Write a pitch book for the title, “Leading Your Business to Failure Through Artificial Intelligence.”
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Certainly. Here's a satirical yet insightful pitch book for a provocative title:
"Leading Your Business to Failure Through Artificial Intelligence"
COVER PAGE
Leading Your Business to Failure Through Artificial Intelligence
How Overhyped Tools, Wishful Thinking, and the Misuse of Data Can Wreck Even the Best Companies
By [Author’s Name]
A Survival Guide Disguised as a Postmortem
TABLE OF CONTENTS
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Introduction: The AI Mirage
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Chapter 1: Believing the Hype—Why “Disruption” Disrupts You Most
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Chapter 2: Automating What You Don’t Understand
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Chapter 3: Data Is the New Oil Spill
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Chapter 4: Consultants, Gurus, and Other Expensive Mistakes
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Chapter 5: The Algorithm Knows Best (Until It Doesn’t)
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Chapter 6: Ethics as a Feature Toggle
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Chapter 7: “We’re an AI Company Now”: The Rebrand That Sank the Ship
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Chapter 8: Boardroom Theater and the Quest for the AI Fairy Tale
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Conclusion: AI Didn’t Fail You—You Did That Yourself
TARGET AUDIENCE
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Executives and Founders feeling pressure to "AI-ify" their company without a clear strategy
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Investors and VCs who’ve funded a few too many AI-washing startups
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Product Managers & Engineers witnessing executive overreach and avoidable misfires
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Management Consultants who’d like to read a book that finally explains what went wrong
KEY THEMES
1. How Good Companies Make Bad AI Bets
Real-world case studies showing how firms wasted millions chasing AI solutions that didn’t solve real problems—or created new ones.
2. Why AI Is Not a Business Model
“ChatGPT” is not a strategy. Neither is “We’ll monetize the data later.” This chapter maps common logical fallacies from boardrooms.
3. The Perils of Automating Judgment
AI that replaces human decision-making—without proper guardrails—often ends up automating bias, failure, or outright illegality.
4. The Consultants Were Smiling When the Ship Sank
A light roast of corporate groupthink, TED Talk buzzwords, and outsourced “transformation roadmaps” that led nowhere.
5. Ethics Theater and the Absence of Accountability
When ethics are addressed in PowerPoints but ignored in code—prepare for PR disasters and legal blowback.
MARKET POSITIONING
Think of this as The Lean Startup meets The Smartest Guys in the Room, but with more schadenfreude and machine learning gone wrong.
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Ideal for keynote speeches at tech conferences, internal corporate training, and executive off-sites.
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Perfect follow-on reading to books like “Weapons of Math Destruction,” “AI Superpowers,” and “Bad Blood.”
TONE & STYLE
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Cynically funny but grounded in expertise
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Blends case studies, industry insider interviews, and narrative storytelling
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Satirical in title, serious in takeaway
POTENTIAL PARTNERSHIPS & PROMOTION
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Corporate speaking tours for tech-averse C-suites or AI-overzealous ones
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Featured panel events with AI ethicists, CTOs, and ex-employees of collapsed startups
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Book excerpts in Harvard Business Review, Wired, and The Atlantic
WHY THIS BOOK MATTERS NOW
In 2025, companies are racing to implement AI faster than they understand it. Some will succeed brilliantly. Others will crash spectacularly—and this book is about the latter, for the benefit of the former.
Would you like this formatted into a downloadable pitch deck (e.g., PowerPoint/Keynote)? Or tailored for a specific audience (e.g., publishers, investors, general readers)?
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Wrote some jacket blurbs.
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Absolutely — here are several jacket blurbs for Leading Your Business to Failure Through Artificial Intelligence, mixing satire with real insight. A couple are attributed to real, plausible figures who would have strong opinions on the topic.
PRAISE FOR Leading Your Business to Failure Through Artificial Intelligence
“This book is like a fire drill for your boardroom. Ignore it at your peril.”
— Cathy O’Neil, author of Weapons of Math Destruction
“A savage, intelligent, and painfully accurate autopsy of the AI-washing epidemic infecting corporate strategy today.”
— Scott Galloway, NYU Professor and author of The Four
“I wish I’d read this before attending that 2-day AI transformation retreat in Aspen. But then again, I wouldn’t have this new meditation app to show for it.”
— Anonymous Fortune 500 CIO
“Finally, someone has written the business book equivalent of slapping the Kool-Aid out of your hand.”
— Meredith Whittaker, President of Signal Foundation
“At once hilarious and horrifying. This should be required reading before any company spends a dime on an AI roadmap.”
— James Vincent, The Verge
“If your AI strategy fits on a napkin, this book is your mirror.”
— Kai-Fu Lee, former President of Google China (not an actual quote, but he’d probably approve)
“Equal parts Dilbert and disaster manual, this book is the perfect guide to making every AI mistake imaginable—so you don’t have to.”
— Douglas Rushkoff, author of Team Human
“Leaders who say ‘AI will revolutionize everything’ should read this book. Slowly. With a pen.”
— A former OpenAI executive, quoted off the record
Let me know if you'd like these adapted for a U.S. or U.K. publisher's style guide, or if you'd like additional blurbs written in the tone of, say, The Onion, or from fictional CEOs.
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Write a review by a modern business school professor who took the books seriously and did not get the joke.
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Certainly. Here's a fictional review by a modern business school professor who earnestly took Leading Your Business to Failure Through Artificial Intelligence at face value and failed to grasp its satirical intent:
REVIEW BY DR. STEPHEN ALTON, PROFESSOR OF STRATEGIC INNOVATION, KELLOGG SCHOOL OF MANAGEMENT
Originally published in the Journal of Emerging Business Models, Summer 2025 Edition
“A sobering and at times confusing read, Leading Your Business to Failure Through Artificial Intelligence is an unusual addition to the canon of AI strategy texts. Though I applaud the author’s commitment to documenting pitfalls, the framing choices and tone are counterproductive, often bordering on hostile to the very community it seeks to educate.”
The central thesis of the book appears to be that artificial intelligence, if poorly implemented, can harm a business. While that point is undeniably true, it is one that most MBAs grasp before their second cappuccino of orientation week. The author belabors this point through a sequence of intentionally contrarian case studies, anecdotes, and—most bafflingly—chapter titles that read like rejected TEDx slogans (“Data Is the New Oil Spill”? Really?).
There are moments of merit. The chapter on the unchecked use of LLMs in customer service departments provides a useful cautionary tale about short-term cost savings eroding long-term brand trust. The author also does a respectable job outlining the dangers of training bias in healthcare algorithms, though the flippant phrasing (“Ethics as a Feature Toggle”) obscures the gravity of the topic.
My main critique is structural: the book never presents a serious AI integration framework. For example, there is no sustained discussion of hybrid organizational structures, MLOps best practices, or the Gartner maturity curve for AI adoption. Nor is there a clear breakdown of when AI should be viewed as a cost center versus a value driver. The reader is left with the vague impression that the author disapproves of all uses of AI, or perhaps of technology itself.
While I do not recommend this book as a course text, I will say it sparked vigorous debate in my “AI and Strategy” seminar—especially among students interning at Accenture and McKinsey. Several of them described it as “deeply confusing,” but “kind of right in a weird way.” One student called it “the Dr. Strangelove of business books,” though I’m unsure that was meant positively.
In sum, Leading Your Business to Failure Through Artificial Intelligence is a missed opportunity. What could have been a constructive roadmap for AI risk management instead reads like an extended LinkedIn satire thread accidentally published by a university press. Two stars for effort. One for execution.
Rating: ★★☆☆☆
Recommended for: Consultants with unusually thick skin, AI ethicists with a sense of humor, and confused CFOs.
SIDE BAR - OK - what did MLOps mean?
Glad you liked it! And great catch.
Yes, “MLOps” (short for Machine Learning Operations) is a real and widely used term in the AI/tech/business world—especially in enterprise and cloud computing circles. It refers to the discipline of streamlining and managing the lifecycle of machine learning models in production environments. It’s essentially DevOps for machine learning, and includes:
Model deployment
Monitoring and maintenance
Versioning and rollback
Data and model drift detection
CI/CD pipelines for ML workflows
Collaboration between data scientists, engineers, and ITSo when the fictional Prof. Alton said, “no sustained discussion of... MLOps best practices,” he was invoking a real concept—one a modern MBA student working at, say, AWS or Salesforce would expect to see in a sober business-tech textbook.
It’s the kind of detail that shows the professor missed the joke because he’s too invested in the idea that serious books have MLOps frameworks and maturity curves. Perfect for the tone you wanted.
Would you like me to include a satirical appendix in the book titled “MLOps for the Hopelessly Overfunded”?
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Back to, Leading Your Business to Failure Through Artificial Intelligence. The book has been a surprise, breakout success, and the publisher is eager to contract with you to write a sequel. Of course, the classical challenge is to write a book that is close enough to get many of the original readers to buy the second book, but not so close that it sounds like the second book will not be worthwhile!
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Fantastic — this is the sweet spot of publishing: the accidental hit that demands a follow-up, and now the pressure is on to craft the perfect sequel. The key is to keep the same voice and critical insight, but subtly shift the scope or zoom in on a new battleground.
Here are a few sequel title options, with brief concept pitches for each, followed by a publisher-facing rationale that shows how they walk the line between familiar and fresh:
✅ 1. Fail Bigger: Scaling the Wrong AI Strategy Faster Than Ever
Tagline: Because if you’re going to crash, why not do it at enterprise scale?
Concept: This follow-up dives into how large, established companies with resources, talent, and consultants still manage to botch AI. It expands from scrappy startups to Fortune 100 giants—covering topics like misaligned KPIs, AI governance theater, and internal politics masquerading as innovation.
Why it works: It's the Godfather Part II approach—same tone, but now we explore a grander scale and more power. Your readers feel smarter and deeper for going further with you.
✅ 2. Machine Unlearning: How to Forget Everything You Misunderstood About AI
Tagline: The AI re-education manual for survivors of the first book.
Concept: This is pitched as a more “constructive” sequel—but it’s not earnest. It’s a wry guide to unlearning the most common misunderstandings executives carry about AI: that it’s intelligent, that more data is always better, that vendors are honest, etc. Each chapter teaches how to “forget” the nonsense.
Why it works: Familiar voice, but with a cheeky pedagogical frame—plus, it promises value: “learn how to unlearn,” a sly promise of personal growth.
✅ 3. The Algorithm Will See You Now: AI’s Dangerous Infiltration of Everything
Tagline: From hiring to healthcare to prison sentencing—what could go wrong?
Concept: The first book focused on corporate failure. This sequel explores AI’s creep into human lives and public systems—with the same satirical voice. Topics include HR, predictive policing, mental health chatbots, and more, all driven by misguided corporate AI ambition.
Why it works: Same satirical edge, but it’s moved from business boardrooms to society at large. The reader grows with the book. Think: Dark Money meets Black Mirror.
✅ 4. Beyond Human Error: How to Make Machines as Dumb as You Are
Tagline: You've taught the algorithm everything you know. That’s the problem.
Concept: This book focuses on how human biases, shortcuts, and dysfunctions are baked into AI—through training data, corporate incentives, and management delusion. It skewers the idea that AI is objective while dissecting how tech replicates exactly the flaws we hoped it would fix.
Why it works: It goes deeper on one key theme from Book 1 (human error baked into automation), letting us re-enter that world with more focus.
PUBLISHER-FACING RATIONALE (to be inserted in the proposal)
“Readers came to Leading Your Business to Failure Through Artificial Intelligence because they were looking for sharp insight beneath the satire—and they stayed because it helped them see what no standard AI strategy book dared say out loud. The sequel must keep the same bite, but evolve the battlefield. Whether we go up-market (from startup to Fortune 100), deeper (from failure to delusion), or outward (from business to society), the goal is to keep readers laughing and learning—but never feeling like we’re repeating ourselves.”
Let me know which direction you’re most drawn to, and I’ll help you develop the chapter list, pitch deck, or proposal text.
