Category Archives: Change

Making Change Stick

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Making Change Stick

GUEST POST from David Burkus

You’ve poured hours into developing a brilliant new strategy. Maybe it’s a streamlined process, a novel team ritual, or a bold cultural shift you know will improve how your team works. You present it to the team. Heads nod. There’s excitement. Applause, even.

And then…nothing.

A few weeks go by and everything’s back to the old way. People are still using the outdated process. The team ignores the new meeting cadence. That cultural initiative? Forgotten. It’s as if your big idea evaporated the moment the meeting ended.

So what happened?

As frustrating as it is, this scenario is all too common. And it reveals an important truth about making change stick: it’s not the brilliance of your idea that matters most. It’s how well that idea is presented and remembered. And for most leaders, that’s where the real challenge begins.

The Flawed Approach to Leading Change

For decades, leaders have looked to process improvement and efficiency as the holy grail of organizational success. From Frederick Taylor’s scientific management to modern methodologies like Six Sigma or Agile, there’s no shortage of change initiatives aimed at helping teams get better.

And in theory, many of them work.

But research from UNC’s Brad Staats and Oxford’s Matthias Holweg and David Upton tells a different story about what happens after rollout. According to their study of over 200 process improvement initiatives across a major European bank, roughly 50% of those projects were abandoned within the first year. And after two years? Only one in three remained.

They call it the improvement paradox—the fact that even successful initiatives often fade over time. And the reason isn’t because the ideas were bad. It’s because of how those ideas were introduced and sustained—or, more accurately, how they weren’t.

Why Good Ideas Don’t Stick

The researchers identified several culprits behind why making change stick is so difficult. And if you’ve ever led a change that didn’t last, these may sound familiar.

Initiative Fatigue
When every quarter brings a new mandate or buzzword, people stop getting excited and start getting cynical. It becomes easier to nod along in the meeting and quietly keep doing things the old way.

Lack of Personal Benefit

When change feels like it’s for the company but not for the individual, motivation suffers. People ask, “What’s in it for me?” And if they can’t find a compelling answer, they’re unlikely to put in the effort required to make the change real.

Loss of Ownership

Many initiatives are handed down from above—or worse, handed over from highly paid consultants—as rigid prescriptions rather than collaborative efforts. When people feel forced to comply instead of invited to contribute, they disengage.

Curse of Knowledge

But perhaps the most overlooked obstacle is this: we fall in love with our own ideas and forget what it’s like not to understand them.

Psychologists call this the curse of knowledge. Once you know something well, it becomes almost impossible to imagine what it’s like not to know it. Which means that when we communicate our change initiatives, we often assume too much.

A classic study by Stanford Ph.D. student Elizabeth Newton illustrates this. Participants were asked to tap out the rhythm of a popular song while another person tried to guess what it was. Tappers thought their partners would guess the song about 50% of the time. In reality? Only 2.5% of the time.

Why? Because the tappers could hear the melody in their head. But to the listener, it was just tapping. They didn’t have the context.

The same thing happens with change efforts. Leaders have been thinking about their new strategy for weeks or months. They’ve connected the dots. They see how it all fits. But their teams haven’t been part of that process—and as a result, they don’t hear the melody. Just the tapping.

Making Change Stick

So how do you overcome the curse of knowledge, and the other traps that make change initiatives fade?

You don’t need to be more charismatic. You don’t need a better slide deck. You need a better design for your message. Authors Chip and Dan Heath argue that “sticky” ideas share a few key principles. And when it comes to making change stick, these three are especially powerful.

1. Be Simple: Find the Core Message

Don’t oversimplify. But do clarify. Strip away the jargon, the background noise, the long-winded rationale—and identify the one idea you want your team to remember.

What’s the slogan behind your initiative? What’s the one phrase they can use to guide their decisions?

If your team walks away from your message and forgets everything else, what’s the one thing they must retain? Then say that. A lot.

2. Make It Concrete: Use Sensory and Tangible Language

People don’t latch onto abstract mission statements. They remember vivid images. One of the best examples of this comes from Jeff Hawkins, lead designer of the original Palm Pilot.

To keep the device simple and user-friendly, he carried around a block of wood shaped like the Palm Pilot. Any time someone proposed a new feature, he’d pull it out and ask, “Where are we going to put it?”

It was a tangible symbol of the constraints—and priorities—of the team. And it worked.

You can do the same by anchoring your message in real-world actions, visuals, or metaphors. Don’t say “streamline communication.” Say, “We’re cutting weekly meetings in half so you can get your Wednesdays back.”

3. Use Stories: Help People Feel the Message

Data informs. Stories stick.

The oldest tool for spreading ideas is still the most effective. When you tell a story—about a time the team nailed collaboration, or a moment when things fell apart because the process wasn’t followed—you help people emotionally experience the change you’re trying to create.

Stories give people something to believe in. Something to remember. And something to model their behavior after. Before your next rollout, ask: What stories do I have that illustrate what good looks like? Or what happens when we get it wrong?

You don’t need a slide deck for this. You just need a story.

The Bottom Line

Making change stick isn’t about shouting louder or using fancier words. It’s about designing your message in a way that overcomes fatigue, sparks ownership, and connects emotionally.

That means asking yourself:

  • Is my idea simple enough to remember?
  • Is it concrete enough to visualize?
  • Is it wrapped in a story people want to be part of?

Because ultimately, people don’t follow mandates. They follow meaning. And your job as a leader is to help them see themselves in the better future your change is meant to bring.

Want to make your next change effort stick? Start by telling a better story.

Image credit: Google Gemini

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We Must Think Less Like Engineers and More Like Gardeners

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We Must Think Less Like Engineers and More Like Gardeners

GUEST POST from Greg Satell

In February, 1919, the famous philosopher Bertrand Russell received a card from his former student, Ludwig Wittgenstein, who was at that time in an Italian prison camp. “I’ve written a book which will be published as soon as I get home,” he would say in subsequent correspondence. “I think I’ve solved our problems finally.”

The “problems” he spoke of had to do with a foundational crisis in mathematics and logic that defied the efforts of the world’s greatest minds. The book, Tractatus Logico-Philosophicus, was an attempt to engineer a perfectly logical language from first principles. It would become enormously influential, leading to the Vienna Circle and the logical positivist movement of the 1920s.

Yet Wittgenstein would later disown the idea and it was, in the end, found to be unworkable. There are limits to what we can engineer. The world is a messy place. Rules inevitably have exceptions, which is why every system will always crash. That’s why we need to think less like engineers making machines and more like gardeners that grow and nurture ecosystems.

The Death of the Secular Gods

The problems Russell and Wittgenstein were working on were part of a larger paradigm shift. By the late 19th century, many intellectuals had begun to question ideas passed down from the ancient Greeks, such as Aristotle’s Logic, Euclid’s geometry and the miasma theory in medicine, overturning two thousand years of conventional wisdom.

It’s hard to overstate the seismic shift that this represented. Aristotle’s use of the syllogism, in which conclusions necessarily followed premises, Euclid’s postulate that parallel lines never intersect and Hippocrates theory that bad air causes disease, were considered to be the basic foundations upon which western thought was predicated.

Yet as human knowledge advanced, people began to see flaws in these precepts. Strange paradoxes called Aristotle’s logic into question. Mathematicians like Gauss, Lobachevsky, Bolyai and Riemann began to imagine curved spaces in which parallel lines did, in fact, intersect and scientists such as Robert Koch, Joseph Lister and Louis Pasteur established the germ theory of disease.

These would be, practically speaking, incredibly positive developments. The rise of non-Euclidean geometry made Einstein’s general theory of relativity possible and the germ theory of disease paved the way for antibiotics and much longer lifespans. Yet they created an unwarranted optimism about what the human mind could achieve.

A New Religion

In the early 20th century, science and technology emerged as a rising force in western society. The new wonders of electricity, automobiles and telecommunication were quickly shaping how people lived, worked and thought. Physicists like Einstein and Bohr became celebrities. It seemed that there was nothing that scientific precision couldn’t achieve.

It was against this backdrop that Moritz Schlick formed the Vienna Circle, which became the center of the logical positivist movement and throughout the 20’s and 30’s. At its core was Wittgenstein’s theory of atomic facts, the idea that the world could be reduced to a set of statements that could be verified as being true or false—no opinions or speculation allowed. Those statements, in turn, would be governed by a set of logical algorithms which would determine the validity of any argument.

Yet even as this logical movement was growing, the foundational crisis in logic continued. To solve the problem, David Hilbert the greatest mathematician of the era, proposed a program to solve the crisis that rested on three pillars. First, mathematics needed to be shown to be complete in that it worked for all statements. Second, mathematics needed to be shown to be consistent, no contradictions or paradoxes allowed. Finally, all statements need to be computable, meaning they yielded a clear answer.

Then things took a surprising turn. A young logician named Kurt Gödel would prove that every logical system is flawed with contradictions. Alan Turing would show that all numbers are not computable. The Einstein-Bohr debates would be resolved in Bohr’s favor, destroying Einstein’s vision of an objective physical reality and leaving us with an uncertain universe.

The Rise Of Faux Scientists

The verdict was in. Facts could never be absolutely verifiable, but would stand until they could be falsified. We could, after thorough testing, increase our confidence, but never be completely sure. Ironically, the demise of logic led directly to the era of digital computing and a new, technological age. Just as we learned that systems would always be fallible, the machines we built became unimaginably powerful.

At the same time, human agency was increasingly called into question. It was, after all, subjective judgements that led to the Great Depression of the 1930s and the enormous wars that followed it. As the Baby Boomers came of age in the 1960s, it seemed like everything was up for debate. All of the fuzziness and uncertainty of relying on human judgment increasingly seemed impractical.

Much like Wittgenstein and the Vienna Circle, a number of thinkers sought to engineer systems that would harness natural forces to create better outcomes. The Austrian School of economics eschewed government regulation in favor of consumer preferences. Neorealism in foreign relations argued that competition and conflict could govern that international order.

Yet unlike the original logical positivists, these ideas wouldn’t stay confined to academia, but would seep into the affairs of everyday people. The consumer welfare standard insisted that market price signals, not government bureaucrats, would decide if a transaction should be permitted, while the principle of shareholder value demanded that the stock market, not managers, should govern business decisions.

The results are clear. Too little antitrust regulation has increased concentration in the vast majority of American industries and strangled competition, which has decreased business dynamism and lowered productivity. Our economy has become markedly less productive, less competitive and less dynamic. Purchasing power for most people has stagnated. By just about every metric, we’re worse off.

We Need To Manage Ecosystems, Not Machines

We like to think of ourselves as rational actors, weighing each piece of evidence before making a decision. Yet our brains don’t work like that. We build up our perspectives through synapses in our brain and through our social networks, which form complex webs of influence. Once we adopt a point of view, we rarely adapt it to new evidence.

Engineers believe in laws that can be understood and put to specific use, so they build machines to perform specific tasks. Gardeners believe in complexity and emergence. They don’t design their garden as much as tend to it, nurture it and support its surrounding ecosystem. They don’t expect the same results every time, but understand they will need to adjust their approach as they go.

We need to think less like engineers and more like gardeners. For most important purposes, we manage ecosystems, not machines. We need to think more in terms of networks that grow and less in terms of nodes whose behavior we can predict and control. Our success or failure depends less on individual entities than the connections between them.

In a world driven by networks and ecosystems, we can no longer treat strategy as if it were a game of chess, planning out each move with near perfect precision and foresight. The task of leadership is to make decisions with full knowledge that many will be wrong and that you will need to make them right.

There’s no system to do that for us, no impersonal forces that will point the way. In the end, we have to put trust in ourselves. There isn’t anyone else.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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Business Leaders Must Learn About Political and Social Movements

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Business Leaders Must Learn About Political and Social Movements

GUEST POST from Greg Satell

Business leaders have long been fascinated by the military. When Alfred Sloan created the modern corporation at General Motors, he based it on the army. In Wall Street, the antihero Gordon Gecko habitually quoted Sun Tzu. Retired generals like Stanley McChrystal earn huge fees advising CEOs and speaking to corporate conferences.

But what about nonviolent conflict? Research has shown non-violent movements are far more successful than violent uprisings, prevailing against powerful regimes against seemingly insurmountable odds. Yet, apart from a stray Gandhi quote here or Martin Luther King Jr. slide there, these go largely unexamined in the business world.

That’s a mistake. As I explained in Cascades, business leaders can learn a lot from the principles of social and political movements. There is abundant scholarship, going back decades, about why efforts succeed and fail. We know what works and what doesn’t. If you’re serious about being a transformational leader, you need to understand these strategies.

We Need To Learn About Not Only Successes—But Failures Too

Organizations are often inscrutable and hard to research. That’s why the preferred mode of analysis is case studies in which insiders are interviewed and a particular situation is interpreted by investigators. These can be helpful, but they also have severe limitations.

First, with shareholders and customers to please, managers are rarely eager to talk about failures. So we usually only hear about successes. Those, of course, are important but also subject to survivorship bias. For example, if a risky strategy results in 1% of the firms being wildly successful and 99% going out of business, then we’ll tend to hear glowing accounts of that lucky 1% and we’ll miss the vast majority that flamed out.

Social and political movements, on the other hand, are largely public events. Gandhi’s Himalayan miscalculation is just as well documented as his triumphant Salt March. We know as much about the failures of #Occupy as we do the ultimate success of the LGBTQ movement. We can look at similar strategies in different contexts and different strategies in similar contexts.

That’s extremely important. We need to learn from failures. It’s one thing to look at a strategy that succeeded, but can it prevail consistently or was that a one-off? Is it a universally successful strategy or highly dependent on context? We need to ask these questions relentlessly and it’s very hard to do that if we only look at the winners.

Change Is Always Multifaceted, We Need to Understand Multiple Perspectives

Another issue with the case study method is that it is necessarily limited. When researchers did a case study on company I used to run, to take just one example, they interviewed insiders (including me) and did their best to interpret what they heard and what they could glean from background information regarding the market.

Yet while I don’t think anything was inaccurate, it wasn’t exactly the truth either. Only a handful of people were interviewed, almost all of them were concentrated in a single part of the business and none of them, besides me, were involved in making decisions. The issues presented in the case study simply weren’t the ones we were actually wrestling with.

Now consider the prominent sociologist Doug McAdam’s paper on recruiting for Freedom Summer during the civil rights movement. He was able to analyze the applications of not only 720 volunteers, but 239 others that withdrew and 55 that were rejected. He conducted 80 in-depth personal interviews and, because the applications asked for social contacts, McAdam was able to document social ties.

That type of documentation simply doesn’t exist in case studies of firms’ internal deliberations and decision making. We rarely get access to internal data, much less insights from partners, customers, competitors and regulators. With social and political movements, on the other hand, we can examine thousands of first-hand accounts from every perspective.

That’s important, because the world is a messy place with a lot going on. Outcomes rarely boil down to a single decision and even key players disagree on which factors were determinant.

We Need To Overcome Resistance

Look at most change management models and what you see is mostly advice that is focused on persuasion. They suggest that the way to drive a transformation is to tell people about it. By creating a sense of urgency and need, you can build a coalition that will implement the change and shift practices for the long term.

Unfortunately, decades of serious research shows that the world doesn’t work that way. Researchers have long been aware of a so-called KAP-gap in which shifts in “knowledge” and “attitudes” don’t necessarily lead to a change in “practices.” For any given change there will also be people who will vehemently resist it, not for any rational logic, necessarily, but for reasons related to identity, dignity and sense of self.

On the other hand, in social and political movements the need to overcome robust—and even violent—resistance is front and center. Practitioners have developed tools such as the Spectrum of Allies and the Pillars of Support as well as innovative strategies like Dilemma Actions. We have decades of documentation on how these worked in a variety of contexts.

Make no mistake. We can’t simply cheerlead change. No one is going to embrace transformation simply because you came up with a fancy slogan. The truth is that whenever you ask people to change what they think or what they do, there will always be some who won’t like it and they will work to undermine what you’re trying to achieve in ways that are dishonest, underhanded and deceptive.

You need to prepare for that and you will learn far more from social and political movements than consultants interpreting case studies.

Change Is Too Important Not To Take Seriously

The most important challenge leaders face is to navigate change. We can optimize operations, streamline our organizations and motivate our people, but eventually our square-peg business will meet its round-hole world and we will need to adapt, build new skills and shift our strategies. Unfortunately, the overwhelming evidence suggests that we will fail.

Consider that, after decades of trying, skills like lean manufacturing, agile development and overcoming unconscious bias are woefully under-adopted in most organizations. Study after study shows that the vast majority of transformational efforts fail. We can’t continue to do the same thing and expect different results.

One reason for this dismal performance is how we research and learn about change. Today’s change management models simply aren’t based on facts or evidence, but rather the interpretation of case studies. Those can help us understand nuance and give us greater depth, but they are no substitute for rigorous research.

The truth is that we know a lot about change. Decades of studies have shown us that new ideas tend to come from outside the community and incur resistance. Research has shown there is a persistent gap between what people know and what they actually put into practice. We also know that transformation follows an s-shaped curve and that ideas are transmitted socially.

Unfortunately, current organizational change practices address none of these challenges. However, social and political movements do and through the work of scholars like Gene Sharp and practitioners Srdja Popović we know what works and what doesn’t. My own work has shown that these principles can be put to use in organizations.

The future is simply too important to be left to superstition and fantasy.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Why an AI Soft Landing Might Look Like Victorian England

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LAST UPDATED: April 18, 2026 at 3:29 PM

Why an AI Soft Landing Might Look Like Victorian England

by Braden Kelley and Art Inteligencia

The Mirage of the Post-Scarcity Utopia

For decades, the prevailing narrative surrounding artificial intelligence has been one of a post-scarcity “Star Trek” future. The logic was simple: as machines took over the labor, the dividends of automation would be harvested by the state and redistributed via Universal Basic Income (UBI), freeing humanity to pursue art, philosophy, and leisure.

The AI Promise vs. The Fiscal Reality

However, this utopian vision ignores the gravity of The Great American Contraction. As we approach 2026 and beyond, the friction between exponential technological growth and a $37 trillion+ national debt (with a $2 trillion annual budget deficit) creates a structural barrier to redistribution. When the tax base of human labor erodes, the math for a livable UBI simply fails to compute.

The Victorian Hypothesis

If UBI is a mathematical and political impossibility fueled by corporate and human greed, we must look toward an alternative “soft landing.” This hypothesis suggests a vertical restructuring of society. As AI drives the cost of production and the demand for goods into a deflationary spiral, the purchasing power of the remaining “employed elite” will skyrocket.

The result isn’t a horizontal distribution of wealth, but a return to a Neo-Victorian social hierarchy. In this reality, the new digital gentry will use their outsized wealth to employ a massive “servant class” to maintain stately homes and personal lives, creating a world where status is defined by the human labor one can afford to command.

Neo-Victorian Hypothesis Infographic

The Great American Contraction: Why UBI is a Non-Starter

The conversation around the transition to an AI-driven economy often treats Universal Basic Income as an inevitability — a safety net that will naturally catch those displaced by the silicon wave. However, this assumes a level of fiscal elasticity that no longer exists. We are entering The Great American Contraction, a period where the traditional levers of government spending are restricted by the sheer weight of historical obligation and systemic greed.

The Debt Ceiling of Compassion

With a national debt exceeding $37 trillion, a $2 trillion budget deficit and rising interest rates, the federal government’s “room to maneuver” has effectively vanished. A livable UBI requires a massive, consistent tax base. As AI begins to hollow out the middle class, the very tax revenue needed to fund such a program disappears. To fund UBI under these conditions would require a level of sovereign borrowing that the global markets simply will not support, leading to a reality where the government cannot afford to be the savior of the displaced.

The Greed Variable

Even if the math were more favorable, the human element remains a constant. Corporate interests, focused on margin preservation and shareholder value, are unlikely to support the aggressive taxation required to fund a social floor. In the race to the bottom of production costs, the primary goal of the “winners” in the AI revolution will be wealth concentration, not social equity. The political willpower to force a massive transfer of wealth from AI-profiting corporations to the idle masses is a historical outlier that we should not count on repeating.

The Velocity of Displacement

Finally, the speed of the AI transition is its most disruptive feature. Legislative bodies move in years, while AI cycles move in weeks. By the time a political consensus for UBI could be formed, the economic floor will have already fallen out. This lag time creates a vacuum that will be filled not by government checks, but by a desperate search for subsistence, setting the stage for the return of the domestic labor economy.

The Deflationary Paradox: Collapse of Demand and Cost

In a traditional economy, unemployment leads to recession, which usually leads to stagflation or managed recovery. However, the AI-driven “soft landing” introduces a unique mechanical failure: the Deflationary Paradox. As AI and advanced robotics permeate every sector, the labor cost of producing goods and services begins to approach zero, but the pool of consumers capable of buying those goods simultaneously evaporates.

The Production Floor Drops

We are witnessing the end of the labor theory of value. When an AI can design, a robot can manufacture, and an automated fleet can deliver a product without a single human touchpoint, the marginal cost of production hits the floor. In a desperate bid to capture the dwindling “active” capital in the market, companies will engage in a race to the bottom, causing the prices of physical and digital goods to deflate at a rate unseen in modern history.

The Demand Vacuum

While cheap goods sound like a boon, they are a symptom of a deeper rot: the Demand Vacuum. As the middle class is hollowed out, the velocity of money slows to a crawl. The economy shifts from a mass-consumption model to a precision-consumption model. Most businesses will fail not because they can’t produce, but because there are no longer enough customers with a paycheck to buy, even at rock-bottom prices.

The Purchasing Power of the “Remaining”

This is where the Victorian shift begins. For the small percentage of Americans who retain their income — the innovators, the orchestrators, and the entrepreneurs — this deflationary environment is a golden age. Their dollars, fixed in value while the cost of everything else drops, suddenly possess exponential purchasing power. When a gallon of milk or a digital service costs mere pennies in relative terms, the “wealthy” find themselves with a massive surplus of capital that cannot be spent on “things” alone. This surplus will naturally be redirected toward the one thing that remains scarce and high-status: the dedicated service of another human being.

The New “Stately Home” Economy

As the Deflationary Paradox takes hold, we will see a fundamental shift in the definition of luxury. In the pre-AI era, luxury was defined by the acquisition of high-tech gadgets or rare goods. In the Neo-Victorian era, where machines produce goods for nearly nothing, “luxury” will pivot back toward the human-centered experience. Status will no longer be measured by what you own, but by whose time you command.

From Software to Service

For the “In-Group” — those entrepreneurs and specialized leaders still generating significant revenue — capital will lose its utility in the digital marketplace. When software is free and manufactured goods are commoditized, wealth seeks the only remaining friction: human presence. We will see a massive migration of capital away from Silicon Valley “platforms” and toward the local domestic economy. The wealthy will stop buying more “things” and start buying “lives” — the total dedicated attention of house managers, chefs, valets, and tutors.

The Modern Manor

This economic shift will be physically manifested in the return of the Stately Home. These won’t just be houses; they will be complex ecosystems of employment. Large estates will once again become the primary employer for local communities. As traditional corporate offices vanish, the residence becomes the center of both social and economic power. These modern manors will require extensive human staffs to cook, clean, maintain grounds, and provide security — services that, while technically possible via robotics, will be performed by humans as a deliberate signal of the owner’s immense “effectively wealthy” status.

The Return of the Domestic Professional

Perhaps the most jarring aspect of this transition will be the class of worker entering domestic service. We are not talking about a traditional blue-collar service shift, but the “Victorianization” of the former middle class. Displaced white-collar professionals — accountants, teachers, and middle managers — will find that their highest-paying opportunity is no longer in a cubicle, but in managing the complex domestic affairs, private education, and logistics of the new digital aristocracy. It is a “soft landing” in name only; while they may live in proximity to grandeur, their survival is entirely tethered to the whims of their employer.

Socio-Economic Stratification: The Two-Tiered Reality

The inevitable result of the “Victorian Soft Landing” is the formalization of a rigid, two-tiered social structure. Unlike the 20th century, which was defined by a fluid and expanding middle class, the post-contraction era will be characterized by extreme polarization. The economic “missing middle” creates a vacuum that forces every citizen into one of two distinct realities: the Digital Gentry or the Dependent Class.

The Corporate and Government Gentry

A small percentage of Americans — likely less than 10% — will remain tethered to the engines of primary wealth creation. This “In-Group” consists of high-level AI orchestrators, strategic entrepreneurs, and essential government officials who maintain the infrastructure of the state. Because their income is derived from high-margin automated systems while their cost of living has plummeted due to deflation, they possess a level of functional wealth that rivals the landed gentry of the 19th century. To this group, the “Great Contraction” is not a crisis, but a refinement of their dominance.

The Dependent Class

For those outside the digital fortress, the reality is stark. Without a national UBI to provide a floor, the majority of the population becomes the “Dependent Class.” Their economic utility is no longer found in the marketplace of ideas or manufacturing, but in the marketplace of personal service. In this neo-Victorian landscape, you either work for the companies that own the AI, work for the government that protects it, or you work directly for the individuals who do.

The Choice: Service or Scarcity

This stratification reintroduces a primal power dynamic into the American workforce. When the cost of basic survival (food and shelter) is low due to deflation, but the opportunity for independent income is zero, the wealthy gain total leverage. The “soft landing” is, in truth, a forced labor transition. Those who are not “useful” to the gentry — either as specialized labor or domestic support — face the grim reality of the Victorian workhouse era: they must find a patron to serve, or they will starve in a world of plenty.

Experience Design in the Neo-Victorian Era

Experience Design in the Neo-Victorian Era

From the perspective of experience design and futurology, the shift toward a Victorian-style social structure will fundamentally alter the aesthetic of status. In a world where AI can generate perfect, flawless goods and digital experiences at zero marginal cost, “perfection” becomes a commodity. Status, therefore, will be redesigned around human friction and intentional inefficiency.

The Aesthetic of Inequality

We will see a move away from the sleek, minimalist “Apple-esque” design of the early 21st century toward a more ornate, human-heavy luxury. Experience design for the elite will emphasize things that AI cannot authentically replicate: the slight imperfection of a hand-cooked meal, the presence of a uniformed gatekeeper, and the physical maintenance of vast, non-automated gardens. Architecture will pivot back to “human-centric” layouts—designing spaces not for efficiency, but to accommodate the movement and housing of a live-in staff.

Designing for Disconnect

The most challenging aspect of this new era will be the Experience of the Invisible. Designers will be tasked with creating systems that allow the Digital Gentry to interact with their environment without acknowledging the vast economic disparity surrounding them. This involves “Social UX” — designing layers of intermediation where the “Dependent Class” provides the comfort, but the “Gentry” only interacts with the result. It is a return to the “back-stairs” architecture of the 19th century, modernized for a digital age.

The UX of Survival

For the majority, the “User Experience” of daily life will be one of Hyper-Personal Patronage. Navigation of the economy will no longer be about interfaces or platforms, but about the “UX of Relationships.” Survival will depend on the ability to design one’s persona to be indispensable to a wealthy patron. In this reality, human-centered design takes on a darker, more literal meaning: the human becomes the product, the service, and the infrastructure all at once.

Conclusion: Preparing for the Retro-Future

The “Soft Landing” we are currently engineering is not the one we were promised. As the Great American Contraction forces a collision between astronomical debt and the deflationary power of AI, the middle-class dream of a subsidized leisure class is evaporating. In its place, we are seeing the blueprints of a Retro-Future — a world that looks forward technologically but moves backward socially.

A Call for Human-Centered Transition

If we continue to view innovation solely through the lens of efficiency and margin preservation, the Victorian outcome is not just possible — it is inevitable. We must realize that without a radical redesign of how we value human contribution beyond mere “market productivity,” we are simply building a more efficient feudalism. True Experience Design must now focus on the social fabric, or we risk creating a world where the only “innovation” left is finding new ways for the many to serve the few.

Final Thought: The Soft Landing Paradox

We must be careful what we wish for when we ask for a “seamless” transition. A landing that is “soft” for the Digital Gentry is one where the friction of poverty and the noise of the displaced have been successfully silenced by the return of the servant class. History doesn’t repeat, but it does rhyme — and right now, the future sounds remarkably like 1837. The question is no longer if AI will change our world, but whether we have the courage to design a future that doesn’t require us to retreat into our past.

Frequently Asked Questions

Why would prices deflate if the economy is struggling?

In this scenario, AI and robotics drive the marginal cost of production toward zero. Simultaneously, massive job displacement creates a “demand vacuum.” To capture what little liquid currency remains, companies must drop prices drastically, leading to a reality where goods are incredibly cheap but income is even scarcer.

How does this differ from the 20th-century middle class?

The 20th century was defined by a “horizontal” distribution where many people owned moderate assets. The Neo-Victorian model is “vertical.” The middle class disappears, replaced by a tiny, hyper-wealthy elite (Digital Gentry) and a large class of people who provide them with personalized human services (the Servant Class).

Isn’t UBI a more logical solution to AI displacement?

While logical in theory, the “Great American Contraction” hypothesis suggests that high national debt and corporate prioritisation of margins make a livable UBI politically and fiscally impossible. Without a state-funded floor, the market defaults to the oldest form of social safety: personal patronage and domestic service.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Google Gemini to clean up the article, add images and create infographics.

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Five Elements of the Changemaker Mindset

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Five Elements of the Changemaker Mindset

GUEST POST from Greg Satell

Chances are, you work in a square-peg business, because that’s the best way to make money. You work diligently to improve the pegs and to get them to where they need to go better, faster and cheaper. It is through quality and consistency that you can best serve your customers, beat your competition and win in the marketplace.

The problem comes when your square-peg business meets a round-hole world. When that happens, following traditional best practices will only result in getting better and better at doing things people care about less and less. Round holes don’t concern themselves how good your square pegs are or how efficiently you can produce them.

Make no mistake. Eventually, every business eventually finds itself in a round-hole world. That’s why good companies fail. Not because they become stupid and lazy, but because the world changes and they lose relevance. Clearly, in the midst of disruption the only viable strategy is to adapt and shift from a traditional manager mindset to a changemaker mindset.

1. Don’t Look For A Great Idea, Identify A Good Problem

“Build a better mousetrap and the world will beat a path to your door,” Ralph Waldo Emerson is said to have written and since that time thousands of mousetraps have been patented. Still, despite all that creative energy and all those ideas, the original “snap trap,” invented by William Hooker in 1894, remains the most popular.

We’ve come to glorify ideas, thinking that more of them will lead to better results. This cult of ideas has led to a cottage industry of consultants that offer workshops to exercise our creative capabilities. They walk us through exercises like Brainstorming and SWOT analysis. We are, to a large extent, still chasing better mousetraps with predictably poor results.

The truth is that every great change leader starts out with a problem they just couldn’t look away from. Change doesn’t begin with an idea. It starts with identifying a meaningful problem. That’s why it’s so important that before you start an initiative you ask questions like, “What problem are we trying to solve? Is there a general consensus that it’s a problem we need to solve? How would solving it impact our business?

Make no mistake. Change isn’t about ideas. It’s about solving meaningful problems that people care about.

2. Anticipate Resistance

The biggest misconception about change is that if everyone just understood it, they would embrace it. That’s almost never true. Make no mistake, if you intend to create genuine impact, you will get pushback. Some people will hate it with every fiber of their being. Not for any rational logic, necessarily, just because for whatever reason, it offends their dignity, their identity, their sense of self.

In Rules for Radicals, the legendary activist Saul Alinsky observed that every revolution inspires a counterrevolution. That is the physics of change.

Every action provokes a reaction because, if an idea is important, it threatens the status quo, which never yields its power gracefully. Clearly, if you intend to influence an entire organization, you have to assume the deck is stacked against you and anticipate resistance.

A simple truth is that humans form attachments to people, ideas and other things and, when those attachments are threatened we tend to lash out in ways that don’t reflect our best selves. As much as we may hate to admit it, we all do it from time to time. Anyone who has ever been married or part of a family knows that.

That’s why anytime you ask people to change what they think or what they do, there will always be those who will work to undermine what you are trying to achieve in ways that are dishonest, underhanded and deceptive. Once you are able to internalize that, you can begin to move forward.

3. Identify A Keystone Change

Every change effort begins with some kind of grievance: Costs need to be cut, customers better served, or employees more engaged. Wise managers transform that grievance into a “vision for tomorrow” that will not only address the grievance but also move the organization forward and create a better future.

This vision, however, is rarely achievable all at once. Tough and significant problems have interconnected root causes, so trying to achieve an ambitious vision all at once is more likely to devolve into a long march to failure than it is to achieve results. That’s why it’s crucial to start with a Keystone Change, which represents a clear and tangible goal, involves multiple stakeholders, and paves the way for bigger changes down the road.

​​For example, when Paul O’Neill set out to turnaround Alcoa in the 1980s, he started by improving workplace safety, which also paved the way to improvements in operational excellence. At Experian, when CIO Barry Libenson set out to move his company to the cloud, he started with internal APIs. In both cases, the stakeholders who were won over in achieving the keystone change also played a part in bringing about the larger vision.

Focusing on a keystone change allows you to get out of the business of selling an idea and into the business of selling a success. When people see that something is working, even at a small scale, they want to be involved. They can bring in others who can bring in others still. That’s how you can grow your initiative to create the critical mass that moves the system toward widespread change.

4. Mobilize People To Influence Institutions

In the early 1990s, writer and activist Jeffrey Ballinger published a series of investigations about Nike’s use of sweatshops in Asia. People were shocked by the horrible conditions that workers—many of them children—were subjected to. In most cases, the owners lived outside the countries where the factories were located and had little contact with their employees.

At first, Nike’s CEO, Phil Knight, was defiant. “I often reacted with self-righteousness, petulance, anger. On some level I knew my reaction was toxic, counterproductive, but I couldn’t stop myself,” he would later write in his memoir, Shoe Dog. He pointed out that his company didn’t own the factories, that he’d worked with the owners to improve conditions and that the stories, as gruesome as they were, were exceptions.

The simple truth is that change rarely, if ever, starts at the top because it is people with power that create the status quo. They are attached to what they’ve built and take pride in their accomplishments, just like the rest of us. That’s why, to bring about genuine change—change that lasts—you need to mobilize people to influence institutions (or those, like Knight, who yield institutional power).

Eventually, that’s what happened at Nike. The protests took their toll. “We had to admit,” Knight remembered, “We could do better.” Going beyond its own factories, the company established the Fair Trade Labor Association and published a comprehensive report of its own factories. Today, the company’s track record may not be perfect, but it’s become more a part of the solution than a part of the problem.

If you want to create change in your organization, think about the institutions—both internal and external—that can bring it about. Which departments have budgets that can be deployed in service of change? Which external organizations, whether those are partners, suppliers, customers, industry organizations or regulators that could impact your change environment? Then think about who you can mobilize to influence those institutions.

5. Shift Your Mindset

Most of the time, we operate with a manager mindset and that works fine. We build consensus and execute with predictable outcomes. Our colleagues are motivated, customers are satisfied and everybody is happy. However, in an era of disruption it’s only a matter of time until we need to adapt and drive transformation. That’s never easy.

To pull it off we need to shift from a manager mindset to a changemaker mindset in which we no longer assume an environment of predictability, but explore unknowns in an atmosphere of uncertainty. Not everybody will be willing to make the journey with us, so rather than relying on a consensus, we will need to build a coalition and leave some people behind.

We start not by trying to convince skeptics, but by going to where there is already energy in favor of change. Once we identify those who are already enthusiastic about change, we can empower them to succeed and build on that success until we hit a tipping point (about 10%-25% of the organization) and the transformation becomes self-sustaining.

What makes our current era so challenging is that we often need to operate with both mindsets simultaneously. We can’t afford to put everything on hold while changes are underway, so we need to approach some things as managers and some as changemakers. It can be difficult and stressful, but it’s what needs to be done.

Perhaps most of all, we need to internalize the reality, proven time and time again, that transformation is not only possible, but that it does not have to come from the top. Anyone, anywhere can achieve enormous change. But first, you need to adopt a changemaker mindset.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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Top 10 Human-Centered Change & Innovation Articles of March 2026

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Top 10 Human-Centered Change & Innovation Articles of March 2026Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are March’s ten most popular innovation posts:

  1. Resilient Innovation — by Braden Kelley
  2. Has AI Killed Design Thinking? — by Braden Kelley
  3. Mapping Customer Experience Risk to the P&L — by Braden Kelley
  4. Moral Uncertainty Engines — by Art Inteligencia
  5. Necesita un Diagnóstico de Riesgo de Experiencia del Cliente y Fuga de Ingresos — por Braden Kelley
  6. Layoffs, AI, and the Future of Innovation — by Braden Kelley
  7. Organizational Digital Exhaust Analysis — by Art Inteligencia
  8. You Need a Customer Experience Risk & Revenue Leakage Diagnostic — by Braden Kelley
  9. Stereotypes – Are They Useful and Should We Use Them? — by Pete Foley
  10. Is There Such a Thing as a Collective Growth Mindset? — by Stefan Lindegaard

BONUS – Here are five more strong articles published in February that continue to resonate with people:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

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Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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Seeds to Grow a Strong Culture

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GUEST POST from Douglas Ferguson

After a long winter, spring has finally sprung! For leaders in our fields, it’s an opportunity to implement some springtime strategies that cultivate and nurture company culture. But healthy cultures don’t grow overnight. Just as a garden is a multi-faceted ecosystem that needs tending, so is your workplace culture. To properly grow your company culture, you must be both patient and nurturing.

As Terry Lee outlines, there is great potential inside everyone. It’s up to great leaders to bring it out in four nurturing ways.

Training

It’s vital for leaders to work with employees to identify what training will position them to be most successful for the job now and for the future. Prior to sending any employee to a training, conference, or seminar, leaders should sit down with the employee to discuss specifics goals, expectations, and takeaways of the training they are attending.

Connecting

Research has shown that talking to house plants can help them grow, thus proving the power of connection. Leaders should connect with their teams as they help them better understand their importance and the value they bring to the organization. Every leader should understand their company’s mission and articulate that message to staff consistently and authentically.

Challenging

Studies have shown that intrinsic motivators are just as important as extrinsic ones. Good managers understand what challenges help generate these motivators. When team members complete meaningful tasks, they may receive an intrinsic reward. One way to amplify this reward is by talking to teams to determine what they think are the most important parts of their job. Then leaders can help them structure their day around tasks that give them a feeling of purpose.

Coaching

Every garden needs a gardener, and every team member needs a coach. Team members need coaches to meet them where they’re at. They help staff identify what options they may have to reach goals and then set the appropriate challenges that lead them to success.

Now that warmer weather has arrived, and the world is opening up again, it’s time to plant the seeds of a healthy work culture. Remember that culture will grow, whether you tend to it or not. Take the time to prioritize nurturing your team, and it will create a strong foundation for a collaborative and supportive workplace.

Need help with creating the foundation for a healthy work culture? Download our Culture Cultivator where you will uncover pain points and plan action items toward growing a healthy and synergetic work culture.

Douglas Ferguson | President, Voltage Control

Image credit: 1 of 1,150+ FREE quotes for your presentations at http://misterinnovation.com

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The Four Psychological Disruptions of AI at Work

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LAST UPDATED: April 3, 2026 at 4:20 PM

The Four Psychological Disruptions of AI at Work

by Braden Kelley and Art Inteligencia

Most AI-and-work frameworks are built around economics – job categories, task automation rates, re-skilling costs. This one is built around something different: the interior experience of the person sitting at the desk. The four disruptions mapped in this infographic were identified not through labor market data, but through a human-centered lens – the same lens used in design thinking and change management to surface the needs, fears, and identity stakes that people rarely articulate out loud but always feel.

The framework draws on three converging sources: organizational psychology research on professional identity and role transition; change management practice, particularly the observed patterns of how workers respond when their expertise is devalued or displaced; and direct observation of how individuals are actually experiencing AI adoption in their workplaces right now – not in surveys, but in the unguarded conversations that happen before and after workshops, in the margins of keynotes, in the questions people ask when they think no one important is listening.

Why these four disruptions

1

Competence Displacement

The skill that defined you no longer distinguishes you.

Professional identity is heavily anchored in the belief that what I know how to do has value. When AI can replicate a signature competency – even imperfectly – it attacks that anchor directly. The disruption isn’t primarily about job loss. It’s about the sudden, disorienting feeling that years of deliberate practice have been, in some meaningful sense, made ordinary.

This disruption appears earliest and most acutely in knowledge workers whose expertise was previously considered difficult to acquire – writers, analysts, coders, researchers, strategists.

2

Purpose Erosion

The meaning embedded in the craft begins to hollow out.

Work is not only instrumental – it is ritual. The process of doing difficult things carefully, over time, is itself a source of meaning. When automation removes the friction, it can also remove the satisfaction. This is subtler than competence displacement and slower to surface, but ultimately more corrosive. People find themselves producing more output and feeling less connected to it.

This disruption is particularly acute for people who chose their profession not just for income but for intrinsic love of the work – and who built their identity around that love.

3

Belonging Disruption

The social fabric of work shifts when AI enters the team.

Work teams are social ecosystems built on complementary expertise, shared struggle, and mutual reliance. AI changes those dynamics in ways that are easy to overlook. When an AI tool makes one team member dramatically more productive, or when collaborative tasks are partially automated, the invisible social contracts of the team – who depends on whom, who contributes what – are quietly renegotiated. Belonging depends on feeling needed. When that changes, isolation can follow.

This disruption tends to surface not as explicit conflict but as a gradual withdrawal – people collaborating less, sharing less, protecting their remaining territory.

4

Status Anxiety

The professional hierarchy is being redrawn by AI fluency.

Workplace status has always been tied to expertise scarcity – the person who knew things others didn’t held power. AI is redistributing that scarcity rapidly. Early and confident AI adopters gain speed, output, and visibility. Those who resist, or who are slower to adapt, find themselves losing ground in ways that feel both unfair and disorienting. The new status question – are you someone who uses AI, or someone AI is used on? – is already being asked in organizations, even when no one says it explicitly.

This disruption is uniquely uncomfortable because it combines external threat (status loss) with internal shame (the fear of being seen as behind).

How to read the framework

These four disruptions are not sequential stages – they are simultaneous and overlapping. A single professional can be experiencing all four at once, with different intensities depending on their role, their organization, and how rapidly AI is being adopted around them. The infographic presents them as discrete panels for clarity, but the lived experience is messier and more entangled.

They are also not uniformly negative. Each disruption contains within it the seed of a corresponding renewal: competence displacement can become an invitation to lead with judgment rather than task execution; purpose erosion can prompt a deeper reckoning with what the work is ultimately for; belonging disruption can surface the human connection that was always the real foundation of team cohesion; status anxiety can motivate the kind of deliberate identity authoring that makes professionals more resilient over the long term.

The framework is designed to give leaders and individuals a common language for conversations that are currently happening in fragments — in one-to-ones, in exit interviews, in the silence after a difficult all-hands. Named things can be worked with. Unnamed things can only be endured.

This framework is a practitioner’s model, not a peer-reviewed clinical instrument. It is designed for use in workshops, coaching conversations, and organizational change programs as a starting point for honest dialogue — not as a diagnostic or classification system. It will evolve as our collective understanding of AI’s human impact deepens.

Framework developed by Braden Kelley as part of the article series Psychological Impact of AI on Work Identity  ·  Braden Kelley  ·  © 2026

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude AI to clean up the article and add citations.

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Why Networks Can Outperform Hierarchies

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(And Vice Versa)

Why Networks Can Outperform Hierarchies

GUEST POST from Greg Satell

I still remember the bright autumn day in 2014 when I turned off of the main road in Exton, Pennsylvania onto a remote path. I was going to meet Brian J. Robertson, the creator of a hot new “flat” management approach called Holacracy. I was skeptical, because it seemed to be a cumbersome way to go about governance, but I was open to learning about it.

Many companies, most famously Zappos, were enthusiastically adopting it and there was no shortage of hype among the punditry about abolishing hierarchies. Brian, for his part, was gracious and patient with me, explaining how and why everything worked. Still, I had my doubts and remained unconvinced.

Recently, Stanford’s Bob Sutton pointed to Ronnie Lee’s research that confirmed my (and his) suspicions. While flatter structures can promote creativity, we need hierarchies to execute well. The truth is that hierarchies form naturally and, rather than trying to ignore that basic fact, we need to design enterprises with hierarchical networks in mind.

Evolution, Religion and Leadership

It’s become common today for many, especially in the academic world, to dismiss religion as the product of ancient superstition. Yet in The Righteous Mind, social psychologist Jonathan Haidt makes a powerful case that it plays an important evolutionary role. “There is now a great deal of evidence that religions do in fact help groups to cohere, solve free rider problems and win the competition for group-level survival,” he wrote.

So while many pundits often portray bureaucratic hierarchies as an anachronistic byproduct of the industrial revolution, it seems significant that religions tend to have hierarchical structures. Even religious activities that can be done individually, such as Buddhist meditation, are often led by someone who has an elevated group status.

So it stands to reason that hierarchy plays a similar governance role in organizations, helping to coordinate group activity by setting priorities, establishing basic rules and norms and, when needed, providing impetus to change direction and adapt to external events. Clearly, these are essential governance functions in any enterprise.

Many would say that, in an increasingly digital environment that helps us communicate and coordinate across boundaries of time and space, we simply don’t need the same levels of bureaucratic governance that we used to. However, what Professor Lee found in the startups he researched was that the levels of hierarchy increased significantly over the last 50 years, most probably due to the greater levels of complexity involved in work.

It’s important to note that, even after years of hype, it’s hard to find examples of successful non-hierarchical organizations. Even the rare exceptions, such as the Orpheus Chamber Orchestra, aren’t quite as flat in how they organize work as it would first seem. Zappos would eventually back away from Holacracy as would other early adopters, such as Medium.

Hierarchies Are Networks

The term “network” is often misconstrued. In management circles, it is often used to mean an organic, unfathomable, amorphous structure, but really a network is just any system of nodes connected by links. So, in that sense, any conceivable organizational structure is a network, even a typically hierarchical organizational chart.

The important question is what kind of networks do we want our organizations to be? If we look at the evidence from thousands of years of human civilization, we’d have to conclude that some sort of command and control mechanism is needed. At the same time, as our competitive environment becomes more complex, we want information to be able to go to where it is needed without getting stuck in leadership bottlenecks.

A bit of network science can be helpful here. For functional purposes, networks have two salient characteristics: clustering and path length. Clustering refers to the degree to which a network is made up of tightly knit groups while path length is a measure of social distance—the average number of links separating any two nodes in the network.

Ideally our organizational networks would have a high degree of clustering—to promote close collaboration and teamwork—as well as short path lengths so that information can get from one part of the enterprise to any other part with speed and efficiency. Intuitively, it seems like those two priorities are in conflict. However, thanks to some breakthroughs in network science in the late 90s, we know that such “small world” networks are not only achievable, but common.

What’s really important isn’t how your organizational chart is constructed, but how you design for connection and there are some common sense ways to do that.

Understanding Formal And Informal Structures

Every organization has both formal and informal structures. For example, while ostensibly open source communities have little formal organization, in practice they are very hierarchical, with high-status individuals driving the direction of the project. At the same time, even in a formal organization, there are informal relationships as when, say, you work in sales and your brother-in-law works in logistics in a very different part of the company.

Network scientists call people who link disparate networks in an organization boundary spanners and they are crucial for maintaining culture as an organization grows. Once you understand the importance of boundary spanners, you can start redesigning programs and platforms to optimize for connection.

There are a number of ways to network your organization by optimizing organizational platforms for connection. Facebook’s Engineering Bootcamp found that “bootcampers tend to form bonds with their classmates who joined near the same time and those bonds persist even after each has joined different teams.” At Experian, leadership found that a biking club led to boundary spanning collaborations at work, so they helped more clubs to get organized.

One striking example of how even small tweaks can improve connectivity is a project done at a bank’s call center. When it was found that a third of variation in productivity could be attributed to informal communication outside of meetings, the bank arranged for groups to go on coffee break together, increasing productivity by as much as 20% while improving employee satisfaction at the same time.

Perhaps most famously, Steve Jobs designed the headquarters both at Apple and Pixar to encourage random collisions among employees. It seems we’ve been asking the wrong question. The problem isn’t how we dismantle hierarchies, but how we connect them.

Leading Hierarchical Networks

For decades we’ve been hearing that we need to eliminate bureaucracy and break down silos. Yet there is little evidence of any success. In fact, when management guru Gary Hamel, who has been leading the call to “bust bureaucracy,” surveyed readers at Harvard Business Review he found that levels of organization had increased, not decreased.

The inescapable conclusion is that we’ve failed to do away with hierarchies because they serve a useful purpose. We need them. In much the same way, the much maligned “silos” form around centers of capability as a result of close collaboration. These are good things. We don’t want to eliminate them, we want to support and empower them.

So instead of trying to break down silos, we need to connect them. Network science tells us that it takes just a small amount of boundary spanning “random connections,” in order to bring social distance crashing down. We can’t just look at organizational charts, but need to focus on how meaningful relationships form in the real world.

The role of leadership in organizations has changed. It is no longer merely to plan and direct work, but to inspire meaning and empower belief. As I wrote in Cascades, the key to transformational change is small groups, loosely connected by united by a shared purpose. The job of leaders today is to help those groups connect and forge a common purpose.

If we are to lead effectively in an increasingly ecosystem-driven world, we need to empower networked hierarchies.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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Accelerating Change in Consumer Packaged Goods

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Accelerating Change in Consumer Packaged Goods

GUEST POST from Geoffrey A. Moore

I had the pleasure of engaging with a team of executives from a Global 2000 Consumer Packaged Goods (CPG) company, and as always from such encounters, I learned something new.

The team is focused on accelerating change, and I was sharing with them the zone management model, and how each zone is intended to keep a characteristic pace. The Productivity Zone, by design, goes the slowest because its job is to take extra time in order to reduce risk and cost. The Incubation Zone, again by design, goes the fastest because its job is to take extra risk and pretty much ignore cost in order to reduce time.

What the team made me realize is that, given all the change coming at them (and, yes, we had been talking a lot about Generative AI and related technologies), they needed their Productivity Zone to speed up, come what may. The more I thought about it, the more I realized that this is not just a single CPG enterprise talking. Every Volume Operations enterprise at its core runs on processes. There is no other way to operate at scale, which means the Performance Zone is completely dependent on them. But here’s the thing—all those mission-critical processes are invented, maintained, and improved by the Productivity Zone.

So, here’s the challenge in a nutshell: How can you possibly speed up something that is inherently designed to go slow? Or, to make the goal more specific, how do you incubate a truly disruptive process and then, at the right moment, use it to transform your most conservative organizations?

Readers of this blog will not be surprised to hear me advocate for aligning the zone management framework with the Technology Adoption Life Cycle as a roadmap for how best to navigate these waters. Here’s how it plays out in four acts:

  1. Act One: Incubate, focusing on early adopters who are looking to explore the opportunities, leveraging a project model. You intend to prove the feasibility of the new process, and you will do whatever it takes to do so. Your goal is to show what good could look like while at the same time taking technical risk off the table, leaving adoption risk as the primary remaining challenge.
  2. Act Two: Transform, focusing exclusively on a single underperforming function led by pragmatists in pain, leveraging a solution model. You intend to use the breakthrough technology to completely revamp the process in question, taking it from underperforming to stellar. Your goal is to create a credible set of references to support your transition to Act Three.
  3. Act Three: Perform, focusing first on processes adjacent to those addressed by Act Two, ones that are performing adequately but could definitely be improved, led by pragmatists who are reluctant to change until they see others go first. You intend to create a groundswell of adoption that will convert their reluctance to change into a fear of missing out. Your goal is to lead with a “killer app,” highlighting whatever portion of your technology that can deliver a quick win, and then follow that up with a complete roll-out.
  4. Act Four: Secure, focusing on the revamped process end to end, monitoring quality from final deliverable back through each step, working with process managers who will be maintaining their portion of the new system. You intend to continuously improve following a data-driven approach supplemented with whatever analytics and AI can provide. Your goal is to operate at scale with unprecedented productivity and agility.

The key point of this framework is that it is linear. You take it one act at a time, and you do not skip over any acts. Your key metric is time to complete, both at the level of each act and of the whole play. With respect to anything transformational, know that most people appreciate it may take more than one year, and no one will give you three years. So you have a maximum of eight quarters to get to Act Four (which will be ongoing thereafter).

That’s what I think. What do you think?

Image Credit: Pexels

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