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In 1996, Steve Jobs sat down for a PBS documentary called Triumph of the Nerds. Robert X. Cringely was interviewing him. The conversation turned to Apple’s 1979 visit to Xerox PARC, the research lab where the graphical user interface, the mouse, and a long list of other foundational computing concepts had been invented.

Jobs said, in essence: Picasso had a saying — good artists copy, great artists steal — and we have always been shameless about stealing great ideas.

The line has aged better than almost any sentence in the history of business commentary. It has become the unofficial motto of Silicon Valley. It hangs on the walls of startups. It gets quoted in MBA classrooms. It is invoked, usually badly, as justification for almost any unattributed borrowing of someone else’s work.

The line is also, almost everyone who quotes it misses, a deeply incomplete version of a much more interesting framework.

This piece is about what the famous version gets wrong, what the better version actually says, and why the better version is the single most useful framework I can offer for any senior leader trying to navigate AI transformation in 2026.

The Picasso problem

Let’s start with the easy correction.

The attribution to Pablo Picasso is almost certainly apocryphal.

There is no documented Picasso source for “good artists copy, great artists steal.” Multiple researchers have looked. The line does not appear in any published Picasso writing, interview, or documented conversation. It has been attributed to him posthumously, and the attribution has stuck because the line sounds like something a defiantly transgressive twentieth-century master might have said.

The same line has been attributed, at various times, to Igor Stravinsky, William Faulkner, and T.S. Eliot. The genealogy is murky. Different researchers have credited different originators.

The most likely actual source — the earliest documented version of the underlying idea, in publication that can be verified — is T.S. Eliot.

In 1920, Eliot published an essay collection called The Sacred Wood. One of the essays was about the Elizabethan playwright Philip Massinger. In that essay, Eliot wrote something that is recognizably the source of everything that came after:

“Immature poets imitate; mature poets steal; bad poets deface what they take, and good poets make it into something better, or at least something different. The good poet welds his theft into a whole of feeling which is unique, utterly different from that from which it was torn.”

Read it slowly. There is more in those two sentences than in the entire Picasso-attributed simplification.

Eliot is not saying “great artists steal.” Eliot is saying something far more specific. He is saying that the stealing is not the achievement. The achievement is what you do with what you stole.

Bad poets deface what they take.

Good poets make it into something better, or at least something different.

That second clause is the entire heart of the framework. It is the part that the Jobs/Picasso simplification quietly drops. And it is the part that turns the line from a permission slip into a discipline.

The Xerox PARC story, told accurately

To understand what Jobs actually meant when he invoked the (probably misattributed) Picasso quote in 1996, you have to understand the specific story he was telling.

In December 1979, a small Apple team — Jobs and several engineers — were invited to visit Xerox’s Palo Alto Research Center, known as PARC. The visit was part of a deal: Xerox had taken a pre-IPO stake in Apple, and in exchange, Apple got two days of access to the PARC research.

What Apple saw at PARC was, in 1979, almost incomprehensibly advanced.

The Alto workstation. A graphical user interface with overlapping windows. A three-button mouse for pointing and clicking. Bitmap displays where pixels could be turned on and off individually. Networked computing. Object-oriented programming in Smalltalk. Laser printing. Ethernet. WYSIWYG document editing.

Almost every concept that would define personal computing for the next forty years was sitting in that one lab, in working prototype form, with researchers who had no real idea how to turn any of it into a product anyone could buy.

Jobs, by every account of the visit, immediately understood two things.

First, that what he was looking at was the future of computing.

Second, that Xerox did not understand what they had built. The research culture at PARC had produced extraordinary technology and almost zero commercial intuition about what to do with it.

Apple took the concepts.

This is the part of the story everyone tells.

What gets told less often is what Apple did with the concepts they took.

The Xerox mouse had three buttons and cost roughly $300 to manufacture. The hardware was research-grade. It was unsuitable for consumer use at consumer price points. Apple’s industrial design team, led by Jerry Manock and later by Hartmut Esslinger’s Frogdesign, redesigned the mouse from scratch. One button. Simple enough that no user would need a manual. Apple worked with manufacturers to bring the unit cost down to roughly $15. By every meaningful measure — usability, manufacturability, price, durability — Apple’s mouse was a fundamentally different product than what they had seen at PARC.

The Xerox interface concepts were research-quality demonstrations on a $30,000+ Alto workstation. Apple put the same concepts on a Macintosh that retailed for $2,495 in 1984 — making the technology accessible to a market that had been completely unreachable to Xerox.

The Xerox GUI used a complicated, technically elegant system that required Smalltalk and was deeply tied to research infrastructure. Apple stripped it down, simplified it, sacrificed some flexibility for usability, and built QuickDraw — Bill Atkinson’s graphics library. QuickDraw made the Mac UI possible at consumer price points and consumer speeds.

Apple stole the idea.

Then they made it into something better.

That’s the framework Eliot was pointing at in 1920. That’s what Jobs was actually doing when he invoked the Picasso line in 1996. The stealing was the entry condition. The transformation was the achievement.

The Microsoft layer

It is worth telling one more layer of the story, because it is the part that completes the lesson.

In 1985, Microsoft released Windows 1.0. The product was, by almost every honest account, derived directly from what Microsoft had seen of the Macintosh during their collaboration with Apple on Office software. The early versions of Windows looked enough like the Mac interface that Apple sued Microsoft in 1988 for copyright infringement.

The lawsuit dragged on for years. Microsoft ultimately prevailed, partly on the argument that Apple itself had derived the interface from Xerox PARC, and partly on a separate licensing agreement Apple had signed with Microsoft.

The lesson Jobs took from this — and the lesson he was implicitly referencing in the 1996 Triumph of the Nerds interview — was that the entire personal computing interface had been a chain of openly acknowledged thefts. Xerox researchers had taken concepts from earlier human-computer interaction research at SRI and at Doug Engelbart’s lab. Apple had taken from Xerox. Microsoft had taken from Apple. Everyone, at every step, had been “stealing.”

What separated the participants was not whether they stole. Everyone stole. What separated them was what they did with what they stole.

Xerox stole brilliantly from earlier research and produced research-quality demos that never became shippable products. Apple stole from Xerox and produced products that reshaped consumer computing. Microsoft stole from Apple and built the dominant operating system of the personal computing era.

In every case, the stealing was the easy part. The transformation was where the value was created.

Why the simplified version is dangerous

The reason I am writing about this in 2026 is that the simplified Jobs/Picasso version of the framework has, over the past three decades, produced a particular kind of executive behavior that is now actively damaging AI transformations.

The simplified version says: great artists steal.

In the hands of an executive who has not read the Eliot original, this becomes: we can copy what other companies are doing without doing the harder work of transforming it for our context.

I have seen this play out, repeatedly, in AI transformations across multiple industries in 2025 and 2026.

A retail company sees that another retail company has deployed AI-powered customer service successfully. They decide to “steal” the same approach. They copy the architecture, the vendor selection, the use case definition, almost line for line. They deploy it. It fails — because their customer base is structurally different, their existing service infrastructure is structurally different, and the parts of the implementation that they needed to transform for their context were the parts they didn’t transform.

A financial services firm sees that another bank has used AI for credit risk modeling with strong results. They copy the model architecture. They deploy it on their portfolio. It fails — because the underlying data, the regulatory context, and the institutional risk culture are all different, and they didn’t do the transformation work.

A consulting firm sees that another consulting firm has been generating polished AI-augmented deliverables in half the time. They copy the workflow. Their consultants try to use the new approach. The work suffers — because what the leading firm had spent eighteen months building was not the workflow but the judgment about when to use the AI and when not to, and the judgment was the thing that took eighteen months and didn’t transfer in a memo.

Every one of these failures has the same root cause. The executives involved correctly identified what to steal. They incorrectly assumed that the stealing was the work.

Eliot, in 1920, had named the failure mode in advance.

Bad poets deface what they take.

What he meant: bad poets steal effectively but fail to transform. The result is a damaged version of the original — recognizable, derivative, less valuable than the source. Defaced.

The contemporary executive version of this is the AI initiative that copies someone else’s successful pilot, deploys it without context-specific transformation, fails publicly, and produces internal cynicism about AI more broadly. The pilot is defaced. The company is worse off than if they had never attempted it.

The Eliot discipline, translated for 2026

If Eliot is right that the stealing is the entry condition and the transformation is the achievement, the question for senior leaders becomes: what does the transformation actually consist of?

I think it has four components.

One: context-specific adaptation.

What you steal has to be modified for the conditions in which it will operate. Apple modified the Xerox interface for consumer price points. Microsoft modified the Apple interface for IBM PC architecture. Your AI initiative has to modify the source approach for your industry, your data, your culture, your regulatory environment, your customer base, your team’s existing skill set.

The transformation is not optional. It is the entire work. Skipping it produces the defaced version Eliot warned about.

Two: integration into the existing system.

The stolen idea has to be welded into a coherent whole, in Eliot’s language. It cannot sit as a bolted-on appendage. Apple didn’t just add a mouse to the Apple II. They redesigned the entire computing experience around the mouse — the operating system, the application paradigm, the file system, the visual metaphors.

Your AI initiative cannot be a separate program running alongside your existing operations. It has to be welded into how decisions get made, how work flows, how value gets delivered. The integration is the transformation.

Three: aesthetic and human-experience resolution.

This is the part that almost no business framework captures and that Eliot’s poetic version surfaces. The Xerox mouse worked. The Apple mouse felt right. The Xerox GUI was technically sophisticated. The Apple GUI was aesthetically resolved in a way that produced an entirely different human experience.

For AI initiatives, this matters more than executives realize. The technical capability is necessary but not sufficient. The version of the AI tool that your people will actually use is the one where the human experience of using it has been resolved — the prompts are intuitive, the outputs land in the right format, the workflow doesn’t require constant context-switching. The aesthetic and experiential layer is most of the transformation.

Four: the willingness to make it into something different.

Sometimes the version that emerges from honest transformation is better than the source. Sometimes it’s just different — adapted to your context in ways the source never was. Eliot was explicit that either outcome counts. Better, or at least different.

The executive temptation is to claim the better. The actual achievement is often the different — the version that works in your context, even if it would not work as well in the source context. This is fine. This is, in fact, the whole point. Your version is supposed to be different. That’s what transformation is for.

Three practical questions

If you want to actually apply the Eliot framework to your AI transformation in 2026, three questions are worth sitting with.

One: what specifically are you stealing, and from where?

Most enterprises are stealing from everywhere all at once — vendor pitches, McKinsey reports, conference panels, industry case studies, peer benchmarks. The result is a patchwork without coherence. Eliot’s framework demands specificity. Pick the three sources you are stealing from most heavily. Name them. Articulate exactly what you are taking from each.

If you can’t do this, you are not stealing in Eliot’s sense. You are pattern-matching to fragmentary impressions, which produces the defaced version more often than the transformed one.

Two: what is the transformation work that has to happen to make this work for your context?

For each source, articulate the specific gap between the source context and your context. Different industry. Different data. Different regulatory environment. Different customer expectations. Different team capabilities. Different cost structure.

The transformation work is the work of bridging those specific gaps. If you cannot articulate the gaps, you have not yet understood what transformation is required. The pilot will fail in exactly the ways you have not yet examined.

Three: who in your organization is responsible for the transformation work?

This is the question most enterprises skip and the question that determines whether you produce the defaced version or the transformed version.

In most AI initiatives, the people responsible for the project are responsible for the stealing — the vendor selection, the architecture, the deployment plan. The transformation — the context-specific adaptation, the integration, the human-experience resolution, the deliberate differentiation from the source — is implicitly assumed to happen on its own.

It does not happen on its own.

The transformation work has to be staffed, resourced, and led, with the same explicit attention as the technical implementation. The leaders who do this produce successful AI initiatives. The leaders who skip it produce defaced versions.

The closing thought

There is a small irony embedded in this entire piece that I want to surface before I close.

The quote that has propagated through Silicon Valley for thirty years — “good artists copy, great artists steal” — is itself an example of the failure mode it tries to describe.

The quote was stolen from Eliot. It was simplified in the stealing. The most important clause — the one about good poets making it into something better, or at least something different — was dropped.

The resulting version is shorter, punchier, and more memorable. It is also less useful. It is a defaced version of a more powerful framework. It has been quoted, in defaced form, for a century.

That is exactly the failure mode the original framework was warning about.

In 2026, with AI transformation rewriting nearly every assumption about how senior leaders create value, the temptation will be to grab the punchy version of every framework, every approach, every case study, and move quickly. Steal. Deploy. Move on.

The Eliot discipline says this is the path to the defaced version.

The harder path is to do the transformation work. To take what you steal and make it into something better, or at least something different, for your specific context. To weld your theft into a whole that is uniquely yours.

This is slower. It requires more skill. It demands the kind of judgment that doesn’t transfer easily between contexts and that cannot be outsourced to a vendor or a consulting firm.

It is also the only path that produces the version of your AI transformation that actually works.

Steal openly. Steal from everywhere. Don’t pretend to originality you don’t have.

Then do the harder work of transformation.

Make it into something better. Or at least something different.

That is the framework Eliot named in 1920, that Jobs lived in the 1980s, and that almost every senior leader in 2026 needs to internalize before they spend the most important years of their career producing defaced versions of other people’s pilots.

The world has changed. The leaders who notice will be the ones the next decade is built around.

The post Framework for AI Transformation in 2026. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

In January 2007, a 20-year-old named Stefani Germanotta was sitting in her parents’ Upper West Side apartment when she got news that should have ended her career.

Island Def Jam Records had just dropped her.

She had been signed three months earlier by Antonio “LA” Reid, then president of Def Jam, who reportedly signed her on the spot after hearing her sing from his office. The album was scheduled for release in May 2007. The label machinery had begun moving. And three months in, before anything happened, it was over.

The court documents in a later lawsuit between her producer and the label would describe the decision with one word: “inexplicable.”

She was devastated.

This is the story most people know, told the way most people tell it: a future global icon faces an early setback, perseveres, becomes Lady Gaga, proves them all wrong. End of motivational poster.

I want to write about the eighteen months between getting dropped and her first hit single. Because that’s the part of the story that matters, and almost no one tells it correctly.

It’s also the most useful eighteen months of any modern career to study if you are a senior leader trying to navigate AI transformation in 2026.

The wilderness period

When Def Jam dropped her in January 2007, Stefani Germanotta had almost nothing left of the conventional path she had been on.

She had dropped out of NYU’s Tisch School of the Arts two years earlier, at 19, against her father’s strong objections. She had been bullied at Convent of the Sacred Heart from age 11 — for her appearance, for her “eccentric habits,” for being, in her own description, “either too provocative or too eccentric” to fit in. Her NYU classmates had created a Facebook group titled “Stefani Germanotta, you will never be famous.”

And now the most powerful record label in the country had decided that her NYU classmates were right.

She was 20 years old. She had no contract. No band. No money. By every available external signal, no future.

What did she do?

She didn’t sit and grieve. She didn’t take a year off to figure herself out. She didn’t pivot to a sensible backup career. She didn’t go back to her parents’ apartment and try to repair her relationship with her father, who, according to multiple biographical accounts, had stopped speaking to her over her drug use and lifestyle choices.

She went back to performing in Lower East Side burlesque clubs.

She co-formed a revue called the Ultimate Pop Burlesque Rockshow with a performance artist named Lady Starlight.

She moved to a tiny apartment on Stanton Street in the Lower East Side.

She took freelance songwriting work, writing songs for other artists at Interscope Records — the same label network whose flagship had just dropped her.

She kept performing wherever she could get a paying gig. Open mics. Burlesque shows. Anywhere a piano and an audience would let her work.

She kept producing. Every single day.

Eight months into this period, in late 2007, the R&B artist Akon heard one of her newer tracks and insisted she sign with his label, KonLive Distribution. She signed with Vincent Herbert at Streamline Records, an Interscope imprint. She spent the rest of 2007 and the first half of 2008 recording the album that would become The Fame.

In August 2008, The Fame released. Within months, “Just Dance” became her first global hit. By 2009, “Poker Face” was the best-selling single in the world.

The eighteen months between the Def Jam drop and The Fame‘s release were not a pause. They were not a recovery. They were not the bad part of the story that happened before the good part started.

They were the story. They were what produced the artist the world would eventually call Lady Gaga.

This is the part almost every retelling gets wrong.

What the motivational version misses

The standard framing of this story is “believe in yourself when nobody else does.”

That framing is too thin. It collapses the actual mechanism into a feeling. It implies that what carried her through 2007 was an emotion — a kind of internal conviction that powered her past the rejection.

I think that reading is dramatically wrong, and the wrongness matters because it makes the lesson useless for everyone trying to apply it.

Belief, on its own, is useless.

There are millions of people who believe in themselves and never produce anything anyone notices. There are millions of people who lost faith in themselves at some point and produced enormous work anyway. The distribution of self-belief in the population does not map cleanly to the distribution of accomplishment. If it did, every motivated MBA graduate would be Lady Gaga.

What worked for her wasn’t belief.

It was discipline.

The discipline of continuing to produce, daily, through one of the lowest periods of her life.

Same piano practice. Same songwriting habit. Same showing-up-to-perform discipline. Executed every day for eighteen months while the world told her she was finished.

She did not, by every account, feel confident every day during this period. She was openly struggling with drug use. She was estranged from her father. Her finances were precarious. The Facebook group from NYU still existed. The Def Jam decision still stung.

What carried her wasn’t the absence of those feelings.

It was the discipline of producing in spite of them.

That distinction is the entire usable lesson.

The motivational version says: feel the conviction first, then the work will come.

The accurate version says: do the work first, daily, regardless of how you feel, and the conviction comes downstream — usually as a byproduct of the work itself.

This is, structurally, the same insight that runs through almost every research-based finding about high performance in any domain. Anders Ericsson’s deliberate practice research. James Clear’s habit-formation work. Daniel Kahneman’s writing on the relationship between behavior and identity. The behavior produces the identity. Not the other way around.

Lady Gaga’s eighteen months in the wilderness are one of the cleanest case studies of this principle in modern entertainment history.

She wasn’t an extraordinary person who happened to keep going.

She was an ordinary person who kept going, and the keeping-going produced the extraordinary person.

Why this matters in 2026

The reason I want to write about this story now, in 2026, is that I think it is the most useful single career narrative for any senior leader navigating AI transformation right now.

Every senior leader I work with is, in some specific way, being dropped.

The AI transformation is telling everyone that the systems that rewarded their old way of working are quietly retooling for something else. The skills that got them here aren’t necessarily the skills that get them there. The expertise that defined their career — the brand they spent twenty years building — is, in some specific way, becoming less central than it used to be.

That feeling — the dropped-by-Def-Jam feeling — is showing up in board rooms, executive committees, and 1:1s across every industry I work in.

It shows up as:

The CMO who notices that AI is now generating, in seconds, the kind of brand strategy work she spent her thirties learning to produce.

The senior consultant who realizes the deck-building skill that earned him promotion is now a 90-second prompt.

The legal partner who discovers junior associates using AI to draft contracts in a fraction of the time it used to take a team.

The CIO whose architectural expertise is being second-guessed by AI tools their teenagers use better than they do.

The CFO who watches AI produce financial analysis that would have taken his team three weeks.

Every one of these senior leaders is, in some structural sense, being told what Stefani Germanotta was told in January 2007: the system that defined your professional identity has decided, for reasons it can’t quite articulate, that you might not be what comes next.

In that moment, the question every one of them is quietly asking themselves is the same question Gaga was asking in early 2007.

What do I do now?

The five lessons from her wilderness year, properly translated, are the most useful possible answers.

Five lessons, translated for the executive moment

One: The rejection isn’t always about you. Sometimes the system doesn’t understand what it’s rejecting yet.

Def Jam didn’t drop Stefani Germanotta because she lacked talent. They dropped her because their machinery didn’t know what to do with her. The system was optimized for a different kind of artist than she was. The mismatch was structural, not personal.

For senior leaders going through AI transformation, the same is often true. The “rejection” you’re feeling — the obsolescence anxiety, the sense that your skill set is being deprioritized — is sometimes a true signal that the world has moved past your skill. More often it’s a signal that the world hasn’t yet figured out how to use your skill in the new configuration.

The discipline is to keep distinguishing between the two.

Two: Keep producing through the wilderness. The wilderness is where the work compounds.

Gaga’s eighteen months on the Lower East Side weren’t a recovery period. They were the most productive period of her early career. She wrote dozens of songs. She refined the persona that would become Lady Gaga. She built the network of collaborators — Lady Starlight, RedOne, Akon — who would shape her sound.

The wilderness wasn’t a pause before the work. It was the work.

For executives, this is the most important reframe of the AI transformation. The period you’re in right now — where you don’t yet fully understand the technology, where your role is shifting, where your authority feels less stable — is not a pause before the future. It is the work that produces the future version of you.

Keep producing through it. Write the documents. Have the conversations. Run the experiments. Build the new fluency. The compounding happens in the wilderness, not after it.

Three: Let the rejection become the material.

Some of Gaga’s most defining early work — the songs about fame, the persona built around the rejection of the rejection — came directly from the experience of being told she wouldn’t make it.

The rejection wasn’t a story interruption. It was the material she used to build the next version of her career.

For senior leaders, this is the underrated executive coaching move of the AI moment. The leaders who write honestly about what they don’t yet know about AI, who admit publicly that they’re learning, who treat their own transformation as part of the curriculum — those are the leaders developing the most distinctive voices right now.

The leaders who pretend to already know are the ones who sound generic. The ones who let the difficulty show, with discipline and rigor, are the ones whose careers will compound through this period.

Four: The systems that reject you in year one are often the same systems that try to claim you in year five.

This is the most quietly satisfying lesson, and also the most strategically important.

The same Def Jam that dropped Stefani Germanotta in January 2007 would have done anything to have her back in 2009. The same NYU classmates who made the “you will never be famous” Facebook group eventually attended her concerts. The same industry gatekeepers who decided she wasn’t viable were, two years later, claiming her as one of their own.

For senior leaders, this is the play. The same boards that aren’t sure what to do with you in 2026 will be the boards that desperately need your leadership through the AI transformation in 2028 and 2029 — if you’ve spent the intervening time building real fluency, real perspective, real distinctive voice.

The reward isn’t immediate. The reward is structural. The systems catch up.

Five: Self-belief is a discipline, not an emotional state.

This is the deepest lesson, and the one most retellings of the Gaga story flatten into something useless.

She did not, by every available account, feel confident every day in 2007. She was struggling. She was using drugs. She was estranged from her father. Her finances were precarious. The professional world had told her she wasn’t going to make it.

The discipline that carried her wasn’t the absence of those feelings.

It was the practice of producing regardless of them.

For senior leaders, this is the only useful translation. You will not feel confident every day in 2026 and 2027. Some days the AI transformation will feel exhilarating. Other days it will feel like everything you’ve built is dissolving. Some weeks your judgment will feel sharp. Other weeks you will feel obsolete.

The discipline is not feeling confident. The discipline is showing up regardless.

The work compounds independent of the emotional state.

That’s the entire mechanism.

The closing thought

There is a sentence Lady Gaga has used in interviews over the years, when asked about the wilderness period, that I want to close with, because it captures the actual texture of what carried her through 2007 better than any motivational version of the story ever could.

She has said, in different forms, the same essential thing: that she knew she had to work harder than everyone else, and that there was no plan B.

Read that slowly.

Not “I believed in myself.”

Not “I trusted the universe.”

Not “I knew I was meant for this.”

The work, harder than everyone else, with no fallback.

That is the entire story.

She wasn’t extraordinary in January 2007. She had been dropped. She was broke. Her father wasn’t speaking to her. She was performing in burlesque clubs for tips. Her NYU classmates were posting about her on Facebook. Def Jam had decided she was finished.

What was extraordinary was that she kept showing up.

For eighteen months. Every day. Through one of the hardest periods of her life.

That is not a story about believing in yourself.

That is a story about being too disciplined to quit while the world catches up.

In 2026, in the middle of an AI transformation that is, in some specific way, telling every senior leader they might not be what comes next, this is the single most useful career narrative I can point at.

Not the proving-them-wrong version. The actual version.

The discipline of producing through rejection.

The willingness to keep working in the wilderness because the wilderness is where the work happens.

The trust that the systems that reject you now may need you in five years — if you’ve spent the five years actually doing the work.

The acceptance that self-belief is downstream of practice, not the other way around.

This is what carried Stefani Germanotta from the Def Jam drop in January 2007 to “Poker Face” being the best-selling single in the world in 2009.

It is what will carry the executives who actually make it through the AI transformation from where they are now to where they need to be.

There is no plan B.

There is just the work.

The world has changed. The leaders who notice will be the ones the next decade is built around.

The post Carry Into the Age of AI. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

There is a bird in Alaska right now, on a tidal mudflat, eating itself.

Not metaphorically. Through an actual cellular process called autophagy — Greek for “self-eating” — the bar-tailed godwit is breaking down its own gizzard, liver, kidneys, and intestines. It is reducing those organs by up to 25%, while simultaneously growing its pectoral muscles and heart.

It is doing this because, in a few days, it will take off from the western coast of Alaska, fly south across the open Pacific Ocean for between 8 and 11 days without stopping, and land in New Zealand. Or sometimes in eastern Australia. Or, on one extraordinary occasion documented in 2022, in northeastern Tasmania.

The current world record, set by a 5-month-old juvenile bird tracked by satellite during its first migration ever, is 13,560 kilometers — 8,435 miles — flown nonstop over 11 days.

No food. No water. No rest. No landing on the ocean. No feeding at sea. 264 continuous hours of flapping flight, day and night, across an ocean that the juvenile bird had never seen before.

This is one of the most extraordinary physiological feats in the natural world. And I think it is the single most precise metaphor we have for what executive transformation in the age of AI actually demands.

Most metaphors for organizational change are bad. They are usually either too gentle (gardening, journeying, climbing a mountain) or too violent (war, surgery, demolition). They don’t capture the specific texture of what real transformation feels like to the people doing it.

The bar-tailed godwit captures it precisely. Because what the godwit is doing is, in the most literal possible sense, biologically uncomfortable. The bird is consuming parts of itself. It is rebuilding its own organism, on the fly, for a journey that the organism it used to be could not have survived.

The biology, as we actually understand it

The bar-tailed godwit’s transformation was documented in detail by two scientists: Theunis Piersma, a professor of global flyway ecology at the University of Groningen in the Netherlands, and Robert Gill, a research biologist at the USGS. Their foundational 1998 paper, published in the ornithological journal The Auk, has one of the great deadpan titles in scientific literature: “Guts don’t fly: small digestive organs in obese Bar-tailed Godwits.”

The paper documented something that, at the time, biologists weren’t quite ready to accept. The godwits being captured immediately before their southbound migration from Alaska had drastically reduced digestive organs compared to what was considered normal for their species. The gizzards were smaller. The livers were smaller. The kidneys were smaller. The intestines were smaller. By up to 25%, in some cases.

At the same time, the same birds had enormous fat reserves. Up to 55% of their body mass was fat — the highest relative fat content ever recorded in any bird species. Dr. Christopher Guglielmo, another leading researcher in migratory bird physiology, later coined the phrase “obese super athletes” to describe them.

The bird wasn’t just carrying fuel for the flight. The bird was reconfiguring its own body for the flight.

After landing, within days, the organs regenerate completely. The bird that arrives in New Zealand is not the same bird that left Alaska. The bird that recovers in New Zealand is not the same bird that arrived. The organism reconfigures itself three times in roughly two months — once to prepare for the flight, once to make the flight, and once to recover from it.

A typical bar-tailed godwit will fly approximately 460,000 km over its lifetime — more than the distance from the Earth to the Moon.

This is not metaphor. This is biology.

The seven lessons for AI transformation

One: You cannot carry everything across.

The most basic biological reality of the godwit’s transformation is that the bird cannot fly with its full digestive system AND its maximum fat reserves AND maintain enough lift to cross an ocean. The math doesn’t work. Something has to be reduced for the journey to be possible at all.

Most senior leaders, faced with AI transformation, are trying to violate this constraint. They want to keep their old organizational structure, their old decision-making processes, their old identity as a leader, their old career-defining expertise — and add AI on top of all of it. They want the transformation to be additive.

It can’t be. The math doesn’t work.

Something has to be consumed. The question is not whether to consume, but what.

Two: The shrinkage is strategic, not arbitrary.

The godwit doesn’t shrink random organs. It shrinks the digestive organs it won’t need during the flight, and it simultaneously grows the pectoral muscles and heart it will need for the flight. The reconfiguration is purposeful.

Most enterprises do this wrong. When budgets tighten during an AI transformation, the cuts almost always fall on the AI investment itself — on the new capability being built. That is cutting the flight muscle. It is the exact opposite of what the godwit does.

The right cuts, biologically, are to the parts of the organization that won’t be useful on the other side of the journey. The legacy process documents that nobody will follow once the new system is live. The approval chains designed for a world where decisions took weeks. The reporting structures that exist to support a hierarchy AI is going to flatten.

Three: The discomfort is biological, not psychological.

This is the lesson that matters most for senior leaders currently going through AI transformation.

The bird is literally consuming parts of itself. That is not metaphor for discomfort. That is discomfort, at the cellular level. The transformation is uncomfortable because it is biologically uncomfortable. There is no version of the journey that doesn’t involve the autophagy.

Most leaders treat the discomfort of AI transformation as a sign that something is going wrong. They look at the resistance, the confusion, the loss of identity, the senior people who can no longer locate their value, the teams whose old work has become obsolete, the cultural strain — and they conclude that their change management plan failed.

The godwit’s biology says: no, this is what a successful transformation feels like from inside the organism going through it. The discomfort isn’t a bug. It is the necessary cost of making the journey at all.

Four: Regeneration happens — but only after the journey.

The godwit’s organs do not regenerate during the flight. They regenerate within days of landing.

If you are a senior leader currently in the middle of an AI transformation and you feel diminished, uncertain, less authoritative than you used to be, less sure of what you uniquely contribute — that is exactly where you are supposed to be. The bird’s intestines don’t return to full size while it’s still over the Pacific. They return after it lands.

The discipline is to let the old identity shrink during the flight, trust that it will regenerate in a new form after landing, and stop trying to perform a role that the journey itself is in the process of dissolving.

Five: The fat is the fuel — but only if you’ve earned it.

The godwit spends two months feeding before takeoff. The journey is biologically impossible without the prior work. A godwit that has not built up sufficient fat reserves cannot make the flight, full stop.

The leaders who succeed in AI transformation are, almost without exception, the ones who built reserves before the journey began. Reserves of credibility with the board, with the executive team, with the workforce. Reserves of personal capacity to absorb discomfort without losing their judgment. Reserves of relationships that will hold under stress.

The two months of feeding before departure isn’t optional. It is the prerequisite that makes the journey possible at all.

Six: There is no stopover.

The bar-tailed godwit cannot rest on water. It is not a seabird. Its feathers aren’t waterproof in the way required for floating. If it lands on the ocean, it drowns. It also cannot feed at sea.

Once the journey begins, it must be completed in one continuous effort. There is no rest. There is no refueling. The 8 to 11 days have to be done in a single push.

Most enterprises are trying to do AI transformation as a series of small pilots with rest periods in between. They run a pilot. They evaluate. They take a quarter to absorb the results. They run another pilot. They take another quarter.

This is not how the journey works. At some point, the executive team has to commit to a continuous transformation effort that cannot be paused. The competitive environment is moving. The technology is moving. The workforce expectations are moving. An organization that pauses for two quarters between AI pilots is, by the time it resumes, working with a different ocean than the one it started across.

Seven: The young ones make it on the first try.

The world record godwit migration in 2022 — 13,560 km from Alaska to Tasmania, the longest documented non-stop bird flight in history — was made by a 5-month-old juvenile on its very first migration.

The bird had no prior experience of the journey. It had no mentorship from older birds during the flight. It had never seen the Pacific Ocean, New Zealand, Tasmania, or any of the islands it might pass on the way. It carried no instruction from its parents about how the crossing worked.

It made the flight anyway. Alone. At five months old. Across an ocean. To a continent it had never seen.

It survived because the capability was in the species, not in individual experience.

This is the lesson that I think matters most for senior leaders looking at the younger generation of AI-native workers in their organizations. The 25-year-olds without MBAs, without decades of industry experience — many of them are making professional crossings right now that veteran leaders are insisting are impossible.

They’re not insisting. They’re doing it. Because the capability isn’t in the experience. It’s in something more fundamental — the fluency with the new tools, the absence of conditioned resistance to new ways of working, the willingness to commit to a journey without a map because they were never given the old map in the first place.

The closing reflection

There is one more detail about the bar-tailed godwit that I want to leave you with, because I think it is the deepest part of the metaphor.

The godwit does not make this journey once. It makes the journey every year. For its entire adult life. A bird that lives 15 to 20 years will reconfigure its body — eating its own organs, growing its own muscles, crossing the ocean, regenerating on the other side — between 15 and 20 times. The transformation is not a one-time event. It is an annual capability that the species has metabolized into its biology.

The enterprise lesson is the most uncomfortable of all the lessons, and I want to say it directly.

AI transformation isn’t a project. It isn’t a one-time crossing that you complete and then return to normal operations afterward.

It is the new annual cycle of how organizations function.

The companies that survive the next decade will be the ones that learn to do what the godwit does — strategically consume parts of their old structure to enable the journey, grow the muscles they need for the crossing, commit to continuous flight without rest, trust that regeneration happens after landing, and then, when the next migration comes, do it all again.

Theunis Piersma, who has spent his career documenting how migratory birds reshape themselves for their journeys, has written that what these birds do is not “endurance” in the way humans usually mean that word. It isn’t pushing through pain. It is, biologically, the organism becoming a different organism for the duration required, then becoming itself again on the other side.

That, I think, is the most precise possible description of what AI transformation actually demands of any senior leader serious about making it.

You become a different leader for the duration required.

You consume the parts of your old self that won’t fly.

You grow the muscles you need for the crossing.

You make the flight continuous, without rest, until you land.

You regenerate on the other side, into a configuration that did not exist when you began.

And then, when the next migration comes — and it will come, on a faster cycle than any previous era of change has demanded — you do it all again.

This is uncomfortable. It is supposed to be uncomfortable. The discomfort is the biological cost of the journey, not a sign that the journey is going wrong.

The bird in Alaska right now, eating its own organs to fuel a 12,000 km flight across the Pacific, doesn’t experience the autophagy as failure. It experiences it as preparation.

The senior leaders who learn to read their own discomfort the same way are the ones who will reach the other side.

The world has changed. The leaders who notice will be the ones the next decade is built around.

The post The Most Precise Metaphor We Have for What AI Transformation Actually Demands — and Why the Discomfort You’re Feeling Is Biological, Not a Failure. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

In 1967, a Maltese physician and psychologist named Edward de Bono published a book titled New Think: The Use of Lateral Thinking. He had recently coined the term “lateral thinking” — a phrase that would, over the next half-century, become one of the most quoted pieces of management vocabulary in the English language.

The Oxford English Dictionary added it. Business schools built curricula around it. Thousands of corporate training programs used it.

Most of them missed the point.

De Bono spent the rest of his life — he died on June 9, 2021, at the age of 88 — trying to explain what he actually meant. He wrote more than 60 books. He consulted with governments and corporations across forty countries. He developed the Six Thinking Hats methodology, the Po provocation technique, and dozens of other tools.

But the core idea, the one that mattered, can be compressed into a single sentence he repeated in interviews and books for fifty years:

“You cannot dig a hole in a different place by digging the same hole deeper.”

This piece is about why that sentence is, in my opinion, the single most useful piece of career advice available to any senior leader navigating AI transformation in 2026 — and why almost nobody is actually applying it.

What de Bono was actually saying

The standard reading of de Bono is that “lateral thinking” means creative problem-solving. Think outside the box. Try unconventional approaches.

That reading is true but shallow. The deeper insight is about how the human mind works.

De Bono’s central claim was that the brain is, fundamentally, a pattern-recognition and pattern-following system. When you encounter a problem, your mind doesn’t reason from first principles. It rapidly retrieves the most familiar framework for dealing with similar problems and routes your thinking along that established pathway.

This is enormously efficient. It is also enormously limiting. Because the most familiar pathway is not always the most productive one.

De Bono called the conventional logical approach vertical thinking. It moves step by step from one premise to the next, deeper along an established pathway. It is the dominant mode of analytical reasoning that schools, professions, and corporations have been training for centuries. It is excellent for problems where the right pathway has already been identified.

He contrasted this with lateral thinking. Rather than digging deeper along the established pathway, lateral thinking deliberately steps off it and approaches the problem from a different angle, generating alternative entry points that vertical thinking would never encounter because they lie outside its trajectory.

The famous quote — “you cannot dig a hole in a different place by digging the same hole deeper” — is the compressed expression of this distinction. If the gold isn’t where you’re digging, no amount of additional vertical effort will produce gold. The only useful move is to stop digging and start somewhere else.

The five-feet-of-dirt problem

The analogy de Bono used to make this land with non-academic audiences was specific and visual.

Imagine you’re digging for gold. You’ve gone five feet down. No gold.

What do you do?

The intuitive answer for almost everyone — and especially for anyone trained in American business culture — is to dig faster. To intensify the effort. To bring in more shovels. To work the night shift. To execute relentlessly.

Sometimes this is right. If you have good reason to believe the gold is at ten feet, dig faster. If you’re in the right hole, the answer is more depth.

But sometimes — and de Bono argued, more often than people are willing to admit — the trouble isn’t that you’re digging too slowly.

The trouble is that you’re digging in the wrong place.

The instinct to dig faster, when you’re in the wrong place, is not just useless. It is structurally counterproductive. It uses up the energy, attention, and capital you would need to dig somewhere else. It deepens your investment in a location that isn’t going to pay out. It produces the appearance of progress without any of the substance.

This is the problem de Bono spent five decades trying to surface for executive audiences.

His repeated diagnosis: in business, in politics, in personal life, the dominant failure mode is not laziness or insufficient effort. It is highly competent vertical thinking applied to the wrong hole.

The personal observation that surfaces the framework

There is an observation about senior leadership that I think is one of the most underexamined experiences in modern executive life.

When I’m in the right context, I feel incredibly smart and capable. When I’m in the wrong context, that feeling disappears.

This is one of the sharpest articulations of an executive insight available, and it points directly at the de Bono framework.

The implicit promise of senior leadership, the way most executives have been taught to think about themselves, is that competence is portable. You are smart, or you aren’t. You are capable, or you aren’t. The capability travels with you across contexts because the capability is yours.

De Bono’s framework, supported by half a century of cognitive research that has confirmed his original intuition, says this isn’t true.

Capability is largely a function of context.

When the context matches the patterns your mind has been trained on, you appear — and feel — extraordinarily capable. The pattern-recognition system retrieves the right framework, the right vocabulary, the right judgment, the right examples, and routes your thinking productively. You make good decisions. You see things others miss. You feel sharp.

When the context shifts — when the problem doesn’t match the patterns you’ve trained on — the same brain that felt sharp now feels slow. You hesitate. You second-guess. You feel obsolete or out of your depth.

This is not a failure of intelligence. It is a structural feature of how the brain works.

For senior leaders, the implication is enormously liberating if you’re willing to accept it: when you feel less capable than you used to, you are not necessarily becoming less capable. You may simply be in the wrong context. The capability hasn’t gone anywhere. The patterns you trained for thirty years are mismatched with the patterns the new context demands.

The de Bono move, in this situation, is not to work harder at being capable in the wrong context.

The move is to change context.

Why this matters specifically in 2026

The reason I think this framework is the single most useful piece of career advice for senior leaders in 2026 is that the AI transformation is, for almost every senior executive I work with, a context shift.

The patterns they trained for thirty years — the patterns that made them feel incredibly capable in 2018, 2019, 2020 — are mismatched with the patterns the AI-augmented world demands.

The CMO who spent twenty years mastering brand strategy is sitting in meetings where AI is producing brand strategy in seconds.

The senior consultant who built her career on synthesizing complex client situations into clear strategic decks is watching clients use AI to produce equivalent decks in 90 seconds.

The CIO who spent his career mastering enterprise architecture decisions is being second-guessed by junior engineers using AI tools he doesn’t fully understand.

The CFO whose value was built on producing financial analysis from scarce data is operating in a world where data is infinite and analysis is automated, and the constraint has shifted to judgment, which is exactly the part of his role he most assumed would always be his.

Every one of these leaders has, by every objective measure, retained their underlying capability. They are not less intelligent than they were five years ago. They have not lost their experience, their judgment, their networks, their credentials.

They are digging in the wrong hole.

And every instinct trained by thirty years of corporate culture — the lean-in instinct, the execute-relentlessly instinct, the double-down instinct — is telling them to dig faster.

The de Bono diagnosis says this is precisely the wrong move.

The right move is to stop digging where they’ve been digging and move to a different location.

The sunk cost of the deep hole

The reason this is so hard to do, in practice, is that the hole you’ve been digging is deep. It represents twenty or thirty years of accumulated expertise. Credentials. Network. Identity.

When de Bono says “move to a different hole,” he is asking executives to walk away from a hole that took them their entire career to dig.

That feels, viscerally, like wasted effort.

It isn’t.

The capability that produced the depth of the old hole — the discipline, the pattern recognition, the judgment, the willingness to work hard at something for years — is the same capability that will produce the new hole. Faster. In a better location.

But you have to be willing to leave the depth behind.

This is, in my coaching experience, the hardest single conversation a senior leader has to have with themselves. Because the sunk cost feels enormous. The identity is bound up with the depth. The professional reputation rests on the depth. And the new hole, by definition, is shallow.

Most senior leaders, faced with this choice, choose to dig their existing hole deeper. They tell themselves the gold must be at fifteen feet, or twenty feet, or thirty. They redouble. They lean in. They execute relentlessly.

They produce, in some cases, the deepest hole in their industry.

And they fail anyway, because there is no gold there.

The de Bono framework is the diagnostic that surfaces this failure mode before it becomes terminal. Not by criticizing the depth of the hole — the depth was real, the effort was real, the expertise is real. But by asking the question that vertical thinking is structurally unable to ask:

Is this the right hole?

What the lateral move actually looks like

The misreading of “lateral thinking” that has dominated business culture is that it means radical creative reinvention. Quit your career. Start a band. Move to Bali.

That’s not what de Bono meant, and it’s not what the framework requires.

A lateral move, in the de Bono sense, is a sideways shift that preserves the underlying capability while changing the location of the work.

The CFO doesn’t quit finance. She shifts from producing analysis to designing the AI-augmented decision architecture that produces analysis. Same financial judgment, different hole.

The CMO doesn’t abandon brand work. He shifts from producing brand strategy to orchestrating an AI-augmented creative process that produces brand strategy. Same taste, different hole.

The senior consultant doesn’t leave consulting. She shifts from being the analyst who builds the decks to being the orchestrator of AI-assisted client engagement. Same client intuition, different hole.

The CIO doesn’t abandon technical leadership. He shifts from architecting systems to architecting the integration of AI into systems. Same architectural judgment, different hole.

In every case, the capability that produced the depth of the old hole transfers. The new hole isn’t a betrayal of the career — it’s a continuation of it, in a location where the underlying capability can still compound.

The leaders who recognize this in 2026 will be the leaders who define the next decade.

The leaders who keep digging the old hole, however deep, will spend the next decade wondering why their effort is producing diminishing returns while less talented peers seem to be effortlessly moving ahead.

The less talented peers aren’t more talented. They’re in a different hole.

Three practical questions

If you want to actually apply the de Bono framework to your own career in 2026, three questions are worth sitting with.

One: what is the central pattern around which your senior career has been built?

Not “leadership” or “strategy” — these are too abstract to be useful. The actual concrete pattern. The specific kind of problem you have spent the last decade getting good at solving. The recurring situation in which you feel most capable, most confident, most valuable.

That pattern is your hole. Identify it precisely.

Two: has the value of that pattern changed in your industry in the last three years?

Be honest. Not whether you still find the pattern useful — whether the market still rewards that pattern at the price it used to. In some industries the pattern is still valuable. In many, the price has quietly collapsed because AI now produces the same pattern at a fraction of the cost.

If the answer is “the price has collapsed,” you are still digging at five feet. The gold has moved.

Three: what lateral move preserves your underlying capability while changing the location of the work?

Not a complete career reinvention. A sideways shift. The same expertise applied to a different problem, where the value is still compounding. Where the pattern you’ve trained on is still rare and useful.

That’s the lateral move. It will feel uncomfortable. The new hole will be shallow for a few years. The depth will take time to rebuild.

But the alternative is digging the old hole deeper, in a location where there is no longer any gold.

The closing thought

There is a phrase from de Bono’s later work that I want to close with, because it captures something his motivational-poster admirers usually miss.

He wrote, in I Am Right, You Are Wrong (1990), that the deepest problem with conventional intelligence is not that it produces wrong answers. It is that it produces highly competent answers to questions that should have been asked differently.

“It is not enough to do your best; you must know what to do, and then do your best.”

That is the framework, compressed.

The American business culture of the last forty years has been overwhelmingly focused on the second half of that sentence. Do your best. Execute relentlessly. Work harder. Lean in. Double down.

The first half — know what to do — has been quietly underweighted, because it is harder to teach, harder to measure, and harder to perform publicly.

You can show effort. You can’t easily show that you’ve correctly identified which hole to dig.

The AI transformation is forcing the first half back into the center of executive practice. Because in 2026, executing relentlessly on the wrong hole produces nothing. The pattern that worked five years ago has, in many domains, no longer the right pattern to be reinforcing.

The leaders who will define the next decade are the ones who, right now, are asking the harder question.

Am I in the right hole?

If the answer is yes, dig harder. Use every ounce of accumulated capability to go deeper.

If the answer is no, stop.

Move sideways. Start a new hole. Trust that the capability that produced the depth of the old hole will produce the new one, in a better location, with the years you have left.

De Bono spent fifty years teaching this. Most people who quoted him missed the point.

In 2026, with AI rewriting the location of value in almost every senior career, the point is no longer optional.

You will not feel as capable in the new hole, at first. The shallow depth will feel like regression. The familiar patterns of the old hole will keep pulling at you.

That feeling — the wrong-context feeling — is the most important piece of executive information available to you right now. It is the signal that the hole has changed.

The discipline is to read the signal honestly, to stop digging, and to start somewhere else, while you still have the energy to make the depth count.

The world has changed. The leaders who notice will be the ones the next decade is built around.

The post Career Advice for the AI Transformation. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

In 2000, Janet Lowe published a biography of Charlie Munger titled Damn Right! Behind the Scenes with Berkshire Hathaway Billionaire Charlie Munger. Buried in it is one of the most-quoted passages in modern business literature — a passage that has, in the twenty-five years since, been reduced to a motivational poster by people who didn’t read the chapter it came from.

Here is the passage, in full:

“Whenever you think that some situation or some person is ruining your life, it’s actually you who are ruining your life. It’s such a simple idea. Feeling like a victim is a perfectly disastrous way to go through life. If you just take the attitude that however bad it is in any way, it’s always your fault and you just fix it as best you can—the so-called ‘iron prescription’—I think that really works.”

Munger called this the iron prescription. He returned to it in talks, interviews, and shareholder letters for the rest of his life. It is, alongside Buffett’s writings on investing temperament, one of the foundational psychological statements of late-20th-century American business.

Most people who quote it treat it as a tough-love motivational line. Take responsibility. Stop blaming others. The basic stoic move.

I think this reading dramatically underestimates what Munger was actually saying. And I think the original context — which almost no one includes when they quote the iron prescription — is the entire reason the principle works.

This piece is about that context, what Munger was really teaching, and why it is the single most useful psychological technology any senior leader can carry into the age of AI.

The context most retellings strip out

In 1953, Charlie Munger was 29 years old. He had been married since he was 21. That year, his wife divorced him. The settlement was severe enough that he lost the family home in South Pasadena and moved into what biographers describe as “dreadful” conditions at the University Club of Pasadena.

Shortly after the divorce, his nine-year-old son Teddy was diagnosed with leukemia. In 1953, leukemia in children had no effective treatment. The death rate was nearly 100%. There was no health insurance. Munger paid everything out of pocket as his savings drained.

Rick Guerin, Munger’s friend from that period, described him going to the hospital, holding his dying son, and then walking the streets of Pasadena crying.

In 1955, when Munger was 31, Teddy died.

Divorced. Broke. Burying his nine-year-old son.

If anyone in modern American business history had earned the right to declare themselves a victim of fate, it was Charlie Munger in the autumn of 1955.

He refused.

What happened next is the part of the Munger story that gets told, but usually without the context above. He rebuilt his law career. He remarried — to Nancy Borthwick, whom he stayed married to for over half a century. They raised a blended family of eight children together. He started investing seriously. He met a young Omaha investor named Warren Buffett. They began what would become one of the most successful business partnerships in human history.

Munger died on November 28, 2023, thirty-three days before his 100th birthday. He had lived 68 years past the worst year of his life.

The iron prescription wasn’t a slogan from someone who hadn’t been tested.

It was a survival technology forged in catastrophic loss.

That distinction matters because almost every glib reading of the iron prescription misses the source of its authority. Munger wasn’t moralizing about other people’s resilience from a position of comfort. He was reporting what he had personally discovered, in 1955, was the only mental position that allowed him to keep functioning.

What Munger was actually teaching

The standard reading of the iron prescription is “take responsibility, stop blaming others.” This is true but incomplete.

The deeper reading is about agency preservation.

Munger’s discovery — and this is what most retellings miss — wasn’t that victims are morally wrong. It’s that victimhood is strategically wrong. It is the one mental position that guarantees you cannot improve your situation.

The moment you decide that some external force is ruining your life, you have located the cause of your problem outside yourself. By definition, you cannot fix something that exists outside yourself. You have surrendered the only lever you actually have.

This is not about moral judgment. It is about what mental positions preserve your ability to act.

The iron prescription is the discipline of always assuming, as your operating frame, that the situation is your fault — not because it always is, but because that’s the only frame that keeps you capable of fixing it.

Even when the situation is genuinely caused by external forces. Even when other people are clearly to blame. Even when the world is genuinely unfair. The iron prescription says: start with what is yours, because that’s the only place you can act from.

This is structurally identical to what Viktor Frankl wrote in Man’s Search for Meaning, which Munger explicitly recommended throughout his life. Frankl, who lost his entire family in the Holocaust, wrote that the one thing the camps could not take from him was his ability to choose his response to what was happening.

It is structurally identical to the central insight of Stoicism. Epictetus, born a slave, wrote that what disturbs people is not events themselves but their judgments about events.

It is structurally identical to the foundation of cognitive behavioral therapy. Aaron Beck’s 1960s work on cognitive distortions identified externalization — locating the cause of one’s distress outside oneself — as one of the central thinking patterns associated with depression and learned helplessness.

The iron prescription is, in this sense, not original to Munger. What was original was his willingness to articulate it bluntly, in business language, after having paid for it with the worst losses a human being can face.

Why this matters specifically in the age of AI

The reason I want to write about Munger’s iron prescription now, in 2026, is that I think it is the single most useful psychological technology any senior leader can carry into the age of AI.

The temptation to externalize has never been higher.

For thirty years, business culture has had a relatively stable set of explanations for failure. Bad strategy. Wrong people. Insufficient capital. Market conditions. Regulatory environment. Competitor actions. These explanations were useful because they pointed at things you could actually fix.

The arrival of AI has given every senior leader an entirely new menu of externalization options. Every one of them sounds reasonable. Every one of them strips agency.

I see five common patterns. Let me walk through each.

Pattern One: “AI is going to make my role obsolete.”

The externalization version: AI is making my expertise obsolete.

The iron prescription version: You haven’t yet decided what you uniquely contribute that AI doesn’t. That’s not AI’s fault. That’s a thinking problem you haven’t done.

The honest reality is that every senior leader’s role is being transformed by AI, and the leaders who will thrive are the ones who have actively done the work of figuring out which parts of their value are augmented by AI versus which parts are commoditized by it. Nobody else can do this work for them.

Pattern Two: “Our AI transformation isn’t working because the vendor underdelivered.”

The externalization version: The vendor failed.

The iron prescription version: You chose the vendor. You wrote the contract. You set the success metrics. You signed off on the implementation plan. Where is the part that’s actually yours?

The structural reality of every enterprise AI program I have seen succeed is that the executive sponsor took ownership of the program’s success in a deep and personal way — including for the parts that other people were nominally responsible for.

Pattern Three: “My industry is being disrupted by AI.”

The externalization version: My industry is being disrupted. This is happening to me.

The iron prescription version: The warning signs were everywhere for at least three years. What did you do during those three years, and what are you going to do in the next three?

Pattern Four: “AI hallucinated and gave us the wrong answer.”

The externalization version: AI hallucinated.

The iron prescription version: Your team used an AI tool they didn’t fully understand, on a high-stakes deliverable, without verification. The error is structurally yours, not the tool’s. You didn’t build the verification habits. You didn’t train your team on what AI is and isn’t reliable for.

Pattern Five: “Our people aren’t AI-ready.”

The iron prescription version: You didn’t invest in training at the scale required. You didn’t model the behavior yourself. You didn’t make AI fluency a meaningful factor in how you hire, promote, and reward people. The readiness gap is one you created through years of optional engagement.

The pattern across all five

In every one of these five cases, two things are true.

The externalization version is, in some real measure, factually accurate.

The iron prescription version is also accurate, and it’s the only version that preserves your capacity to act.

This is the deepest thing Munger was teaching, and it’s why I think the iron prescription is so much more sophisticated than the motivational-poster version of it.

He wasn’t denying that external causes are real. He was pointing out that the frame of starting with your own contribution is the only frame that produces resilient action.

You can hold both truths at once. The vendor genuinely did underdeliver, and you chose them. The technology is genuinely imperfect, and you put it in production without verification habits. The industry is genuinely being disrupted, and you had three years to prepare and didn’t.

Holding both truths is the discipline. Defaulting to the second one — your own contribution — is the iron prescription.

The limits, honestly

It would be dishonest not to flag the limits of this principle, because the iron prescription is genuinely dangerous when applied as a moral judgment of other people’s circumstances.

The iron prescription is a personal discipline for people with agency. It is not a moral framework for evaluating people without agency. A single mother working two minimum-wage jobs in a city with no affordable housing is not failing to apply the iron prescription. A 14-year-old growing up in a war zone is not failing to apply the iron prescription.

Munger’s framework is best understood as a gift you give yourself — a way of holding your own circumstances that maximizes your agency — not a judgment you impose on others.

The senior leaders I work with are, almost without exception, people with substantial agency. They have resources, education, networks, options, capital, and credibility. They are squarely in the population for whom the iron prescription is the most useful possible mental technology.

If you are a senior leader reading this, the iron prescription is for you. You have the agency. The question is whether you’ll use it.

The practical discipline

If you want to actually apply the iron prescription in 2026, here is the simplest possible version.

Every time you catch yourself saying — out loud or internally — that something or someone is ruining something about your work, run the translation.

The original thought: “X is ruining Y for me.”

The translation: “What am I doing, or not doing, that is making this worse — and what am I going to do differently this week?”

You will not always have an answer. Some situations are genuinely external. Some weeks you will run the translation and conclude that there is, in fact, nothing more you can do this week, and that the situation is mostly outside your control.

Even in those cases, the discipline is doing work. The habit of looking inward first preserves your capacity to act, even when looking inward doesn’t yield an immediate answer.

Over months and years, this discipline reshapes how you carry yourself. You stop being a person who is constantly being acted upon by circumstances. You start being a person who is constantly looking for the leverage that is actually available to you.

That second person is the kind of senior leader the next decade rewards.

The first person is the kind the next decade quietly retires.

The closing thought

There is a small, somewhat painful detail about Munger that I want to close with.

He was asked, multiple times across his life, how he survived Teddy’s death. The answers he gave varied in detail but converged on the same core idea. He said something to the effect of: self-pity is a guaranteed way to make a bad situation worse, even when the situation is unimaginable. So I just refused to engage with it.

He was not denying that the loss was catastrophic. He was not pretending that grief wasn’t real. He was reporting a structural decision: self-pity, however justified, was the one mental position that guaranteed his life would get worse from there. So he refused it. Not because he didn’t feel the grief. Because he had decided, in advance, that the grief would not be the thing that ran his life.

That decision, made by a 31-year-old man in 1955 who had every reason to surrender to despair, is what made the next 68 years of his life possible.

The iron prescription is the same decision, generalized.

It says: whatever is happening to you, whatever the cause, whatever the apparent injustice, the only useful mental position is to ask what part of this is yours and what you’re going to do about it.

In the age of AI, with the temptation to externalize at an all-time high, with the menu of plausible excuses growing wider every quarter, this is the most useful single discipline any senior leader can carry.

Munger paid for the insight in a way none of us would ever wish on ourselves.

We get to use it for free.

The world has changed. The leaders who notice will be the ones the next decade is built around.

The post Charlie Munger’s Can Carry Into the Age of AI first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.

Syndicated analysis. Originally published on Agile Agilist. Figures are as presented by the original author; for fully cited data see our Statistics Library.

The first quarter of 2026 confirms something critical:

AI development is accelerating faster than its economic value realization.

But the gap is starting to close.

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The Big Picture (2024 → 2026)

📊 AI Adoption & Economic Impact Trend

Year        Adoption       Investment       Revenue Impact
---------------------------------------------------------
2024        Medium         High             Low
2025        High           Very High        Emerging
Q1 2026     Very High      Extreme          Scaling (early)

1. AI Investment Explosion

• Global AI market: ~$298B in 2026, growing ~36% CAGR
• AI-related spending projected at $2.53 trillion in 2026
• Top tech companies alone plan $562B in AI capital investment
• 96% of tech leaders say AI remains a top priority

2. AI Adoption Reaches Critical Mass (Q1 2026)

• 50% of U.S. employees now use AI at work
• Daily/weekly usage reached 28% (all-time high)
• AI agents deployed in 80% of tech environments

👉 AI has moved from tool → habit → default behavior

3. Productivity Gains Are Real

• 30–35% productivity gains in software development
• 65% of employees report positive productivity impact

4. Revenue Impact Is Still Catching Up

• Only 20% of organizations currently see revenue growth from AI
• 74% expect revenue growth in the future
• Only 25% of S&P 500 firms report measurable AI impact in Q1 2026 (up from 13% in 2025)

👉 The pattern: Adoption leads → value lags → then scales

5. AI Is Reshaping Entire Industries

• TSMC revenue: +40.6% YoY in Q1 2026 driven by AI demand
• AI-related imports surged 73% from 2023–2025
• Data center & chip demand is outpacing supply globally

Why This Gap Exists

AI creates value in three stages:

1. Automation (Efficiency)
2. Augmentation (Productivity)
3. Transformation (Revenue & Growth)

Most organizations are still in Stage 1–2. Very few have reached Stage 3.

Final Thought

2026 is not the year AI proves its value. It is the year AI forces organizations to prove their ability to use it. The winners will be those who move fastest from efficiency → revenue, turn adoption → transformation, and convert capability → economic impact.

Syndicated analysis. Originally published on Agile Agilist. Figures are as presented by the original author and may not carry primary-source links; for fully cited data see our Statistics Library.

State of AI 2025 – Key Statistics

The chart shows five core numbers that represent the current maturity of AI:

Metric from McKinsey (2025)	Percentage
Generative AI is used regularly in a function	78%
Generative AI used regularly in a function	71%
Organizations that track GenAI KPIs	<20%
Orgs where ≥5% of EBIT is due to GenAI	~17%
Orgs experiencing negative GenAI consequences	47%

🔑 Insight:

Companies aren’t struggling to adopt AI. They’re struggling to capture value and manage risk.

AI in 2026 is defined by a paradox: adoption is nearly universal, capability is compounding, and capital is flooding in — yet most organizations still struggle to turn it into profit. Here are eight sourced statistics that capture where things actually stand, each linking to the full data on our Statistics Library.

Published June 2026 · Every figure links to its primary source.

1. 88% of organizations now use AI

Up from 78% a year earlier, AI adoption is effectively universal among organizations — the differentiator is now depth, not presence. → AI Adoption Statistics 2026

2. 900 million people use ChatGPT every week

Double a year earlier — generative AI has been adopted faster than the PC or the internet. → Generative AI Statistics 2026

3. But only 39% see EBIT impact

Adoption hasn’t translated into broad financial return — value is concentrated in a small group of “AI high performers.” → Enterprise AI Statistics 2026

4. AI took 61% of global venture capital

About $258.7 billion of 2025’s VC went to AI — more than double AI’s 30% share in 2022. → AI Investment & Funding Statistics 2026

5. A net +78 million jobs by 2030

The WEF projects 170M new roles and 92M displaced — but with 22% workforce churn and a sharp squeeze on entry-level hiring. → AI & Jobs Statistics 2026

6. SWE-bench jumped from 4.4% to 71.7% in a year

Model capability is improving faster than benchmarks can keep up — MMLU is now saturated above 92%. → AI Models & Benchmarks Statistics 2026

7. 362 AI incidents — and tougher rules

Reported incidents rose ~55% as the EU AI Act (€35M / 7% fines) and 131 new US state laws raised the stakes. → AI Safety & Governance Statistics 2026

8. Data centers heading toward 945 TWh

AI is driving data-center electricity demand to roughly double by 2030, with AI infrastructure spending on track to exceed $1 trillion by 2029. → AI Infrastructure & Compute Statistics 2026

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The throughline: 2026 is the year AI became infrastructure — ubiquitous, capable, and expensive — while the gap between adoption and realized value, and between capability and governance, defines who wins. Explore the full, sourced data in our Statistics Library.

There’s a number that’s been floating around the business press for two decades now. Different versions, different sources, different transformations — but the headline never really changes.

Roughly 70% of major transformations fail.

Cloud adoption. Digital transformation. ERP. Platform-based architectures. Agile. Now AI. The technology changes. The failure rate doesn’t.

That should be the most uncomfortable statistic in modern business, because it’s telling us something the industry has been studiously refusing to hear. The failures aren’t about the technology. They’ve never been about the technology. They’re about everything that has to happen around the technology for it to actually work.

And the consulting firms whose entire business is studying this — McKinsey, BCG, Deloitte, the Stanford HAI Index — have been remarkably consistent on the math. Let me walk through what the most recent research actually says, because the picture in 2026 is sharper than the old “70% fail” cliché.

What the 2025-2026 data actually shows

McKinsey’s 2025 State of AI survey tested 25 attributes across nearly 2,000 organizations and found that workflow redesign had the single strongest correlation with EBIT impact from AI. Not model selection. Not data infrastructure. Not vendor choice. Workflow redesign.

High-performing organizations — the roughly 6% that reported meaningful financial returns — were nearly three times more likely to have fundamentally redesigned their workflows around AI rather than layering AI onto existing processes.

BCG’s 2025 global study of 1,250 companies tells the same story in different words. Only about 5% create substantial AI value at scale, while 60% generate no material value from their AI investments despite meaningful spending.

Sixty percent. Spending real money. Getting nothing back. Not because the technology doesn’t work — the technology works fine — but because the differentiator is not the technology. It’s whether the organization treats AI as a tool to add or a reason to redesign how work gets done.

This is the punchline buried under thousands of pages of consulting research: the technology is the easy part.

The 10-20-70 principle

BCG has been promoting a framework that captures this better than any other framing I’ve seen. They call it the 10-20-70 principle.

Companies should devote 10% of their efforts to algorithms and 20% to technology and data; the remaining 70% of their efforts should focus on people and processes to make sure that the changes stick.

Read that again, because it’s almost the inverse of how most enterprise AI budgets are actually allocated.

Most leaders I work with are spending roughly the opposite. Seventy percent of the budget goes to platforms, tools, models, vendors, and infrastructure. Twenty percent goes to data and integration. Ten percent — if that — goes to the people and processes that determine whether any of it actually changes the way work gets done.

Then they’re surprised when nothing changes.

The 10-20-70 numbers are directional, not absolute. MIT Sloan found that 70% of AI’s value depends on complementary investments in people and process, not on the sophistication of the technology. Different study, different methodology, same conclusion.

The technology is increasingly commoditized. Any mid-market company can access the same frontier models from OpenAI, Anthropic, or Google. What’s not commoditized is your organization’s capacity to absorb, adopt, and operationalize that technology. That’s the moat now.

This pattern is not new

Here’s the part that should give every leader pause.

This isn’t a new finding. We’ve seen this same pattern in every major technology wave of the last quarter-century.

Cloud adoption (2010-2020). Most enterprises that moved workloads to AWS or Azure without changing their operating model got cloud bills instead of cloud value. The 70% that struggled weren’t fighting the technology — they were fighting their own org charts, procurement processes, and engineering cultures.

Digital transformation (2015-present). McKinsey’s research on digital transformation has reported failure rates of 70-80% for over a decade. Same root cause. Companies bought new tools and tried to run them on old operating models.

Platform-based architectures (2018-2024). Microservices, APIs, event-driven systems. The tech worked. The pattern of failure was the same — teams structured around the old monolith couldn’t suddenly behave like teams owning loosely coupled services, no matter what the architecture diagram said.

Agile adoption (2010-present). This one is almost a parody at this point. Every large enterprise has “adopted agile.” Very few have actually changed how decisions get made, how funding flows, or how work gets prioritized. The framework is on the wall. The behaviors haven’t moved.

In every case, the pattern is identical:

• The technology works.
• The leaders adopt it.
• The org doesn’t absorb it.
• Two years later, someone declares the transformation “stalled.”
• A new technology arrives.
• The cycle restarts.

AI is going to follow this script unless leaders deliberately choose otherwise.

What “deliberately choosing otherwise” actually looks like

The good news is that the small minority of organizations getting this right are extremely well-studied. We know what they do differently.

High performers are 3.6x more likely to pursue transformational change and 55% fundamentally rework workflows when deploying AI. They’re not adding AI on top. They’re rebuilding the work around AI.

They invest the 10-20-70 the way BCG describes it — most of the money goes to change capacity, not to compute.

They have executive sponsors who are publicly committed and operationally engaged, not just funding the program from a distance.

They build shared language across the organization, so the CEO, the CIO, the frontline manager, and the analyst all mean the same thing when they say “AI.” Without shared language, every conversation restarts from zero.

They redesign workflows end-to-end rather than automating existing ones. If you take a broken, manual, approval-heavy workflow and add AI on top of it, you get a slightly faster broken workflow. That’s not transformation. That’s expensive friction reduction.

They measure value rigorously. Not “did we deploy the tool” but “did the work change, did the cycle time drop, did the cost-to-serve improve, did the customer outcome improve.”

And — this is the one most leaders skip — they invest in workforce capability ahead of deployment. Companies that are realizing the most value from AI also have the most ambitious upskilling programs, and put the resources in place to support them.

The hard truth for Canadian enterprise leaders in 2026

If you’re running an AI program right now, you are statistically much more likely to end up in the 60% generating no material value than in the 5% capturing real returns.

That’s not pessimism. That’s what the data says.

The path to being in the 5% is not picking a better LLM. It’s not waiting for the next model release. It’s not hiring more AI engineers.

It’s investing the 70% of effort that almost everyone underspends — change capacity, workflow redesign, executive fluency, shared language, workforce capability, governance that doesn’t choke delivery.

This is exactly why the AI-Native curriculum is structured the way it is. Foundations builds shared language. Change Agent develops the people who can actually land the change. Leading the AI-Native Organization equips executives to make the release-rate, prioritization, and workforce decisions that determine whether the 70% gets the attention it deserves.

The data has been telling us this story for twenty years. Cloud. Digital. Platforms. Agile. Now AI.

The pattern doesn’t change because the technology changes.

The pattern changes because leaders choose to invest where the value actually lives.

The closing thought

There’s a comforting story leaders sometimes tell themselves: this time it will be different, because this technology is more powerful.

It’s not going to be different. It’s never been different.

The technology was powerful in 2005. It was powerful in 2015. It’s powerful in 2026. And in every era, the same 60-70% of organizations have failed to extract value from it for the same reason — because they treated transformation as a technology problem when it has always been a people-and-process problem.

The 5% who get this right aren’t smarter. They’re not better resourced. They’re not luckier.

They’re just willing to spend their effort where the value lives — not where the marketing budget is loudest.

Build the 70%. The other 30% will follow.

The post Success in the Age of AI Isn’t a Technology Story. first appeared on Agile Agilist | SAFe® Gold Partner for Agile Transformation, Innovation & Leadership Training.