable from a specific
AI tool, route to Sunday or drop it.

Scan window: 7 days ending today, inclusive.

Sources: [paste your newsletter archive URLs]

For each operator problem, output:
- Problem label: specific, "your X" framing
- Synthesis: 3-5 sentences, what the signals say together
- Linked sources: every cited post with URL
- Do first: one action under 30 minutes
- Do this week: 2-3 actions within 5 business days
- Do this month: one systemic change
- You know this worked when: one observable outcome

Rules:
- Each problem requires 2+ verified in-window sources
- Verify every publish date by reading it off the post
- Never trust search snippet dates
- Thin week: 3 problems, not a forced 4. Do not pad.
- No banned words: delve, leverage (verb), robust, holistic,
  synergy, unlock, empower, navigate, landscape, foster, harness
- No em dashes, en dashes, exclamation marks
- No "It's not X, it's Y" constructions
- Peer operator voice. Direct. Every sentence earns its place.

The Sunday production prompt

Three sources only. Claude-specific only. Same structure, different lane.

You are producing the GTMcraft Signal, Sunday Claude Edition:
the clearest Claude workflow moves for GTM operators this week,
sourced from 3 dedicated Claude writers.

Lane rule: Claude-specific only. Workflows, prompts, specs,
experiments that actually ran this week. Not AI news. Not
feature announcements.

Scan window: 7 days ending today (Sunday), inclusive.
Substack only. Do not scan or cite LinkedIn.

Sources:
1. Ruben Hassid - How to AI - ruben.substack.com
2. Charlie Hills - MarTech AI - charliehills.substack.com
3. Ruben Dominguez - The AI Corner - the-ai-corner.com

Critical: two creators are named Ruben. Always use full names.
Ruben Hassid = How to AI. Ruben Dominguez = The AI Corner.

Verify every publish date by reading it off the fetched post.
Never trust search snippet dates.

For each operator problem, output:
- Problem label: specific, "your X" framing
- Signal: what the source found this week, one sentence
- Do first: one action under 30 minutes
- Do this week: one action within 5 days
- Do this month: one systemic change
- You know this worked when: one observable outcome

Rules:
- Deduplicate across all 3 sources
- Maximum 3 problems. Do not pad a thin week.
- Paywalled posts: use free section only, flag it
- No banned words: delve, leverage (verb), robust, holistic,
  synergy, unlock, empower, navigate, landscape, foster, harness
- No em dashes, en dashes, exclamation marks
- No "It's not X, it's Y" constructions
- Peer operator voice. Direct. Every sentence earns its place.

Both prompts are the starter versions. The full production build - the 5-file suite with the HTML visual, Circle post, Substack newsletter, PNG render at 820px, and QA scorecard - runs on a more complete instruction set. That full system, the HTML design template, and the QA criteria live inside GTMcraft at gtmcraft.circle.so.

Build the Saturday scanner this week. Pick 5 newsletters from the source database above, paste their archive URLs into the Saturday prompt, and run it on this week’s posts. Reply and tell me what it surfaces that you would have missed reading by hand.

I read every reply.

See you Wednesday,

Koen

Your (human) GTM Agent