2026.11: Myth 1 - Every Company Needs an AI Strategy
Happy Friday, from wet and cold Toronto.
This week I published Five Myths About AI Transformation. If you've been reading along the last few months, you've seen me pull at these threads already. AI fatigue. Feature stuffing. Organizational readiness. The gap between what tools can do and what companies are actually prepared to use them for. The paper ties all of that thinking together and goes deeper, connecting the patterns I've been seeing in the field to the systems thinking and business theory that explain why they keep repeating.
Starting this week and for the next 5 Fridays, I'm pulling each myth apart in its own issue. Deeper research. More context. Specific things you can do about it.
We're starting with the myth that got the whole paper started: Every company needs an AI strategy.
Let's break it down.
The Signal:
Microsoft's Copilot Reality Check Landed. Hard.
In January 2026, Microsoft finally revealed the number they'd been sitting on for 8 quarters. 15 million paid Copilot seats. Satya Nadella called it "record AI momentum." He said Copilot is "becoming a true daily habit."
The number he didn't volunteer: Microsoft has 450 million commercial Microsoft 365 users. 15 million is 3.3%.
After 2 years on the market. After $37.5 billion in capital expenditure in a single quarter.
Microsoft Spent $37.5 Billion on AI. Only 3.3% of Users Are Paying for It.
It gets more interesting. Recon Analytics surveyed 150,000 enterprise users in January. 70% initially preferred Copilot because it was already in their Office apps. After trying alternatives, only 8% kept choosing it. Copilot's paid subscriber share dropped 39% in 6 months.
Even Microsoft is walking it back. Reports surfaced that the company is pulling AI features from Windows 11 where usage doesn't justify the investment. SemiAnalysis put it bluntly: an outside competitor shipped a better AI experience on Microsoft's own application than Microsoft's $30-per-seat product could deliver.
The Scale:
Myth 1: Every Company Needs an AI Strategy
Reality: Most companies need to fix their foundation before AI can help.
Microsoft's Copilot adoption numbers aren't an AI failure. They're a foundation failure. Companies bolted Copilot onto dirty data, broken processes, and workflows nobody had examined. The tool worked. The systems underneath it didn't.
This pattern is everywhere. MIT's Project NANDA reviewed over 300 AI deployments in 2025 and found that 95% of enterprise AI pilots delivered no measurable impact on profit and loss. Between $30 and $40 billion in enterprise investment. Almost all of it producing zero return.
The core issue wasn't the models. MIT found that enterprise AI systems didn't retain feedback, didn't adapt to context, and didn't fit into real workflows. A CIO told the researchers: "We've seen dozens of demos this year. Maybe 1 or 2 are genuinely useful. The rest are wrappers or science projects."
Gartner put a number on the data problem. 63% of organizations either don't have or aren't sure they have the right data practices for AI. Their prediction: through 2026, organizations will abandon 60% of AI projects because the data isn't ready.
RAND Corporation measured an 80% overall failure rate. But here's the number that should change how you think about this: projects with sustained executive involvement succeeded 68% of the time. Projects that lost executive sponsorship within 6 months? 11%. That gap isn't technology. It's commitment.
This is the Substitution trap. Dr. Ruben Puentedura's SAMR model maps it precisely. Substitution swaps 1 tool for another with no functional change. That's what most AI adoption looks like. Swapping ChatGPT into a broken workflow and calling it transformation.
Sangeet Paul Choudary made this case in Harvard Business Review last month. He studied Figma vs. Adobe. Adobe did everything right. Moved to the cloud. Subscription model. Collaborative features. Still lost. Because Adobe changed the delivery mechanism. Figma changed how design work was organized. Substitution vs. Modification.
Donella Meadows wrote that you can't improve a system you can't describe. A Microsoft survey of 500 enterprise decision-makers across 13 countries found that only 22% strongly agreed their organization has clearly documented key processes and data dependencies.
If you're in the other 78%, the first honest question isn't "what's our AI strategy?"
It's "can we describe, in detail, how our business actually works right now?"
If the answer is no, start there. The AI can wait.
The Deep Dive:
This week's deep dive goes all the way in on Myth 1, including the practical steps for what to do instead and the business theory that explains why this pattern keeps repeating. Read: You Don't Need an AI Strategy. You Need to Know How Your Business Actually Works.
Thanks for reading!
Next week: Myth 2. "AI Is the Technology That Changes Everything." Why the biggest wins still come from boring, proven technology applied to the right problem.*
If this landed, forward it to someone who's about to spend 6 figures on an AI strategy without documenting their processes first. They'll thank you later.
If you disagree, hit me up. I’d love to hear your thoughts. That's what fills my cup.
See you next Friday.
Best,
JT
P.S. — Read the full Five Myths Paper on Substack.
