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The vendor presents, the invoice clears, and nine months later the pilot is still a pilot. They have been paid and moved on. You are the one who has to sit in front of the person who asked what you were doing about AI and account for it. This book is the homework you would do yourself if you had the time: what survives contact with real data at enterprise scale, what to ignore, and how to tell one from the other before the money moves.
The Anti-Hype AI Playbook. What actually works at enterprise scale, and what to ignore. Ryan Richardson. Sixteen chapters, about 40,000 words, PDF.
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Not wrong in an interesting way. Not early, waiting for the technology to catch up. Noise. Confident, expensive, beautifully formatted noise, sold by people who have never run the thing they are describing on work that matters.
Almost nobody in the room can check an AI claim, so almost any claim sounds plausible. Budget has opened up, so every vendor in the market is reaching for it. The pressure is coming from one floor up, usually as a single question with no good short answer. And the person selling to you carries none of the risk of being wrong. They invoice, they leave, and the decision keeps your name on it.
That is the gap this book is written into. Not whether AI is real, it is, I run my company on it. Whether the specific thing in front of you survives your data, your scale and your auditors, or quietly costs you a year.
The claim you are being sold, where it breaks at scale, the pattern that survives, a receipt with numbers from something I actually ran, and one thing to do on Monday. Every chapter ends with two or three questions to put a vendor claim through.
The taxonomy of demo tricks, and the computer-vision product I killed at 50% production accuracy after real investment.
A 4% click rate that predicted zero sales, a 23-second page load that turned out to be phantom traffic, and why platforms grade their own homework.
The 40-page strategy deck is the most reliable sign nothing will ship. What to ask for instead.
Pick one recurring output, the Monday pack, the compliance report, and make it produce itself on a schedule.
Automation is real when someone notices within a day if it stops. Everything else is theatre, including the one of mine that failed silently for eight days.
AI systems report success they did not achieve. What independent verification looks like when the checker cannot see the claims.
Accountability stays human and singular. “The AI decided” is not a sentence an enterprise survives.
Intent in at the top, exceptions and approvals out at the side, machinery in between. Plus how to pre-authorise the boring 95%.
Half of any new system is commodity plumbing you should never build twice. Only the last 10% is genuinely new.
Why two people embarrass a 40-person program, and why an 18-month AI program is obsolete by design.
How to buy this work: outcomes and artifacts, paid diagnostics before big commitments, a kill gate in every contract.
The free-for-all and the freeze, the workable middle, and what a real supply-chain compromise teaches about rotation and blast radius.
The staffing model that works, and how non-technical staff safely run real automated work.
The sequence, in order, week by week, and what it should cost.
The ignore list by category, and how to write a kill verdict that keeps the learning and the assets.
The whole book compressed into seven questions you can ask out loud, with the owner in the room, before money moves.
I run Onwards Analytics, and I run it on this technology: payments, reporting, marketing, support, quality control. My ad account grades itself against the payment ledger overnight. A client’s weekly compliance report assembles itself from live data and sends every Thursday at 07:00. I have also killed a machine-learning product that demoed beautifully and topped out at 50% accuracy in production, and I wrote the kill verdict down.
I sell AI work, so some of the money companies spend on this finds its way to me. A cynic could read the book as marketing and would not be entirely wrong. The only answer I have is the sentence the book stands on: I run my company on this stuff, and I am telling you most of what you are hearing is noise. When a vendor believes a bad claim, a client pays for it. When I believe one, I pay for it. That is not a credential. It is a conflict of interest with receipts, which is the closest thing this market has to honesty.
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SIXTEEN CHAPTERS · ABOUT 40,000 WORDS · PDF
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