by Robyn Bolton | Aug 11, 2026 | AI, Metrics
“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.
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.
by Robyn Bolton | Aug 4, 2026 | AI, Leadership
“Try AI. It will make you more efficient,” every executive at least once in the past three months.
And it always makes me laugh.
Yes, AI will make save you time and effort. Eventually. But not on the timeline you, or your boss, is comfortable with.
A story that proves the rule
A few days ago, I spent 4 hours building a single AI tool for one research task.
Some context: I use AI Daily and have built prompts, tools and agents to streamline regular tasks. The idea came from a Whop site that I use regularly, which outlined the specific tools, prompts, and steps required to successfully build an AEO (answer engine optimization) scanning tool.
That’s why I only blocked four hours for the build.
To build the tool, I created new accounts on four different websites, worked in Terminal, Claude Cowork, and Claude Code, and referred to instructions from both the originating website and Claude.
If I had a boss or an IT department, neither would be happy with me.
Why? After all, I built a tool for a task that was too complicated and time-consuming to do previously, will run automatically every week, recommends practical fixes, and measures impact against meaningful business metrics.
I’ve also had bosses that, if I told them I spent 10% of my week on this, would say, “that’s nice but where do we stand on (fill in the blank).”
The cost of efficiency
“Lean is the enemy of learning,” according to MIT professor, Ben Armstrong. That’s as true for AI in the office as it is on the manufacturing floor.
Learning is inherently inefficient. Baked into the experience are user mistakes, unexpected outcomes, and dead ends. But those mistakes, outcomes, and dead ends generate the insights required to increase the odds the next attempt will succeed.
As Thomas Edison said, “I have not failed. I’ve just found 10,000 ways that don’t work.”
When we do ultimately succeed, we almost immediately forget time, money, and effort the learning cost us. But forgetting is what makes harder to keep learning. And it results in nonsense like, “try AI. It will make you more efficient.”
The value of waste
Next time you’re tempted to say, “try AI,” stop and ask yourself these five questions:
- “Why am I saying this?” Do you really believe that AI is a solution to the problem you’re hearing or are you just saying it because the company is pushing AI? If it’s the latter, just provide a solution to the problem. If it’s the former, keep reading.
- “What does ‘efficiency’ look like?” As with all experiments, define success first. Do you expect time to completion to be cut in half? Or for it to take just as long, with fewer people? Or maybe it simply costs less. Without a goal, AI is just a toy.
- “How long am I willing to wait for efficiency?” Efficiency doesn’t magically appear the moment a prompt or tool spits out a response. Are you willing to dedicate an hour a week, even if it takes six months to be efficient. Or is success expected in a day, no matter how many hours?
- “Am I willing to pay the cost of learning?” The costs of learning go beyond trainings and tools. It’s the opportunity cost of time spent on another project, calls to customers, conversations with consumers, engagement in meetings. If you’re not willing to pay all those costs, don’t encourage people to “try AI.”
- “How will I reward and spread this learning?” Your “learning waste” can create value for your organization. But only if you reward the people who did the work and spread the learning beyond your team, enabling people to build on what’s known with less “waste.”
“Try AI” is a shortcut to avoid the hard and inefficient work of learning. But paying the cost is the only way to earn the efficiency.
by Robyn Bolton | Jul 29, 2026 | AI
Peak profit is a good news/bad news sort of thing. It’s good news when other metrics, like revenue, customer satisfaction, and employee engagement, are also at their peak. It’s bad news when it signals that you’re in the endgame of disruption. But how can you tell which situation you’re in?
Most students of Creative Destruction or Disruptive innovation will tell you it’s a “bad news” situation if peak profit is preceded by the launch of a radical new technology. They’re partly right.
But it’s not the technology that should scare you. It’s the new business model it unleashes.
What is a business model?
“Business model,” like so many other words popularized during innovation’s heyday, is a buzzword that everyone uses and no one defines in the same way.
A business model is how an organization creates, captures, and delivers value.
That’s it. It’s that simple.
What’s not simple is how to represent all the types of business models, how they work, and what elements make them work. That’s why the Business Model Canvas became so popular. It made visible an interrelated system of decisions that was once assumed, or worse, unknown.
Changing any one element of a business model typically requires changes to other elements, resulting in a new business model.
is also why it’s so hard for existing companies to change their business models or even copy successful new ones.
As a result, companies don’t change their business models unless they’re forced to. Usually by a new technology.
When does disruption actually occur?
When a new technology bursts into the market, it’s an event. The new tech is suddenly everywhere: on the news, in stores, dominating our conversations. It becomes a “where were you when” moment. Where were you when you first logged on to the internet (Miami University’s computer lab, fall 1995)? Used a smartphone (Natick, 2007)? Prompted ChatGPT (Watertown, winter 2022)?
What isn’t an event, but is far more disruptive, is the business model that emerges from the new technology:
While new business models are appearing more quickly after the debut of new technologies, it still takes years for the new business models to “prove” themselves in the marketplace. That’s why it’s so easy to dismiss them even if, like Borders and Blockbuster, you see them emerging.
Why should I worry about it now?
As Hemingway wrote, “How did you go bankrupt? Two ways: Gradually, then suddenly.”
I’m sure executives at Borders and Blockbuster never thought they would be able to speak from experience when quoting that line. I’m also sure there are at least a dozen CEOs right now, reaping the rewards of record revenue and profits, thinking the same thing.
After all, AI and LLMs felt disruptive in 2022 but by 2026, companies have harnessed the technology’s power to cut costs, increase efficiency, and maximize profit.
But in just the last 90 days, people have started to openly discuss the need for new business models:
- SaaS: From seat/feature pricing to outcomes
- Marketing agencies, law firms, and consulting: From charging by the hour to value delivered
- Network operators: From usage-based (volume) to per-connection pricing
How much time do you have?
The disruptive technology is here. The new business models it’s driving are appearing. Peak profits and revenue aren’t far behind. The question you need to ask is how long will it take for “gradually, then suddenly” to hit your business?
Special shoutout to my friends at Jedi On the Fly for their research support via their Jedi Signals intelligence platform
by Robyn Bolton | Jul 14, 2026 | AI
You could practically hear the soaring, triumphant anthem playing over a scene of unwashed yet unbowed humans crawling out of hiding as the machines’ ominous hums slowed and the evil tech overlords realized that their reign was ending.
“Ford’s AI Hiccups Lead Carmaker to Rehire ‘Gray Beard” Engineers’ the Bloomberg headline proclaimed.
It was only one company, but the news was received as if winning this battle foretold winning the war.
Sure, other companies, like IBM, Commonwealth Bank of Australia, and Klarna, rehired humans after AI-motivated layoffs. But the Ford decision just hit different because of the honesty that accompanied the announcement: “Mistakenly we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that that would produce a high-quality product.”
It’s the honesty, more than the action, that signals a new era emerging.
The fever dream takes hold
Since ChatGPT burst onto the scene in November 2022, companies scrambled to “adopt AI” and cram it down employees’ throats in the most striking demonstration of the Red Queen hypothesis since Lewis Carroll penned the words, “Now here, you see, it takes all the running you can do, to keep in the same place.”
The past three years saw a lot of running (and spending) with not a lot of progress. MIT reported earlier this year that 95% of AI pilots fail and even published an article titled, “What leaders still get wrong about AI” listing the following;
- Treating AI as something you do, not a tool to get results
- Starting AI projects without a clear path to value
- Getting stuck in pilots instead of scaling
- Overlooking how AI changes the business itself
- Mistaking productivity gains for value
Two months after publication, there are signs that leaders are starting to get things right.
Signs we’re waking up
After spending $2.5-$3T between 2022 and 2025, companies’ approach to AI isn’t going to change overnight. But there are indications that leaders are learning and adopting new strategies for AI adoption and implementation.
Executives are admitting mistakes.
After boldly committing to AI and promising step-changes in efficiency, innovation, and earnings, executives are moderating their tone and even admitting their mistakes:
- “Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it.” Charles Poon, VP Vehicle Hardware Engineering , Ford
- We “did not adequately consider all relevant business considerations…we should have been more thorough in our assessment of the roles required.” CBA announcing its reversal of AI-related job cuts
- “Really, investing in the quality of human support is the way of the future for us,” Sebastian Siemiatkowski, CEO of Klarna, when rehiring 700 customer service agents
Managers are changing who they hire
After years of layoffs, recent analysis indicates that technical jobs aren’t going away. They’re changing what’s required for success.
In a review of 2.85 million job descriptions posted between June 2025 and June 2026, researchers found a dramatic increase in skills related to “judgment, design, and accountability,” and a decrease in skills related to routine work like “boilerplate coding” and manual testing.
Companies are engaging employees
With 70% of large companies monitoring employee AI activity, it’s no surprise that fatigue and anxiety are increasing, trust is plummeting, and employees are resisting.
But in a switch from the authoritarian, top-down, “because I said so” AI implementation model of the past, companies are starting to engage employees as advocates and trainers. Some are going a step further and shifting their approach from “use AI” to “what tools, including AI, do you need to become the professional you aspire to be.”
Slow then fast
Just like waking up from a dream or crawling out of hiding after the apocalypse, the shift from “AI IS EVERYTHING!” to “AI is a tool” will take time. But the process is beginning.
by Robyn Bolton | Mar 25, 2026 | AI, Leadership, Strategy
“AI is the new cigarette.”
When a colleague said this in the waning days of 2022, days after ChatGPT burst on the scene, she took my breath away. The idea that this miracle would kill us seemed confined to hysterical handwringing foretelling the birth of Skynet.
She was right.
But neither of us knew it was designed to be that way.
Designed for addiction
My friend predicted that ChatGPT would stay free and helpful until usage reached “critical mass,” and then we’d have to pay. Less than three months after its November launch, OpenAI introduced its $20 per month service.
But it’s not the “first one’s free, the next one will cost you” aspect of drugs that makes AI addictive. It’s the design decisions at its core that keeps you coming back:
- Purchase Decoupling in which you convert real money into tokens, creating psychological distance between you and your actual spending
- Difficulty Curve where skills and benefits accumulate quickly giving you the sense that you’re becoming more capable over time and therefore more committed after progress slows.
- Skill Atrophy where every skill you stop practicing because the machine does it for you, quietly disappears.
Even casual AI users have experienced one or more of these:
- You get a message mid-chat telling you you’ve used all your tokens and need to come back in three hours even though you’ve paid your monthly $20 fee
- You’re prompting in all caps because it’s the only way you can think of to get the LLM to stop hallucinating, while reminiscing about the days when it was a brilliant thought-partner
- You’ve relied on AI to outline articles for the last several months, but you need to write in a different style and have no idea how to get started.
And yet, we keep going back.
But it’s not just individuals who are addicted. It’s entire organizations.
Signs that your organization is addicted to AI
Your CFO asks for the total AI spend across the organization. Three weeks and four departments later, the number is three times what anyone expected because the licenses are buried in IT infrastructure budgets, the pilots are expensed as innovation projects, and half the tools were purchased by business units on corporate cards.
The board approved the AI transformation initiative based on the pilot results. Eighteen months later, the pilot case study slide hasn’t changed, headcount has been reduced in anticipation of productivity gains that haven’t materialized, and the team running the pilot has quietly moved on to other work.
You eliminated the analyst pool two years ago because AI could do in minutes what they did in days. Now you need to evaluate whether the AI’s output is actually correct, and you’ve just realized there’s nobody left in the organization to check it because everyone who’s done it is gone.
Sound familiar? Your organization is an addict.
Recovery is possible
Addiction can’t be cured, only managed. The same is true for AI.
The road to recovery starts in a similar place: Visibility
- Centralize AI spending the way you centralize other business processes AND allow some flexibility by setting strict spending limits and clear decision-making criteria and ownership.
- Start pilots with the end in mind by establishing success metrics and scaling plans at the start of the pilot, not when it’s already in process.
- Treat certain human capabilities as strategic reserves the same way you’d treat any critical operational dependency. Before automating a function, explicitly document what judgment and expertise currently lives there, who holds it, and what it would cost to rebuild it if needed.
Unlike cigarettes or gambling, we’ve reached a point where we can’t quit AI.
But we can be aware of our addiction and we must manage it.
The first step is admitting that it’s real. And by design.
by Robyn Bolton | Mar 18, 2026 | AI, Customer Centricity, Leadership, Leading Through Uncertainty, Strategy
If you’re uncertain, you’re not alone. According to data from FactSet, 87% of Fortune 500 companies cited “uncertainty” during their 2025 Q1 earnings calls. And while things are definitely a tad chaotic in the world, I’ve started asking my clients, “What would you do if you were certain?”
It’s not an academic thought experiment. It’s a very practical exercise that radically shifts the way the think about and lead their businesses.
An Example That Proves the Rule
Most leaders facing disruption do one of two things: freeze and hope that “this too shall pass” or follow and hope that there is safety in numbers.
Neither is a strategy. Both are knee jerk reactions rooted in fear and communicated in the language and buzzwords of business.
This behavior didn’t start with AI. It happens every time a disruptive technology or philosophy bursts onto the scene. The printing press. The industrial revolution. Microchips. Each time, a new leader and paradigm emerges. How do they do it?
They’re certain.
Not because they’re omniscient. But because they know the answers to three questions
Question 1: Who Are You?
When photography made academic realism obsolete, Picasso didn’t freeze. He didn’t pick up a camera. He created something entirely new. Why? Because he knew exactly who he was. “I don’t seek,” he said. “I find.”
Today’s business icons are no different. Richard Branson describes himself as curious and someone who challenges the status quo. Lou Gerstner, when he arrived at a floundering IBM, declared himself a results man, not a visionary.
These self-definitions aren’t marketing. They’re decisions filters that define what you are and aren’t willing to do, agnostic of events, technologies, and capabilities.
Question 2: What Does Your Organization Actually Do?
Not what you make. Not what you sell. What Job to be Done do customers hire you to do?
Nintendo’s answer has been consistent across 130 years of radical product change: help me have fun with friends and family. From playing cards to the Game Boy, Wii, and Switch, their products changed completely. The Job didn’t.
IBM has done the same. From punch card tabulators to consulting and AI, the Job of helping customers make sense of complex information to run better never change. Amex moved from freight forwarding to credit and debit cards, but it’s commitment to move value securely when direct exchange isn’t an option never wavered.
When you know the Job you do, you stop chasing trends and start making choices.
Question 3: How Do You Move Forward?
You can’t answer this question without answering the first two. When you try, you get caught in the same freeze/follow trap as everyone else.
But when you answer the first two questions, the answer to this one becomes clear. For Picasso and Branson, they create. For Gerstner, he optimized the status quo. For most businesses, the answer is “And, not Or.” They must stabilize today’s business, step into (even follow) the next wave, and invest in creating the new.
Satya Nadella’s transformation of Microsoft is a perfect example. He defined himself as a learner, not a knower. He defined Microsoft’s job as helping people make a difference in their roles. From those two answers, every major move followed logically: maintain Office 365, step into cloud, create quantum computing technology.
None of it was reactive. All of it felt certain.
Your Moment Is Now
Yes, the world is uncertain. You don’t have to be.
Before you close this tab and tell yourself you’ll think about it later, answer the first two questions. You can change your answers later, but you need to start now.
The leaders who navigate this moment won’t be the ones who wait and see or follow the crowd. They’ll be the ones who know themselves and their organizations well enough to be certain.
