We Gave Everyone a Ferrari and Blamed the Engine

I keep running into the same response.

Someone shares the stat—“95% of GenAI pilots are failing”—and treats it like a mic drop. See? The tech doesn’t work. Let’s pump the brakes.

It’s starting to wear on me. Not because the stat is wrong. Because the conclusion is.

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The Speed Problem Nobody Talks About

When automobiles arrived, adoption was slow. Cost was a barrier. Infrastructure didn’t exist. We had decades to figure it out—build roads, write traffic laws, teach people to drive.

With AI, we skipped all of that.

We went from walking to 100mph overnight. No ramp-up. No gradual exposure. Every knowledge worker suddenly had access to a supercar.

Of course they crashed.

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Here’s the Thing About That 95%

The stat doesn’t tell you what you think it tells you.

It means we put people in the driver’s seat who’ve never driven before. No governance. No workflow design. No strategy for where they’re even going.

And when they crashed, we blamed the car.

The pattern is always the same: prototype works in the demo, leadership gets excited, then six weeks later it’s quietly shelved. Not because the tech failed. Because nobody thought through what happens when the bot confidently gives wrong answers. Who reviews the output? What’s the escalation path? How do you even know it’s drifting?

They built the car. They forgot the roads.

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The Conclusion Everyone Draws

“Ferraris are bad vehicles. Let’s go back to the horse and buggy.”

That’s what I hear when someone uses failure rates to argue against AI adoption.

But that’s not the lesson. The lesson is that we need driving instructors. Traffic laws. Roads.

Not fewer cars.

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The Gap That Matters

The failure happens between “fun demo” and “production value.” That gap isn’t technical—it’s operational.

I’ve started thinking about it in layers:

• Governance — the traffic laws. Who’s accountable when it goes wrong?
• Workflow engineering — the roads. How does AI fit into existing processes?
• Strategy — knowing the destination. What problem are we actually solving?
• Cost management — understanding the fuel economics. Is this sustainable?

If you treat AI like a magic trick, it fails. If you treat it like a business system that needs an operator, it works.

I’ve been building toward this for a while. The Aegis Framework was my attempt at the governance layer—stage gates, policy packs, audit trails. The kind of infrastructure that catches drift before it becomes disaster.

But governance alone isn’t enough. You also need people who understand why it matters.

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The Part That Stings

Here’s what frustrates me most: the people citing these stats think they’re poking holes.

They’re actually posting job ads.

The 95% failure rate isn’t a technology problem. It’s a training problem.

The market is flooded with employees who can chat with a bot. It’s starved for professionals who can deconstruct a complex business process into atomic units suitable for AI execution—then govern the whole thing so it doesn’t blow up.

That gap has a name. And it’s not “prompt engineer.”

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What I’m Trying to Build

I don’t have all the answers. I’m not sure anyone does yet.

But I keep coming back to the same conviction: if 95% of pilots are failing, and the technology itself works, then we’re missing something in the middle. Some combination of skills, frameworks, and operational discipline that turns a chatbot into a business capability.

I’ve been building curriculum around this—what I’m calling the AI Analyst path. Systems thinking first, tools second. Not magic prompts. Not another ChatGPT tutorial. The actual skills that make pilots succeed:

• Process decomposition
• Agent orchestration
• Risk and governance frameworks
• The economics of intelligence

Whether it works, I don’t know yet. But the 95% failure rate tells me the experiment is worth running.

The question isn’t whether AI works. It’s whether we’re willing to teach people to drive.

That’s the direction, anyway.