The $100 Million Post-It Note: Why Your AI Strategy Should Fit in Your Pocket

Where I explain why the 5% succeed by abandoning AI transformation theater for single-sentence strategies

MIT’s recent ANDA Initiative report found that 95% of enterprise AI investments produce zero returns has executives scrambling for answers. But a lot of commentary is asking the wrong question. Instead of dissecting why pilots fail, we should study why the 5% succeed and what they do differently.

As an AI solutions builder, who studies, researches, and teaches AI and now advises¹ innovation teams about AI transformation, I’ve watched this movie before. The companies succeeding with AI aren’t the ones with the biggest models, grandest aspirations, or fattest budgets. They’re the ones who’ve abandoned the transformation theater entirely and focused on quick and compounding wins.

Spoiler alert: The difference isn’t technology. It’s strategy, or rather, the lack of it.

If this sounds interesting to you, read on…

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The $100 Million Post-It Note

Cursor, an AI coding assistant, went from zero to $100 million in annual recurring revenue in twelve months without spending a dime on marketing. Replit grew from $10 million ARR to $100 million ARR in 5.5 months. Lovable reached $17 million ARR in three months. Young founders are building $20 million businesses in a year with teams of fewer than ten people.

What’s their secret?

Their entire AI strategy fits on a Post-It Note: “Help developers write code faster.”

Compare this to McDonald’s AI drive-thru debacle, where the company spent years and millions with IBM trying to transform the ordering experience. The AI ended up suggesting adding bacon on ice cream. Taco Bell tried the same thing and has rethought its strategy when a customer crashed the system by ordering 18,000 water cups. Both approached AI as transformation; successful startups approach it as a singular call to action.

Why Speed Matters More Than Strategy

Companies integrating AI successfully move at startup speed, not enterprise speed. They’re shipping value in weeks, not planning transformations for quarters.

Deployment Speed = Faster Learning ⇒ Faster Learning = Better Tool; Rinse and Repeat

For about a quarter of current Y Combinator startups, 95% of their code is written by AI. These companies reach $10 million in revenue with teams of less than ten people. They don’t need strategic plans because their entire strategy is one sentence: “We use AI to do X cheaper/faster/better.”

This speed differential isn’t just about time to market, it’s about learning cycles. The startups use AI coding assistants to ship code faster and learn from feedback how to improve. While enterprises are still defining governance frameworks, startups have already pivoted three times based on customer feedback. By the time the enterprise ships its pilot, the startup has product-market fit.

The Excel Principle

The companies succeeding with AI treat it like Excel, not ERP. This mental model changes the game.

Nobody held a transformation summit for Excel. There wasn’t a steering committee. Someone in accounting just started using it, saved three hours, and told their desk neighbor. Within months, everyone from finance to marketing had built their own spreadsheets. Excel succeeded because individuals could adopt it without permission, solve specific problems immediately, and see ROI at the task level.

Now think about your last ERP implementation: SAP, Oracle, Workday, whatever enterprise system consumed two years and $50 million of your life. ERP systems promise to transform your entire business by connecting everything to everything else. But first, you need to redesign all your processes to match the software’s logic. Retrain thousands of employees. Migrate decades of data. Hire an army of consultants. And pray that when you flip the switch, your business doesn’t grind to a halt.

The 95% failing with AI are implementing it like ERP, as an enterprise initiative requiring fundamental process changes, governance committees, and culture transformation. They’re treating ChatGPT like SAP. They’re building three-year roadmaps when they should start with a week-long sprint.

The Back-Office Secret

While companies pour more than half their AI budgets into visible initiatives like sales and marketing, the highest returns consistently come from the unsexy stuff: back-office automation, document processing, and operational efficiency.

MIT’s research found that successful AI implementations overwhelmingly target backoffice automation: back-office deployments often delivered faster payback periods and clearer cost reductions. These aren’t transformational use cases or attractive enough for press releases. They’re labor intensive, repetitive tasks that companies already pay humans or outsourcers to handle.

Y Combinator’s latest batch tells the same story. About 80% of this year’s cohort are AI-focused, but unlike previous tech waves, these companies have actual commercial validation with real customers paying real money. The whole batch is growing 10% week on week. Why? They’re not selling transformation. They’re selling tools that do specific jobs cheaper and faster than humans.

The Line Manager Revolution

Successful AI adoption has another counterintuitive characteristic: Line managers lead it, not innovation labs or innovation officers. The person who owns the P&L for the problem owns the AI solution.

This isn’t how enterprises typically approach technology adoption. They create centers of excellence, innovation labs, and tiger teams. But S&P Global reports that 42% of companies abandoned most of their AI initiatives in 2025, up from 17% in 2024. Why? Because the people building the solutions don’t feel the pain of the problems.

When line managers lead, they pick problems that actually matter. They already have a budget. They can measure success. Most importantly, they can kill initiatives that aren’t working without organizational drama or approval.

The Flaw in Most AI Strategies

If your AI strategy is longer than a paragraph, you’re doing it wrong. If you have an AI transformation office, you’re doing it wrong. If you’re building proprietary models instead of buying tools, you’re probably doing it wrong.

The data bears this out starkly. MIT’s research shows purchasing AI tools from specialized vendors succeeds about 67% of the time, while internal builds succeed only 33%. Yet most enterprises, especially in regulated industries, insist on building proprietary systems. They’re choosing the path with double the failure rate.

The companies succeeding with AI have abandoned the transformation narrative entirely. They’re not trying to become “AI-first” They’re solving expensive, tractable problems with tailored tools.

Three Moves for Tomorrow

1. Kill your AI transformation initiative. Seriously. Shut it down. Fire the consultants. Dissolve the steering committee. You’ll save millions and increase your odds of success.

2. Find your most expensive repetitive task. Not your most visible one: your most costly. Calculate what it costs per transaction/document/decision. That’s your baseline.

3. Buy, don’t build. Find a vendor who sells an AI tool for your specific task. Prove ROI in 30 days, not 30 months. If it doesn’t beat your baseline cost by 50%, kill it and try the next task.

Closing Thoughts

Johnson & Johnson started with 900 generative AI experiments. They cut it down to a handful tied to specific outcomes: drug discovery, supply chain optimization, sales enablement. Each one is a tool solving a specific problem, not a transformation changing the company.

The MIT study’s 95% failure rate isn’t an indictment of AI, it’s a reflection of enterprise thinking. We’ve confused experimentation with transformation, tools with strategy, and complexity with sophistication.

The 5% succeeding with AI aren’t smarter, better funded, or more innovative than the 95% failing. They just understand something fundamental: AI is a tool, not a belief system.

Stop asking: “How do we transform with AI?”

Start asking: “What expensive problem can we solve this week?”

That’s not just a better question; it’s the only question that matters.

1. Disclosure: Paradoxically, I am writing a book about AI transformation in legal. Why you might ask? Because although no one benefits from transformation theater, everyone benefits from a plan that is well conceived and well received. Stay tuned!