Hi Adopter,

Before you ask AI to write another article, open a page on your website. Would it help a customer understand what to do next, or decide whether you’re right for them?

AI can produce a page that sounds fine but says very little about your actual work. “We provide excellent service” doesn’t tell a customer what to expect.

Google’s content guidance gives its human quality raters four things to check: effort, originality, skill, and accuracy. In plain English, show the work, add what you know, make it easy to understand, and check the facts. These are useful checks, not a guarantee of ranking higher.

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Here’s how I’d use AI: ask it to find what’s missing from the page. Then give it something real to work with.

A contractor could explain how they found a leak, why they chose a particular repair, and how they tested it. A consultant could answer a common question using an anonymized example from their work.

AI can help you explain that clearly. You supply the experience and check that the result is true.

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Premium subscribers get the nine-prompt pack below. It helps you review your website and the other content customers see, a few pieces at a time.

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Hi Adopter,

Think of something you’ve been putting off because you don’t know how to do it. Mine was an app. I’m not a developer, but I now have one running on my iPhone and Apple Watch.

Experimenting with AI is the best way to learn about it!

I hate running, so while I was out walking I explored an idea with Grok. Could I make interval walking easier to follow with a beat, a simple Watch screen and honest feedback from the walk? I brought the idea onto my Mac, then used Codex and Xcode to turn it into Eggsitus.

You can watch the build-along here:

But this isn’t mainly a lesson about building apps. Eggsitus is evidence of something more useful: AI can help you do work beyond your current skills when you use it to think, learn, make decisions and produce one testable result.

The catch is that you have to know which job belongs to whom.

Four jobs, kept separate

A conversational AI helps me reason. It can ask questions, explain an unfamiliar term, compare choices and challenge a weak assumption. That conversation helps me understand the work before anything changes.

Codex is a file-changing agent. It can inspect the project, edit code, run checks and show me what changed. Unlike a chat answer, those actions affect the thing I’m building, so I review the result and keep recoverable versions.

I still make the decisions. I decide what problem matters, what tradeoffs I’ll accept and whether the result is good enough.

Then the real world gets the final vote.

Eggsitus runs on my iPhone and Watch, but it’s still a work in progress. Real walks exposed Siri and AirPods audio problems, confusing setup, a crowded Watch screen and uncertainty about commanded versus measured pace. Some responses have been implemented, but I haven’t confirmed every repair outside yet. Revenue, release status and medical benefit aren’t established.

The project record reports 139 tests of shared, device-independent rules passing, along with simulator and signed builds. That’s useful evidence from the project, not an independent reliability audit. A passing rule test doesn’t prove a beat survives a real walk, a Siri interruption or a tired thumb on a small screen.

That gap between reassurance and evidence is where the learning happens.

Below, I’ll give you three prompts I’d use to move from “I have no idea” to one result you can inspect. They’re recommended adaptations, not transcripts of my historical AI chats. The 24-page premium workbook turns the same method into an AI-assisted thinking and work process with five fillable worksheets.

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Hi Adopter,

Every campaign comes with the same questions: who’s this for, what can we promise, and how should it sound?

This week, I’ll show you how to keep those answers in Codex and set up two specialists to help with the writing, with you approving the result. I’ve included seven prompts to walk you through the whole setup.

If you have questions, hit reply. I read every email, and I’ll do a follow-up if you’re interested.

Know someone who’s starting a small AI team? Share this setup.

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Understand who does what

You give the main Codex agent the outcome.

It coordinates the job and can spawn subagents: separate work threads with their own context that report back.

A specialist profile defines a role. It only becomes a working helper when Codex spawns it. For our campaign, the planner develops the brief; the content producer writes from that brief. They don’t both need to start at once. I’d start with two. More agents mean more handoffs to check before you know whether the workflow helps. Codex subagents

Skills hold reusable instructions for a job, such as turning an approved brief into three posts and landing-page copy. Tools perform actions, such as reading files or searching the web. MCP connects Codex to additional tools and context. Plugins can bundle skills and MCP connections. Installing a skill doesn’t grant account access. Skills, MCP

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Hi Adopter,

this article is about putting AI to work in your company and becoming the person who takes ownership of making it useful.

It’s 2026. You already know AI matters. But between agent demos and the race to name the best model, it’s easy to feel you need to follow every release before you can do anything useful. You don’t need to win that race to lead a useful project.

After three years advising companies big and small on AI, I keep coming back to the basics. Understand the work. Get the right people involved. Test an improvement and measure the whole job.

The model choice matters when it changes quality, cost, or what your workflow can do. Compare upgrades on the same cases before changing tools. Even Anthropic’s agent-building guidance recommends starting with the simplest solution and adding complexity when it earns its place.

Follow a report all the way through

Take a weekly client report assembled from a CRM, a finance export, and a spreadsheet one colleague fully understands. Ask them to walk you through the last report they finished, with the files open.

What starts the job? Which figures get copied? Where does it wait? Who decides it’s ready?

Then ask for a troublesome case. Maybe finance removed a canceled order but the CRM still showed it as open. Find out who spotted the difference and how they resolved it.

Compare what you saw with the instructions and ask the system owner about rules hidden in code. A mismatch might be an outdated document, a useful workaround, or a defect. Investigate before replacing it.

Give the owner something small to approve

Choose one recurring bottleneck with permitted inputs and someone responsible for the result.

For this report, I’d try drafting commentary from verified figures and approved account notes. The account manager checks it and sends the report.

Bring the owner a concrete proposal. Name the task, tool, permitted data, reviewer, time needed, and decision you’ll make after testing.

If you don’t know whether those notes are permitted in the AI tool, resolving that is your next step. Having a license doesn’t answer the data question.

Check whether an existing feature or spreadsheet formula would solve the bottleneck more easily.

Agree what a finished job looks like

Before testing, record completed reports, turnaround time, active preparation time, checking, corrections, and recurring cost. Keep waiting separate from active work, and record setup costs separately.

Agree acceptance checks with the recipient. I’d propose correct reporting dates, figures matching the approved source, commentary supported by account notes, and unresolved discrepancies flagged for review.

Try ten representative authorized historical cases, including missing notes and conflicting figures. Ten gives you a first look, not proof of a company-wide gain.

Measure the full job. If drafting saves 20 minutes and checking adds 25, you’ve added five minutes of work. That’s an illustration, not a forecast.

METR’s 2025 coding trial found perceived speed and measured completion time diverged. Its 2026 follow-up encountered selection and time-tracking problems. These studies don’t predict your reporting results; they reinforce the need to measure carefully.

Give each part a clear job

For the reporting test, I’d divide the work this way.

Existing systems: Supply the approved customer records and figures.

Code and established rules: Calculate totals and validate required fields.

AI: Draft commentary and flag missing or conflicting information.

People: Resolve exceptions, accept the report, and authorize sending.

Write down the data definitions and business rules. The AI shouldn’t decide what revenue means because two systems disagree.

Have the system owner enforce access, spending limits, and required approvals in the tools. Telling an agent to ask before sending doesn’t establish an approval control. Anthropic’s permission documentation makes that distinction explicit.

Leave your colleagues something they can run

With the owner’s agreement, let a small group try the workflow after the historical checks. Compare similar work against the same acceptance standard, including everyone’s checking and correction time.

Log failures and turn them into test cases. Agree when to stop, who handles an incident, and how to return to the existing process. Name who reviews changes to the prompt, tool, or rules, consistent with NIST’s voluntary guidance.

If the test earns its place, teach a colleague to run it and name who maintains it. Choose the next project partly by what you can reuse. Use the evidence to review handoffs and approvals.

Get support for the project you own

I’m focusing more of my time on personal, one-to-one coaching because I’ve seen companies make more progress when one person takes ownership and gets support to follow through.

If that’s you, agree the time and authority you need with your manager, and how your contribution will be recognized and rewarded in your role. This work needs room in your week.

I work with you on a real project in your business. Explore one-to-one AI coaching.

The companion Right Click Prompt asks about your role and one frustrating task, then builds a custom plan, checklist, blank scorecard, and note to your manager. If permissions are unclear, it starts with discovery work.

Build my first AI project plan

Book 30 minutes with the person doing the job. Ask them to show you the last case that went wrong.

Adapt and Create, Kamil

Hi Adopter,

A customer calls after hours with no heat. The office is closed, the technician schedule is changing, and the decision lands back with the owner.

Repeat that every evening, then add pricing, complaints, scheduling, and staff questions. One important process still depends too much on you.

Rescue Air & Plumbing offers a useful case. According to Avoca’s Rescue account, its AI customer service representative answers inbound calls and books appointments in the company’s CRM. Avoca also says its Coach product scores conversations against Rescue’s service rules, and that two customer service representatives moved into management without replacements being hired for their old jobs.

Those are vendor and operator reports, not audited results. The Boxwood deal record separately documents Rescue’s majority sale in December 2023. A Boxwood transaction roundup names HomeTown Services as the buyer, and HomeTown’s brand list includes Rescue. In an Avoca founder interview, Rescue’s founders said buyers were interested in their AI tools. No public source gives the price, a buyer statement about AI, or proof of an AI premium. Today’s named products and results also can’t be projected backward to the sale.

Still, the operating lesson matters. Focus on one customer call becoming a correct appointment, a useful record, and a clear next action without dragging the owner back in. The published account doesn’t measure total human checking time. That’s something I’d test, not assume.

CRM, in plain English: the system holding customer and job records. A booking only helps if the right details reach the team doing the work.

Start with the interruption

Write down every time someone needed you last week. Pick one repeated task with rules you can explain. For phone intake, separate an answered call from a correct booking, a completed job, and collected payment. Those are four different results.

Count the time spent checking and correcting calls, including those that never become bookings. Add exception handling and upkeep across the whole team, not only the owner. Moving the interruption from your phone to someone else’s review queue hasn’t necessarily improved the process. Track setup time separately, then compare ongoing human minutes per correct booking without lowering the quality bar.

You also need intelligence independence. Keep your own booking rules, escalation instructions, examples, and performance records. If the vendor changes, you should still understand how the work gets done.

Below, I’ve laid out a 30-day test, the owners and safety fallback, a financial worksheet, and the continue, change, or stop decision. The premium case study adds the evidence review and four fillable worksheets for your team.

The 15-page Rescue Air case study contains the full evidence review and four fillable worksheets for your team.

Design the handoff before you automate it

Here’s the phone workflow I’d test. This is our proposed design, not a verified map of Rescue’s architecture.

1. The AI interprets why the customer is calling and captures the details.

2. Ordinary software checks the service area, service type, technician capacity, and real calendar availability.

3. A named person handles exceptions, including complaints, safety concerns, unavailable appointments, out-of-area requests, and callers who want a human.

4. The integration confirms the appointment was saved in the job system before the customer is told it’s booked.

That last check matters. A confident voice saying “you’re all set” is not a booking if nothing reached the calendar.

Run one 30-day test

Choose after-hours or overflow calls, not every call. Name one owner for intake, one for technician capacity, and one for emergency routing and the human fallback.

For days 1–5, record the current numbers: eligible calls, correct bookings, cancellations, completed jobs, collected revenue, staff minutes, and errors. For days 6–10, write the booking rules and test awkward cases, including an unavailable appointment, a repeat caller, a complaint, an out-of-area request, a caller asking for a person, and a safety-critical problem.

For days 11–25, run the limited workflow with a person ready to take over. Review every booking at first, including routine ones that appear correct. For days 26–30, compare results and calculate contribution. Stop immediately if emergency routing fails, required human help is unavailable, or customer information reaches an unapproved system.

Intense review at the start is normal. It should not become a growing job supervising the AI. Reduce review only after the workflow demonstrates reliable performance, and only where the risk allows it. Keep human judgment on safety, complaints, unusual requests, and policy changes. Keep sampling ordinary bookings too. If errors rise, return to the previous review level.

Eval*: testing the workflow against agreed examples and rules. “The demo sounded good” doesn’t count.

Do the math without fooling yourself

Here’s invented arithmetic to show the method. It is not a Rescue result.

Say 100 eligible after-hours calls produced 10 bookings before the test and 30 during it. If 80% of bookings become completed, paid jobs, the increase is 16 jobs. At $180 contribution per job, that’s $2,880.

Now subtract $900 for software, $600 for staff review, and $200 for errors and rework. The illustrative monthly operating contribution is $1,180.

Contribution*: collected revenue minus the cost of delivering the job. Revenue alone doesn’t tell you whether extra bookings helped.

Don’t count saved hours as cash unless they reduce overtime or support measured additional work. Don’t count the same staff capacity once as labor savings and again as new revenue. And don’t compare a busy month with a quiet one and hand the difference to AI.

Decide what happens next

• Continue when safety and service checks pass, completed paid work increases, ongoing human effort per correct booking falls, and conservative contribution stays positive after review and rework.

• Change when calls are recovered but scheduling, service quality, or staff workload gets worse. Narrow the hours, rewrite the rules, or improve the fallback, then test again.

• Stop when safety escalation fails, errors stay above your agreed limit, or the benefit disappears after the real costs are included.

The Top Flight Electric account reports relief from night and weekend calls, but its revenue figures conflict and aren’t independently audited. The Apollo Home account reports using recorded field conversations to coach more technicians. Both illustrate a specific task with a human owner. Measure finished work in your own test.

Keep your instructions, accepted examples, records, exports, and fallback procedure. Check local recording and disclosure requirements, who owns recordings, whether data trains models, and what happens if the vendor fails. Intelligence independence matters when the process touches customers.

If you’re following this idea, start with AI babysitting to spot the hidden cleanup. Read accepted work to measure the whole team’s effort. Then try the Toyota case study, which looks at making specialist knowledge easier to reach without constantly asking the same experienced person. The first two are free. Toyota has a free preview and a premium implementation section.

What’s the one interruption you’d stop carrying first?

Adapt and Create, Kamil

Hi Adopter,

Your sales manager uploads a customer spreadsheet to AI. The names are removed, so everyone feels better.

The file still shows margins, renewal dates, discount limits, and negotiated terms. A competitor wouldn’t need the names to learn quite a lot about your business.

That’s why sensitive data is a concern for any company. It includes customer information, intellectual property, trade secrets, and the small operational details you spent years figuring out.

Palantir CEO Alex Karp has been arguing that customers want control over their models, data, and “alpha.” For a business owner, think of alpha as the knowledge that gives you an edge. He has software to sell, but the ownership question is useful. Who gets access to what makes your company valuable?

What happens after you press send

“Does it train on our data?” is a good question. It needs company.

Training. Can your inputs or outputs be used to improve a future model? Don’t assume every AI service does this. For example, Anthropic’s commercial products exclude training by default, with exceptions including feedback and explicit opt-ins. Check the exact product and settings you use.

Storage. What gets retained, where, and for how long? A no-training promise can still allow stored conversations or files. Retention rules can differ by feature and agreement.

Access. Which people and systems can see the information? A confidential forecast shared with the wrong colleague is still a problem, even when no model learns from it.

Actions. Where can connected tools send the data? An assistant with email access introduces a different risk from one that only drafts text.

These are separate controls. A reassuring answer to one doesn’t settle the others.

What this looks like inside real companies

Mercedes-Benz describes its employee Direct Chat tool alongside explicit usage guidance, including avoiding personal or internal data in employee inputs and reviewing generated content. The page also describes knowledge agents connected to internal systems. Even a company-provided tool needs rules for how information reaches it.

In its Debrief launch announcement, Morgan Stanley describes using client consent before AI generates meeting notes and action items. The tool drafts a follow-up email for an advisor to review, edit, and choose to send. That gives AI a useful job with a human decision before the message goes out.

Both are company descriptions of their own practices. They show the kinds of boundaries to look for when choosing your own setup.

If you’d like help doing that, we can map one workflow together in coaching. We’ll look at the data it needs, who can access it, and where human approval belongs.

Give AI only what the job requires

Suppose you want AI to draft an overdue-invoice reminder. It might need the amount due, how overdue it is, and your approved payment options. It probably doesn’t need every customer’s history, your bank details, or the sales team’s private notes.

Your business software can keep the real customer record, prepare the necessary facts, and give AI that smaller version. The model drafts the message. Your software connects it back to the right customer and checks permissions before sending. Start with a person reviewing those drafts.

Have software remove unnecessary fields before data reaches the model. Writing “ignore the confidential columns” in a prompt has already sent those columns.

Removing names alone won’t protect a pricing formula or make every record anonymous. When sensitive information is necessary for the task, use a service approved for that information, with suitable terms, retention settings, and access controls. Some work may belong on systems you operate yourself. Those still need security and maintenance.

You can start this week with one recurring task. Name its owner, list the information it actually needs, and trace where that information goes. Begin with a copy containing fictional details. Check what appears in saved conversations, logs, and connected tools. Review the output before allowing real actions.

Then decide what additional access, if any, the result justifies.

AI becomes useful when it understands enough about your business to help. Deciding what “enough” means is part of the work.

Adapt and Create, Kamil

Hi Adopter,

Imagine you run an online fragrance store (as I used to ;).

Every week, you check competitor prices, products, and offers. You spot changes, but by next week you’re relying on memory.

This issue shows how to build a private competitor monitor with Codex. It saves public observations, compares each week, and gives ChatGPT or Codex evidence to help you decide what deserves a response.

By the end, you’ll have a prompt package that builds a monitor for your own stores. You don’t need to edit a template. Codex will ask for your store URL, one to three competitors, your market, and the question you want answered each week.

Let’s walk through a simple fictional example first.

A price change is not a decision

Our fictional store is Northline Parfum. One of its competitors, Scent Studio, has sold the same bottle for $120 across several weekly checks. This Monday, the monitor records it at $108.

That tells you the price moved by 10%.

The product page also says the bottle is 32.5% off because it compares $108 with a higher reference price. That is the promotion the customer sees. It is not the same as the competitor’s weekly price change.

Without history, you see a big discount and might rush to match it. With history, you see what changed and ask a better question: does a 10% move matter enough to change our offer?

The answer depends on your costs, product fit, customers, and goals. The monitor doesn’t decide for you. It gives you clean evidence before you decide.

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Hi Adopter,

If your team handles private information, you’ve probably asked whether AI can help without sending the work to another company’s model. Most conversations jump straight to cost. Honestly, that misses the more important choice.

Who controls the model, where the information goes, what gets logged, and when a person takes over? I call that intelligence independence. You don’t need to build a frontier model. You need a real choice in how your business uses one.

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2026 09 24 Local Ai Law Firms Case Study

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A law firm’s very small AI job

In an anonymized case study from Nexus Technology Consulting, a Sydney boutique law firm had a specific problem: a partner returning from leave faced a mountain of email and calendar items before they could get back to client work.

Nexus says it put an open-weight, roughly 8-billion-parameter model on the firm’s own RTX 4090 graphics card. The system pulls messages from the firm’s Google Workspace account, writes a one-line summary for each email, and puts a daily rollup into a local spreadsheet. The partner reads the rollup, then opens the original messages before acting on anything important.

The vendor reports that one longer-leave catch-up fell from about two hours to 25 minutes, and that the system summarizes about 210 emails per hour. Those are vendor-reported figures from an unnamed client, not independently measured ROI. The exact model checkpoint, total build and running cost, and error rate aren’t public.

Still, the design is worth studying. The model was given a small, repeatable reading job. It wasn’t asked to decide whether a deadline had been met, advise a client, or send a reply.

And “local” needs a little honesty. The AI inference runs on the firm’s machine, according to Nexus. The email still lives in Google Workspace. Local AI removes one outside AI processor from this workflow; it doesn’t make the whole information chain independent or automatically secure.

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The choice it creates

Madgett Law describes a different use of local AI. Its published approach gives clients a choice in their engagement letter: no AI, in-house AI only, or a hybrid that can use contracted outside providers. The firm says the in-house-only choice must be enforced by the routing software, not left to someone’s memory. A lawyer still checks the work and verifies citations in every tier.

That’s a firm-authored policy, not proof of a fully local deployment or a measured saving. But it points to the real prize: a business can make a promise about how work will be handled and have a way to honor it.

Below, I’ve put these cases beside North Dakota’s local bill-summary pilot and built a decision test you can use on one workflow. The full case-study PDF includes the stack evidence, the missing numbers, a security checklist, and a worksheet for deciding whether local, cloud, or hybrid is right for your team.

What three legal-work cases actually show

Nexus is the most concrete small-firm hardware story, but it is also the least independently checkable. We have a vendor’s description of a 38-person firm, an RTX 4090, an “LFM2-8B class” model, a local spreadsheet, and a partner reviewing original messages. We don’t have the firm’s name, test set, security audit, cost, or before-and-after task logs. I wouldn’t build a return-on-investment slide from it.

North Dakota is a useful counterweight. Meta’s account of the Legislative Council names Llama 3.2 1B Instruct, fine-tuning with Unsloth, a retrieval step that brings in relevant bill text, and Ollama running on local hardware. The system generated three draft summaries per bill for legal review during the 2025 session. Meta calls its 15–25% time saving and 25 legal hours per session estimates. The 2027 use is a plan, not an observed result. It is a public legal team, not a law firm, and its exact machine specifications and operating costs aren’t disclosed.

Madgett supplies something neither case gives us: a reason to own the option. A client can choose a handling tier because the firm says its routing follows that choice. The published article gives hardware and model-size guidance, but no measured workload, deployed checkpoint, or ROI for the firm. Treat the tier design as a practitioner example, not a legal standard.

Put together, they show three forms of control: a bounded task on owned hardware, a locally served model adapted to a legal task, and a business rule about when outside AI is allowed. None proves that every job should run locally.

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What you need to own

If you want this kind of independence, start with four questions. You don’t need to buy a server to ask them, but someone must own the answers.

1. What information can the model see? Trace the data from its original system to the model, the output file, logs, backups, and any human inbox. In the Nexus story, “local AI” still starts with Google Workspace.

2. Who controls the model version? Name the exact checkpoint and its license. Keep a tested copy you can roll back to. An open-weight model can still carry use restrictions.

3. What is the model allowed to do? Summarizing for review is different from filing, sending, advising, or making a decision. Put the handoff to a person at the point where an error has consequences.

4. Can you switch paths? If a provider changes price or terms, can you move the same task to another approved model? If the local model isn’t good enough, can you route a carefully chosen subset to a contracted provider, or keep it human-only?

That last question is why I don’t think this is only a cost story. A cheap API can be the right choice. So can a managed cloud service with strong controls. The question is whether you’re choosing it, or whether your business has quietly lost every other workable option.

A test small enough to finish

Pick one task that produces a draft, not a final decision. Email catch-up, document tagging, or a first-pass summary are reasonable candidates. Use 20 real, permission-cleared examples as an initial screen, not as proof of safety. Include ordinary cases, awkward cases, and cases where the right output is “I can’t tell.” Keep the original task and source documents available to a reviewer.

Before testing a model, record how long the current process takes and what an acceptable result looks like. Then compare three paths on the same examples: the current human process, a suitable managed service if your rules permit it, and one local model that fits the machine you actually have. Don’t buy a graphics card because a case study had one.

Have a qualified reviewer score correctness, missing information, source traceability, and whether the system knew when to stop. Add the person’s checking time, setup time, maintenance, electricity, and hardware to the local cost. For a cloud path, include usage fees, contracts, administration, and the cost of keeping sensitive material out of it. This is a business comparison, not a benchmark contest.

I would stop the pilot immediately for an access leak or a wrong answer in a predefined high-consequence case. Otherwise, decide at the end whether the result is good enough to continue, needs a narrower task, or should stay with the current method. A local model that makes everyone double-check every line hasn’t freed anyone.

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The full case study and worksheet give you the evidence table, architecture and control map, a cost sheet, and a one-page continue/change/stop decision. The point isn’t to convince you to run everything in your office. It’s to help you know which choices you’re making, and which ones a vendor has made for you.

What is one piece of work in your business that you would want the freedom to run on your own terms?

Adapt and Create, Kamil

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Hi Adopter,

Your team uses AI to finish a proposal faster. Someone else checks the pricing, rewrites the recommendation, and asks where the customer quote came from. Before you count the time saved, count their time too.

That’s why I’d put this question in your next leadership meeting: How much accepted work are we producing per unit of human attention?

You can start answering it with one recurring workflow and the tracker you already use. The useful result is a proposal someone can send, a support case resolved, or a change ready to deploy. A pile of drafts doesn’t count.

Make the question measurable

Agree on two definitions before you change anything.

Accepted work means a complete business task that meets checks agreed with the person receiving it. For a sales proposal, that might mean approved pricing, requirements tied to discovery notes, and delivery promises the delivery team has signed off on. Count the whole proposal once, not every section AI helped write.

Human attention needs a practical proxy. Start with active human hours spent preparing, producing, checking, and correcting the output through acceptance. Include the recipient’s review and handling, but exclude the ordinary business activity that follows accepted work. Hours won’t capture every difference in cognitive effort, so keep specialist review bottlenecks visible too.

Your starting measure is:

Here’s an illustrative comparison. Your team attempts ten comparable proposals. Eight are accepted, and the whole batch takes ten human hours. That’s 0.8 accepted proposals per hour. A later batch produces nine accepted proposals in nine hours. That’s 1 per hour, provided the scope and quality standard are the same.

Keep the time spent on rejected work in the denominator. Otherwise, the dashboard quietly deletes the failures. Track turnaround time separately, because ten minutes of approval can still hold up a deal for three days.

Improve the ratio without lowering the standard

There’s evidence for measuring beyond the first draft. In a September 2025 BetterUp Labs and Stanford Social Media Lab survey of 1,150 full-time U.S. desk workers, 40% reported receiving “workslop” in the previous month. Recipients estimated about two hours to resolve an incident. Those are self-reported experiences, not measured company-wide productivity losses.

AI’s value also varies by task. A randomized experiment with 758 BCG consultants using GPT-4, first reported in 2023, found better performance within the model’s capabilities and worse correctness on a task outside them.

I’d use six controls to find out what improves your own workflow.

1. Agree on acceptance before generating. Write three to five checks with the recipient. Specify the permitted tools, data, and source material. The checks should describe usable work, not whether someone followed a prompting technique.

2. Keep ownership with the sender. If AI helped you produce it, you still own it. Verify important claims and link the evidence before handing work over. Name unresolved questions. Managers need to provide the time and expertise to do that checking; responsibility without support becomes a ceremonial tick in a box.

3. Match review to consequence. Private brainstorming gets an owner check. A team recommendation needs checks against original sources. A contractual promise needs the designated specialist’s approval. Agree on review triggers once and use existing approval owners. The same paragraph carries different risks in a scratchpad and a customer commitment.

4. Make the full effort visible. Record rough active minutes once per job, split into preparation/drafting, checking/fixes, and downstream work. Use existing time records where available and label estimates. Count each minute once. Necessary review isn’t waste, but it still belongs in the calculation.

5. Check whether the work is needed. Name the recipient and the decision or action the output supports. Keep required records; stop producing unrequested variations. Don’t improve your ratio by generating easy documents nobody needs.

6. Fix whatever keeps coming back. Log why work gets returned. Wrong prices should change the pricing source or validation check. Unsupported promises should change the template and approval route. Give someone responsibility for that fix, so checking gets easier with each batch.

These are practical proposals, consistent with NIST’s voluntary AI risk guidance on responsibilities, testing, risk-based priorities, and learning from failures. They aren’t a proven productivity intervention.

Run it in your organization

Pick one workflow and name its owner. Agree on the acceptance checks, risk level, and a simple difficulty measure. Compare the last ten consecutive qualifying jobs with the next ten under the new rules. Record setup effort separately so you can judge whether recurring savings repay it.

Alongside the ratio, track first-pass acceptance, reasons for returns, and errors found after acceptance. Use the same follow-up window for both batches. If a later correction is required, remove that job from the accepted total and include the cleanup time. Zero accepted jobs means zero accepted output per hour. Ten jobs is a diagnostic, not proof of a company-wide gain.

Review the results with the sender and recipient. Pause if a serious error escapes review. Otherwise, fix the biggest repeated source of effort before expanding. Keep scope and quality fixed; don’t chase a higher score by making acceptance easier.

Your AI expert can own this improvement process instead of checking everyone else’s work forever.

Bring the question to your next team meeting. Let the people doing the cleanup help you answer it.

Adapt and Create, Kamil

Hi Adopter,

If you’ve got a quote waiting to go out, customer complaints you haven’t looked through, or competitors you keep meaning to check, you’ve got something useful to try with AI this Monday.

I’ve put three prompts below. Each starts by asking about your business, then produces something you can check. There’s also a repeat mode for work you want to run again. Everything in this edition is free, including those instructions.

If your AI tool is already connected to your CRM or email, identify the exact records or folder and date range it should read. Otherwise, paste the material or attach an export. Naming an app alone doesn’t give it access.

You don’t need to set up an automation today. Start with one inquiry, a small batch of feedback, or a few competitor pages. Remove customer details the task doesn’t need, and use a tool you’ve approved for that material.

Become a paid member ➖ See all the perks

Get the next quote moving

You know the inquiry I mean. It’s sitting there because you need to find the rate card, check what the customer asked for, and decide what belongs in the scope.

This prompt turns those pieces into a draft you can review. Give it the inquiry, your current pricing rules, and a past proposal if the format helps. Check the scope and arithmetic before anything goes to the customer.

The useful result might be a mostly finished quote. It might also be three questions you need answered before you can price the job. Both move the inquiry forward.

What to provide Paste a CRM inquiry, customer email, or form response, plus your current rate card and scope rules. An attached export works too. Keep the record ID and date so the draft points back to the request.

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You’ve got a tool that spots a problem in seconds. Great. Who checks it? Who calls the crew? Without that next step, you’ve produced a very fast addition to somebody’s inbox.

Before your team adds AI to another process, decide where it belongs in the job and what people still need to do. Hawai‘i has some useful examples.

I lived there for almost a decade, and my friend Ian Kitajima introduced me to design thinking. Our recent conversation got me looking at AI projects across the islands. These are separate efforts by different organizations, not one coordinated statewide rollout.

Start with the job

Road maintenance. Hawai‘i’s transportation department is collecting dashcam footage to help identify potholes, damaged guardrails and other road conditions. Its Eyes on the Road update describes information flowing to maintenance staff. Someone still needs to check a finding and decide what gets fixed. Spotting a pothole isn’t the same as repairing it.

Wildfire detection. Hawaiian Electric’s camera system uses AI to flag possible smoke. People review the imagery before notifying the utility and emergency agencies. The human review is part of the system, not an afterthought.

Evacuation planning. Maui’s contract for evacuation tools combines traffic modeling with public communication. The county said first responders would review proposed zones using their local knowledge. Software can propose a plan; people who know the roads need to question it.

Learning. In a Hawai‘i Island classroom, students defended historical inventions against a skeptical chatbot. AI challenged their reasoning instead of doing the assignment. The students still had a job.

That last example connects to something Ian told me. He ran a design-thinking workshop where AI took over much of the work people normally do together. He recalls participants leaving demotivated, rather than energized. It’s one workshop, not a study, but the question is worth asking: are you removing tedious work, or the part people need to learn and feel involved?

Watch our conversation to find out more about it.

Below, I’ll walk through how these lessons apply to your team. Which job should you try with AI? Who needs to check its work? And how do you tell whether you’ve saved time or just moved the work onto someone else?

You’ll get a four-step checklist for testing one recurring task, plus the full 24-page Hawai‘i case study. The PDF includes the project details, source links and a printable pilot approval sheet you can fill in with whoever owns the work.

Become a paid member ➖ See all the perks

Read more

Hi Adopter,

You’ve probably seen the number by now. 32.3 percent false positive rate on Flock’s license plate cameras, straight from LAPD’s own audit, repeated everywhere like it’s settled.

So I went and read the audit. The number isn’t in it. Not on one page.

What’s actually in there is two counts. 161 alerts where the plate read correctly but the car turned out not to be stolen. 337 where it was. A journalist at Futurism divided one by the sum of both and landed on 32.3 percent. The LA Police Commission’s Inspector General never built that math themselves. They published the two counts and left it there.

Follow the number back further and it falls apart three more ways.

Those 161 errors weren’t misread plates. LAPD’s own audit attributes them to stale hot list data, cars that had already been recovered but never got pulled off the flagged list. That’s a stale database, not a camera getting something wrong.

The footage wasn’t even from Flock’s cameras. It came from roughly 1,500 Axon in-car cameras, run through Flock’s software. Flock was the middle layer that day, not the hardware.

And LAPD didn’t walk away over accuracy anyway. Their contract expired, and the department’s CIO pointed at data ownership and security terms. Not the plate reads.

So the most quoted number in the entire Flock story has the wrong source, the wrong camera, and the wrong reason attached to it. Four mistakes stacked into one stat, and it’s still the headline.

Honestly, that’s the real story here. Not whether Flock’s plate reader is any good. Whether we’re all arguing about the wrong layer of the product.

A Flock deployment isn’t one AI doing one job. It’s a chain. A camera captures the plate. A model reads the characters and guesses the car’s make and color, and that part can genuinely be wrong, an actual computer vision error. Then it hits a hot list, which is just a database lookup, a string match, nothing intelligent happening there at all. Then a person decides whether to act on it. Three very different things can go wrong in that chain, and only one of them is the model. The 32.3 percent story blamed the AI for an error that happened two steps later, sitting in a database nobody had gotten around to cleaning.

None of this means the tool doesn’t work, to be fair to it. That same LAPD audit, over two months, recorded 337 stolen cars recovered off Flock alerts. The plate reader does what it says on the label. The question was never whether it works. It’s what you’re actually paying for when you buy it.

I live inside AI tools for a living. Most of my job is figuring out what people are actually buying versus what they think they bought, and the pattern in the Flock story is one I run into constantly. Just turned up loud here.

Reading a plate off a photo isn’t hard anymore. Every serious competitor in this category does it, Axon, Rekor, Motorola. Flock’s own page comparing itself to Axon doesn’t even argue it reads plates better. It argues on network scale, on pricing, on how fast you can deploy it. That’s the company itself telling you where the value sits.

The model is the cheapest part of what you’re buying. It’s table stakes.

What you’re actually buying is everything wired around it.

A network of cameras spanning the whole country, where one department can search footage another department captured. Hardware sold as a subscription, roughly $3,000 a camera a year, all in, on multi-year terms that renew themselves unless someone remembers to cancel. Purchasing contracts that let any city or county buy in without running its own competitive bid, sometimes riding inside a solicitation written for something else entirely. One of Flock’s cooperative-purchasing deals started life as a weapons-detection contract and quietly became the vehicle for its whole camera catalog. And a workflow that plugs straight into how police already operate: alerts, hot lists, human review, action.

That’s the product. The camera is just what gets you in the door.

This is where it gets useful for anyone thinking about AI moats more broadly, not just cameras. That same network, the thing that makes Flock worth billions, is the exact thing cities name when they cancel.

Denver dropped Flock this year and moved to Axon, on the same poles, cameras cut from 110 down to 50. Asked why a direct competitor couldn’t just slot into the gap, Denver’s mayor said Axon doesn’t maintain a national cross jurisdiction database the way Flock does. Douglas County made the same swap, and their sheriff put it simpler: trust was lost. Syracuse pulled its readers after an eight-month fight that started with a single officer’s error, one login that connected the city to Flock’s national network for nearly a year. Federal immigration agents reportedly ran more than 2,000 searches touching Syracuse drivers before the city caught it, according to local reporting.

None of those departments are complaining that the camera misreads a plate. They’re leaving because the network reaches further than they’re comfortable with, and because who gets to search it turned out to be hard to control once it was built.

How far it can actually reach is the part that tends to get people’s attention. According to a congressional letter citing search logs, one sheriff’s office in Texas reportedly ran a search whose stated reason was logged as “had an abortion, search for female.” That single query is reported to have touched more than 6,800 separate Flock networks and over 83,000 cameras, reaching into Illinois and Washington state along the way. Nobody in either state signed off on it. The network didn’t stop them, because letting one agency reach into another is exactly what it’s built to do.

So the moat and the liability are the same asset, looked at from two different angles. The thing a competitor can’t copy is also the thing that gets you canceled.

One more piece, and I want to hold this one carefully because it’s genuinely unresolved, not a gotcha. Flock holds a patent, granted in 2022, whose written description covers classifying people by sex, race, and clothing, and matching faces across footage. Flock’s own public pages say flatly, “Flock does not use facial recognition technology” and “It cannot recognize people.” Both are real statements, about two different documents. Nobody’s independently audited the running software to say which one describes what actually ships today. That gap, between what a company is legally allowed to build and what it says it currently runs, is worth watching in any AI vendor. Not just this one. A patent isn’t a product. It’s also not nothing.

If you’re the one deciding whether to buy an AI tool, or build one, this is what actually carries over.

Stop grading the model first. That’s the part everyone pokes at, because it’s the easiest thing to argue about: accuracy, false positives, benchmarks. It’s also usually the least defensible part of the whole business, the part any competitor can match inside a year.

Look instead at what’s built around it. The data it accumulates, and who else can reach it. The contract terms, and what it actually costs to leave. The workflow it’s wired into, and how hard that would be to rip out once it’s in. That’s where the real value sits. It’s also where the real risk sits, in the same place, because most of the time they turn out to be the same thing wearing two names.

If you’re the one building the AI product instead of buying it, the same lesson just runs in reverse. Your trained model probably isn’t your moat either. Anyone with similar data and enough compute can get close to it in a year, the way Axon got close enough to Flock’s plate reading to walk right in behind it. What’s actually expensive to copy is everything you built around the model: the workflow your customers get stuck inside, the data you’re the only one holding, the distribution that gets you in the door without a fight.

Next AI pitch you sit through, on either side of the table, don’t just ask what the model can do. Ask what it’s plugged into, and who else gets to touch it once you say yes.

Companion Case Study

The Model Was the Cheapest Part

AIAC-CSR-2026-015 · AI Adopters Club

Read the full case study

Adapt and Create,
Kamil

Sources

1. Los Angeles Police Commission Office of Inspector General, “Review of the Department’s ALPR System and Use in the Field,” BPC #26-184, July 10, 2026.

2. Futurism, July 2026 (the 32.3 percent calculation).

3. TechCrunch, July 13, 2026 (LAPD’s non-renewal, CIO cites data ownership and security terms).

4. The Denver Post, February 24, 2026 (Denver’s mayor on the Axon switch).

5. Axios, July 23, 2026 (Douglas County sheriff on “trust was lost”).

6. Central Current, July 20, 2026 (Syracuse’s removal of Flock readers).

7. US Patent 11,416,545 B1, Garrett Langley, granted August 16, 2022. https://patents.google.com/patent/US11416545B1/en

8. Flock Safety, “What Is Flock” and “Trust and Safeguards” pages, accessed July 23, 2026. https://www.flocksafety.com/what-is-flock

9. Flock Safety, Flock vs. Axon comparison page, accessed July 24, 2026. https://www.flocksafety.com/vs/axon

Hi Adopter,

You’ve had the call. The client sounded interested. Now you’re staring at a proposal and realizing you’re still guessing about what they need.

For your next client conversation, start by giving AI the relevant background. Then give it a job before the call and another one after you’ve heard the answers. These prompts help you prepare questions, check your reading of the notes, and draft a useful next step.

You might come away with a small test or a clearer proposal. You might also find a question you missed, or realize you aren’t the right fit. All useful things to learn before spending Friday writing the proposal.

Get specific before you recommend anything

Take a fictional example. A client says their weekly report is frustrating. You’re an automation consultant, so it’s tempting to start thinking about a faster way to produce it.

But what makes it frustrating?

If different teams define the numbers differently, producing the report faster could leave the disagreement untouched. You need to understand that before recommending the work.

Use the conversation to find out what’s happening, whether the client wants to change it, and what a worthwhile improvement would look like to them.

Give AI the context before you give it the job

A useful question starts with knowing what’s already been discussed. Your CRM notes, email exchanges, and call transcripts may contain answers you’d otherwise spend the meeting asking for again

.

Think about the setup in three parts: context, connectors, and capabilities.

Context: what does the AI need to know?

Start with a small set of relevant material for this client:

• Your offer: what you do, what you can deliver, and any limits that matter. A service document or a few plain notes will do.

• The relationship so far: relevant CRM notes, email exchanges, and any proposal or commitments you’ve already discussed.

• The client’s own words: a previous call transcript, meeting notes, or a brief explaining what they want to achieve.

Use what you have. Missing information can become a question for the call. Include dates and who said what, especially when a newer conversation changes an earlier assumption.

Connectors: how will the AI get that material?

You can paste text, attach files, or use an app that can read files in a workspace you’ve made available. A document sitting on your computer isn’t automatically available to the AI.

If your app supports connectors to your CRM, email, or transcript tool, use them to retrieve the relevant records for this client. Be specific about the client and the conversations you need. You can use this whole workflow with files and pasted notes, too.

Before moving on, have the AI list which files or records it actually read, with their dates, and flag missing access, older information, or conflicting accounts. Bring the retrieved material into the client chat. If you’re using a summary, include the relevant client excerpts and source references so the AI can check what supports it. Share only information you’re permitted to use.

Capabilities: what should the AI do with it?

The prompts below provide the instructions for preparing, reviewing, and following up. A skill can package a repeatable process too; for this edition, the copyable prompts are all you need.

Finish gathering the context before running them. These prompts work with the material you’ve made available in the chat. They don’t retrieve your client history or update your CRM for you.

Coming up: your complete client discovery workflow

Get the three prompts to prepare your call, review what the client actually said, and draft a useful next step, plus an optional rehearsal prompt and instructions for keeping the context together.

Your premium membership also includes:

• Weekly AI implementation case studies, the full premium archive, and a library of 65+ case studies with downloadable deep dives.

• 75+ AI business workflows and prompts, plus implementation guides, checklists, and resources.

• The AI Data Readiness Assessment, AI Adopters Bot, and Claudia Installation Workshop.

• 50% off your first year of Right Click Prompt Pro on the standard annual plan: $29 instead of $58, renewing at the regular annual price, currently $58.

Explore all subscriber perks and offer details.

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Hi Adopter,

Think about the person everyone asks when an old project comes up. They remember why a decision was made, which shortcut failed, and where the useful notes are buried. Helpful, until they’re in a meeting, on vacation, or gone. Apparently, “ask Jim” isn’t a knowledge system.

If you’re bringing AI into a team, this is for you. You’ll see how Toyota made specialist engineering knowledge easier to reach, where the public evidence stops, and how to run a small version of the idea without creating another expensive search box.

One real question inside Toyota

Toyota engineer Takehiro Nakamura described asking O-Beya’s regulatory specialist for the specifications of equipment used to measure exhaust emissions. Microsoft’s original November 2024 interview also reported that roughly 800 powertrain engineers had access from January 2024. Access doesn’t mean 800 active users.

O-Beya’s specialist assistants search approved internal material and combine focused responses. Toyota’s published technical account describes internal reports, regulations, and permission controls. It also says Toyota hadn’t yet measured the exact research time saved.

This is a useful design case, not an ROI result. Toyota didn’t disclose measured time savings or financial return in that account.

The job behind the assistant

An answer only stays useful when someone keeps its sources useful. ABI Research reported in September 2025 that Toyota staff add and update sources and validate responses. That’s a secondary analyst account, not a published Toyota operating manual, but the responsibility matters in any company.

The unit of value isn’t the answer. It’s the question that stops interrupting your best person without lowering the quality of the decision.

Below, you’ll turn that lesson into one narrow test your team can finish and judge in 30 days.

Premium members get:

• A 30-day test using 20 real questions, including the awkward ones

• A worked project-decision example you can copy

• A review method that counts checking and maintenance time

• A stop gate that can end the pilot instead of protecting it

• The full Toyota O-Beya case study and 30-day worksheet

Read more

Hey Adopter,

A prospect filled in your contact form ten minutes ago. Name, company, what they need. It landed in your inbox looking like a proper lead. Right now it is doing nothing, because nobody has opened it yet, and by the time someone does, that lead has cooled from warm to lukewarm to who is this again.

Why this breaks in the handoff

Lead qualification rarely fails because the offer is wrong or the form is ugly. It fails in the gap between three systems that were never built to talk to each other, a form, an inbox, and a sales board. Someone has to be the glue holding that chain together, and in a small business, that someone is usually the owner, who is also busy running the actual business.

When the glue person is in a meeting, on a job site, or asleep, the lead just sits there. Nobody reviews it, nobody asks the missing question, nobody moves the card. A reply sent in minutes and a reply sent next week produce very different outcomes, and most owners already know this in their gut. What they do not have is a process that reviews every submission the same way, at the same speed, whether or not someone happens to be at their desk that hour.

Fixing this does not need a developer or a CRM migration. It needs six tools you likely already pay for and four prompts, written out in full below, that you paste into ChatGPT in order. No code, no guessing at the wording. Here they are, one step at a time.

Share

Connect these before you start

Six tools do the work, and if you run a small business with a website, you likely have accounts for most of them already.

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Hey Adopter,

When I say model, I mean the engine inside the chatbot on your laptop. ChatGPT. Copilot. Whatever your company already pays for. The bit that writes the draft and answers the question. Think of it like the engine in a car. You can argue Ford versus Toyota all afternoon. The car still does not move if nobody knows the route.

You’re trying to become the person your boss asks when they want AI to do something real. Reciting product names will not get you there. The person who gets the seat can look at one messy process, invoices, refunds, the weekly report nobody wants to write, and say whether a chatbot is even ready to touch it.

Subscribe now

Yesterday OpenAI put a new ChatGPT out. They call it Astra. Your invoices did not get smarter overnight. The meeting that starts with “did you see it” will not get you a seat.

Companies bought the tools. The licenses sit there. There’s a Slack channel called AI-something that three people still post in. Private equity put about $1.5 billion into a company that does not build chatbots at all. It only puts AI into real work inside real businesses.

You can spend six months comparing tools. Someone else in the building, or a vendor in the room, will become the person the VP trusts. That is the cost.

If you cannot answer four questions about one process you already own, you do not have a system. You have a demo that has not failed yet.

Those four questions are the whole job.

The $1.5 billion is Ode, a company Blackstone helped stand up whose whole job is putting AI inside real businesses. Eddie Siegel, their chief technologist, said which chatbot you pick matters, but that is not where most of the work goes. One ingredient in a system.

Gartner asked 353 data and AI leaders the same thing another way. Organisations that report good AI results spend up to four times more, as a share of revenue, on data quality, rules, people, and getting the team on board, than the ones with poor results. Not four times more on the chatbot.

And S&P Global found that 42% of companies abandoned most of their AI work in 2025, up from 17% the year before. The average organisation killed 46% of pilots before anything went live.

All of that is a work problem.

Four questions that kill the chatbot argument

I will not let a build start until these have answers. You can walk into a meeting with the same list. You do not need to become an engineer.

What does it need to know, and where does that live?

If the chatbot has to go hunting through five systems and a folder called FINAL_v3, it will guess. Guessing looks confident. It is still guessing. Your job is to write down the facts first. Claim type. Customer status. The rule that changed last quarter. Hand it a packet, not a junk drawer.

The red flag is a bot that rummages through live company files on its own. That is an intern with the master password.

What is it not allowed to do, and what stops it?

If the only rule is a sentence you typed into the chatbot, that is a hope. A hope does not survive a clever prompt. Customer files, prices, anything you would not paste into a public chat, should never reach the engine. The output should hit a hard check. If it is off the list, the system does nothing.

How will you know it still works next month?

Vendors ship new versions. Your prices change. Write twenty real examples from last quarter, with the right answer. Run that pack before anything goes live, and again when the vendor updates. If you cannot produce a score, you have a demo.

If they triple the price, do you start over?

If swapping ChatGPT for Copilot means rebuilding the whole thing, you built a wrapper. The packet of facts and the tests should stay. The engine should be the part you can change.

A vendor who wants to skip this list is selling you a demo with a bow on it. Your value in the room is being the person who will not skip it.

If it is refunds, do this before the meeting

Pick one process you already own. Not an AI strategy. Refunds. Invoice exceptions. The weekly report. One job.

Say it is refunds. Write down where the truth lives. Order number in the shop system. Payment status in finance. The policy in a doc nobody updated since March. The tribal bit, which manager always says yes under fifty dollars.

Take twenty refunds from last quarter and write the right answer next to each. Approved, rejected, needs a human. That pack is your test. When the vendor ships a new chatbot next month, you run the pack again.

Write the don’ts. No issuing money above a set amount. No touching a VIP account. No sending the customer file to the engine. Then write whether a person or a system enforces each one. If the answer is “we told the chatbot not to,” that is not an answer yet.

Bring that to the meeting. Leave the product comparison at home. You will sound more useful than the vendor, because you showed up with the work, not with a logo.

If the room still wants to argue about ChatGPT versus Copilot, let them. Then ask the four questions out loud. The argument usually dies.

The next time they ask which chatbot to buy

Do not spend this week comparing chatbots. Spend it on one process you already do, and those four questions.

The chatbot will keep changing. The person who knows the work, the failure modes, and the tests becomes the one everyone has to go through. That person is hard to fire.

How will you answer, the next time someone asks which chatbot to buy?

Adapt & Create, Kamil

Want help running this on your own work?

Book A Call

Hi Adopter,

This one is for leaders and internal champions trying to move AI from a pilot into real work.

South Korea is preparing to give every citizen access to a free AI service. It is meant to answer questions, find public benefits, and help people apply through platforms they already use, including KakaoTalk, SK Telecom, and KT.

Instead of forcing everyone into a new government app, the service will meet people inside familiar private platforms. The state is trying to create public access and new demand for Korean AI models at the same time.

That sounds like a chatbot launch. Look closer and it is really an operating model being written in public.

Three operator groups have been selected. Agreements and beta testing come next, followed by a planned launch later in 2026. So this is not a national success story yet. Nobody should order the victory cake.

What makes the case useful is everything Korea is deciding before millions of people arrive: what stays free, which models can answer, how personal information should be handled, who pays for the computing power, and what counts as a completed public-service task.

The initial support includes 512 Nvidia B200 AI chips for development and early launch. That is a starter pool, not the permanent national system. A proposed 2027 budget adds 250 billion South Korean won, but that is not a full lifetime cost.

Most companies do this backward. They buy access first, then discover the assistant cannot enter the systems, use the right permissions, or finish the workflow people expected it to handle.

When access becomes the KPI, a rollout can look successful while the work stays exactly the same. A thousand active users do not tell you whether a claim reached Finance or a citizen completed an application. Usage shows attention. Completion shows value.

Read more

Hey Adopter,

Your boss drops a new area or responibility on your desk. A vendor’s tool you have to evaluate, or the finance behind a business case you are presenting Friday. You need to learn it, fast.

So you buy a course, or you open a chatbot and ask it to explain the thing. Both fail the same way, and the numbers back it up. About 88 in 100 people who start an online course quit before the end. The course was never built for you.

The point is, you end up sounding informed without being able to use it. You nod through the meeting and cannot run the numbers or push back on the vendor. The colleague who picked up the same area in a week becomes the one your boss trusts with the next project. They picked up a method for exactly this. You have not switched it on yet.

We have known the method since 1984. Work one to one with a tutor and you beat a classroom by a mile, enough that the average tutored student scores above 98 percent of the room. Too expensive to give everyone a private tutor. That was the wall.

AI knocks the wall down, and almost nobody sets it up to. Most people run it in the one mode that keeps them stuck at 88 percent. Two prompts change that.

Subscribe now

Ask a question, take the answer, move on. That is search with extra steps. The model has no idea where you are stuck, so it explains everything to everyone the same way. Same reason the course lost you.

A tutor does two things a search box never will. It interviews you before it teaches. And it will not let you nod along without understanding. The two prompts below force that behavior. Copy them as they are.

The advisor prompt

Use this when you need to learn something and want a plan built around you, not a course built for the average student.

It will not give you a plan until it interviews you and pulls out five decisions, one question at a time. It asks where you are starting and what you need to be able to do at the end, then works back from there. Most people skip this and ask for the six-week plan straight away. They get a syllabus that burns week one on things they already know and never reaches the part they came for. The interview kills that.

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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.