by Robyn Bolton | Feb 23, 2026 | Leadership, Stories & Examples, Strategy
Congratulations, you’ve done the hard part required to get buy-in! You asked instead of told, said “I don’t know” out loud, and got genuine buy-in. Your team believes, is engaged, and ready to go. And yet execution is stalling.
What gives?
Activity without Achievement
There’s no doubt that people are working hard. You can see it in their schedules and you hear it in your one-on-ones. But projects are moving slower than they should, decisions that seem straightforward take weeks, and agreements made in meetings are quietly undone. Strategies, buy-in, timelines are powerless against an invisible and unnamed force.
So, you consider your options. A team offsite can provide a helpful rest but there’s no guarantee it sticks when you’re back in the office. Training can help shore up skill gaps, but your team is already capable, so this doesn’t feel like a skill problem. You could reorg but that creates new problems.
Your People Aren’t the Problem
The problem isn’t your people, your team, or even your culture. The problem is the hidden seams between people, teams, and cultures, that create friction.
Because of friction, people hesitate to share information across functional or hierarchical seams. They make assumptions about other generations. They work to achieve individual or functional, rather than collective, goals.
These friction points have been part of your organization for so long that they are accepted as normal. As immoveable and unchangeable as your company’s mission and vision. And because they’re so ingrained, you shift your efforts to things that feel changeable: skills, org charts, and communication plans.
You’re addressing symptoms because the root cause seems impossible to fix.
It’s not impossible.
How One Company Resolved the Friction and Tightened the Seams Without Extra Work
When a K-5 curriculum company decided to expand into the Middle School market, they knew they were asking the project team to do something new that was complex, ambiguous, and fraught with high-stakes decisions.
Six months in, the project was breaking down. Decisions that should have taken a day took weeks or months. Work got stuck as different functions weighed in at different times with different mandatory requirements. People hid problems and gave optimistic updates.
The executive who owned the project had seen this before. In fact, she was seeing it in every project team across the entire company. So, she knew that the problem wasn’t the project or the people, it was something much deeper, something that was such a part of the company’s standard operating process that it had become invisible.
So, she brought in someone (me) who could see things differently and together we sought out the seams, naming the moments when friction occurred, and engaging the team in developing and experimenting with solutions.
And we did it all as part of the daily work.
We redesigned hand-offs in real time, experimented with decision-making rules until we found what worked for multiple decision types, and rewarded people for saying “I don’t know.”
Within six months, the project was back on track and engagement and morale were sky-high. Other teams took notice and asked for advice. New products began shipping on time, on budget, and to rave reviews.
Now the Real Work Begins
Where are your seams showing up? A cross-functional initiative that’s losing momentum? A decision that never seems to stick? A team that’s aligned on paper but stuck in execution?
That friction has a name. And it’s findable.
If you’re ready to find the seams and resolve the friction, set up a SeamSpotter Session. It’s a 60 to 90-minute conversation, no prep required, and you’ll receive a written summary and recommended next steps within 48 hours.
If your team is bought in, but execution keeps stuttering, you can fix it. Email me at robyn@milezero.io to get started.
by Robyn Bolton | Feb 16, 2026 | Leadership, Leading Through Uncertainty, Strategy
“None of it worked. When I pulled the executive team back together and asked what went wrong, these executives said, ‘You told us what to do. You never asked us what to do.
“What I should have done is just said, ‘I don’t know.’ And when you say those words, what happens is everybody wants to help you.”
That is how Josh D’Amaro, the newly named CEO of the Walt Disney Company, characterized his defining leadership development moment.
Sound familiar?
Every executive, at some point in their career, has faced this moment. The business is doing poorly, the future is uncertain, and everyone is looking to you for answers.
But few of us learn the lesson that Mr. D’Amaro did. So, we keep telling and wondering why compliance isn’t generating the results we expected.
Compliance and Buy-In are not the same
In our world of “using positive words to describe uncomfortable realities,” we often characterize compliance as buy-in. And that’s a dangerous mistake.
“Compliance,” explains innovation expert Tendayi Viki, “comes from external pressures to follow rules and policies due to fear of consequences. In contrast, buy-in comes from internal motivation where people genuinely view the initiative as valuable and legitimate.”
Compliance is what happened when D’Amaro convened the market and sales executives of Hong Kong Disneyland together and told them “to adjust, build, and set ourselves up for the future.”
When things are not going well and the future is uncertain (and therefore scary) it’s normal to think that, because you are in a role with authority, that you need to have all the answers. But you don’t. Because you can’t. Because no one has the answers.
You need help.
Why Buy-in, not compliance, is required for success
No one is going to help you when they’re afraid. Instead, they’re going to execute orders regardless of their own experiences or judgment, which may be more informed and likely to result in the desired outcome (as was the case with D’Amaro and his team).
But when you ask for help, people help. They feel ownership of both the problem and the solution and seek out creative ideas and alternatives. They work across traditional organizational boundaries, like functions and levels, and they’re more resilient when faced with adversity. Even better for you, they don’t require constant instruction, surveillance, and micromanagement.
Getting buy-in frees you up to do the very thing you want to do: lead a team to a common goal and better future.
Buy-in is NOT another Change Management initiative
I’m sorry to say that getting buy-in is much harder than running the standard Change Management playbook.
Change management gives leaders a structured playbook of communication plans, training schedules, governance milestones. It’s systematic, observable, and leader-driven. And it’s not wrong. It’s just not sufficient to gain buy-in.
Buy-in is individual, nonlinear, and rooted in belief, not process. It forms one person at a time based on trust, relevance, and whether the individual sees themselves in the future state. It happens when one human being trusts the motives and behaviors of another human being.
How to get Buy-In
Earning buy-in requires you to do what D’Amaro eventually learned: invite dissent, share incomplete thinking, and say “I don’t know.” But that’s just the beginning.
You also have to find where things are breaking down internally, the gaps that allowed the situation to grow ever more concerning and dire. And it’s rarely at the obvious boundaries between silos that everyone can see and org charts try to fix.
It’s at the seams: the hidden disconnects between people, decisions, handoffs, and incentives where functions, levels, and priorities intersect. These seams are where compliance lives and buy-in dies. And until you make them visible, you’ll keep mistaking one for the other. But they can be made visible and that changes everything.
Now that you see the difference, where is compliance masquerading as buy-in in your organization?
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.
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?
by Robyn Bolton | Jan 25, 2026 | AI, Leadership, Leading Through Uncertainty
Spain, 1896
At the tender age of 14, Pablo Ruiz Picasso painted a portrait of his Aunt Pepa a work of brilliant academic realism that would go on to be hailed as “without a doubt one of the greatest in the whole history of Spanish painting.”
In 1901, he abandoned his mastery of realism, painting only in shades blue and blue-green.
There’s debate over why Picasso’s Blue Period began. Some argue that it’s a reflection of the poverty and desperation he experienced as a starving artist in Paris. Others claim it was a response to the suicide of his friend, Carles Casagemas. But Bill Gurley, a longtime venture capitalist, has a different theory.
Picasso abandoned realism because of the Kodak Brownie.
Introduced on February 1, 1900, the Kodak Brownie made photography widely available, fulfilling George Eastman’s promise that “you press the button, we do the rest.”
An ocean away, Gurley argues, Picasso’s “move toward abstraction wasn’t a rejection of skill; it was a recognition that realism had stopped being the frontier….So Picasso moved on, not because realism was wrong, but because it was finished.”
Washington DC, 2004
Three years before Drive took the world by storm, Daniel Pink published his third book, A Whole New Mind: Why Right-Brainers Will Rule the Future.
In it, he argues that a combination of technological advancements, higher standards of living, and access to cheaper labor are pushing us from a world that values left brain skills like linear thought, analysis, and optimization towards one that requires right brain skills like artistry, empathy, and big picture thinking.
As a result, those who succeed in the future will be able to think like designers, tell stories with context and emotional impact, and combine disparate pieces into a whole greater than the sum of its parts. Leaders will need to be empathetic, able to create “a pathway to more intense creativity and inspiration,” and guide others in the pursuit of meaning and significance.
California, 2026
Barry O’Reilly, author of Unlearn, published his monthly blog post, “Six Counterintuitive Trends to Think about for 2026,” in which he outlines what he believes will be the human reactions to a world in which AI is everywhere.
Leadership, he asserts, will cease to be measured by the resources we control (and how well we control them to extract maximum value) but by judgment. Specifically, a leader’s ability to:
- Ask better questions
- Frame decisions clearly
- Hold ambiguity without freezing
- Know when not to use AI
The Price of Safety vs the Promise of Greatness
Picasso walked away from a thriving and lucrative market where he was an emerging star to suffer the poverty, uncertainty, and desperation of finding what was next. It would take more than a decade for him to find international acclaim. He would spend the rest of his life as the most famous and financially successful artist in the world.
Are you willing to take that same risk?
You can cling to the safety of what you know, the markets, industries, business models, structures, incentives that have always worked. You can continue to demand immediate efficiency, obedience, and profit while experimenting with new tech and playing with creative ideas.
Or you can start to build what’s next. You don’t have to abandon what works, just as Picasso didn’t abandon paint. But you do have to start using your resources in new ways. You must build the characteristics and capabilities that Daniel Pink outlines. You must become the “counterintuitive” leader that embraces ambiguity, role models critical thinking, and rewards creativity and risk-taking.
Do you have the courage to be counterintuitive?
Are you willing to embrace your inner Picasso?
