“AI Adoption is a Myth,” declared the headline of a recent viral blog post.
I do love a dogmatic declaration of a controversial opinion and the article opened with a compliment (“you’re already in the top 1% of AI users”), so I kept reading. Eight minutes later, I couldn’t help thinking, “Oh boy. We’re in trouble.”
AI adoption IS a myth
The author is the CEO of a company that implements AI and builds agents for companies earning $500M or more in revenue, which gives him a unique perspective across a wide variety of organizations. In his experience, he estimates that for the average Claude Cowork, or similar, rollout:
- 5-10% of employees become power users (use Cowork daily, create and use skill files for repeated tasks, install connectors to other programs, etc.)
- 20% use it a couple of times a day and either poorly or for “check the box” purposes
- 70% don’t use it at all
Now 10% adoption may be a huge win in the organic baby food market, but it isn’t going to excite a CFO who just spent $2 million on initial deployment and integration and is looking at annual operating expenses of $1-2 million.
Spending on AI is expected to hit $2.6 trillion by the end of 2026, a 47% increase from 2025. The only way that gravy train keeps moving is if CEOs, CFOs, and CIOs believe that their investments will show ROI quickly and increased profit soon after.
Huge swaths of the economy rely on AI spending continuing to rise so it’s in “everyone’s” interest to show data that justifies the spending, namely AI adoption rates, even if the math is a little squirrely.
After all, “frequent use” mean “a few times a week” as it does in Gallup’s report that 28% of employees are “frequent” AI users. Or that adoption should be measured at the enterprise, rather than employee level, as it is in AI Business Weekly’s “AI Adoption Statistics” report that proclaims “88% of organizations now use AI in at least one business function.” (underline added).
AI adoption is NOT a myth
It’s hard to claim that AI Adoption is a myth when research shows that 60% of US adults used GenAI within 3 years of ChatGPT’s launch and by November 2024, 99% had used at least one AI-enabled product in the past week (though 64% didn’t realize they were using AI).
With such widespread adoption, it’s logical to assume that even if AI usage began outside the home, as GenAI becomes widely available with companies, more and more employees will adopt the new tools available on their employer-issued laptops and smartphones.
AI adoption DOES NOT MATTER!
Harvard Business School professor Ted Levitt, famously said “people don’t buy a quarter-inch drill. They buy a quarter-inch hole.”
Yet, with regards to AI, we’re obsessing over the number of quarter-inch holes drilled. Who cares what percentage of your employees use AI? You didn’t install it so everyone could have access to a chatbot. You installed it to get work done faster, cheaper, and/or more accurately. Measure that!
Yes, AI adoption is easy to measure (and easier to “adjust” as needed), and it’s easy to benchmark where your company falls relative to its peers, and it makes everyone feel great when your adoption rate is higher than your competition.
But it’s all vanity unless you’re getting the real results that justify the investment.
FAQs
Is a high AI adoption rate just innovation theater?
It can be. High adoption numbers are easy to report and easy to feel good about, especially next to a competitor’s lower number. But adoption alone doesn’t tell you if the work got faster, cheaper, or more accurate. Measure what changed in the work AI touched, not how many employees opened the app.
Why do AI adoption stats vary so wildly between reports?
Because the definitions move to fit the story. Gallup counts 28% as “frequent” users, meaning a few times a week. AI Business Weekly reports 88% adoption by counting at the company level: one business function using AI counts the whole organization as an adopter, regardless of how many employees actually use it. Check how a number was measured before you compare it to yours.
If adoption rate is the wrong number, what should I actually measure?
Not how many people opened the app. Measure whether the work AI touched actually got faster, cheaper, or more accurate. As the saying goes, people don’t buy a quarter-inch drill, they buy a quarter-inch hole. You didn’t install AI so employees could have a chatbot. You installed it to get specific work done better. Track that outcome, not usage.