Hey Adopter,

Nobody likes slop. That’s why I’m convinced that AI-generated videos that feature fake characters will never catch up to the authentic you. However, that doesn’t mean that you cannot use the power of AI to help you with the deliverables.

Today I’m showing you how to use Claude code to fully cut and edit and deliver you a finished video from just a raw file that you give it.

By the end of this you will have the whole thing running on your own machine.

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What separates a real edit from AI slop

A professional editor turns up with two things. A cut engine, what gets kept, in what order, at what pace. And a design system, what the graphics look like, which moments earn one, what the sound does. The cut engine is mechanical. The design system is taste, top to bottom.

Here is the part most people miss. Taste survives being written down. Set the palette once, the type floor once, the rule for which lines earn a graphic once, and Claude works inside your look instead of its own best guess. That beats any single prompt you will ever write.

Below is the entire build. The install, the skill files, the first-video walkthrough, and the copy-paste prompts that drive it. Thirty minutes to set up, then you own a studio instead of renting a trick.

Everything from here is the actual setup. Commands, files, prompts, in order.

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I’m starting the AI Adopters Coffee Club: small, local tables for people who actually put AI to work in their field. Not another webinar, not a feed. A real room of operators in your city, comparing what’s working and sending real opportunities to each other. Coffeehouse, not conference.

Here’s why I’m writing to you specifically. The map is mostly open on purpose. Whoever shows up first in a city gets to anchor it, and becomes the person everyone there turns to for AI. That could be you.

It costs nothing to claim your seat. Two ways in:

• Join a table forming near you.

• Or lead the first one in your city, and I’ll hand you the playbook and put the AI Adopters Club behind you.

• and even if you’re not an AI implementer, there’s something for you

Either way you start on the same list.

Adapt & Create
Together!

Tesla reported Q1 on April 22, and the number worth your attention was not the one on the chyron. Not deliveries, which missed. Not the $477 million in net income, up a polite 17%. The number was this: 1.28 million active Full Self-Driving subscriptions, up 51% year over year, attached to a fleet that just crossed 10 billion cumulative miles of real-world driving. At $99 a month, with the one-time purchase option killed off in February so subscription is now the only door. Services and other revenue hit $3.745 billion for the quarter, up 42%, and that is the line where this money lives.

Here is the part that should make every operator sit up, and it has nothing to do with robotaxis. Tesla turned its paying customers into its workforce. Every one of those cars is a sensor. Every mile someone drives to the grocery store is a labeled training example the company did not pay a vendor to collect, did not stage in a demo, did not beg a regulator for permission to gather. The customer bought the car, then pays monthly for the privilege of improving it. That is not a product. That is a meat grinder with a subscription attached, and the customers are feeding it voluntarily.

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The Data Moat Meat Grinder

You have sat through the pitch. Some autonomy startup, a beautiful deck, a sizzle reel of a car gliding through a sunny intersection with nobody touching the wheel. Then the Q&A, and you ask the only question that matters: where does the data come from. And the answer is always some version of we have a test fleet, we are scaling our operations, we are in conversations with the city.

Translation: we rent the miles. We pay drivers, we pay for permits, we pay to map a few square miles, and we burn money on every single one of those miles whether the demo works or not.

That is an open loop. Money goes out, miles come in, and the loop only closes if you eventually launch a paid service big enough to cover the burn. Most never get there.

Tesla runs the loop the other direction. The miles come in with a check stapled to them. Look at the mechanics, because the mechanics are the whole point:

• The fleet collects for free. Roughly 9.2 million Teslas have been delivered. They are on the road regardless. The marginal cost of harvesting another driving scenario from a car someone already owns is close to zero. The fleet is now logging around 29 million FSD miles a day, up from 14 million at the start of the year. No vendor invoice. No safety-driver payroll.

• The training stack turns miles into product. The v12 software in early 2024 famously deleted more than 300,000 lines of hand-written C++ and replaced it with a single neural net trained on video clips. The April release, v14.3, rewrote the AI compiler and cut reaction time about 20%. That improvement ships back to the entire fleet over the air, overnight, for free.

• The customer pays to receive it. $99 a month. The same customer who generated the data pays to get the upgraded version of the thing they already bought. Then they drive more, generate more data, and the grinder turns again.

Collect for free, improve for cheap, charge to deliver, repeat. Every competitor without a million-car fleet is standing outside this loop trying to buy their way in one expensive mile at a time. That is the moat. Not the cars. The loop.

The three levers that actually move the money

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Hi Adopter,

Everyone is sick of AI right now, and they are not wrong to be. The frustration is real, the mandates are real, and a wave of new data backs all of it. But the conclusion underneath the noise, that AI itself is the problem, is wrong, and getting it wrong is about to be expensive. Read to the end and you will have one clean test for whether AI is making you sharper or quietly making you replaceable, and you will know which side of the split now forming you want to be standing on.

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“Nobody actually wants AI anymore.” That video, and the forty like it, are pulling millions of views this month. The hotel bot with the fake Southern charm that swears it is human, then transfers you to another bot. Amazon’s Rufus introducing itself with a disclaimer that it might get things wrong. The “leverage AI to complete this task” email that lands like an insult to anyone who spent years learning their craft.

The frustration is real, and the data backs it. Writer’s 2026 enterprise survey asked 2,400 people who already use AI at work. Forty-eight percent of leaders now call their company’s AI adoption a “massive disappointment,” up from 34% a year earlier. Only 29% report meaningful return from generative AI, 23% from agents. Three in four executives admit their AI strategy is “more for show than for actual internal guidance.” And 60% say they plan to lay off employees who can’t or won’t use it.

Tools that mostly do not pay off, mandated by people who admit it is theater, enforced with your job. The anger is earned. Anyone telling you to just be more positive has not read the room.

And it is not only the bots. This May, former Google CEO Eric Schmidt stood in front of ten thousand University of Arizona graduates, told them AI would shape their world, and said that when someone offers you a seat on the rocket ship you do not ask which seat, you just get on. A good part of the stadium booed him. Some of that was organized, and it was not only about AI. But the nerve it hit was real: nobody wants to be handed the future by the people who already cashed out of the present.

But the video makes one move that will cost you if you accept it: it aims all that justified anger at a single target. AI itself. The tool.

Look at what is underneath the complaints and you find two different things wearing the same word. There is AI as theater: the usage dashboards, the slop, the mandate, the budget somebody upstairs has to justify. And there is AI as leverage. The same month those videos went viral, Uber’s engineers tore through a full year’s AI-tools budget in four months. Nobody forced them. Seventy percent of their code commits now involve AI. ChatGPT crossed 900 million weekly users. That is not compliance. That is pull.

The backlash is not a referendum on whether AI is useful. It is the sound of those two things separating, and the fatigue is what it feels like to be handed the theater version and told it is the leverage version.

Here is the part nobody quoting the scary research mentions. The most-cited study on AI making people worse, a five-university trial this spring, found the damage was real but concentrated entirely in people who used AI to get direct answers. The people who used it for hints and kept thinking showed no measurable decline. The same study being passed around to prove AI rots your brain proves something narrower and far more useful.

The villain was never the tool. It was the posture.

Which makes the backlash the best news an adopter has gotten all year, if you can read it. The market is sorting people into two piles right now, and you can tell which one you are landing in with a single question: is the tool making your judgment sharper, or replacing it?

Stay on the right side of that line.

• Refuse oracle mode. Use AI for the messy middle: drafts, options, pressure-testing your own thinking. Never the final call. The moment you stop checking its work is the moment you join the pile that atrophies.

• Build a loop, not a habit. A repeatable workflow with a verification step beats scattered prompting. Those enterprise disappointment numbers come from companies that bought tools and skipped the redesign. Do not make the same mistake at the scale of one.

• Watch your own load. The “brain fry” study everyone cites (BCG, 1,488 workers) found productivity rising as people went from one tool to three, then falling at four or more. More agents is not more leverage. Mastery is.

The last time a tool sorted a profession this hard, it was the spreadsheet. (VisiCalc, the first one, shipped in 1979 and turned the personal computer from a hobbyist toy into a serious business machine almost overnight.) Bookkeeping roles fell by about a third over the following decades. The people who moved up into analysis and judgment became more valuable, not less. The difference this time is the mandate bolted on top, and that mandate is exactly what people are revolting against. Fair enough. But VisiCalc never waited for anyone’s permission, and neither will this.

Nobody is coming to sort it for you. That is not the threat the video thinks it is. It is the opening. If this reached you because someone forwarded it, they were telling you something: you are probably already the kind of person who can tell theater from leverage. The people who can, and who keep their own judgment in the loop, are about to be worth a great deal. Everyone else is waiting for a rescue that was never on the schedule.

Adapt & Create,
Kamil

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Go deeper. Everything above traces to a primary source:

• The video that started it: “Nobody Actually Wants AI Anymore”

• The commencement that went viral: Eric Schmidt booed at the University of Arizona, May 2026 (press coverage)

• The enterprise data: Writer, “AI Adoption in the Enterprise,” 2026

• The persistence study: Liu et al., “AI Assistance Reduces Persistence and Hurts Independent Performance,” 2026

• The cognitive-load study: BCG / HBR, “When Using AI Leads to Brain Fry,” March 2026

• The Uber numbers: Uber’s Anthropic push, via Yahoo Finance

• The usage figure: OpenAI, “Scaling AI for everyone,” Feb 2026

Before we start today, please reply to this email and answer the following questions 🙏

1. What is one thing that if I took it away from the newsletter, this would not be a valuable resource for you anymore?

2. What is the one thing that you wish I added that would make you stay and read forever?

Hey Adopter,

Some decisions you can test. Run it small, read the numbers, roll it back if it flops. This edition is about the other kind. The three-year supplier contract. The hire that reshapes your team. One shot, no undo, and you are standing over two or three options trying to pick.

So you do the smart thing. You hand the options to AI and ask which one wins.

It answers. Confident, structured, reasonable. You pick. Six months later the number you chased looks better and something you never measured is quietly bleeding out. The fix worked. The business got worse.

The clerk and the advisor

That gap has a cause. The tool ranked your two or three options against the one goal you named. It never asked whether those options touch the thing holding you back, or whether that goal is a symptom of something one layer down. It could not. You gave it a shortlist and a metric, so that is what it sorted. Garbage frame, confident answer.

This is the line between a clerk and an advisor. A clerk sorts your list. An advisor looks at your list, looks at your business, and tells you the list is wrong. On a reversible call, who cares. On a call you live inside for years, that difference is everything.

The fix is a tool that refuses to sort anything until it understands the system your decision sits in. So it starts where a good advisor starts. It interviews you. And it will sometimes tell you none of your options were right, then point at the move you never thought to list.

Below: the prompt that runs the interview, pulls the data out of you when you think you have none, finds the real constraint, then judges the call.

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The UK government published the most detailed civil service AI trial in history. Twenty thousand civil servants, twelve departments, three months. The headline number traveled the world: 26 minutes per day saved. Nearly two weeks per year per civil servant. Eighty-two percent of users said they would not go back to working without the tool.

Three months later, the same government quietly published a second study of the same trial period. That study found “no robust evidence” that the time savings translated into improved productivity. Excel work got worse with Copilot. Twenty-two percent of users identified hallucinations. The Department for Work and Pensions later ran a third study with a comparison group of 2,535 non-users and reported 19 minutes per day, not 26.

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In March 2026, the chair of the Public Accounts Committee wrote to the Cabinet Office permanent secretary calling the original 26-minute figure “curiously specific” and asked her to explain how it was calculated. As of mid-May, no public response.

That is the most rigorous public-sector AI trial ever conducted. And it is still arguing with itself.

Which raises the question this edition is actually about. Why did the UK run the trial in the first place?

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Hey Adopter,

Google spent two hours on stage telling you the work will get done while you do something else. Gemini Spark runs on its own cloud machine, around the clock, chewing through email, monitoring, research and project tasks under your direction. The search box got its biggest change in 25 years. New audio glasses you talk to. A quicker default model in Gemini 3.5 Flash. And a fresh $100 a month plan that slots in beneath the old top tier, which dropped from $250 to $200.

Big day. Here is the bit the keynote skipped.

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Why the demo works and your week breaks

Every announcement pointed the same way. AI that reasons, acts in the background, and strings tools together without you babysitting each step. The same agent logic now sits across Search, Workspace and YouTube, with persistent agents that monitor things for you and a search box that builds its own interactive widgets. The Verge’s roundup of the biggest announcements walks through the lot. Google says Spark handles long-running jobs on its own, plugged into Gmail, Docs and outside apps through MCP, which you can read in 9to5Google’s breakdown of the Gemini app changes.

On stage, the task is clean. The inbox is tidy. The goal fits in one sentence. Your real work is messier than that.

The agent does not fall over because the model is weak. Gemini 3.5 Flash is fast and sharp, and a stronger Pro lands in June. It falls over because the job you handed it was never defined. A thin brief produces thin output, and now that output arrives at 3am while you sleep.

That points at a skill no keynote sells.

Delegation is the real skill here

Most people are bad at handing work to a human assistant. The brief is half a sentence. The constraints live in your head. Nobody said what done looks like. You correct it after the fact and call that managing.

An agent makes the habit expensive. A person senses a half-baked instruction and asks a question. An agent takes the half-baked instruction and runs with it, on a schedule, with confidence. Feed it a fuzzy task and it returns a tidy pile of wrong work overnight.

The people who pull value from Spark are the ones who already write tight briefs. If you cannot tell a new hire what good looks like in plain words, a cloud machine acting on your behalf will not save you. It will scale your vagueness and bill you monthly for it.

So before you pay for the agent, earn the right to use it.

What to run this week

Pick one task you repeat every week. Weekly report, inbox triage, meeting prep, status update. One.

Write the brief as though onboarding a person on their first morning. Name the inputs. List the steps in order. Show the exact shape of the output you want. Write the single line that tells you the job is finished.

Run it as an ordinary prompt first, inside the Gemini app or whatever sits on your desk. No automation yet. Watch where it guesses wrong.

Fix the brief, not the tool. Tighten the step it fumbled. Run it again. Repeat until it produces something you would sign your name to without edits.

Only once it works by hand do you let anything run it on a schedule. Google showed the automation first and the definition never. You do it the other way round.

Get that order right and the next part stops being frightening.

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Read the price tag before you reach for it

Google moved the money around too. A new Ultra plan at $100 a month, and the old $250 tier trimmed to $200. The full list of what dropped sits at the bottom of this email if you want the receipts. The pitch writes itself. Pay more, get an employee who never clocks off.

The question for you is not which tier. It is whether a background agent on a pricier plan beats a sharper prompt on the plan you already pay for. For most weekly tasks, it does not. Buy the capability after a defined job demands it, not because a keynote made it look effortless.

The permission problem nobody put on a slide

A background agent needs standing access to your mail, your documents, your calendar and third-party tools. That is a different animal from a chatbot you paste text into. The keynote spent its energy on what the agent can do, almost none on what it can touch.

Before you grant that access, settle three things. What it may act on without asking. What it must bring to you before doing. How you check its output before it leaves the building. Treat it like a junior with keys to the office, not a faster search bar.

None of this needs a $100 plan to begin. The thinking is free. The tool is the easy part.

If a colleague is about to pay for an agent before they have defined a single task for it, forward this. You will save them a month of confident nonsense and one awkward chat with their boss.

This is the work I am building a room for. Professionals and executives putting these tools into live workflows, not only reading about them. We pressure-test each other’s briefs, swap what runs and what flops, and build the systems together instead of guessing alone. Pull up a chair at skool.com/aiac.

The agents are coming whether your work is defined or not. Define it, and they finally earn their keep.

Adapt & Create, Kamil

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The full I/O 2026 rundown if you want it

Everything worth knowing from the keynote, in one place. Skim it, click what matters to you.

• Gemini 3.5 Flash, the new default. Faster output, frontier reasoning, and agent skills built in. A stronger Pro version lands in June. 9to5Google’s live news hub

• Gemini Spark, the 24/7 agent, plus new pricing. A persistent agent on a dedicated cloud machine, wired into Gmail, Docs and Workspace. New Ultra plan at $100 a month, top tier cut from $250 to $200. WIRED’s full announcement list

• Antigravity 2.0, the agent builder. A desktop app, CLI, SDK and multi-agent orchestration for developers shipping agents. Google’s developer highlights

• Gemini Omni, multimodal creation. Reasoning fused with video generation and conversational editing, with better physics and motion. 9to5Google on Gemini Omni

• Search, rebuilt. Its biggest change in 25 years. Persistent monitoring agents, generative interfaces, and a Universal Cart spanning Search, Gemini, YouTube and Gmail. Tom’s Guide live coverage

• Audio glasses on Android XR. Voice-first eyewear with Samsung, Warby Parker and Gentle Monster, aimed at a fall 2026 launch. Google’s Android XR post

• Productivity odds and ends. Docs Live voice dictation, Ask YouTube video search, a Daily Brief agent, and wider content provenance through SynthID and C2PA. CNET’s recap

Hey Adopter,

Most agency owners, consultancy founders, and SMB operators start Monday by opening email. Within ten minutes, three people have told you what your priorities are. By lunch you’ve answered everyone except yourself.

Start by pasting your numbers into a prompt and getting a one-page brief back. Graduate to connectors so Claude fetches its own data. Convert the whole thing into a skill that updates itself every Monday based on what you tell it. Then, if you want the brief waiting for you before you’ve touched your phone, automate the entire workflow.

Level 1 you can run today. The bonus level is what you’ll be running six months from now. Here are all four, plus a filled example so you know exactly what you’re building toward.

For skimmers. Scroll to Level 1 below, copy the prompt, paste in five numbers from your business, get your brief in two minutes. Come back for the rest when you want to take it further.

What a finished brief looks like

This is what the system produces. Real shape, illustrative numbers. Yours will run shorter once you trim sections you don’t care about. For things like this I always ask the output to be in HTML format.

Two minutes to read. Five minutes to act on. That’s the whole game.

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Why the brief is the highest-leverage prompt you’ll run all week

Information without structure is just noise. You already have the numbers. The bank balance is in your banking app. Last week’s sales sit in Stripe, Square, or QuickBooks. The deals are in your CRM or your head. The week’s commitments are on your calendar.

The problem is none of these talk to each other on Monday morning. You either spend ninety minutes hopping between tabs, or you skip the review and let the day happen to you.

A briefing reverses that. Ten minutes of input, two minutes of output, and you walk into the week knowing what to defend, what to ignore, and where the leverage sits.

Urgent stuff has built-in pressure. Leverage stuff needs the brief.

The compound effect matters more than the weekly one. After four weeks you start to see patterns. The same deal stalls every month at the same stage. Cash dips every third week. Tuesday afternoons are where your strategic work goes to die. The brief surfaces what your gut already suspects but couldn’t prove.

What you need before you start

Ten minutes Sunday evening or Monday before 7am. Same time every week.

Access to whatever data you have. You don’t need all of it. The prompt degrades gracefully when sections are missing.

An AI chat that handles long inputs. Claude or ChatGPT, free tier is fine for Level 1.

A place to save the output. A Google Doc, a Notion page, or your notes app. Don’t trust your memory to hold it.

That’s it. No connectors, no plugins, no API keys for Level 1.

Won’t work if any of these are true

The brief is not a fix-all. Skip it if:

• Your Monday starts at 9am with a meeting. The brief needs ten quiet minutes before the world catches you. If your team is already in your calendar by the time you wake up, fix that first.

• You won’t be honest about what’s on your mind. The brain dump at the end of the input is the most valuable line in the whole prompt. If you’d lie to a therapist, you’ll lie to the prompt, and the output will lie back.

• Your business is in a genuine crisis this week. Stabilise the urgent fire before optimising for leverage. The brief is for operators who already have the basics under control.

• You expect the AI to make decisions. The brief surfaces signal. You still call the shots. If you want a tool that thinks for you, this isn’t it.

If none of those apply, keep reading.

Level 1, paste-based, runs in any AI chat

This is where every reader starts. You paste your data, Claude generates the brief. The version I run myself takes about eight minutes most Sunday evenings, less when the inputs are stable.

Manual, slightly tedious, and the friction is the point. Pasting forces you to look at the numbers yourself before the AI synthesises them.

Copy everything between the lines into Claude or ChatGPT. The bracketed sections are where your data goes.

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Hey Adopter,

I’m at Jim Kwik’s Limitless Live this weekend, where I was an AI voice on a panel of human-performance experts. I left the stage convinced of something I’ve been circling for a while.

There’s too much AI noise out there: the takes, the threads, the constant new tool. The newsletter helps you cut through it. But understanding isn’t doing. The people actually moving forward are the ones learning together: comparing what’s working, collaborating on experiments, asking “dumb” questions out loud, watching someone else’s workflow before building their own.

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Chegg lost $14 billion in market value over 30 months. The CEO publicly told the truth about why the entire time.

He acknowledged the ChatGPT threat on a live earnings call. He went on stage at Fortune and called himself “the poster child for getting your ass kicked in the public markets by A.I.” He launched an AI product within two weeks of the bad news. He said all the right things in public, from May 2023 through October 2025.

They still lost 99 percent of the company’s peak value.

This is a case study about the difference between acknowledging AI disruption and actually adapting to it. The two are not the same thing, and Chegg is now the public face of the gap between them.

Here is what is in this piece:

• Why telling the truth about AI did not save Chegg. The mechanics of acknowledgment without adaptation.

• The wrapper trap. What CheggMate was, why it failed at launch, and why most companies running internal AI strategy reviews are about to build the same thing.

• What Khan Academy, Duolingo, and Quizlet did differently in the exact same 30-month window. Each one had a sharp answer to one question Chegg never answered.

• Three rules for any leader sponsoring AI strategy in 2026. Doable on a board agenda this quarter. None require special technology.

If your company is at the “we see this, we know it’s real, what do we do” stage, this one is for you. The full case study PDF (sources, timeline, references, full diagnosis) is the premium download for this edition.

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Most AI rollouts that stall don’t stall at the model. They don’t stall at the vendor. They don’t stall at the data infrastructure. They stall at the layer of people responsible for translating strategy into how teams actually work.

Middle managers are the decisive translation mechanism between AI strategy and the team that adopts it. When they model AI use, the team adopts. When they don’t, the rollout stays in pilot purgatory regardless of how much budget sits behind it.

This is the layer your AI strategy lives or dies at. Below: how to read the resistance, the four levers that change the dynamic, and the 60-day sequence that beats every training program.

The pilot-purgatory pattern

The pattern repeats across industries and tools. The executive announces an AI strategy. The press release goes out. Tooling gets procured. Training runs. Six months later, adoption is shallow, the metrics are stuck, and the post-mortem reaches for the model, the vendor, or the team.

The actual cause sits upstream. Some teams use the tools well. Most don’t. The variance is enormous, and the predictor of that variance is not the team, not the technology, and not the budget. It’s whether the team’s direct manager actively uses and models the AI tools the strategy promised.

BCG’s 2025 analysis of “future-built” versus laggard organisations finds the gap is starkly visible at the manager layer: 88 percent of future-built organisations have managers actively role-modeling AI, against 25 percent in laggards. The technology stacks and budgets across both groups are usually comparable. The manager-layer behavior is not.

The gap between organisations that scale AI and those that don’t tracks closely with one thing: whether middle managers are treated as active orchestrators of hybrid human-AI teams, or as passive recipients of mandates they were never asked to design. The last mile of cultural and operational embedding goes unaddressed. The pilot dies. The post-mortem blames the model, the vendor, or the team. The actual cause was upstream.

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The identity-threat mechanism

The mistake is to read manager resistance as obstructionism. It isn’t. It’s rational hedging against a real threat to the value the role provided.

AI automates the things middle managers historically did to add value: coordination, information synthesis, status reporting, routine oversight, gating decisions on incomplete information. When those tasks get absorbed by tooling, the manager is left with a role whose contour has changed but whose KPIs and incentives haven’t. The accountability anxiety is concrete (who gets blamed when the AI errors?). The skill anxiety is concrete (most managers don’t yet feel ready for the new capabilities they’re being asked to lead). Resistance manifests as hedging, escalation of edge cases that could have been resolved, or quiet reversion to legacy workflows where the manager’s authority is undisputed.

This is not a culture problem. It is a role-design problem. Tools deployed without redesigning the role they’re meant to amplify will get used reluctantly at best and resisted defensively at worst. Tools deployed alongside a redesigned role, from transactional coordinator to AI orchestrator, boundary manager between human and machine agency, coach for contextual judgment, meaning-maker who translates AI outputs against business reality, will get used as designed.

The technical subsystem (agents, dashboards, workflows) only works at scale if the social subsystem (role definitions, incentives, authority thresholds, feedback rituals) is updated in parallel. Organisations that do this in sequence, tooling first and role second, typically don’t get to the second.

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Four levers that change the manager layer

The standard response to manager underperformance on AI is more training. The standard outcome is generic AI literacy that doesn’t translate into the manager’s actual workflow. Four levers do translate.

Lever 1. Skills for orchestration, not just tool use. The manager-specific capability set is narrower and more concrete than generic AI literacy: identifying high-value AI-augmentable tasks, designing hybrid workflows where human and machine decisions are explicitly partitioned, coaching the team on prompt design and output validation, spotting ethical drift before it propagates, pairing reverse mentoring so junior AI fluency reaches senior business judgment. BearingPoint’s 2025 survey of 300-plus managers across Europe and the US, paired with an analysis of roughly a thousand middle-manager job descriptions, surfaces a similar capability profile: embracing AI’s strategic potential, leveraging reverse mentoring, providing workforce strategic outlook, and promoting team AI literacy to reduce fear. Notice what’s missing. Tool training is not on the list. The tool is the easy part. The orchestration around it is the hard part.

Lever 2. Protected time and structured learning rituals. Adoption layered on top of existing workload produces compliance theater. Adoption with dedicated capacity, typically 10-20 percent protected time or cohort blocks, produces actual capability shifts. The effective formats are cohort-based learning in groups of 8-12 grounded in real workflows, structured familiarisation periods per capability area, and safe sandboxes with defined baselines where managers can test new workflows without production-quality consequences.

Lever 3. Outcome-based incentives that don’t punish AI use. Legacy KPIs measure throughput, activity, or output volume. AI makes the same work faster and often better-quality. If the manager’s review or variable comp is tied to legacy metrics, the AI-enabled gain is invisible. The reward goes to the legacy behavior. The manager hedges accordingly. Shift to outcome-based measures: quality-adjusted productivity, adoption depth at the team level, reuse of validated AI assets, and upward feedback on implementation friction valued as data rather than penalised as complaint.

Lever 4. Coaching, segmentation, and role redesign. Treat managers as a heterogeneous population. The intervention that works for an Early Adopter actively pulling on resources is wrong for a Blocker whose authority feels threatened, which is wrong again for a Skeptic burned by past technology waves. Early Adopters get resources, visibility, and platform. Skeptics get safe use-case sandboxes and peer references. Blockers get governance and quality ownership, converting identity threat into legitimate authority over the oversight layer.

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The 60-day sequence

The single biggest mistake is sequencing tool rollout before role redesign. The single biggest fix is reversing the order.

A 60-day role-redesign program looks like:

• Days 1-15. Diagnose. Run the segmentation. Pull adoption data. Conduct skip-level interviews. Identify the two or three functions where the role redesign will pilot.

• Days 16-30. Redesign the role explicitly. Have the conversation: “Your role post-AI includes reviewing outputs for contextual accuracy. That judgment is yours. The coordination work that used to take half your week is now automated. We’re giving you protected time for coaching and quality. Here’s what we measure now.”

• Days 31-50. Cohort learning kicks off. 8-12 managers per cohort. Real workflows. Peer models drawn from Early Adopters. Sandboxes for the new capabilities.

• Days 51-60. Amplify. Champion networks form. Lessons feed back into the next cohort. Performance system tweaks land in parallel so the new behaviors get rewarded.

This is not a training curriculum. It is an operational role redesign that uses training as one of several tactics. Organisations that treat it as a curriculum produce graduates who go back to the same role they had before. Organisations that treat it as a redesign produce a different role.

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Once the technology baseline is in place, the manager layer is the highest-leverage remaining variable in any AI rollout. It is where most organisational variance gets controlled or released. It’s also the cheapest fix on the menu compared to another vendor contract or another data platform.

Gartner projects that 20 percent of organisations will use AI to eliminate more than half of their current middle-management positions by 2026. The flattening pressure is real, and the temptation is to read it as proof that the layer is dispensable. The evidence cuts the other way. Organisations that flatten without deliberate role redesign create either vacuums of translation capacity or defensive middle layers that slow rather than accelerate. Gartner’s same research finds that employees who perceive an AI rollout as a job threat are 27 percent less likely to stay, which means the cost of getting this wrong shows up in attrition before it shows up in adoption metrics.

The reason it stays unfixed is not that it’s hard or expensive. The reason is that nobody has owned it. Someone reading this will, within their organisation. The question is whether it’s you.

Adapt and Create, Kamil

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Hey Adopter,

On 7 May 2026, an engineer on the Claude Code team at Anthropic posted an essay declaring that HTML had replaced Markdown as the right default for AI-generated artifacts. He liked the density, the diagrams, the interactivity. He admitted HTML takes two to four times longer to generate. He admitted the diffs are noisy. He said he was, in his own words, far on the maximalist side of things. The post hit four million views inside a week

On 29 January 2026, Mintlify shipped the opposite default. Their head of product marketing wrote that documentation served to AI agents should be clean Markdown, not HTML. They reported a thirty times reduction in token usage. Cloudflare, three weeks later, measured the same thing on their own blog post and reported eighty percent fewer tokens when the same page was served as Markdown.

A token is the unit AI models use to count text. Roughly four characters of English in one token. Every word in, every word out, you pay per token.

Both sides are smart. Both report wins. The internet decided this was a methodology fight.

It wasn’t. It was two teams naming different constraints and choosing accordingly. The Anthropic engineer was optimising for cognitive density on his own review artefacts. He read his own output. He wanted it pretty. He accepted the cost. Mintlify was optimising for the token bill of the agents reading their docs, not the humans. Different constraint, different choice, no contradiction.

The fight on social media was held one level above where the decisions were made. People were picking a format. The two named players were picking a constraint and letting the format follow.

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The actual disease

This is the dominant failure mode of AI adoption in 2026, and the format fight is just where it shows up most loudly.

People default to maximum because the model supports it, then call it strategy. Bigger context windows. Richer output formats. More agents in the loop. More tokens through the pipe. None of it decided. All of it defaulted to, then defended as a methodology when someone questions it.

The context window is the maximum amount of text a model can read in one go. The bigger the window, the more you can stuff in. That doesn’t make stuffing it in a good idea.

The honest name for this is token maxing. It’s what happens when nobody at the table has named the constraint they’re optimising against, so the default becomes “use everything the model will let us use.” It feels like progress because the numbers go up. The window grows. The output gets richer. The agent gets more autonomous. The bill grows too, but slower than the demo wow factor, so nobody notices until the quarterly review.

An agent is an AI system that runs multiple steps automatically instead of waiting for you to prompt each one. More steps means more tokens. More tokens means more cost.

The opposing camp calls itself token budgeting and assumes it owns the moral high ground. Cheaper, leaner, more disciplined. But budgeters who refuse density without naming what density would buy them are making the same mistake in the opposite direction. They picked a number to minimise and called it a strategy. The thing they’re defending isn’t a position. It’s a vibe.

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What the evidence says about defaulting

The case against thoughtless maxing isn’t ideological. It’s measured.

In April 2026, researchers from ETH Zurich’s SRI Lab published a study testing whether AGENTS.md files, the industry-standard practice of giving coding agents rich contextual instructions, actually helped. They ran 438 real-world coding tasks across Claude Code, Codex, and Qwen Code. The result was the opposite of what every operator in the field assumed. LLM-generated context files cut task success rates by three percent and raised costs by over twenty percent. Human-written ones added four percent on outcomes but still raised costs by nineteen percent. Their recommendation was that context files should describe only the minimum needed, and that LLM-generated ones should probably be omitted entirely.

AGENTS.md files are documentation files placed in code repositories to tell AI coding tools how the project works. The community treats them as best practice. The study tested whether they actually help.

Read that again. The most common advice in the agent-tooling community, followed religiously, was actively making things worse. Not in theory. In a peer-reviewed study, on real tasks, with named models.

The mechanism is what the Anthropic Applied AI team called context rot in September 2025. Every additional token in the window depletes what they call the model’s attention budget. Chroma tested eighteen frontier models in July 2025 and found that performance degrades at every length increment, not just near the limit. A model with a one million token window still rots at fifty thousand tokens. The window is real. The usable window is much smaller, and it shrinks as you fill it.

Bai et al. at Stanford and MIT published a paper in April 2026 with two findings that should end the maxing argument on their own. First, running the same agent on the same task produces up to thirty times variance in token spend. Same model, same input, same job, thirty times range on what it costs you to find out. Second, the models themselves systematically underestimate their own token usage, with prediction correlations no higher than 0.39. The models can’t predict what they’ll spend. The humans operating them definitely can’t.

Maxing in that environment isn’t ambitious. It’s flying blind and calling the lack of instruments a feature.

What budgeting actually means

Budgeting is the part most people get wrong. It isn’t austerity. It isn’t picking the smallest model and the leanest format and the shortest prompt. It’s the discipline of naming a primary constraint and letting the choice follow.

The Anthropic engineer named his constraint as cognitive density on artefacts he personally read. Given that constraint, HTML was correct. He budgeted, he just budgeted toward density instead of cost.

Mintlify named cost on the consuming agent. Markdown was correct. Same skill, different constraint.

Anthropic’s own multi-agent research system burns fifteen times more tokens than chat. They accepted that multiplier because they named breadth-first parallelism as their primary constraint. They didn’t default. They decided.

The teams that win at this aren’t picking between two formats or two modes. They’re answering one question before they start. What does this token spend actually buy me, and at what scale does that calculation flip? Once you can answer that, the format follows, the model follows, the agent shape follows, the budget follows. Without that answer, every choice is a vibe defended as a strategy.

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The steel-man, on the record

The honest pushback to all of this is that budgeting is harder than maxing. It requires caching infrastructure most teams haven’t set up. It requires naming what stays static across requests, which most teams haven’t thought about. It requires measurement, which most teams aren’t doing. Telling the market to budget is telling it to do something it has no muscle for yet.

Caching means storing a stable prompt once so the model doesn’t pay full price to re-read it on every request. Anthropic, OpenAI, and Google all support it. Done well, it cuts input costs by up to ninety percent.

That objection is real, and it doesn’t change the answer.

Manual server deployments persisted for a decade after continuous integration existed. The reason wasn’t that CI was wrong. The reason was that the skill gap was real and the alternative shipped. Maxing is the manual deployment of 2026. It works. It ships. It also leaves money and reliability on the table that the operators who acquired the skill collect quietly while the rest argue about format defaults on Twitter.

Budgeting is the right default. Maxing is what you reach for when you’ve named what budgeting would cost you and decided the trade is worth it. If you can’t name the trade in one sentence, you’re not maxing. You’re defaulting and dressing it up.

How to know which mode you’re actually in

Three tests, in order.

1. Can you name your primary constraint in one sentence? Not “we want it to be fast and good and cheap.” One constraint. Latency floor of two seconds. Cost ceiling of fifty cents per task. Accuracy floor of ninety-five percent on the eval set. If you can’t say it in one breath, the constraint doesn’t exist yet, which means whatever you’ve picked is a default.

2. Have you measured what your current default costs you? Not the per-token price. The per-task spend, the run-to-run variance, the cache hit rate, the format overhead. If those numbers are vibes, your strategy is a vibe.

3. What would you give up to relax the constraint by half? If you can’t answer, you don’t have a constraint. You have a preference.

The teams running these tests aren’t fighting about HTML versus Markdown. They’re fighting about whether their caching architecture is set up to make the format question irrelevant, whether their retrieval is tight enough that context rot stops mattering, whether their agent loops have a stop condition that catches the runaway run before it costs them four figures.

Retrieval means pulling only the right snippets of information from a larger store and feeding them to the model, instead of giving it everything and hoping it finds the right bit.

The format wars get attention because they’re visible. The real work is invisible. That’s the actual gap between operators who are winning at this and operators who are still arguing about defaults.

Token maxing is what you do when you can’t budget. Token budgeting is what happens when you can answer one question.

Adapt & Create,
Kamil

Hey Adopter,

Boring AI is where small businesses make real money, and hiring the wrong person to build it is where they lose it. Below the line, the five workflows that pay for themselves inside a quarter, plain English, one piece of proof each. Behind the paywall, the questions, the email scripts, and the trial structure that stop a five figure cheque burning on a demo that never goes live. Read it once, copy the scripts, start screening today.

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Five workflows. That is the whole list. Almost every service business bleeds money in the same five places, and the builders making real revenue in 2026 fix one at a time without the theatre.

Speed-to-lead. A new form submission to first human contact in under 60 seconds. Most businesses sit at 14 hours. That gap is the difference between a 12% close rate and a 25% one.

Document processing. Invoices, intake forms, contracts. Work a person does in 15 minutes that a properly built system handles in 2, with a lower error rate, running while your team sleeps.

Follow-up sequences. The deals dying because nobody chased them. Personalised messages that stop the second a prospect replies. Webinar conversion rates double when this is built right.

Database reactivation. Every CRM has dormant contacts worth real money. A small gym ran one sequence on 4,000 lapsed members and pulled mid five figures with zero new ad spend.

Internal reporting. Daily numbers in your inbox before your first coffee. The reports your team builds by hand at 7pm on a Friday.

Pick the right workflow and the revenue lift pays for the build inside a quarter. Pick the wrong builder and you burn three months and a five figure cheque on a demo that never survives production.

That second problem is the one nobody warns you about. The market is full of consultants who can pitch and cannot ship. Below the line, you get the five questions that expose them, the trial structure that protects you, and four emails you send the next time one slides into your inbox.

Do your own math before any call

You don’t need a consultant to find your bottleneck. You need an hour and a notepad. (bonus points if you can hook up the right connectors to your claude account and have the AI do it for you)

Pull last month’s numbers. Answer these in writing.

• Leads in, leads converted, average response time to a new enquiry

• Hours your team spent last week copying data between systems

• Number of past customers not contacted in the last 90 days

• Who builds your weekly reports by hand, and how long it takes them

• Where your team complains the loudest about manual work

If you can’t answer those in writing, that’s your first problem. No builder can fix what you can’t measure.

Walk into a sales call already knowing your response time is 14 hours and your close rate is 8%, and you stop being a mark. You become a buyer. The consultant trying to sell you AI transformation loses to the one who says, let’s get your response time under five minutes by Friday.

Refer a friend

The five questions that expose fake expertise

Ask every one before money moves.

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Hi Adopter,

Your Claude subscription is subsidized. So is mine. The economics do not work yet, which is why Anthropic announced a SpaceX compute deal and doubled five-hour limits across Pro and Max on Wednesday. That sounds like good news until you read it as a confession: they need cheaper power, and they are throwing capital at the gap between what you pay and what it costs to serve you.

Gaps like that close. Always. Cursor restructured pricing twice in 2025 and power users felt the second move as a doubling. Replit’s effectively unlimited tier tightened. The free million AWS Lambda invocations became a memory. Subsidized growth phases end with someone learning to budget the resource that used to feel infinite.

Forget saving 20 dollars this month. Token efficiency is a hedge against the next pricing letter. The earlier the habit forms, the smaller the adjustment when it hits, and the further your existing subscription stretches in the meantime.

You already know this in your gut. You are using maybe 30 percent of what claude.ai does. Your sidebar is a graveyard 47 chats deep, each one starting with the same paragraph of background about your business. Your team has started using it and you have no idea what good usage even looks like, which means you have no idea how to coach them. The viral “seven hacks” post was not written for you. This one is.

Five habits. None of them require Claude Code, the API, or anything you have to install. They all live inside the claude.ai app you already pay for.

Stop letting Claude pad. Set Concise once.

Long threads punish verbosity. Every reply gets re-ingested as input on the next turn. A wordy assistant doubles your bill across a working day.

Two moves. Open the style picker at the bottom of the chat and switch to Concise. That alone is worth a chunk. Then go to Settings,General, Profile, Instructions for Claude, and paste this:

Lead with the answer. No preamble, no “great question,” no narration. 
Code blocks or bullet lists first when the answer is structural. 
Add one short sentence of justification only when correctness depends on it. 
Maintain 100 percent accuracy on facts and numbers.

That is your house style now. It rides on every chat across every project.

The guilt you feel about asking your $20-a-month subscription to write things faster is the wrong instinct. Padding is the bug. Concise is the fix.

Live in threads, not new chats.

This is the biggest single move on the page.

You start a new chat for every task because you do not trust Claude to remember. So you re-paste the same paragraph of background about your business, your team, your client. sixty-seven times. That is input tokens being burned to recreate state Claude already had two days ago.

The shift: pick three to five long-running threads per area of your work. One for “Q3 board prep.” One for “Acme client strategy.” One for “Hiring decisions.” Continue them. Stop starting new chats for the same recurring work.

When a thread feels heavy, do not abandon it. Ask:

Summarise the decisions, current state, and open questions in 250 words.

Paste that into a new thread to continue. State preserved, tokens released.

Two settings to flip while you are in there. Settings, Capabilities, turn on “Search and reference past chats” and “Generate memory from chat history.” Both shipped in 2026. The first stops you re-opening the same answer from a Tuesday in March. The second turns “I have to re-explain my situation every time” into “Claude already knows.”

Stable threads are cheaper threads. Your existing background gets cached server-side. You stop paying input tokens to remind a system that has the receipts.

Projects are the leverage move you are not using.

If you have one Project and it is called “Untitled,” you are leaving the single biggest claude.ai feature on the table.

The shift: one Project per major client or function. Not one per task. Upload your reference files once. Brand guidelines, contract templates, SOP documents, past briefs, your business background, your style guides. Set the project’s custom instructions to the rules that always apply: your tone, what you sell, the decision criteria you use, the things Claude should never assume.

Every chat inside that project inherits everything. You stop pasting your “about us” paragraph at the top of every thread. You stop re-uploading the same PDF.

When the project’s knowledge gets large, RAG activates automatically on paid plans. This shipped in March. You do not configure it. Claude switches to a search-the-knowledge-base tool when the window starts to fill, and capacity expands up to ten times.

One trap. Do not build one giant “Everything” project. Group narrowly. RAG does worse on broad collections than on tight ones. If you have an Acme project and a Beta Corp project, do not merge them into “Clients.” Keep them separate.

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Pick the right model. Stop burning Opus on email.

The model picker exists for a reason. Most Pro users set it once during onboarding and never touch it.

Sonnet 4.6 is your daily driver. It handles drafting, analysis, summarising, briefing, decision support at near-frontier quality and burns less of your quota.

Opus 4.7 is for hard cross-cutting work. Complex reasoning, novel research, board-level strategic decisions where being right outweighs the bill. One thing to know: Opus 4.7 launched on 16 April with a new tokenizer. The same input text can produce up to 35 percent more tokens than the same text on Opus 4.6 or Sonnet 4.6. Pro and Max quotas are not priced by token, but every Opus session eats your five-hour window faster than it used to. Use Opus when the quality of the answer matters more than the cost of the answer. Otherwise switch back.

Haiku 4.5 is the bottom of the picker. Use it for routine extraction, classification, “summarise this five-page PDF,” “categorise these 30 inbound emails.” It is fast, it is cheap on quota, and it is good enough for the work that does not deserve Sonnet.

If you cannot articulate which model you used last Tuesday for the email draft you sent, you are eating expensive quota for cheap output. Look at the picker. Make it a decision.

The team move. The 30 percent problem multiplies.

If you use 30 percent of claude.ai and your five team members each use 20 percent, your team’s collective fluency is below your individual fluency, and the bill is six times yours. This is the conversation procurement is going to start having with you.

Three operator moves.

1. Write a one-page team standard. Which model when. The Concise + custom instruction setup from habit one. The shared Projects everyone should be in. The data your team should never paste into Claude. One page, not ten. Make it the onboarding doc for the next hire.

2. Audit usage once a month. Workspace and Team plans show per-user patterns. Outliers point in two directions: power users worth shadowing on a Friday afternoon to learn from, or coasters worth a quiet coaching conversation. Most teams have one of each they have not identified.

3. Make one person the AI lead. Not full-time. A few hours a week. They own the team standard, they update the Projects, they answer “should we use Claude for this?” so you do not. This is how operators who get the AI question right are different from operators who keep getting blamed for the bill.

That’s it, pick one habit. Build it this week. Add the next one next week. By the time the next pricing letter arrives, your claude.ai subscription will stretch further than your peers’, your team will have a standard to fall back on, and you will be the operator who already knew this conversation was coming.

Adapt & Create,
Kamil

P.S. for the developers reading

A subset of this list runs Claude Code on top of claude.ai. Skip this block if that is not you.

Two free tools that compound the habits above.

caveman by JuliusBrussee is habit one as a skill. Cuts output 22 to 87 percent across coding tasks, ships sub-skills for one-line PR reviews and tight conventional commits. macOS and Linux:

curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.sh | bash

Windows PowerShell:

irm https://raw.githubusercontent.com/JuliusBrussee/caveman/main/install.ps1 | iex

Activate with /caveman lite for professional-but-terse, /caveman full for grunt mode. Do not run ultra mode on legal, medical, security, or learning content.

code-review-graph by tirth8205 is habit three for repos. Persistent local graph, 28 MCP tools, 23 languages, self-reported 6.8 times fewer tokens on reviews and up to 49 times on daily coding tasks vs full-file ingestion.

pip install code-review-graph
code-review-graph install --platform claude-code

Run “build the code review graph for this project” once. Updates incrementally in under two seconds.

Both are actively maintained. Neither replaces the five operator habits.

Hey Adopter,

Wendy’s AI handles 86 percent of drive-thru orders without human help. The other 14 percent get handed cleanly to a person. That handoff is why Wendy’s keeps scaling. Taco Bell paused after going viral on TikTok. McDonald’s pulled the plug after thirty months. Same era. Same vendor pool. Different architecture.

The decision that splits Wendy’s from Taco Bell is not the model. It is whether you build the human escalation path before customers can use the system, or after viral failures force you to. If you sponsor a customer-facing AI deployment in 2026, the worst minute of your rollout is going to be a TikTok, not a press release. The architectural call you make right now decides whether that minute kills the rollout or stays inside the percentage of orders the system was built to absorb.

The four-point gap that broke the promise

In January 2024, Yum Brands announced voice AI was ready for hundreds of Taco Bell drive-thrus. Improved order accuracy. Reduced wait times. Easing of task load for team members. Two years of fine tuning, the company said.

By August 2025, the same Chief Digital and Technology Officer told the Wall Street Journal it was “really, really early.” Five months earlier, Yum had announced going one hundred percent digital across drive-thru lanes as NVIDIA’s first AI restaurant partner. Five months between the investor pitch and the operator’s admission.

The data caught up faster than the rollout. The 2025 InTouch Insight QSR Drive-Thru Study ran 120 mystery shops across three AI-equipped chains. AI accuracy averaged 83 percent. Human accuracy averaged 87 percent. The clean number is the four-point gap. The harder one: staff intervened in 62 percent of AI errors. The promised easing of task load did not arrive. Employees were repositioned from taking orders to supervising AI taking orders. Same headcount on the floor. Different job description.

On the Q3 2025 earnings call, the only voice-AI metric that reached the analysts was that voice AI stores were up 14 percent quarter over quarter. No accuracy. No customer satisfaction. No labour cost movement. Deployment count.

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The number that changes the conversation

Wendy’s launched FreshAI in 2023 with the explicit counter-architecture. AI as assistant. Human escalation built in from day one, not bolted on after viral failures.

86 percent of orders completed autonomously. Approximately 99 percent accuracy when staff escalation is included. An 80 basis point improvement in restaurant-level margin. Expansion from 160 to 500-plus locations through 2025.

The 14 percent of orders Wendy’s escalates to a human are not failures. They are the system working correctly. They are the design.

Taco Bell ran a similar autonomous completion rate but framed every human intervention as a problem to be eliminated. McDonald’s hit the same accuracy ceiling and pulled the plug after two and a half years rather than restructure around the gap. The architectural decision is the line. Hybrid systems with mandatory escalation, published thresholds, and labour repositioned around the AI scale. Full-autonomy systems with reactive monitoring, no public threshold, and labour savings that never arrive retreat.

Below the paywall, premium subscribers get the full breakdown:

• Three architectures, three results. What Taco Bell, McDonald’s, and Wendy’s actually built, with the disclosed numbers each side put on the record.

• Three patterns that explain why customer-facing AI rollouts retreat at the same accuracy threshold regardless of vendor or chain.

• The viral math vendors leave out of every ROI deck.

• The adopter’s playbook. Six moves to make this quarter, lifted from Wendy’s architecture and Chipotle’s contrarian behind-the-counter strategy.

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Hey Adopter,

A kindergarten teacher in a small classroom just delivered the sharpest AI prompting lesson I’ve seen this year. She doesn’t know it. She wasn’t trying to. She was teaching descriptive writing.

Watch the video. It runs about three minutes. Bread, peanut butter, jelly, a knife, and a teacher who promised her five-year-olds she would follow their written instructions exactly. No assumptions. No common sense. Nothing added.

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The setup

Each kid wrote out how to make a peanut butter and jelly sandwich. The teacher performed the instructions to the letter.

“You get bread. You get peanut butter. You get jelly.” She picks up the bread. Picks up the jars. Holds them. Asks the class, “Did I make it?” They laugh.

“Spread jelly on the bread.” She spreads jelly with her fingers, because nobody mentioned a knife. “Oo, it’s crunchy,” she mutters to a room full of giggling kindergarteners.

A few more attempts pile jelly and peanut butter straight onto bare bread. No plate. No knife. No flipping. Just a mess on the table.

Then the class revises together. “Put bread on the plate. Grab the knife. Scoop peanut butter. Spread it. Get another piece. Put jelly on it. Flip it onto the first one.”

Sandwich. Music swells. Kids cheer.

The reversal

Here is what nobody in that room said out loud. The kids weren’t the ones being taught.

Every adult watching that video has the exact same problem with AI. You ask for “a polished email.” You ask for “better copy.” You ask it to “summarise this properly.” The model nods and hands you mush, because you forgot the plate. You forgot the knife. You forgot to flip the bread.

You assumed common sense. AI doesn’t have any.

The expert curse

Here’s the uncomfortable part. The more senior you are, the worse you tend to be at this.

A junior analyst writing a procedure has to think through every step, because nothing is automatic to them yet. A 20-year veteran skips half the steps in their head, because their expertise has gone underground. They genuinely cannot articulate what they do anymore. Ask a senior salesperson how they close a deal and you’ll get vibes, not steps. Ask a marketing director what makes a campaign land and you’ll get taste, not a recipe.

That tacit knowledge is invisible to them. It’s invisible to your AI. It’s the gap where most “we tried AI and it didn’t work” stories actually live.

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Who’s already good at this, who needs to catch up

Some roles have lived in checklists their whole career. Software engineers. Operations and process people. Pilots and surgeons. Lawyers drafting contracts. QA, audit, compliance. Anyone who has ever written a runbook or a standard operating procedure. These people moved their tacit knowledge onto paper years ago. AI feels native to them, because they were already translating expertise into instructions every day.

Other roles built their value on ambiguity. Executives selling vision. Marketers chasing taste. Salespeople reading rooms. Strategists pointing at the horizon. Most middle managers. Their work has rarely required a written procedure, and now they’re asked to brief a machine that needs one. The hierarchy quietly inverts.

If that’s you, the fix isn’t a prompting course. It’s a 30-minute conversation with the most checklist-obsessed person on your team. They’ve been dissecting workflows for years. Borrow their brain.

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The kindergarten test

Here’s the easy lesson you can run today.

Pick one task you want AI to handle this week. Just one. Now write the instructions like you’re handing them to a five-year-old who has never made a sandwich. Plate. Knife. Order. Flip.

Then read your prompt out loud. Every time you catch yourself saying “obviously” or “you know what I mean” in your head, that’s a missing step. Add it.

Run that test once and you’ll see what your AI has been seeing for months. A pile of jelly on the table. A jar held in the air. A confused machine doing exactly what you told it to do.

A kindergarten teacher in a small classroom with a jar of jelly is doing better AI training than most consultancies on LinkedIn. Steal her method.

Adapt & Create,
Kamil

This issue gives you the one move separating the 6% of teams pulling real profit from AI from the 94% stuck in pilot mode. You get the numbers, the cases, and a five-stage playbook used by global firms.

The 21 percent rule nobody mentions

Most teams treat AI as a tool upgrade. Better chatbot. Smarter assistant. Faster summary.

That is the trap. McKinsey’s 2025 research on workplace AI shows only 21% of organisations have rebuilt workflows around AI. The other 79% bolt models onto processes designed for filing cabinets and email chains. The result is predictable. Half of all AI initiatives sit in pilot purgatory. Maturity across the board sits at 1%.

The top performers do something different. They are nearly three times more likely to redesign workflows end to end, 55% versus 20%. They also pull at least 5% EBIT impact from AI. The rest pull pocket change.

Tools are cheap, redesign is the moat

A randomised field experiment from INSEAD and Harvard Business School tested this directly. 515 startups got equal AI access and training. One group also got training on workflow reorganisation. That group generated 90% higher revenue, found 44% more use cases, won 18% more paying customers, and needed 40% less capital.

Same tools. Same access. Redesign was the multiplier.

Layering AI onto a brittle process creates new friction. Trust drops. Value caps below 5%. Failures here are 70% organisational, not technical. Worth asking this week, which workflow are we patching that should be rebuilt?

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The DGRI playbook the top 6% are using

The California Management Review published a five-stage method called DGRI in late 2025, field-tested across global firms with measurable before-and-after results.

Stage 1, diagnose and align

Map decision flows in your top three workflows. Surface root inefficiencies. Get leadership buy-in on outcomes, not features. Most teams skip this and start with vendor demos. Do not.

Stage 2, governance and accountability

Assign data and model owners. Set KPIs. Build escalation paths with visible dashboards. Without owners, AI projects drift into politics and stall.

Stage 3, redesign for scalability

The multiplier stage. Replace manual chains with AI-orchestrated sequences. Route exceptions and judgment calls to humans. Build feedback loops that produce proprietary data. This is where the 90% revenue uplift comes from. Skip this stage and you have an expensive demo, not a workflow.

Stage 4, reuse and build literacy

Create shared feature stores, data dictionaries, and training programmes. Your assets compound when teams stop rebuilding the same prompts and pipelines from scratch.

Stage 5, iterate via MVTs

Treat pilots as learning vehicles. Minimum viable transformations. Scale only after outcome metrics prove out.

Real numbers from operating-model surgery

A global manufacturer cut its financial-close cycle from 12 days to 6. Manual adjustments dropped 40%. Audit fees fell 15%. None of that came from buying better software. It came from redesigning reconciliations and embedding alerts where humans used to chase reports.

JPMorgan Chase saved 360,000 legal hours a year through its COiN platform, and its $2 billion AI spend returned $2 billion in 2024 benefits. PwC reported 20% to 50% productivity gains across audit, tax, marketing, and IT after resequencing workflows around human judgment. Mayo Clinic automated lab tasks, increased test volume, and reduced staff burden.

These are operating-model wins, not prompt-engineering wins.

The superagency point most leaders miss

Satya Nadella has been clear on the design principle. AI works when human agency stays at the centre, not when it gets pushed aside. Demis Hassabis says the same. Breakthroughs come from the collaboration between people and algorithms, not the algorithms alone.

Employees use AI for over 30% of their work right now. That is three times what leaders estimate. Your team is ahead of you. Redesign closes the gap by giving humans the high-judgment work and giving AI the orchestration, parallelisation, and grunt analysis.

The agentic shift makes this urgent

Agentic AI from OpenAI, Salesforce Agentforce, and Google Gemini now runs full multi-step outcome loops. Legacy bolt-ons cannot support that. If your processes still depend on humans copy-pasting between tools, agents will sit idle or produce noise.

Three pitfalls to skip

Treating AI as IT. Procurement-led rollouts ignore workflow change and stay in pilot purgatory.

Bolting agents onto a process designed for humans. Agents need clean inputs and outcome ownership, not a slot in someone’s email queue.

Rewarding tool usage instead of outcome metrics. Bonuses that count “AI prompts sent” produce noise. Bonuses tied to time-to-outcome produce results.

Your move this quarter

Pick one workflow with a clear outcome metric. Proposal generation. Claims processing. Financial close. Whichever has the highest pain and the cleanest data.

Form a digital-steward team with a domain owner and a tech owner. Map the current decision flow. Mark which steps are AI-only, which need human judgment, and which need orchestration.

Set three metrics. Time-to-outcome. Exception rate. Reuse rate. Tie bonuses to workflow outcomes, not tool usage. Firms that invest over 20% of digital budgets in redesign see three to five times the returns of firms that do not.

Pilots fail because the workflow underneath them was never rebuilt. Fix that, and the rest follows.

Adapt & Create,
Kamil

Hey Adopter,

Most people use AI like a vending machine. Type a request, hit enter, copy whatever comes back. Then complain that it sounds generic.

The output is generic because the input is generic. You gave the model nothing about your business, none of your past writing, and a one-line prompt that could have come from anyone. It averaged the entire internet and handed you the median. That’s the slop you keep getting.

The fix isn’t a better prompt. The fix is a setup that makes every prompt 100x more useful without making it harder to write.

Three folders. Twelve answers. One prompt shape.

That’s the setup the working operators have moved to in the last twelve months, and most won’t tell you about it because it took them two months of false starts to find. Once you build it, you stop typing prompts from scratch. You stop getting bland output. You start producing content that genuinely reads like you, in a fraction of the time.

The shape is boring. That’s the point. You make a folder called ai-assets on your drive. Inside it, three sub-folders. One holds a single text file answering twelve questions about your business. One holds your past writing as plain text. One holds reusable prompt files in a four-part structure. The whole setup takes an afternoon.

What changes after you build it: every piece of content you produce for the next twelve months runs through the same three folders. The model never starts from a cold prompt. It starts from your voice, your business, your data.

Below, the full method, the twelve questions, the four-part prompt shape, and the cloneable file pack you can drop on your drive today.

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Hey Adopter,

I spend a lot of time advising businesses on AI adoption. The conversation almost always starts with software pricing. It almost never ends there.

This piece is built on enterprise AI deployments across logistics, telecom, professional services, and technology. Every number below comes from that report or the published research it cites.

Forward this to whoever signs off your AI budget. If they only see the software line item, the project is already in trouble.

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The math nobody puts on the first slide

Executives keep budgeting AI like they budget SaaS. Pick a vendor. Approve a licence. Add some implementation hours. Wait for the productivity gains.

Stanford’s data tells a different story. When researchers asked 50 practitioners what was the hardest part of their deployment, 77% of the answers had nothing to do with the model. Process documentation came first. Data quality came second. Change management came third. Technology was consistently described as the easiest part of the work.

The macro picture is worse. Accenture estimates that 80% to 85% of companies are stuck in what they call a Proof of Concept Factory, running experiments that never scale and absorb the budget anyway. McKinsey’s State of AI research shows that high performers, the organisations attributing more than 5% of EBIT to AI, are not the ones with better models. They are the ones rewiring their processes and data products. Same software. Different ratio of investment.

Which leaves the obvious question. What is the right ratio?

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The number that changes the conversation

Erik Brynjolfsson’s productivity J-curve research, cited in the Stanford report, found one ratio that explains why so many tech investments underperform their business case in year one.

“For every $1 of tangible technology investment, companies spend up to $10 on intangibles, process redesign, reskilling, and organisational transformation.”

That is the rule. One to ten. The model is the cheap part. The deployment is where the budget goes, and most CFOs never see that number on the proposal.

It also explains the second Stanford finding nobody discusses on a vendor call. Sixty-one percent of the AI projects that succeeded were preceded by a failed attempt at the same problem. The failure was a sunk cost that never appeared in the successful project’s ROI calculation, but it was usually essential to it. The first attempt taught the team what could not be automated until the work itself was redesigned.

So who is doing this right, and what does the spend look like in practice?

Below the fold, premium subscribers get the full breakdown:

• The $1B logistics case. How a refrigerated trucking firm cut seven invoice clerks to two in eight weeks using off-the-shelf Azure tools, and where the real money went.

• Three contrasting approaches from telecom, professional services, and technology companies showing how the $1 to $10 rule shows up at scale.

• Five rules that separate the AI budgets that pay off from the ones that fund pilot graveyards.

• The adopter’s playbook. What a 50 to 500 person company can copy, counter, and avoid this quarter, with the exact spend categories your CFO needs in the proposal.

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