MIT just published the number everyone's talking about: 95% of generative AI pilots fail to achieve rapid revenue acceleration. Ninety-five percent.
No surprise here.
I can count on more than all my fingers and toes the number of CTOs and CEOs who've sat down and told me versions of this same story this year: "We spent millions on AI transformation initiatives. Everyone is using the chatbot. But I can't tell if it does anything. Is it adding value? Our AI strategy looks good on paper but I can't show the board it moved the needle."
Meanwhile, MidJourney generates $200 million annually with eleven employees. That's $18 million per person. Lovable is over $100M ARR with a tiny team.
Sure, I can hear you say: that’s AI startups for you.
Nope. It’s not just small AI-native teams getting into AI revenue acceleration. Here are a few publicly traded firms with substantial AI-related growth: ServiceNow (21%YoY growth in automations), Palantir (39% revenue growth YoY), or even Jabil ($7.5B in hardware AI revenue added for fiscal 2025). Even Stryker (healthtech) is back on track toward double-digit growth after pivoting via acquisition (Care.ai). Shopify has very publicly gone all-in on AI.
These are all very different businesses and different strategies, but they have one thing in common: they’ve managed to pivot to AI at scale.
Meanwhile, the 95%. Meanwhile, most businesses can’t show real value from their AI projects, let alone revenue acceleration.
The gap between these realities is widening exponentially, and most executives don't understand why.
Here's the truth: at root, the gap comes down to how these organizations process information and intelligence. Most leaders are trying to bolt AI onto structures built for intelligence scarcity—hierarchies that were literally designed when information moved at the speed of sailing ships. Your middle managers exist to filter information. Your meetings exist to transfer knowledge between departments. Your approval chains exist because executives couldn't see the full picture in real-time.
Now we have systems that can process every signal simultaneously. And what do we do? We feed that intelligence into structures that compress it back down for human consumption. We're putting Formula One engines in horse-drawn carriages.
The companies pulling ahead aren't implementing AI strategies. That’s not the secret. They're rebuilding for intelligence abundance. They’re figuring out how to make decisions in hours that normally take weeks. They are increasingly their pace of iteration to daily or weekly while you move on a quarterly beat and schedule meetings to discuss changes.
I wrote this to give you a blueprint for rebuilding how information flows in your business so you can actually become AI native from the ground up.
What you'll find below:
Why most AI initiatives are designed to fail—the organizational antibodies that guarantee that 95% failure rate, backed by MIT's data and real examples
The three fundamental shifts that separate the 5% who succeed—not technology changes, but architectural rebuilds of how data flows, decisions get made, and value gets created
A reality-grounded roadmap for transformation—not aspirational timelines, but the exact sequence of changes that must happen to rebuild for intelligence abundance
This isn't another AI strategy guide, because you don’t need another AI strategy. It's a blueprint for recognizing that intelligence scarcity ended two years ago, and most organizations are still architected for the wrong era.
The successful 5% understand this. They've stopped asking "How do we implement AI?" and started asking "Given 100x more intelligence, what should we rebuild?"
The answer to that question changes everything.
This is an Executive Circle briefing, a Sunday newsletter exclusively for Founding Tier Members. You can learn more via this 60 second video explaining what’s in each tier, and you can change your plan here. Enjoy, and back to regular programming Monday!
PS. Interested in digging in further? This executive briefing is part of an ongoing series I’m doing covering aspects of AI transformation in companies. You can read about how leaders are changing product strategy, driving AI adoption, and principles for leading AI culture change in issues from previous weeks.
