by Robyn Bolton | Apr 15, 2026 | Customer Centricity, Innovation, Leadership
In October, at InnoLead’s annual conference in Boston MA, everything was AI. When the facilitator of a LEGO Serious Play workshop announced we would not talk about AI, the room erupted in applause.
In April, at Inside Outside Innovation’s biannual conference in Lincoln NE, everything was human. By day’s end, speakers and attendees alike were celebrating the sweet relief of a human-led, AI-supported future.
Why the difference? AI hasn’t fallen out of the news cycle, nor have AI-driven layoffs ceased.
Perspective.
InnoLead’s conference featured practitioners living the day-to-day reality of change and innovation. IO 2026 spotlighted thought leaders like Eric Ries, David Bland, and Erin Stadler, advisors able to see across organizations and invited into the C-Suite’s inner sanctum.
One conference talks about what is. One about what will be.
So, if you want to know what your C-Suite will task you with in six months, look to Nebraska.
To move forward, we must face hard truths
Eric Ries, the creator of Lean Startup and author of the forthcoming Incorruptible, exposed the myth that free markets reward value creation. They reward value extraction. Companies focused on extraction forget their purpose, serve themselves over their customers, and ultimately fail.
Elliott Parker, CEO of Alloy Partners and author of The Illusion of Innovation, declared corporate innovation to be alchemy. Isaac Netwon spent his life pursuing alchemy (creating calculus was just a side quest) but failed because the basic building block of matter, the atom, is immutable. The same is true of big company executives pursuing innovation. The atomic elements of corporations (efficiency) and entrepreneurship (autonomy, passion, urgency, skin in the game, and freedom) are immutable and incompatible. Just as lead cannot become gold, companies can’t create like startups.
To do better, we must focus on people
Erin Stadler, founder of Design Culture and author of one of my all-time favorite articles on innovation, shared a forgotten truth: “When we lead with people, the human element, the science, the innovation comes with it.” To do this requires leaders and organizations to find and state their purpose, to build principles and values, and to act on them every day
Dan Hassenplug, VP of Design at sport tech company Hudl, boldly declared that customer obsession is the “real AI strategy.” After all, getting 10x faster at something doesn’t matter if it’s on something that doesn’t matter. And what matters are your customers. Living with them, talking to them, listening to them. You’ll get radical and game changing insights that no competitor, survey, or synthetic persona can.
David Bland, founder of Precoil and author of Testing Business Ideas, implored the audience to flip the 80/20 ratio of feasibility experiments to desirability experiments. Why? “We can make anything these days. It doesn’t matter if you can make it if no one wants it.”
To focus on people, we must serve them
Ted Ullrich, co-founder of Tomorrow Lab, reminded us that “simplicity is earned,” not a starting point. We start by trying to do all the thingsfor customers, but that’s overwhelmng and unnecessary. Only by listening to humans and staying humble can we create the simple solutions that create value.
Julie Ann Crommet, founder of Collective Moxie and former VP at Disney, dazzled us with the simple fact that “the more specific the story, the more universal.” She backed this up with data that films with Authentically Inclusive Representation perform nearly 3x better at the box office and the story behind how Coco became Pixar’s highest grossing movie in China, despite content that is typically banned.
The future is wonderfully human
AI isn’t going away and it will change almost all aspects of life and work. But if the thought leaders, advisors, and designers in Nebraska are right (and I think they are), the future will be far more human than machine.
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 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 | Mar 3, 2026 | Leadership, Leading Through Uncertainty, Strategy
“Change is changing: How to meet the challenge of radical reinvention” – McKinsey
“End to End Reinvention Unleashes a Technology’s Full Potential” – BCG
“Reinvention: The Overlooked Skills Leaders Need Right Now” – Forbes
Don’t look now but we’ve got a new buzzword!
Hello, REINVENTION
Wait, what happened to Transformation?
Oh hon, “Transformation” is so 2025 and for good reason. In a survey of 750 global organizations, researchers found that 52% of respondents suffer from “transformation fatigue,” 44% cite constant change as the reason for their burnout, and more than one-third are considering quitting as a result of never-ending transformations.
Unfortunately, massive technologic, economic, and societal shifts demand executives rethink every aspect of their organizations. So, what do you do when you need to transform but using the word is likely to lead to a revolution?
As fans of The Wire know, you rebrand.
So, Reinvention is the new Transformation?
Yes and no.
Both terms apply to large-scale organizational changes that often hit at the heart of an organization’s operations. As a result, they require leadership commitment, employee buy-in, and lots of money and time to execute.
The difference is that Transformation is positioned as a finite endeavor to increase performance, usually through technology adoption and integration or restructuring. Reinvention, however, “requires leaders to embrace more radical approaches and actions – in effect, to embrace the creative destruction of the company so it creates value in new ways.”
On-going. Radical approaches. Creative destruction.
Just what C-Suite execs want.
Honestly, it sounds like Reinvention is needed so why is it BS?
To be fair, it’s only two-thirds BS.
Building a capability for ongoing change, iteration, and learning isn’t BS. In fact, it’s mission critical in a world of constant change and uncertainty. But this capability requires new mindsets and skills that take time, consistent role modeling by senior leaders, before they stick.
What is BS is the need for radical approaches and creative destruction.
Instead, leaders need to return to their roots and reimagine their future.
Return and Reimagine?
Return
Jørgen Vig Knudstorp is widely credited with saving LEGO from bankruptcy and turning it into the world’s biggest toy company. At the 2025 Thinkers50 Summit, he shared his 10 rules for a successful transformation. Number one, “Why do we exist?” He spent three years trying to answer this question.
Why do we exist? What makes us relevant, valuable, rare, hard to imitate?
The answer isn’t your industry, products, or processes. It’s something more fundamental. It’s the Job to be Done that your organization and ONLY your organization can do.
John Fallon, who led Pearson’s turnaround as their CEO, answered this question in a recent conversation with Outthinkers’ Kaihan Krippendorf.
“The job to be done was not publishing textbooks. The job to be done was empowering people to progress in their lives through learning.”
Reimagine
When you know why you exist, you’re able to go beyond rebuilding to reimagining what your organization could be. Knowing your Why changes how you think about your organization and its potential. It enables you to step out of the hype, ignore the peer pressure, and explore all the future Whats and Hows before committing to action.
Then, and only then, do you commit to action. To concrete changes in business models, operations, and capabilities. To Reinvention.
I think I get it. Reinvention is BS not because it’s wrong but because it skips two essential steps.
Reinvention implies rebuilding, but if you don’t know why your company exists, how can you be sure you’re building something that matters?
And, if your “reimagining” is focused only on the latest tech or doubling down on a dying business model, you’ll never see all the other possibilities that may be more resilient.
Return. Reimagine. Reinvent. The 3Rs. That’s a buzzword I can support.
by Robyn Bolton | Feb 2, 2026 | AI, Leadership, Strategic Foresight, Strategy
It was a race. And the whole world was watching.
In 1911, Captain Robert Scott set out to reach the South Pole. He’d been to Antarctica before and because of his past success, he had more funding, more expertise, and more experience. He had all the equipment needed.
Racing him to fame, fortune and glory was Norwegian Roald Amundsen. Originally heading to the North Pole, he turned around when he learned that Robert Peary had beaten him there. He had dogs and skis, equipment perfect for the Arctic but unproven in Antarctica.
Amundsen won the race, by over a month.
Scott and his crew died 11 miles from the South Pole.
When the Playbook Stops Working
Scott wasn’t guessing. He’d tested motor sledges in the Alps. He’d seen ponies work on a previous Antarctic expedition. He built a plan around the best available equipment and the general playbook that had served British expeditions for decades: horses and motors move heavy loads, so use horses and motors.
It just wasn’t right for Antarctica. The motors broke down in the cold. The ponies sank through the ice. The plan that looked solid on paper fell apart the moment it met the actual environment it had to operate in.
The same thing is happening today with AI.
For decades, when new technologies emerge, executives have followed a similarly familiar playbook: assess the opportunity, build a business case, plan the rollout, execute.
And for decades it worked. Cloud migrations and ERP implementations were architectural changes to known processes with predictable outcomes. As time went on, information grew more solid, timelines became better understood, and the playbook solidified.
AI is different. Executives are so focused on picking the right AI tools and building the right infrastructure that they aren’t thinking about what happens when they hit the ice. Even if the technology works as designed, you have no idea whether it will deliver the intended results or create a ripple of unintended consequences that paralyze your business and put egg on your face.
Diagnose Before You Prescribe
The circumstances of AI are different too, and that requires a new playbook. Make that playbooks. Picking the right playbook requires something my clients and I call Calibrated Decision Design.
We start by asking how long it will take to realize the ultimate goals of the investment. Do we need to break even this year, or is this a multi-year bet where results slowly roll in? Most teams have a sense of this, so it allows us to move quickly to the next, much harder question.
What do we know and what do we believe? This is where most teams and AI implementations fail. To seem confident and indispensable, people present hypotheses as if they are facts resulting in decisions based on a single data points or best guesses. The result is a confident decision destined to crumble.
Where you land on these two axes determines your playbook. Apply the wrong one and you’ll either waste money on over-analysis or burn through budget on premature action.
Pick from the Four Playbooks
Go NOW!: You have the facts and need results now. Stop deliberating. Execute.
Predictable Planning: You have confidence in the outcome, but the payoff takes patience. Build a flexible strategy and operational plan to stay responsive as things progress.
Discovery Planning: You need results fast, but you don’t have proof your plan will work. Run small, fast experiments before scaling anything.
Resilient Strategy: The time horizon is long and you’re short on facts. The worst thing you can do is go all in. Instead, envision multiple futures, identify early warning signs, find commonalities and prepare a strategy that can pivot.
Apply it
Which playbook are you using and which one is best for your circumstance?
