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 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.
by Robyn Bolton | Mar 11, 2026 | AI, Leading Through Uncertainty, Strategy
Thursday, February 26.
3:35 pm PST – Jack Dorsey said thank you and goodbye to 4,000 people. Block;s profitability was growing, but the promise of “intelligence tools…paired with flatted teams” enabled a fundamental shift in how the company could be run
4:12 pm PST – He posted his farewell announcement to X for the world to read. In it he wrote, “I know doing it this way might feel awkward. I’d rather feel awkward and human than efficient and cold.”
Is there anything more darkly humorous than a CEO trying to avoid appearing efficient and cold when communicating a decision to make the company more efficient and cold?
Only the moment when your boss calls to ask how your plans to grow the business and going and then informs you that the C-Suite wants a plan “to do what Dorsey just did”
Tuesday, March 10.
Time unknown – The agenda of Amazon’s weekly “This Week in Stores Tech” focused solely on investigating why “the availability of the site and related infrastructure has not been good recently.”
More specifically, why, for SIX HOURS, Amazon customers could not access their accounts, view product prices, or complete checkout. That is nearly $300M in lost revenue assuming the outage only affected North America.
All because, after years of cutting headcount and ramping up AI, junior engineers basically vibe-coded production changes..
As best practices and safeguards are yet to be “concretized,” it’s now the responsibility of senior engineers to review all production changes prepared by junior programmers.
How efficient is that AI looking now?
What we lose when we bet on hype, not proof
Researchers at Oxford have documented companies using AI as justification for cuts they had already planned. A January 2026 survey of 1,006 global executives found that 60% have or will make cuts in anticipation of AI’s impact while 29% plan to slow hiring. Only 2% have laid off staff as a result for actual AI-driven results.
Thousands of people are being laid off based on hype, not proof.
It’s reasonable to expect that, one day, AI will live up to the hype and deliver on all the promises promoters are making. But that’s a long-term bet that only pays out if you survive the inevitable crashes in efficiency, revenue, and institutional knowledge.
When organizations swap out people for “intelligence tools,” they lose institutional memory, the subtle, often unspoken, sometimes subconscious knowledge that makes things work. These are the people who understand your clients, your controls, and why past decisions were made. AI can automate workflows. It cannot replicate that knowledge. And once it’s gone, it’s gone.
And the loss continues even amongst the people who remain.
Research from MIT shows that regular AI use reduces activity in brain networks responsible for creativity and analogical thinking by 55%, and the atrophy persists even after people stop using AI tools. You are not trading people for AI. You are trading people for AI while simultaneously reducing your remaining team’s capacity to think creatively, adapt quickly, and catch mistakes. Operations get fragile. Innovation stalls. And when the AI-assisted work fails, as it did at Amazon, there’s no one left to fix it.
The root of growth is never hype
When the call comes down from on high to “do what Dorsey did” it’s hard to counter with cautionary tales like Amazon or reality checks about the state and capability of the organization.
But you can ask questions:
- Are you cutting based on what AI has delivered or what we expect it to?
- How will we ensure essential institutional knowledge isn’t lost?
- If (when) AI-assisted work fails, who fixes it? Amazon’s answers were still on staff. Will ours be, too?
Growth is essential to every organization. But you can’t cut your way to growth.
AI doesn’t change that fact.
It just makes it easier to believe the hype.
by Robyn Bolton | Feb 8, 2026 | AI, Leadership, Leading Through Uncertainty, Strategic Foresight, Strategy
In 2023, Klarna’s CEO proudly announced it had replaced 700 customer service workers with AI and that the chatbot was handling two-thirds of customer queries. Labor costs dropped and victory was declared.
By 2025, Klarna was rehiring. Customer satisfaction had tanked. The CEO admitted they “went too far,” focusing on efficiency over quality.
Like Captain Robert Scott, Klarna misjudged the circumstance it was in, applied the wrong playbook, and lost. It thought it had facts but all it has was technical specs. It made tons of assumptions about chatbots’ ability to replace human judgment and how customers would respond.
Calibrated Decision Design, a process for diagnosing your circumstances before picking a playbook, consistently proves to be a quick and necessary step to ensure success.
When you have the facts and need results ASAP: Go NOW!
General Mills, like its competitors, had been digitizing its supply chain for years and so facts based on experience and a list of the facts it needed.
To close the gap and achieve end-to-end visibility in its supply chain, it worked with Palantir to develop a digital twin of its entire supply chain. Results: 30% waste reduction, $300 million in savings, decisions that took weeks now takes hours. It proves that you don’t need all the answers to make a move, but you need to know more than you don’t.
When you have hypotheses but can’t wait for results: Discovery Planning
Morgan Stanley Wealth Management’s (MSWM) clients expect advisors to bring them bespoke advice based on mountains of analysis, and insights. But it’s impossible for any advisor to process all that data. Confident that AI could help but uncertain whether its would improve relationships or create friction, MSWM partnered with OpenAI.
Within six months, they debuted a GenAI chatbot to help Financial Advisors quickly access the firm’s IP. Document retrieval jumped from 20% to 80% and 98% now use it daily. Two years later, MSWM expanded into a meeting summary tool to summarize meetings into actionable outputs and update the CRM with notes and follow-ups. A perfect example of how a series of experiments leads to a series of successes.
When you have facts and time to achieve results: Patient Planning
Drug discovery requires patience and, while the process may be predictable, the results aren’t. That’s why pharma companies need strategies that are thoughtfully planned as they are responsive.
Lilly is doing just that by investing in its own capabilities and building an ecosystem of partners. It started by launching TuneLab, a platform offering access to AI-enabled drug discovery models based on data that Lilly spent over $1 billion developing. A month later, the pharma giant announced a partnership with NVIDIA to build the pharmaceutical industry’s most powerful AI supercomputer. Two months later, it committed over $6 billion to a new manufacturing facility in Alabama. These aren’t billion-dollar bets, they’re thoughtful investments in a long-term future that allows Lilly to learn now and stay flexible as needs and technology evolve.
When you’re making assumptions and have time to learn: Resilient Strategy
There’s no way of knowing what the global energy system will look like in 40 years. That’s why Shell’s latest scenario planning efforts resulted in three distinct scenarios, Surge, Archipelagos, and Horizon. Multiple scenarios allows the company to “explore trade-offs between energy security, economic growth and addressing carbon emissions” and build resilient strategies to recognize which one is unfolding and pivot before competitors even spot what’s happening.
Stop benchmarking. Start diagnosing.
It’s easy to feel like you’re behind when it comes to AI. But the rush to act before you know the problem and the circumstances is far more likely to make you a cautionary tale than a poster child for success.
So, stop benchmarking what competitors do and start diagnosing the circumstances you’re in, so you use the playbook you need.
