Hi Adopter,

Here’s the long sung problem: Your AI pilot works in the demo. Then it meets the real process.

One team uses a different spreadsheet. An approval still lives in email. Exceptions go to the employee who has handled them for years, because nobody else knows the workaround. The AI didn’t create that confusion. It walked into it.

The series follows six business decisions:

1. Fix the process before adding AI.

2. Give AI reliable company information.

3. Define success and failure before choosing the system.

4. Move important AI work into managed company systems.

5. Assign an owner, limits, and a way to stop or recover the work.

6. Connect workflows only after each one works on its own.

Part 1 starts with a two-week handoff test:

If the person who knows how this process works were unavailable for two weeks, could the rest of the team still run it?

If the answer is no, you’re not ready to automate the process. You’re about to automate one person’s memory and hope they never take a vacation. Bold continuity plan.

The problem is widespread. In Deloitte’s August 2026 survey of 501 U.S. leaders at organizations already piloting agents, only 5% said their business processes were highly prepared. Just 21% called them prepared or highly prepared.

Three company cases show what this looks like in practice.

PepsiCo moved expensive engineering decisions into simulation before changing a physical facility. McDonald’s put voice automation into a live drive-through and ended the IBM test without publishing final operating results. Stripe broke compliance reviews into small questions, let AI gather evidence, and kept people responsible for every answer.

Three different uses of AI. One operating question: where should the software enter the work, and what decision must still belong to a person?

Below the paywall:

• The decision PepsiCo moved before committing physical capital

• Why McDonald’s early 85% accuracy figure does not describe its final test

• How Stripe redesigned a white-collar review without automating the final judgment

• The CEO approval test to run before funding another AI pilot

• The complete 15-page case-study PDF

Paid members also get the combined 15-page case-study PDF with the three timelines, evidence limits, six-part series map, decision tree, 90-day pilot plan, and printable process-redesign worksheet.

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Hi Adopter,

Somewhere in your building, someone’s scoping an AI agent to run a process nobody’s ever written down. You’d expect that to get rarer as the tools mature. It’s doing the opposite, and somewhere a steering committee is meeting about it right now, which is usually where these things go to die.

Before you scope an agent team, or sign off on the budget for one, there are four questions worth answering first, the same four that separate the agents that make it to production from the ones quietly shelved along the way.

The share of companies abandoning most of their AI initiatives before they ever reach production nearly tripled this year, from 17% to 42%, according to S&P Global Market Intelligence. RAND has the likely reason: it interviewed 65 people building AI systems inside real companies, and 84% pointed to the same root cause, leadership and the technical team misunderstanding, or flat-out miscommunicating, what problem the project was supposed to solve. Not compute. Not data quality. Not the model.

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If you’re the one greenlighting an agent team this quarter, sit with that for a second. The thing you’ve been worried about is rarely the thing that sinks it.

McKinsey’s newest survey, published this week, backs it up with a number: nearly three-quarters of the highest-performing companies redesigned their workflows before scaling agents on top of them, up from 55% a year ago. Everyone else? One in four. Gartner expects the gap to keep punishing everyone else, forecasting over 40% of agentic AI projects canceled by the end of 2027.

Get the pack: rightclickprompt.com/s/e3b57f57, free, no card required, or scan the QR code below.

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Here is what the prompts are for:

1. Define the initiative in one sentence

Most AI pitches die from vague scope before anyone even argues about the model. This prompt interviews you until the fog clears, then calls out whether what you’ve described is a real outcome or just activity wearing one. Run it from the pack →

2. Pull the real stakeholder language

Skip this and you’re writing requirements from your own head instead of the people who’ll actually fund or use the thing. This one builds a stakeholder profile from real conversations, and refuses to fake one from thin material. Run it from the pack →

3. Turn the process into an owned, timed sequence

This is the actual map. If a process only lives in one person’s head, no agent can run any part of it, this prompt interviews you phase by phase until every step has an owner and a time attached. Run it from the pack →

4. Stress-test it before AI touches it

Run this on what question three gives you, before anything runs unsupervised. It hunts for the single point of failure, the handoffs that lose information, and anything touching money or compliance that shouldn’t run without a human checking it first. Run it from the pack →

You don’t need a better model this month. You need one process, mapped well enough for a stranger to run it, before you assign any part of it to something that doesn’t ask before it acts.

Pick the one your team’s already arguing about for an agent. Grab the pack, run the four questions on it. What does question one alone tell you that nobody’s said out loud yet?

Adapt and Create, Kamil

Sources

1. S&P Global Market Intelligence (451 Research), Generative AI shows rapid growth but yields mixed results, October 27, 2025

2. RAND Corporation, The Root Causes of Failure for Artificial Intelligence Projects and How They Can Succeed, August 13, 2024

3. McKinsey, The state of AI in 2026: On the road to ROI, August 25, 2026

4. Gartner, Gartner predicts over 40 percent of agentic AI projects will be canceled by end of 2027, June 25, 2025

About the author

Kamil Banc is an AI culture and adoption advisor. He helps organizations put AI to work through corporate advisory, coaching, and assessments, and writes AI Adopters Club.

Hey Adopter,

You ask AI to find your audience, create the offer, plan email and LinkedIn, build the follow-up, and decide what success looks like. It hands you a very organized pile of guesses.

Honestly, I get why mega-prompts are popular. They look complete. But the “marketing team” inside that text box knows almost nothing about your company, your customers, or which leads are real.

The prompt asks for “data-driven insights” without naming the data. It wants a comprehensive campaign inside 2,000 characters, with no requirement to show sources, admit what’s missing, or stop before contacting people.

The prompt is trying to skip the work

The five sections are useful. Audience, offer, outreach, follow-up, and measurement. Asking AI to finish all five in one shot isn’t. A long prompt can’t replace missing context.

xAI’s sales-outbound example starts with real accounts and finishes at a review list. OpenAI’s account-prioritization workflow requires source links and missing context, then stops before contacting customers.

Give AI a smaller job and make it show its work. Think fast intern, still supervised.

Keep the useful parts

Keep the five-section structure, but turn it into a test you can judge.

One audience. One offer. One outreach channel. Ten researched companies. Three drafts. Seven days.

Now you can see where AI helps, where it guesses, and how much editing is left.

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Run the 10-lead test

Use this with an AI tool that can review approved websites or files. It will learn the business first, then wait for confirmation before researching leads. Keep the first run manual.

Make AI check its own work

Open a fresh chat, paste the result, then use this:

You still need to open every source yourself. AI checking AI is useful, but let’s not make the student grade the final exam alone.

Know what good looks like

For the first run, seven of the 10 leads should survive review, and two of the three drafts should need only light edits. Those are test thresholds, not industry benchmarks. If the output misses, fix the inputs and try again.

Don’t automate the first run. LinkedIn prohibits unauthorized bots from scraping or sending messages. In the U.S., CAN-SPAM covers business-to-business commercial email too. Check your local rules.

Once the process works twice, save it as a reusable instruction or skill. Until then, keep your hands on the wheel.

Sources

1. xAI, Grok Bot use cases, updated August 11, 2026

2. xAI, Get started with Grok Bot, updated August 20, 2026

3. OpenAI, Prioritize accounts, accessed August 24, 2026

4. LinkedIn, User Agreement, effective November 3, 2025

5. U.S. Federal Trade Commission, CAN-SPAM Act compliance guide, August 2023, edited January 2024

Your move

Pick one audience. Run the prompt on 10 companies. Review every source before anything leaves your screen.

If the work holds up, build on it. If not, at least AI didn’t introduce itself to half of LinkedIn.

Adapt & Create,
Kamil

About the author

Kamil Banc is an AI culture and adoption advisor. He helps organizations put AI to work through corporate advisory, coaching, and assessments, and publishes the AI Adopters Club. Work with him at aiadopters.club

Hi Adopter,

Moderna’s stock jumped 177% after the company and Merck said their personalized mRNA treatment hit both main goals in a large Phase 3 melanoma trial.

The market, as usual, didn’t wait for the appendix.

You can already feel the headline getting away from the facts. AI cured cancer. The vaccine worked. Moderna is back.

Not quite.

The 1,137 patients had high-risk melanoma removed by surgery before treatment. The combination of Moderna’s intismeran and Merck’s Keytruda kept the cancer from returning or spreading long enough to cross the trial’s interim success boundaries. That matters. But the companies haven’t released the effect size, absolute benefit, detailed safety results, or evidence that patients lived longer overall.

So no, this isn’t a cancer cure. It is a serious signal that a very unusual operating system can work at Phase 3 scale.

The part most coverage missed

Every patient’s medicine is different. Moderna sequences tumor and normal tissue, analyzes the mutations, and uses a proprietary prediction system to rank potential targets. Up to 34 are placed into one custom mRNA construct.

That sounds like the AI story. It’s only the branching point.

The harder part is getting one patient’s digital design through manufacturing, quality testing, release, shipping, clinical scheduling, and treatment without losing the identity of the person or the product. Every patient is effectively a batch of one.

Honestly, most companies are still celebrating the model recommendation. Moderna had to build the factory around it.

That is why this case travels well beyond biotech. The same pattern shows up anywhere customer needs split into thousands of possible paths while the delivery process still has to be safe, repeatable, and economical.

Below the paywall:

• Where AI actually sits inside Moderna’s patient-to-dose workflow

• Why standardization, not model novelty, makes customization scalable

• The adoption metric Moderna shared that sounds impressive but proves very little

• A six-question test for finding the branching point in your own business

Paid members also get the full case study PDF, including the clinical evidence boundary, technical system map, business economics, and source ledger.

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Hi Adopter,

Opus 5 is the smartest model Anthropic has shipped, and your team still rewrites the first draft. The client email. The meeting notes. The update that should have taken five minutes. The answer was usually fine. It just arrived wrapped in so much talking that nobody could hit send.

Anthropic wrote this down. Default Opus 5 replies run longer than prior Opus models. Turning effort down cuts how much it thinks. It does not reliably shorten what you see. You have to put the talking rules in the system prompt.

Two weeks ago I showed you output styles in Claude Code. Keep that. That’s a voice file for the terminal. This is the other layer, the one in the video going around right now: the system prompt. The law for every task. You write it once and append it. You do not type “be concise” at the top of every chat.

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Here’s the setup

If you work in the Claude website, which is where most of the email and notes actually happen, click your initials, open Settings, and paste the file into Instructions for Claude. That hits every new chat. If the work lives in one Project, use that project’s instructions instead.

If you work in Claude Code, save the file and start the session with:

claude --model opus --append-system-prompt-file ./opus-contract.md

Append. Do not use --system-prompt-file. That replaces Claude Code’s own prompt. And do not drop this in CLAUDE.md and call it done. That’s project context, not the system prompt.

The prompt below should hand you the file on the first reply. Not a questionnaire. A working Opus 5 contract for email, notes, and client updates, built the way that video builds it: what to do, what never to say, a scope line, short codes, and two real examples. If you then paste one reply you already had to rewrite, it swaps the banned list for your actual tics. Skip the paste and you still leave with something you can install today.

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Hi Adopter,

You’re sitting in a budget meeting when someone puts two lovely, clean numbers on the screen. A hospital AI voice assistant cut staff workload by 43% and lifted patient satisfaction by 28%. The case study names Memorial Healthcare System, a six-hospital public system in South Florida.

That’s the sort of slide that gets a pilot approved before anyone asks an annoying question like, “Where did those numbers come from?”

The answer is an AI vendor’s blog post. No link, because it doesn’t deserve the click. It doesn’t name Memorial’s deployment owner, the measured population, or the method behind either percentage.

Memorial does have a credible AI story. It’s just not that one.

What Memorial actually did

Memorial bought focused tools for work people could already name: flagging missed issues in medical images, drafting clinical notes, watching high-fall-risk patients, routing contact-center work, and alerting stroke teams. It didn’t build a custom moonshot. It put vendors into real workflows.

Honestly, that sounds almost boring. Good. Boring systems with owners, baselines, and a place inside the workflow tend to survive after the launch photos are filed away.

Its Care Coordination Center cost $1.7 million and was projected to save $1.6 million in its first year. That’s a serious test case, not a victory lap. Memorial hasn’t published an audited one-year result.

Then the owner left

Jeff Sturman, the digital chief who publicly narrated and led the program, left Memorial in September 2025. As of late June 2026, Becker’s still listed Memorial among health systems without a permanent CIO. The seat was still empty.

That’s the real story. The tools may be useful, but could the operating model keep moving when its most visible champion walked out the door?

Here’s the free question from the continuity check: who’s the deputy who could brief the board tomorrow if the sponsor got hit by a bus? If nobody has a name, you don’t have a two-deep bench. You have a fire drill nobody scheduled.

Below the paywall this week:

• The five deployments Memorial can actually substantiate

• The near-payback project and the caveat hidden inside its headline number

• The vendor claims worth testing without copying them into your board deck

• The other four continuity questions, the deployment table, and the evidence ledger

Paid members get the operator playbook and full case study PDF, with the economics, evidence grades, governance risks, and source ledger.

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Hey Adopter,

Honestly, most AI mistakes never get caught. They get presented, in a meeting, a client email, a board deck, sounding exactly as sure of itself as the correct answer beside it.

Wrong and right come out looking identical. Same tone, same confidence, same clean formatting. No red squiggly line under a made up number.

Say you pull a competitor’s pricing for a proposal, or a stat for a client deck. AI hands it back fast and clean. You paste it in, and it has your name on it now, not the AI’s.

If you are a manager, one wrong number in a meeting hands the skeptics on your team the ammunition they wanted. You made the case against AI for everyone watching.

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If you are a consultant, it is worse. Clients are not paying for information, they are paying for someone who already checked it. Hand them an unverified AI answer and you have quietly skipped the one job they hired you for.

It is not that AI needs a few more model updates before it gets reliable. There is a specific, provable reason it misses its own mistakes, and it has nothing to do with how smart the model is.

Read more

TL;DR:
Use custom output styles to wrangle opus 5 verbosity to a level you like.
👉 Here is the full prompt set.

Hey Adopter,

Opus 5 landed a few weeks ago. By every benchmark that matters, it is the strongest model Anthropic has shipped. Talk to it in plain conversation, though, and something else happens.

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Ask it a plain question and the reply arrives wrapped in paragraphs of throat clearing, packed with terms it half invents. You finish reading more tired than when you started. Reddit split into camps within the first couple of weeks and coined its own words for it. “Claudeslop.” “Benchslop,” for the gap between the benchmark score and how the model actually reads.

“It’s too talkative.”

That was the top-voted answer to whether the launch was hype or the real thing. One reader, quoted in a widely read review of the launch, said his eyes now glaze over reading Claude’s replies, and that human writing in the same register has started doing the same thing to him. The model is not wrong more often. It is just exhausting to decode.

So what’s the issue?

Anthropic told us why themselves. Thariq, who works on Claude Code, posted that the team had removed more than 80 percent of Claude Code’s system prompt for models like Opus 5, with no measurable drop on their coding evaluations.

“We were over-constraining Claude Code.”

Their own transcripts showed the old system prompt fighting itself. One rule said add documentation. Another said do not comment. Multiply that across a year of accumulated skill files and CLAUDE.md rules, and the model was spending part of every reply untangling contradictions instead of doing the work. So they cut the rules and let the model use its own judgement.

Where it helps and where it doesn’t

Good trade for a coding benchmark. Anthropic never said this is also why the conversational voice got denser, but fewer guardrails almost certainly means more of the model’s own instincts show through unfiltered, including its instinct to write for another version of itself, not for you.

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Here’s what you can do about it.

Do not fight that instinct every message. Decide the rules once, the way Anthropic did. Claude Code has a feature for exactly this, called output styles. One markdown file, added to the system prompt for the whole session, and every reply follows it from then on.

---
name: no-slop
description: Direct human voice. Zero AI filler patterns.
keep-coding-instructions: true
---
Speak like a precise senior engineer who is tired of corporate writing.

Lead with the point. Specific nouns and verbs. Cut every sentence that does not change what the reader will do or understand. No "it's worth noting," rule-of-three padding, or vague intensifiers. Opinion is preferred when a recommendation is required. Code stays exact.

Save that as no-slop.md in .claude/output-styles, then run /output-style and pick it. I built six of these for different situations, one for when you are running on no sleep, one for executive updates that answer first and explain after, plus two prompts that turn a bad reply you paste in into a brand new style file. The full set is in the Shut up Opus prompt pack on Right Click Prompt.

Opus 5 is not getting simpler. The layer between you and its default voice is still yours to set. Spend the two minutes today, not the next time a reply wears you out.

Adapt & Create,
Kamil

Hey Adopter,

You have been writing instructions for your AI for about two years now. The settings box. The project instructions. The documents you uploaded so it would stop getting things wrong. In this edition, I’m sharing four prompt templates that you can use today to clean out your system today!

None of it ever gets deleted. It only grows.

And all of it gets read every time you send a message, before your actual question is even looked at.

Last month Anthropic deleted more than 80% of the instructions it had written for its own coding tool. Test scores did not move. Then the engineer who built that tool went on stage and told everyone else to do the same thing. Delete your files. Delete your skills. Every six months.

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The reason it works is simple. Most of what you wrote was there to stop a mistake. Mistakes from 2025, that the model no longer makes. You are still paying for the correction on every message you send. You stopped getting anything back for it a long time ago.

Picture sticky notes left for a new starter. Two years on they run the department, and the notes are still on the desk telling them where the kettle is. They read every one before they do anything.

Now the part that stops most people. Deleting the wrong things is worse than deleting nothing. Cut your audience, your formats, your house style, and you will spend a month wondering why the output went flat and generic.

So the whole job comes down to one question.

Most people ask the wrong one. They ask whether it is still true. It always is. That is exactly why nothing ever gets deleted and the pile keeps growing.

The question

Would a brilliant new colleague, who already knows my field, still need to be told this?

If yes, keep it. A sharp new hire genuinely does need telling who your audience is, what your word counts are, which spelling you use, how you sign off, and that you hate em dashes. None of that is guessable. Losing it is what makes output go flat.

If no, cut it. Check your facts. Do not make things up. Be careful with numbers. Make sure the tone is right. A competent person does not need any of that, and neither does the model any more.

Ten seconds a line. Clears out about two thirds.

Three things people get wrong

1. More instructions means better output. True at first, then it flips. Anthropic found it had over-constrained its own tool through the settings, the files and the skills all at once. One rule said document as appropriate. Another said never add comments. The model read both and had to pick.

2. If I bothered to write it down, it must be helping. Almost certainly not, because you never tested it. Anthropic’s method is to delete the whole thing, then add it back one line at a time to see what each line actually does. Most lines never come back.

3. So delete everything. This is how people wreck a working setup. Anthropic cut corrections, not preferences. Your house style stays.

One caveat, because this audience checks. A developer captured the prompts models actually receive and found Opus 5 gets a longer one than Opus 4.8. Most of the cut landed a version earlier. The idea holds. The headline number is softer than it sounds.

What comes next

The question is the easy part. Pointing it at the right places is where people stall, because your instructions live in three separate places and most people only remember one of them.

Below the line, in order.

Three passes. Settings, projects, skills. Exactly where to click for each one, and what to look for that is specific to that layer.

The four prompts I use, written out in full. Paste, run, done. No setup.

And the rule for what earns its way back in, which is the part that stops you rebuilding the same pile again by Christmas.

Skip it and you will do what almost everyone does with advice like this. Read it. Agree with it. Delete nothing.

Pass one, your settings

Click your name, then Settings, then General. Scroll to the box marked Instructions for Claude.

This is the expensive one. It loads into every conversation you have, in every project, forever.

Your box is probably a wall of prose, not a tidy list. One sentence in there is doing three jobs at once. So the first thing this prompt does is pull it apart.

Paste the kept version back in. Then work normally for a week and write down anything that goes wrong.

Pass two, your projects

Open a project, then its instructions, then the files you uploaded to it.

Same test, plus the thing that does most of the damage at this layer. Two documents agreeing in spirit and differing in wording will give you worse output than either one alone.

Pass three, your skills

Sidebar, under Customize, then Skills.

Skills are the exception. They only load when you call them, so they cost you nothing the rest of the time. Judge them on whether they fire when they should, not on length.

When something breaks

It will, once or twice. That is what the week of normal work is for.

Wait until the same problem happens twice. Once is a bad day, not a pattern. This is the rule that keeps the pile from growing back, so hold the line on it.

Do this today

The full prompt pack can also be downloaded on rightclickprompt.com for our premium members.

Adapt & Create, Kamil

Sources

• The new rules of context engineering for Claude 5 generation models, Thariq Shihipar, Anthropic, 24 July 2026

• Boris Cherny: Building Claude Code, Y Combinator

• Prompt length analysis from captured traffic, 36Kr

Hey Adopter,

So, quick version of what you’re getting today. Why most people chasing the “build a small AI tool” idea pick the wrong thing, where the good ones hide, and how to test one before you burn four months of evenings on it.

The prompt pack that runs the whole process is here.
Grab it now, read the rest, then go use it.

The guy who did this with air filters

David Heacock took over his family’s failing machinery business in Alabama and turned it into an air filter company. North of $250m a year now, bootstrapped, in a category nobody thinks about.

His point about AI is the one I keep coming back to. In software, AI improves something already built to scale. In a physical business it changes the arithmetic, one operator handling work that used to need layers of staff. At Filterbuy he pointed it at scheduling, forecasting and error rates. Not the factory floor.

LinkedIn has already turned this into a side hustle pitch. Find a dull industry, build a small tool, charge $500 a month. Honestly, the pitch isn’t wrong. It skips the one part that matters.

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Most of these die at the picking stage

Freemius mapped the revenue spread across micro-SaaS. Around 70% earn under $1,000 MRR. Another 18% sit between $1,000 and $5,000. The top 1% clear $50,000.

Those aren’t builds gone wrong. I spend 10 to 12 hours a day in Claude Code and I’ll be honest with you, shipping something functional stopped being the hard part a while ago. What kills these is picking a workflow nobody was suffering over, then finding out four months later.

The expensive bit flipped. Writing the software got cheap. Knowing which two hours of somebody’s Tuesday are worth deleting did not.

So where does that knowledge live? - Not on Product Hunt

It lives with people mid-task.

An insurance broker rekeying renewal data out of three carrier portals into one spreadsheet, because the portals don’t talk to each other. A construction admin building certified payroll every week out of timesheets, union rates and four subcontractor emails, by hand. A food distributor updating allergen specs across 200 product sheets every time a supplier swaps an ingredient.

Same shape every time, right? High frequency. Low judgement. Expensive when wrong. Invisible to anyone who hasn’t done the job.

Which gives you a test. If you can’t name the workflow and the person who does it without looking anything up, you don’t know it well enough to build for it yet.

Most of you reading this hold a better hand than the people writing the threads.

You already sit inside the best version of this

Bessemer’s vertical AI playbook notes that Shopify and others started life as internal tools. Someone solved their own problem first, then noticed other people had it too.

You’ve got what an outsider spends a year trying to buy. Domain knowledge. Access to the people doing the work. A salary while you figure it out. And an employer who’ll be your first user for free.

So start there. Pick the ugliest recurring thing on your team, the one everyone works around. Build the smallest version handling the ordinary case correctly. Put it in front of the person who does the task and watch them use it without you. Then don’t add anything until somebody asks twice. Overbuilding ahead of proof is how most of these fall over.

Two outcomes, both good. Either you’re the person who took a day a week off your department’s plate, which beats any certificate. Or the pain turns out to be real and shared across the industry, and you’ve got a validated product with no one to acquire. Put a price on it the second a different company asks. Free pilots teach you nothing about whether anyone would pay.

One thing separates both of those from a wasted quarter.

Ten conversations beat ten estimates

Ask a model to size a workflow and it hands you weekly hours and error costs with total confidence. Those numbers are made up. Fine as hypotheses. Useless as evidence.

Test them the slow way. Ten conversations with the people doing the task. One question does most of the work.

Walk me through the last time you did this.

Then shut up and listen. You’re waiting for the workaround. The shadow spreadsheet. The colleague they text to check a number. The step they do at 7am because the system crawls after nine. That’s where the hours hide.

Three out of ten describing the same workaround, you’ve got something. Ten different answers, you’ve got a preference.

Your next seven days

Write down ten painful workflows in your own function without looking anything up. Rank them by how many people do them and how often. Book three conversations about the top one.

More on that prompt pack

Five prompts, built to run in order. Feed each output into the next.

One narrows you to an industry. Two surfaces the workflows hiding inside it, in the words the people doing them use. Three pressure-tests your top five against software already on the market. Four picks a winner and names your first ten customers. Five asks whether the thing holds up or gets shipped as a feature by somebody bigger next quarter.

One rule holds the whole thing together. Every hour estimate, error cost and market size these prompts give you is a guess, so I’ve written them to admit it. Each number comes back with a confidence label. Take the top three guesses into your ten conversations and let the people doing the work confirm or kill them.

Prompts do the narrowing. Calls do the deciding.

Grab The Prompt Pack Here

The barrier used to be an engineering team you couldn’t afford. Now it’s knowing where the hours go. And you already do.

Adapt & Create, Kamil

Hi Adopter,

Your support queue costs more than anyone in the room says out loud. Three to eight dollars a contact once you count wages, tooling, QA, and the manager who cleans up after all three.

So when a model turns up that’s fast, cheap, and good enough for the boring half of that queue, you look. Airbnb looked. The model was Alibaba’s Qwen.

Brian Chesky said it plainly last October. Qwen was “very good” and “also fast and cheap.” He also said OpenAI’s integration wasn’t “quite ready” for what they needed. Seven months later, two House committees sent him a letter about that decision.

What makes this worth twenty minutes of your attention isn’t the letter. It’s that Airbnb’s answer wasn’t a lawyer’s answer. It was an architecture answer, and it only existed because of something they’d built a year earlier for completely unrelated reasons.

What actually happened

The rollout was quiet. Airbnb started testing AI on narrow queries in 2024, began the US rollout in April 2025, and within about a month Chesky reported a 15% drop in people needing a human.

That first win wasn’t clever. It was triage. A chunk of their inbound was repetitive, lookup-shaped, and never needed a person to begin with.

By October, average resolution had gone from roughly three hours to six seconds. By Q4 2025, AI handled about a third of US and Canada support. By Q1 2026, 40%, and Chesky told investors cost per booking had fallen “about 10 percent year over year.”

Then in April, the House Select Committee on the CCP and the Homeland Security Committee wrote to ask what exactly a Chinese model was doing inside an American company’s customer data.

The part worth stealing

Chesky’s response was short. “We are not providing data to any Chinese companies. They don’t have access to any data.” He’s right. Qwen is open weights, which means Airbnb downloads it and runs it on their own hardware. Nothing phones home.

But the sentence that did the real work came next. He said Airbnb uses “a variety of open-source models, including U.S. open-source models.”

That sentence is only available to a company that built a router before it built a dependency. Airbnb runs thirteen models. Qwen does the heavy lifting on volume, and everything above it, the routing, the policies, the escalation rules, transfers to whatever they swap in next.

They can drop Qwen in a quarter and lose nothing that took time to build. Most companies making the same bet can’t say that.

Below the paywall this week:

• The four-layer teardown of their stack, and which layer actually costs you money to rebuild

• The reconstructed unit economics, including what a 10% cost-per-booking drop is worth in real dollars at their volume

• The two June incidents where the agent did something expensive, and the one design gap behind both

• The move most teams reach for first that quietly destroys the reversibility this whole case depends on

Members get the full teardown plus the 13-page case study PDF with the exhibits and source ledger.

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Hey Adopter,

is your company running on one default AI model that handles everything? Contract review and bullet point summaries, same token price. In this newsletter, you will find out how to test different models on your tasks and be more efficient as a company. The big ones are already doing it.

Most of what you send it is not hard. You pay top rates anyway.

The bill grows. Finance asks why. Nobody has an answer, because nobody has run the same task on two models and compared the results.

You are guessing. Guessing is expensive.

The waste sits in routine work. Extraction, classification, cleaning up notes, drafting standard replies. All charged at the top of the range, because the top of the range is already selected and switching for one task feels like effort.

Same thing at your desk. One model for everything means your daily limit goes on admin, and you have nothing left when the hard problem lands.

Public benchmarks will not settle this for you. They score someone else’s tasks on someone else’s data. Your work is the only test that counts, and testing it is easier than people assume.

The fix takes an afternoon.

I built five prompts. Each one makes a model show its working on a different failure mode:

• How deep it reasons before it commits

• How many tokens it burns getting there

• Whether it invents sources

• Whether it changes its answer when the audience changes

• Which tier any new task belongs in

Run them on two models. Score what comes back. You end up with a routing rule based on your own work, a list of jobs the cheap model handles fine, and a number you hand to finance.

No engineering required. No new tool to buy. Two browser tabs and a scoring sheet.

All five are below, ready to copy.

Take the whole pack instead of copying

Every prompt I use lives in Right Click Prompt. This pack consists of seven prompts, two more than this edition covers. One builds your task list for you. One scores two model outputs head to head.

Scan the code or open rcp.ad/benchmark. Import once, it sits in every AI tab you open, and shares with your team in a click.

Over 3,000 people on it now. Thank you, the support has been overwhelming. There is a discounted lifetime deal running at the moment.

What the gap is worth

Ramp routes more than 100 internal AI use cases through a router they built. Their Router page reports 30% lower LLM costs at about 30 ms of added latency, across 2.75 trillion tokens a month.

Glean CEO Arvind Jain estimates around 95% of enterprise AI usage still runs on the most expensive frontier models. Cognition CEO Scott Wu puts the gain on routine work at five to ten times better cost efficiency. Both spoke to CNBC. Wu’s example is asking which president came third. Every model says Thomas Jefferson. You paid frontier rates to hear it.

On waste specifically, OckBench tested 49 model settings and found top open source models now match commercial ones on accuracy while spending up to 26 times more tokens. Same answer, very different bill.

Those numbers are someone else’s work. Here is how you get your own.

Step one, build the task list

Pull eight to twelve real tasks from the last fortnight. Mix routine jobs with one analytical piece, one thing that went to a client, and one where a wrong answer would have cost you money.

This is the task list I walk business owners through building in the seven lessons edition, alongside how to turn it into a standing benchmark. Build it once. You reuse it every time a model launches.

Then pick two models. Your current default, and a new model you consider switching to. Run each prompt on both. Do not help either one.

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Hi Adopter,

There’s probably a file on your computer you wouldn’t paste into ChatGPT. Maybe it’s a customer list, maybe it’s a contract, maybe it’s the thing your legal team has opinions about. You know exactly which one I mean.

Most companies handle that file with a policy nobody reads. Or a ban, which people route around within a week.

The University of Florida had the same problem, just worse. Student records under FERPA, clinical data, unpublished research, export-controlled work, and a whole campus of people who wanted AI yesterday. So they built their own: NaviGator AI, running on HiPerGator, their own supercomputer.

And look, they spent real money on it. A $70 million AI initiative in 2020, another $33 million on Blackwell hardware, roughly $6 million a year just to keep it cool and running. Easy to look at that and think, well sure, they solved it by being rich.

But the supercomputer isn’t the part protecting the data.

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Every model in NaviGator carries a tag saying which classes of data it’s allowed to receive. All 104 of them. The ones running locally on UF’s own GPUs are cleared for open, sensitive and restricted data. The frontier cloud models, the GPT and Claude and Gemini everyone actually wants, are cleared for open data only. Same chat box, same login. The tag on the model is the whole control.

So the crown jewel of a nine-figure AI program is, honestly, a permissions column in a table. Which is the good news, because a permissions column is free.

What I like about UF is they say the awkward part out loud. Straight from their own documentation: “When interacting with cloud models hosted by vendors, your messages and subsets of your documents will be sent to a LLM instance provided by Microsoft, Amazon, or Google.” Then, same breath, “None of this data contributes to training the large language model.”

That’s a hybrid, and they’re straight about it. A front door that decides which way your request goes, and they tell you when yours goes outside the building. Almost nobody selling you “private AI” this year is that specific about where your text ends up.

Below the line I get into the questions this raises about your own setup:

• How do you build the three-tier version of this when you’ll never own a GPU, and what does each tier actually cost you?

• Where does this design still leak, and why has UF left that gap open on purpose?

• Is a “private” tier ever real, or is it always theater? Their clinical model settles that one.

• Are your own AI rules actually enforced, or just written down somewhere? Five questions will tell you.

• What has to be true before you let any cloud model near a regulated data class?

If you’ve got data you can’t paste into a chat box, this is the one.

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Hi Adopter,

I keep getting the same question. Someone watches the AI job listings pile up, month after month, and asks me which one is actually worth chasing. And honestly, most of them aren’t. Prompt engineer is already fading. “AI specialist” means basically nothing to a hiring manager, it’s a shrug with a salary attached.

But there’s one role hiring like crazy that hardly anyone can explain to me properly, sometimes not even the people applying for it. Forward deployed engineer. I do a version of this for a living, walking into companies and working out where AI actually earns its keep versus where it just looks clever in a demo, so this one keeps landing in my inbox.

The job is deployment, not intelligence

What changed is that the model stopped being the hard part. Every company can buy the same one now, OpenAI, Anthropic, whoever, so the intelligence itself is more or less a commodity. What almost nobody has is the person who can take that model and get it running inside one company’s actual mess. The process nobody ever wrote down. The data sitting in one guy’s head. The security team that trusts none of it. And then, on top of all that, show it moved a number the CFO actually cares about.

So that’s the role, roughly. Part engineer, part consultant, and the person on the hook for whether the thing works in the end. That last part is where most people quietly fall out.

Why it’s exploding right now

The money tells this better than I can. OpenAI spun up a whole subsidiary for it, the Deployment Company, put more than four billion behind it, and the first thing they did was buy an applied-AI consulting shop just to get roughly 150 of these engineers on staff overnight. Anthropic ran its own version, Ode, worth north of a billion and a half, with Blackstone and Goldman Sachs in on it, built to embed engineers inside mid-market and private-equity companies and rebuild how they actually work.

And the reason all that money moved is kind of grim, if you think about it. MIT went and looked at enterprise AI projects and found something like 95% of them produce no measurable return. Not because the models are weak. Because getting a model to actually work inside a real, messy company is genuinely hard, and most places have nobody who can do it. That's the whole opening, right there.

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What it pays, honestly

On money, I’ll only tell you what’s actually posted, because everything past that is rumour. OpenAI’s listings put base pay somewhere between $162,000 and $280,000, plus equity. Anthropic sits around $200,000 to $300,000. You’ll hear people throw around “a million dollars,” and sure, that exists, but it’s the top of the ladder for senior folks whose equity has already paid off, not the thing you walk into. I’d plan around the base and treat the rest as upside.

Where you fit

Where you land in all this mostly comes down to where you’re starting from. And the uncomfortable bit nobody running a bootcamp will tell you is that you don’t study your way into this one. You build your way in. So do the work first, then go looking for the title, not the other way around.

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The one move that beats a course

Forget applying for a second. The thing that actually gets you in the door is one real system you built, start to finish, that you can walk somebody through. Roughly like this.

There’s a simple gut-check for whether you’ve earned the title. Can you show evidence for each of those steps, or can you only describe them? If it’s describe, you’re an AI implementation lead for now, and there’s no shame in that. The most convincing piece isn’t even the code, weirdly. It’s the short doc where you explain what you deliberately chose not to build. That’s the thing that makes a hiring manager exhale.

The honest part

I’m not going to pretend this is a soft gig. The real listings want 25 to 50% travel. A lot of the time you’re the only technical person in a room full of people who don’t trust you yet, untangling politics nobody warned you about. And you have to actually ship into production, not demo something slick and disappear.

If your idea of a good day is headphones on, no meetings, just you and the code, this one will grind you down fast. But if you like walking into a mess and owning how it turns out, it’s honestly one of the best-paid, fastest-growing corners of this whole thing right now. Both of those can be true about the same job.

Your move

You don’t have to quit anything or sign up for anything. Just pick one real problem and finish it. Right now the market’s moving fast enough that a working thing beats a polished resume, and that window won’t stay open forever.

Since where you’re starting from changes the whole plan, I put together a prompt that builds a version of this for your own background. Paste it into whatever AI you use, answer a few honest questions, and you’ll get your own six-week build instead of mine.

Your personal roadmap

Copy the prompt, paste it into your AI of choice, and answer the questions. You’ll get an honest, specific plan built on everything above, tuned to your background.

FDE-Roadmap Prompt

You are a Forward Deployed Engineer (FDE) career strategist. Turn my background into an honest, specific, and actionable roadmap to become an FDE, or the closest role I can realistically win, based on how the role actually works in 2026.

The premise you operate from:
- Intelligence is now a commodity. Any company can buy a frontier model. The scarce, expensive skill is DEPLOYING that intelligence into one company’s messy real workflows and proving measurable business impact.
- An FDE is a consultant who can actually engineer and build, embedded with a customer, owning the outcome end to end.
- The winning move is “do the job before you have the title.” The proof is one real, production-style system you built and measured, not a certificate or a course.
- Be honest, not flattering. If I am far from ready, tell me plainly. Never promise a salary or a specific company.

STEP 1 - Ask me these questions in one numbered message. If I have already answered some, use those and only ask for what is missing. Then wait for my answers.
  1. Your current role and years of technical experience.
  2. Your coding depth: (a) non-technical, (b) AI-assisted builder / vibe-coder, (c) I ship small production systems, (d) senior full-stack or infrastructure engineer.
  3. Customer-facing experience: discovery with stakeholders, presenting to execs, or leading a delivery? One or two lines.
  4. Your domain or industry expertise. This is where we anchor your project, so be specific.
  5. Hours per week you can commit, and your target start date.
  6. Travel and on-site tolerance, your location, remote-only?
  7. Your goal: independent paid engagements, a full-time role, or both? Any dream companies?
  8. Honest self-test, yes/no with one line each: (a) shipped something real users depend on? (b) turned a vague business problem into a scoped MVP with real stakeholders? (c) measured a system’s cost, latency, or business impact? (d) handled a production failure?

STEP 2 - Classify my starting point and state it plainly, with a realistic timeline:
  - Builder-ready (ship production code): fastest, a six-week deployment sprint produces your proof.
  - Customer-facing-ready (strong discovery/exec/delivery, lighter coding): ~6-10 weeks, gap is production engineering evidence.
  - Early-career / some coding: longer; often win a software or solutions role first, then transition.
  - Non-technical switcher: the long game; become a real builder first.
  Name my single biggest gap in one blunt sentence.

STEP 3 - Produce my roadmap in exactly this structure:
  1. WHERE YOU STAND - archetype, realistic timeline, biggest gap named plainly.
  2. YOUR POSITIONING - 3-5 best-fit target titles (Forward Deployed Engineer, Forward Deployed Software Engineer, Technical Deployment Lead, Forward Deployed AI Lead, AI Solutions Engineer, Customer Engineer, Deployment Strategist, Applied AI Consultant), plus one positioning line I can use, modeled on: “Forward Deployed AI Lead: I diagnose the operational bottleneck, build the first production AI workflow, and lead deployment until the system is adopted and measurable.” Say which companies to target first and which to earn toward.
  3. YOUR ONE PROJECT - a deployment sprint anchored in MY domain. Pick one messy workflow from my world, week by week (stretch the timeline honestly if I’m below builder-ready):
     Week 1: Audit. Map current state, catalogue undocumented exceptions, baseline (time, error rate), success criteria, security/access needs, explicit non-goals.
     Week 2: Deterministic foundation. Real stack (Python, FastAPI, PostgreSQL, Docker, a cloud, CI/CD). Ingestion, schema, rules-based logic, API, auth, tests. No toy CRUD.
     Week 3: Add AI judgment only where rules fail. Structured JSON, confidence score, source references, human approval for every consequential action, full trace logging, no irreversible actions.
     Week 4: Evaluation system. Golden dataset of 75-100 real cases including ugly ones. Report accuracy, false-approval/escalation rates, cost per run, median + worst-case latency, before and after each change.
     Week 5: Deploy safely. Local, private test, shadow mode, limited pilot, production with human approval. RBAC, secrets, monitoring, retries, rollback, incident runbook.
     Week 6: Turn it into proof. Demo, architecture diagram, workflow map, evaluation report, threat model, decision log, before/after metrics, one-page exec case study, README on what you deliberately did NOT build. Synthetic data for anything public.
  4. YOUR PROOF - the public artifact checklist; flag the “what I did not build” doc as the most persuasive item.
  5. YOUR READINESS TEST - 10 checkpoints, marked against my answers (have vs still need):
     mapped a workflow by observing users; found exceptions the SOP never documented; decided where AI belonged vs plain rules; built or directly contributed to the system; integrated databases, APIs, identity, existing tools; created an evaluation dataset; measured failures, latency, cost, impact; deployed in shadow mode before increasing autonomy; handled a production failure; turned a customer-specific lesson into a reusable component. Until most are true, “AI implementation lead” or “technical deployment lead” is more defensible.
  6. THIS WEEK - the single most important action in the next seven days.

CONSTRAINTS: honest over flattering; ground everything in my actual answers, domain, hours, travel tolerance; real tools and metrics only; if I lack a production track record or dislike customer interaction/travel/ambiguity, say so and suggest a better fit; do not invent compensation figures (verified 2026 base bands at frontier labs run ~160K-300K USD for 4-5+ years experience, total comp reportedly higher with equity, entry and mid-market pay less).

Start now by asking me the Step 1 questions.

Run it yourself in ten minutes. Or just reply and I’ll help you map your first build on a workflow you actually own.

Adapt and Create,
Kamil

Sources

1. OpenAI, “OpenAI launches the Deployment Company,” May 2026.

2. Anthropic, “Ode with Anthropic” enterprise AI services announcement, 2026.

3. Exponent, “What Is a Forward Deployed Engineer? Complete 2026 Guide,” 2026.

4. MIT NANDA, “The GenAI Divide: State of AI in Business 2025,” 2025.

5. OpenAI, “Forward Deployed Engineer (FDE) - SF” job posting, accessed July 2026.

6. Anthropic, “Forward Deployed Engineer, Applied AI” job posting, accessed July 2026.

Hi Adopter,

Executives don’t understand what their people actually do all day. Your employees wrote those. And honestly, on the thing that matters most this year, they’ve got a point.

Your team is already using AI. You’re just not the one they tell.

I walk into companies for a living, and the numbers back the comment section. Ask executives if their people are excited about AI and 76% say yes. Ask the people themselves, it’s 31%. Ask leaders how many of their staff use AI for a real chunk of the working day and they guess around 4%. The staff say 13%, three times higher. And most of what they’re using isn’t yours. At more than 90% of companies, people already run their own AI for work, while fewer than half of those companies pay for an official one.

So the adoption problem you think you have might be the wrong problem. You’re measuring excitement in the boardroom while the real work happens two levels down, on tools nobody signed off on. The usage you’ve been trying to manufacture with rollouts and town halls is already here. It’s just off the books, on personal logins, quietly out of your sight.

Which is a governance headache, no argument. It’s also the best news you’ll get this quarter. That’s demand you didn’t have to beg for. You just can’t see it yet, so you keep buying seats nobody opens and running pilots that die in a slide deck.

The move is finding what your team already reached for and making it safe to use in the open. That starts with an amnesty, and meaning it. One message: whatever you’ve been using on your own to get work done, tell me, nobody’s in trouble, I want to make the good stuff official. The credibility of that first sentence is the whole game. Break it once and you’re blind again for a year.

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Then triage what comes back on two questions: how much real work is it doing, and what data is it touching. A summariser running on public meeting notes is a green light. The same tool pointed at customer records is a different conversation. Bless the high-value, low-risk ones and buy the seats. Move the sensitive data off the useful-but-risky ones before you keep them. Kill the pure exposure, and name the safer swap in the same breath so it doesn’t land as a no.

The people who found the value on their own are your champions, and they cost you nothing. Right now you’re leaving them to solve it in the dark.

Your best AI adopters are already on the payroll. They found the value your rollout promised and never delivered, and they did it on their own logins because that was easier than asking. Find them before that quiet advantage walks out the door with them. What would next quarter look like if the people already doing this could stop hiding it?

If you want a hand running the amnesty on your real workflows, just reply to this one. No form, no funnel.

Adapt and Create, Kamil

Sources

1. Deborah Lovich (BCG) & Stephan Meier (Columbia Business School), “Leaders Assume Employees Are Excited About AI. They’re Wrong,”

2. McKinsey & Company, “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential,”

3. MIT NANDA, “The GenAI Divide: State of AI in Business 2025,”

About the author

Kamil Banc is an AI culture and adoption advisor. He helps organizations put AI to work through corporate advisory, coaching, and assessments, and writes AI Adopters Club. Work with him at aiadopters.club.

Hi Adopter,

You’ve seen the ladder going around this month. Boris Cherny, the person who runs Claude Code at Anthropic, mapped AI adoption in five steps by how many agents you’re running: zero, one, ten, a hundred, a thousand and up. He’s earned the right to draw it. He’s had AI writing basically all his code for months and runs agent fleets most of us can’t picture.

And the shape is right. What breaks changes as you scale. Early on your problem is access. Later it’s whether you can review, govern, and pay for what the machines are doing.

Here’s where it falls apart for the rest of us. The number of agents you run tells you almost nothing about whether you’re any good at this. A hundred agents nobody’s checking isn’t maturity. It’s a bigger invoice and a slower incident.

I see the same thing in almost every company I walk into. The tools are there. Somebody ran a pilot. And then it just sits. Not because the model’s weak. Because nobody redesigned the work around it, nobody’s measuring anything, and the review pile quietly ate the time savings.

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Read the ladder by what’s actually blocking you

So here’s how I’d read the stages instead. Not by agent count. By the one thing that’s actually blocking you at each step:

• Blocked. People technically can’t use it, so half of them use it in secret anyway. What’s in your way is a real owner and a clear yes. (Cherny calls this Gated.)

• Assisted. One person, one AI, real work. What’s in your way is trust. Can every output show its receipts, and do you know your rework rate?

• Delegated. Lots of tasks running at once. What’s in your way isn’t the model, it’s your capacity to check the output without drowning. (Cherny calls this Parallel.)

• Governed. The system’s doing work you can’t watch action by action. What’s in your way is turning your policy PDF into controls the system actually enforces. (Supervised autonomy.)

• AI-native. You’re running a portfolio of automated work. What’s in your way is money and judgment. Every workflow needs an owner, a real cost model, and a kill switch for the ones quietly automating busywork.

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A couple of traps

The obvious one: more agents means more mature. It doesn’t. Agent count is a vanity number. You can be Stage 2 in engineering and Stage 0 in finance in the same building, because it’s per-workflow, not per-company.

The bigger one: treating this like a software rollout. It’s an operating-model change. What’s blocking you is almost always people and process, your review capacity, your incentives, whether anyone actually owns the outcome, not the tech.

And watch the mood in the room. In a 2026 study of software engineers, 84% felt more productive while the work itself got worse to do: less flow, more time spent checking the machine instead of building. Feeling good about your AI isn’t the same as being good at it. If your only evidence is vibes, you’re probably at Stage 1 telling yourself you’re at Stage 3.

So the real question isn’t how many agents you’re running. It’s which gate you’re stuck at, and what specifically clears it.

That’s what the premium below does, and it does it for you. It’s a prompt. You paste it into your AI, feed it a few honest facts about your setup, and it scores you across the things that actually matter: access and governance, data, verification, what you measure, cost, skills, risk. Then it tells you your stage, the one constraint holding you back, and a 30 to 90 day plan with exit criteria you can measure. Not “name a sponsor.” The actual gate and the number that proves you cleared it.

Run it yourself in ten minutes. Or reply and I’ll run it with you on your real workflows.

The self-assessment prompt

Paste this into Claude or ChatGPT. Answer what it asks, or paste in whatever you already know and let it press you for the rest. Be honest. It’s more useful when you don’t flatter yourself.

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Two things after you run it.

One, it’s built to be blunt. If your data and governance aren’t ready, it’ll tell you to fix that before you scale, not cheer you up the ladder. That’s the whole point. Most maturity advice is nice to read and useless on Monday morning. This one’s meant to ruin your week in a productive way.

Two, if you’d rather I run it with you, on your actual workflows, with a plan we build together, just reply to this email. No form, no funnel. We’ll find a time.

Wherever the prompt lands you, pick the one workflow that costs you the most every week and clear its next gate. Not ten. One. What would it change about next quarter if that one actually worked?

Adapt and Create, Kamil

Kamil Banc is an AI culture and adoption advisor. He helps organizations put AI to work through corporate advisory, coaching, and assessments, and writes AI Adopters Club. Work with him at by connecting directly.

Hi Adopter,

You’ve probably got a part of your business that still runs on a spreadsheet somebody rebuilds by hand every week. Most companies I walk into do. For Rutland, a 31-person workshop in Chesterfield that makes fire-door closers, it was their stock. Export the numbers out of Sage, drop them in Excel, guess what to reorder.

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Then lead times hit four months. Steel got scarce, containers got dear, and the spreadsheet that had always sort of worked just stopped. So they handed the reordering to an AI tool that bolts onto the system they already had. Within a year they’d pulled about a million pounds back off their shelves, pushed their fill rate from 92 to 97 percent, and turned a full day of purchasing admin into an hour.

Now put that next to Walmart, with its $330 million robot warehouse and a digital twin of its whole network. Different budget. Same play.

Everyone’s buying it. Almost nobody’s running it.

Here’s what gets me. 57 percent of operations leaders say they’ve integrated AI. Only 23 percent have a real strategy behind it, and in retail and wholesale, only 10 percent have it actually live in the daily workflow.

So the winners aren’t the ones who spent the most or bought the fanciest platform. They’re the ones who took one painful process and got it running. That’s it. The forecasting Walmart spent years building in-house, a 31-person shop now rents by the month.

The benefit isn’t robots. It’s cash. Inventory is money you’ve stacked on a shelf as cardboard, and McKinsey puts the fix at 20 to 30 percent less of it, plus 5 to 20 percent off logistics.

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Below, for premium readers, the full teardown:

• The before-and-after numbers for all three, side by side

• What a grocery chain did to cut fresh-food waste by 30 percent, and why perishables are the smartest place to start

• A five-line self-check to tell if you’re in the operating 10 percent or the stuck 90

• The one tool category a small team can switch on this quarter, no data team

If you run stock, freight, or fulfillment, this is the one.

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I have spent two years advising companies on AI implementation and AI culture, diagnosing readiness, building the first workflows, staying on until adoption sticks. Success has nothing to do with which model sits on the desktop.

If you are the one signing off on AI spend right now, there is a decent chance your own team is doing this too. Paying full price for enterprise access while half the department still pastes prompts into a free account, because nobody showed them the paid one exists.

Two years watching this play out across companies of every size taught me seven things. Not seven tool recommendations. Seven behaviors separating the businesses where AI adoption compounds from the ones where it flatlines by month three.

The first one is the most overlooked. Most leadership teams treat getting AI like a talent some people have and others do not. Wrong instinct, wrong strategy, and it is quietly costing you the one thing AI adoption runs on.

Thank you , , , and many others for tuning into my live video! Join me for my next live video in the app.

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Hey Adopter,

Deutsche Telekom handed its mobile network to a team of AI agents. Response time on major events fell from an hour to a minute. First month, the system fixed more than a hundred problems on its own, nobody touching the controls. An outside analyst checked the maths, so this is not the usual press-release confetti.

Most people read that and clap for the AI. The AI was ordinary. What did the work were three decisions so dull your competitors will skim right past them. Which is the good news, because dull is copyable.

Every AI pilot dies in the same ditch

The model almost never fails. Your data does. It sits in six systems with field names one person in accounting still remembers, nobody wrote down what a finished job looks like, and the second the tool tries something with consequences, everyone learns at once that no one knows who signs off.

The World Economic Forum studied hundreds of companies running this in production and said it flat: data quality is the number one barrier to AI success. Deloitte found the same wall from the other side. Three-quarters of companies want AI agents inside two years. One in five has any real way to govern them. Everyone is flooring the accelerator. Almost nobody bolted on brakes.

So drop the question of which AI to buy. The better question is which small job you can hand off tomorrow while keeping a hand on the wheel.

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Steal this one move

Telekom did not aim AI at “the network.” That is how you buy a six-month failure and a bad board meeting. It aimed at one job with clean edges: spot the public events that flood the towers, check whether the towers nearby can take the crowd, adjust before your call drops at the Christmas market.

One job. Clear input. A win you can measure against a real before.

That is the whole trick. Smallest job that still matters. Define good before you build. Let the machine own only the part you can undo.

You have a job like that sitting in your week right now. The report someone rebuilds by hand every Monday. The invoices that rot for three days waiting on one person’s eyeballs. The tickets a human sorts when they would rather do almost anything else.

Pick one. The how is below.

If you are the person your boss looks at when they ask “so how do we actually use AI here,” this is the section that hands you a plan instead of a shrug. Upgrade, and the next 20 minutes become something you can put on one page and defend in a room full of people waiting to poke holes in it.

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Hey Adopter,

Today you get a real GEO audit of a real business, and the prompt to build the whole audit skill inside your own Claude and keep it. (Consultants charge $1000-$2000 to run it)

GEO stands for generative engine optimization. Plain version: when someone asks ChatGPT or Gemini “what is a good golf club near Jupiter, Florida,” GEO decides whether your business shows up and whether the facts are right. The old game was ranking on Google. The new game is getting quoted by an AI, correctly.

So I ran the audit on a real one. A private golf club in Florida. Arnold Palmer once played an exhibition there. Dick Wilson designed the course in 1957, Tom Fazio II restored it in 2013, and the history page reads beautifully. A human would be sold.

The audit scored the site 51 out of 100. Schema, the machine-readable label telling an AI what a page is, scored 3.

The club’s most important public page, membership, runs about 65 words and signs off with “contact Dianne,” typo included. The social icons in the footer link to nowhere. And because the club never published its own course yardage, the AIs went hunting and found three different numbers, 6,421, 6,875, and 6,925. Now they repeat whichever one they grabbed. The club is losing an argument about the length of its own golf course, and it is not even in the room.

Two moves sit below the line. How people turn this audit into a raise or an invoice, and the one prompt building you the whole GEO audit skill inside your own Claude, yours to keep.

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