Category Archives: Change

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

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 2026 Drum 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:

Resilient Innovation — by Braden Kelley
Has AI Killed Design Thinking? — by Braden Kelley
Mapping Customer Experience Risk to the P&L — by Braden Kelley
Moral Uncertainty Engines — by Art Inteligencia
Necesita un Diagnóstico de Riesgo de Experiencia del Cliente y Fuga de Ingresos — por Braden Kelley
Layoffs, AI, and the Future of Innovation — by Braden Kelley
Organizational Digital Exhaust Analysis — by Art Inteligencia
You Need a Customer Experience Risk & Revenue Leakage Diagnostic — by Braden Kelley
Stereotypes – Are They Useful and Should We Use Them? — by Pete Foley
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:

The Architecture of Organizational Agility — by Braden Kelley
Neuroadaptive Interfaces — by Art Inteligencia
Innovation Lessons from the 50 Most Admired Companies of 2026 — by Braden Kelley
Why It Matters WHO Conducts Your Customer Experience Audit — by Braden Kelley
Values Always Cost You Something — by Greg Satell

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!

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

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.

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.

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.

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.

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:

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.
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.
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.
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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Change Starts with Empathy

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(Even for Your Enemies)

Change Starts With Empathy

GUEST POST from Greg Satell

On September 17th, 2011, protesters began to stream into Zuccotti Park in Lower Manhattan and the #Occupy movement had begun. “We are the 99%,” they declared and as far as they were concerned, it was time for the reign of the “1%” to end. The protests soon spread like wildfire to 951 cities across 82 countries.

It failed miserably. Today, a decade later, it’s hard to find any real objective that was achieved except some vague assertions about “building awareness” and Bernie Sanders’ two failed presidential campaigns. Taking into the count the billions of dollars worth of resources expended in terms of time and effort, that is abysmal performance.

As I explained in Cascades, there were myriad reasons for #Occupy’s failure. One of the gravest errors, however, was the insistence on ideological purity and the lack of any effort to understand those who had different ideas from their own. If you expect to bring change about, you need to attract, rather than overpower. Empathy is a good place to start.

Finding Your Tribe

In 1901, before he became employed by the patent office, a young Albert Einstein put out an advertisement offering tutoring services in math and physics. Maurice Solovine, a Romanian philosophy student, responded to the ad but, after a brief discussion, Einstein told him that he didn’t need lessons. Still, he invited Solovine to come and visit him whenever he wished.

The two began meeting regularly and were soon joined by another friend of Einstein’s, a young Swiss mathematician named Conrad Habicht, and the three would discuss their own work as well as that of luminaries such as Ernst Mach, David Hume and Henri Poincaré. Eventually, these little gatherings acquired a name, The Olympia Academy.

Einstein had found his tribe and it became a key factor in the development of his “miracle year” papers that would turn the world of physics on its head in a few years later. It gave him a safe space to let his mind wander over the great questions of the day, formulate his ideas and get feedback from people that he trusted and respected.

This is a common pattern. Similar tribes, such as, the Vienna Circle, the Bloomsbury Group and the “Martians” of Fasori have, if anything, led to even greater achievement. So it’s easy to understand how those protesters descending on Zuccotti Park, finding themselves amongst so many who saw things as they did, felt as if they were on the brink of a historic moment.

They weren’t. And that’s what’s dangerous about tribes. Although they can lend support to a fledgling idea that needs to be nurtured, they can also blind us to hard truths that need to be examined.

Developing A Private Language

A tribe is a closed network that, almost by definition, is an echo chamber designed to develop its own practices, customs and culture. Perhaps not surprisingly, it is common for these networks to develop their own vocabulary to describe these unique aspects of the tribal experience and to make distinctions between members of the tribe and outsiders.

Consider what happened when Congressman John Lewis, the civil rights legend, showed up at an #Occupy rally in Atlanta. The protesters refused to let him speak. He left quietly and issued a polite statement, but an opportunity was lost and real damage was done to the movement and its cause. If John Lewis wasn’t welcome, what about the rest of us?

Later, the man who led the charge to prevent Congressman Lewis from speaking explained his reasons. He cited his suspicion of Lewis as part of the “two-party system,” which he felt had betrayed the country. Yet even more tellingly, he also explained that his main objection was due to the “form” of the event, which he felt was being violated.

It is common for tribes to fall into this kind of private language trap. The function of communication is inherently social and, if the customs and vernacular that you develop becomes so archaic and obscure that it is unable to perform that function, you have undermined the fundamental purpose of the activity.

Clearly, in any dialogue both the speaker and the listener have a responsibility to each other. However, if you consistently find that your message is not resonating outside your tribe, you probably want to rethink how you’re expressing it.

Shifting From Differentiating Value To Shared Values

Once you start separating yourself off and creating a private language for your adherents, it’s easy to fall into a form of solipsism in which the only meaningful reality is that of the shared experience of the tribe. Many aspiring revolutionaries seek to highlight this feeling by emphasizing difference in order to gin-up enthusiasm among their most loyal supporters.

That was certainly true of LGBTQ activists, who marched through city streets shouting slogans like “We’re here, we’re queer and we’d like to say hello.” They led a different lifestyle and wanted to demand that their dignity be recognized. More recently, Black Lives Matter activists made calls to “defund the police,” which many found to be shocking and anarchistic.

Corporate change agents tend to fall into a similar trap. They rant on about “radical” innovation and “disruption,” ignoring the fact that few like to be radicalized or disrupted. Proponents of agile development methods often tout their manifesto, oblivious to the reality that many outside the agile community find the whole thing a bit weird and unsettling.

While emphasizing difference may excite people who are already on board, it is through shared values that you bring people in. So it shouldn’t be a surprise that the fight for LGBTQ rights began to gain traction when activists started focusing on family values. Innovation doesn’t succeed because it’s “radical,” but when it solves a meaningful problem. The value of Agile methods isn’t a manifesto, but the fact that they can improve performance.

You Never Have To Compromise On Common Ground

One of the things that sticks in my head about my experiences during and after the Orange Revolution in Ukraine was an interview with Viktor Pinchuk. who is not only one of the country’s richest oligarch’s, but also the son-in-law of the former President and, at the time, a member of the Rada, the Ukrainian Parliament.

He was, by any definition, a full-fledged member of the “1%” that #Occupy took to the streets to protest. Before reading the article I would’ve expected him to be bitter about the abrupt shift in power. Yet he wasn’t. In fact, he explained that his biggest concern during the protests was that his own children were in the streets, and he feared for their safety.

The insight underlines one of the fundamental fallacies of failed change efforts like #Occupy and others, both in the streets and in the corporate world. They imagine change as a Manichean struggle between two countervailing forces in which we must either prevail or accept defeat and compromise. That is a false choice.

The truth is that any change we win by vanquishing our opponents is bound to be fleeting. Every revolution inspires its own counter-revolution. Lasting change is always built on common ground. The best place to start is by building empathy for your most ardent adversaries, not to give in to them, but to help you identify shared values.

After the Orange Revolution was over, we would learn that Pinchuk’s father-in-law, Leonid Kuchma, who was still in power, ordered the most reactionary forces in his regime to stand down. As it turned out, there were some places that even the famously corrupt leader would not go. In the end, he understood that his legacy, and therefore his interests, lay with the protesters in the streets.

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

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Organizational Digital Exhaust Analysis

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Unlocking the Invisible Signals That Shape Innovation and Change

LAST UPDATED: March 20, 2026 at 5:44 PM

Organizational Digital Exhaust Analysis

GUEST POST from Art Inteligencia

The Invisible Byproduct of Work: What is Digital Exhaust?

Every organization is producing more data than ever before. Dashboards are full, KPIs are tracked, and reports are generated with increasing frequency. And yet, despite this abundance, many leaders still find themselves asking a fundamental question: “What is really happening inside our organization?”

The answer often lies not in the data we intentionally collect, but in the data we unintentionally leave behind. This is what we call digital exhaust—the invisible trail of signals created as people interact with systems, processes, and each other in the course of getting work done.

Digital exhaust includes everything from collaboration patterns in tools like email, Slack, and Teams, to clickstreams in customer journeys, to the subtle workarounds employees create when processes don’t quite fit reality. It is not designed, structured, or curated. It simply exists as a byproduct of activity.

Most organizations focus their attention on intentional data—metrics they define in advance: sales targets, operational efficiency scores, customer satisfaction ratings. These are important, but they are also inherently limited. They reflect what leaders thought would matter ahead of time.

Digital exhaust, by contrast, captures what actually does matter in practice. It reveals:

Where employees are struggling despite “green” metrics
How work really flows across teams, not how it is designed to flow
Where customers encounter friction that was never anticipated
Which informal behaviors are compensating for broken systems

In this sense, digital exhaust is not just data—it is a form of organizational truth-telling. It exposes the gap between the designed experience and the lived experience.

For leaders focused on human-centered change and innovation, this distinction is critical. Traditional measurement systems tend to reinforce existing assumptions. Digital exhaust challenges them. It brings visibility to the moments of friction, improvisation, and adaptation where real innovation opportunities are hiding.

Perhaps the most powerful way to think about digital exhaust is this: It is a passive, always-on listening system for your organization.

Unlike surveys or interviews, it does not rely on what people say after the fact. It reflects behavior in real time, at scale, and often without the filters that come with formal reporting. It captures the signals people don’t even realize they are sending.

And that is precisely why it is so valuable. Buried in this exhaust are the early indicators of change resistance, subtle signs of employee disengagement, and the unarticulated needs of customers. It is where inefficiencies whisper before they become visible problems, and where innovation opportunities emerge before they are formally recognized.

The challenge is not whether digital exhaust exists—it already does, in massive quantities. The challenge is whether organizations are willing and able to see it for what it is: not noise, but signal.

Organizations that learn to listen to their digital exhaust gain something incredibly powerful: a clearer, more human-centered understanding of how work actually happens. And with that understanding comes the ability to design change and innovation efforts that are grounded in reality, not assumption.

Why Digital Exhaust Matters for Change and Innovation

Most change initiatives don’t fail because of poor strategy. They fail because leaders are operating with an incomplete—or worse, inaccurate—understanding of how their organization actually functions. This is where digital exhaust becomes a game changer.

At its core, digital exhaust provides a continuous, behavior-based view of the organization in motion. It captures the difference between how work is designed and how it is actually performed. And in that gap lies the truth about why change efforts stall and where innovation opportunities emerge.

Traditional change management relies heavily on lagging indicators—survey results, adoption metrics, and post-implementation reviews. By the time these signals appear, the organization has already absorbed the impact, for better or worse. Digital exhaust, on the other hand, offers something far more valuable: early visibility into emerging patterns of behavior.

This early visibility allows leaders to detect and respond to critical dynamics in real time, including:

Change Resistance: Not through what people say, but through what they do—avoiding new tools, reverting to old processes, or creating parallel workarounds.
Process Friction: Identifying bottlenecks, repeated handoffs, or excessive rework that signal misaligned or poorly designed workflows.
Cultural Misalignment: Revealing disconnects between stated values and actual behavior patterns.
Hidden Work: Surfacing informal, often invisible effort employees expend to compensate for gaps in systems or processes.

For innovation leaders, this is where things get especially interesting. Digital exhaust doesn’t just highlight problems—it illuminates possibilities. Every workaround is a signal of unmet need. Every friction point is a potential innovation opportunity. Every unexpected behavior pattern is a clue about how people are adapting to constraints in ways the organization did not anticipate.

In other words, innovation lives in the gaps between designed experience and lived experience.

When organizations ignore digital exhaust, they effectively blind themselves to these gaps. They continue to invest in solutions based on assumptions, often optimizing for a version of reality that no longer exists. This is how well-intentioned initiatives end up driving “hallucinatory innovation”—building elegant solutions to problems that don’t actually matter.

Conversely, organizations that leverage digital exhaust gain the ability to:

Continuously validate whether change is working as intended
Identify emerging needs before they are formally articulated
Adapt strategies dynamically based on real-world behavior
Reduce the gap between leadership perception and employee/customer reality

This shifts the role of leadership from one of prediction to one of perception and response. Instead of trying to anticipate every outcome, leaders can sense what is happening and adjust accordingly.

The implications are profound. Change becomes less about large, episodic transformations and more about continuous alignment. Innovation becomes less about isolated breakthroughs and more about systematically uncovering and addressing real human needs.

Ultimately, digital exhaust matters because it reconnects organizations with reality. It grounds strategy in behavior, not intention. And in a world where the pace of change continues to accelerate, that grounding may be the most important competitive advantage of all.

From Data to Meaning: The Practice of Digital Exhaust Analysis

If digital exhaust is the raw signal of how work actually happens, then digital exhaust analysis is the discipline of turning that signal into meaning. This is where many organizations struggle—not because they lack data, but because they lack a systematic way to interpret it in a human-centered way.

The first step is recognizing the breadth of digital exhaust across the enterprise. Every interaction, transaction, and workflow leaves behind traces of behavior. Individually, these signals may seem insignificant. Collectively, they form a dynamic, continuously updating picture of how the organization actually operates.

Common sources of digital exhaust include:

Collaboration Tools: Email, messaging platforms, and meeting systems that reveal communication flows, decision bottlenecks, and collaboration overload.
Customer Interactions: Support tickets, chat logs, call transcripts, and clickstream data that expose friction, confusion, and unmet expectations.
Operational Systems: CRM, ERP, and workflow platforms that capture how processes actually unfold, including delays, rework loops, and exception handling.
Content and Knowledge Systems: Document creation, editing patterns, and knowledge-sharing behaviors that reflect how information is accessed, reused, or lost.

But volume alone does not create insight. The real shift comes from applying analytical approaches that focus on behavior rather than static metrics. Instead of asking “What happened?”, digital exhaust analysis asks “How and why did it happen this way?”

Effective analysis typically combines multiple techniques:

Behavioral Pattern Recognition: Identifying recurring actions, deviations, and anomalies that signal friction, adaptation, or emerging habits.
Process Mining and Journey Reconstruction: Rebuilding actual workflows and customer journeys based on real activity, not designed processes.
Language and Sentiment Analysis: Examining tone, word choice, and context in communications to uncover emotion, confusion, or resistance.
Network and Interaction Analysis: Mapping how people and teams connect to reveal informal influence structures and collaboration patterns.

A critical principle in this work is triangulation. No single data source tells the full story. Only by combining multiple signals can organizations distinguish between noise and meaningful patterns.

Equally important is the shift from retrospective reporting to continuous sensing. Traditional analytics looks backward, summarizing what has already occurred. Digital exhaust analysis, when done well, enables organizations to monitor patterns as they emerge and evolve—creating the opportunity to respond in near real time.

This does not mean automating decisions blindly. On the contrary, the goal is to augment human judgment. The role of digital exhaust analysis is to surface signals that prompt better questions, deeper inquiry, and more informed action.

Ultimately, the practice is not about mastering tools—it is about building a new organizational capability: the ability to see clearly, move beyond assumptions, understand behavior in context, and translate that understanding into smarter, more human-centered decisions about change and innovation.

Human-Centered Interpretation: Avoiding the Measurement Trap

One of the most dangerous assumptions organizations make is that data is objective. It isn’t. Data is shaped by what we choose to measure, how we collect it, and the context in which we interpret it. Digital exhaust may feel more “real” because it is behavior-based, but it is still incomplete without thoughtful, human-centered interpretation.

This is where many digital exhaust initiatives go off track. Leaders see a new stream of rich behavioral data and immediately move to optimize against it—reducing time, increasing throughput, or eliminating variance. In doing so, they risk falling into the very trap they were trying to escape: mistaking signals for truth and metrics for meaning.

The reality is that every data point carries ambiguity. A spike in after-hours activity could indicate high engagement—or it could signal burnout. A reduction in collaboration might reflect improved efficiency—or growing silos. Without context, interpretation becomes guesswork dressed up as insight.

This is why digital exhaust analysis must be grounded in a human-centered mindset. The goal is not to monitor people more closely, but to understand their experiences more deeply.

There is also an important ethical dimension to consider. The same data that can illuminate friction and unlock innovation can also feel invasive if misused. Employees who believe they are being surveilled will adapt their behavior—not to improve outcomes, but to protect themselves. When that happens, the integrity of the data itself begins to erode.

Organizations must therefore be intentional about how they approach digital exhaust:

Transparency: Be clear about what is being analyzed, why it matters, and how it will (and will not) be used.
Purpose: Focus on improving systems and experiences, not evaluating or policing individuals.
Context: Combine behavioral data with qualitative insights—interviews, observation, and direct feedback—to understand the “why” behind the patterns.
Humility: Treat insights as hypotheses to explore, not conclusions to enforce.

At its best, digital exhaust analysis becomes a tool for empathy at scale. It helps leaders see where people are struggling, where systems are failing, and where expectations are misaligned—not in theory, but in lived experience.

This requires a fundamental shift in mindset: from control to curiosity. Instead of asking, “How do we make people comply with the process?” leaders begin asking, “Why does the process not work for people?” That shift is where real transformation begins.

Because the ultimate goal is not to create perfectly optimized systems. It is to design organizations that work with humans, not against them. And that means recognizing that behind every data point is a person making choices, adapting to constraints, and trying to get their work done.

Digital exhaust can show you what is happening. But only a human-centered approach can help you understand why—and what to do about it in a way that builds trust rather than erodes it.

Use Cases That Actually Move the Needle

Digital exhaust analysis only becomes valuable when it drives better decisions and meaningful outcomes. While the concept can feel abstract, its impact becomes very concrete when applied to real organizational challenges. The key is to focus on use cases where behavior-based insight can close the gap between intention and reality.

The following are some of the highest-impact applications of digital exhaust analysis across change, experience, and innovation:

Change Management: Seeing Adoption as It Happens

Traditional change management relies on training completion rates, survey feedback, and delayed adoption metrics. These signals often arrive too late to correct course effectively.

Digital exhaust provides a real-time view of how people are actually engaging with new tools, processes, or ways of working. Leaders can identify:

Where employees are reverting to legacy systems or behaviors
Which teams are adopting quickly—and why
Where informal workarounds are emerging

This enables faster intervention, targeted support, and ultimately a higher likelihood of sustained change.

Employee Experience: Detecting Friction and Burnout Early

Employee experience is often measured through periodic surveys, which provide valuable but infrequent snapshots. Digital exhaust fills in the gaps between those moments.

By analyzing collaboration patterns, workload signals, and communication behaviors, organizations can detect:

Meeting overload and fragmentation of focus time
After-hours work patterns that may indicate burnout risk
Breakdowns in cross-functional collaboration

Instead of reacting to disengagement after it occurs, leaders can proactively redesign work environments to better support how people actually operate.

Customer Experience: Uncovering Hidden Friction

Customer journeys are carefully designed, but rarely experienced exactly as intended. Digital exhaust reveals where those designs break down in practice.

Through analysis of clickstreams, support interactions, and behavioral flows, organizations can identify:

Points where customers hesitate, abandon, or seek help
Inconsistencies across channels and touchpoints
Unmet needs that are not captured in structured feedback

These insights enable more precise, evidence-based improvements to the customer journey—reducing friction and increasing satisfaction in ways that traditional metrics alone cannot achieve.

Innovation Discovery: Finding Opportunity in Workarounds

One of the most overlooked sources of innovation is the set of informal solutions people create to get their work done. These workarounds are not failures—they are signals.

Digital exhaust analysis helps surface:

Repeated deviations from standard processes
Shadow systems and tools adopted outside official channels
Emerging behaviors that indicate shifting needs or expectations

Each of these represents an opportunity to design better solutions that align with how people naturally work, rather than forcing them into rigid structures.

Operational Excellence: Moving Beyond Efficiency to Effectiveness

Many operational improvement efforts focus narrowly on efficiency—reducing time, cost, or variability. Digital exhaust enables a broader view that includes effectiveness and experience.

By reconstructing actual workflows, organizations can identify:

Hidden loops of rework and redundancy
Misaligned handoffs between teams or systems
Disconnects between formal processes and real execution

This allows for redesign efforts that not only streamline operations but also make them more intuitive and resilient.

Across all of these use cases, the common thread is speed of learning. Digital exhaust shortens the feedback loop between action and insight. It allows organizations to move from periodic evaluation to continuous adaptation.

And in an environment where change is constant, that ability—to learn faster than the pace of disruption—is what ultimately separates organizations that struggle from those that thrive.

Digital Exhaust Flow

The Technology Ecosystem Powering Digital Exhaust Analysis

While digital exhaust is created naturally through everyday work, unlocking its value requires a rapidly evolving ecosystem of technologies. No single platform owns this space. Instead, it is an emerging convergence of analytics, artificial intelligence, process mining, and digital twin capabilities—each contributing a piece of the broader puzzle.

Understanding this ecosystem is critical, not because organizations need to adopt every tool, but because it reveals where the market is heading: toward a future of organizational observability—the ability to continuously sense, interpret, and respond to how work actually happens.

Enterprise Platforms: Scaling Insight Across Complex Systems

Large enterprise technology providers are embedding digital exhaust analysis into broader platforms that integrate data across operations, customers, and assets. These solutions often combine IoT, analytics, and simulation to create end-to-end visibility.

Siemens: Leveraging digital twin technology to simulate and optimize complex systems, capturing exhaust signals from both physical and digital environments.
General Electric: Applying industrial data analytics to monitor performance, predict issues, and improve operational outcomes.
Dassault Systèmes: Enabling virtual modeling of organizations and ecosystems to better understand how processes and interactions unfold.
PTC: Integrating IoT and augmented reality to connect frontline activity with enterprise systems, generating rich behavioral data streams.

These platforms are particularly powerful in environments where physical and digital systems intersect, but their broader impact is the normalization of continuous data capture and analysis at scale.

Advanced Analytics and Simulation Engines

A second layer of the ecosystem focuses on making sense of complexity. These tools excel at modeling, simulation, and high-dimensional analysis—turning raw exhaust into predictive and prescriptive insight.

ANSYS: Known for engineering simulation, increasingly applied to model system behavior and test scenarios before changes are implemented.
Altair: Combining data analytics, AI, and high-performance computing to uncover patterns and optimize outcomes across complex environments.

These capabilities allow organizations to move beyond hindsight and into foresight—understanding not just what is happening, but what is likely to happen next under different conditions.

Process Mining and Behavioral Analytics Innovators

One of the fastest-growing segments in this space is process mining and behavioral analytics. These solutions reconstruct workflows and interactions from event logs, revealing how processes actually execute across systems and teams.

They provide:

End-to-end visibility into real process flows
Identification of bottlenecks, deviations, and rework
Data-driven opportunities for automation and redesign

By grounding analysis in actual behavior, these tools bring a level of objectivity and clarity that traditional process mapping rarely achieves.

Emerging Startups: Democratizing Insight

Alongside established players, a new generation of startups is pushing the boundaries of what digital exhaust analysis can do. These companies are often more focused, more agile, and more explicitly human-centered in their approach.

They are exploring innovations such as:

AI-driven pattern detection and anomaly identification
Natural language processing applied to communication data
Lightweight tools that make insight accessible beyond data science teams
Privacy-first architectures that balance insight with trust

Their collective impact is to lower the barrier to entry—making it possible for more organizations to experiment with and benefit from digital exhaust analysis without massive upfront investment.

The Convergence Toward Organizational Observability

What is most important is not any individual tool, but the direction of travel. These technologies are converging toward a shared goal: creating organizations that can continuously observe themselves.

In software engineering, observability transformed how systems are managed—shifting from reactive troubleshooting to proactive monitoring and adaptation. A similar transformation is now underway at the organizational level.

The implication is clear. In the near future, leading organizations will not rely on periodic reports to understand performance. They will operate with a living, breathing view of how work unfolds—powered by digital exhaust and the technologies that bring it to life.

The question is no longer whether these capabilities will exist, but how quickly organizations will learn to use them in a way that is both effective and human-centered.

Building the Capability: From Experiment to Enterprise Muscle

Recognizing the value of digital exhaust is one thing. Building the organizational capability to use it consistently and effectively is another. Many organizations start with enthusiasm, launch a pilot, and then stall—unable to scale insight beyond isolated use cases.

The difference between experimentation and impact lies in treating digital exhaust analysis not as a tool, but as a core organizational muscle—one that must be intentionally developed, embedded, and sustained over time.

Start Small, But Start Where It Matters

The most successful organizations resist the urge to boil the ocean. Instead, they begin with a focused, high-value problem—typically a journey or process where friction is both visible and consequential.

This might include:

A struggling change initiative with uneven adoption
A critical customer journey with known pain points
An internal process plagued by delays or rework

By instrumenting relevant systems and analyzing the resulting digital exhaust, teams can generate early wins that demonstrate both value and feasibility.

Build Cross-Functional Alignment Early

Digital exhaust does not belong to a single function. It spans IT, HR, customer experience, operations, and innovation. As a result, siloed approaches quickly run into limitations.

Leading organizations bring together cross-functional teams that combine:

Technical expertise (data engineering, analytics, AI)
Domain knowledge (HR, CX, operations)
Human-centered design and research capabilities

This combination ensures that insights are not only technically sound, but also contextually meaningful and actionable.

Establish Clear Governance and Ethical Guardrails

As digital exhaust analysis scales, questions of trust, privacy, and appropriate use become unavoidable. Without clear guardrails, even well-intentioned efforts can create resistance or unintended consequences.

Effective governance includes:

Transparency: Communicating openly about what data is being used and for what purpose
Boundaries: Defining what will not be measured or inferred, particularly at the individual level
Accountability: Ensuring that insights are used to improve systems, not penalize people

Trust is not a byproduct of capability—it is a prerequisite for it.

Shift the Mindset: From Reporting to Sensing and Adapting

Perhaps the most important transformation is cultural. Traditional organizations are built around reporting—periodic snapshots of performance against predefined metrics.

Digital exhaust enables something fundamentally different: continuous sensing. But to realize this value, leaders must embrace a new operating model—one that prioritizes learning and adaptation over control and prediction.

This means:

Acting on directional insight rather than waiting for perfect data
Testing and iterating in shorter cycles
Empowering teams to respond to what they observe in real time

Over time, this shift transforms digital exhaust analysis from a specialized capability into an embedded way of working.

Scale What Works, Systematically

Once early use cases demonstrate value, the focus should shift to scaling—not by replicating tools, but by codifying practices. This includes:

Standardizing data pipelines and integration patterns
Creating reusable analytical models and frameworks
Embedding insights into existing decision-making processes

The goal is to make digital exhaust analysis repeatable, reliable, and accessible across the organization.

Ultimately, organizations that succeed in this space do not treat digital exhaust as a one-time initiative. They build it into the fabric of how they operate—continuously listening, learning, and adapting.

And in doing so, they move closer to something every organization aspires to, but few achieve: the ability to evolve as quickly as the world around them.

The Future: From Digital Exhaust to Adaptive Organizations

The journey from collecting digital exhaust to building a fully adaptive organization is both a technological and cultural evolution. It requires more than tools or analytics—it demands a mindset shift where organizations listen continuously, respond intelligently, and innovate in alignment with real human behavior.

Organizations that master digital exhaust will develop capabilities similar to observability in software systems: they will sense emerging issues, anticipate bottlenecks, and detect opportunities before they become urgent. This real-time awareness allows leadership to act proactively rather than reactively.

Key hallmarks of adaptive organizations powered by digital exhaust include:

Continuous Sensing: Systems and processes generate ongoing behavioral data, providing a real-time view of organizational health and performance.
Rapid Feedback Loops: Insights flow quickly to decision-makers, enabling faster course corrections and iterative improvements.
Behavior-Informed Innovation: Emerging patterns reveal unmet needs, workarounds, and latent opportunities, fueling human-centered innovation.
Trust-Centered Design: Analysis is conducted ethically and transparently, preserving employee and customer confidence.

The implications are profound. Change initiatives no longer rely solely on annual plans or post-implementation reviews. Innovation is no longer limited to isolated labs or ideation workshops. Instead, the organization becomes a living, learning system, continuously adapting based on how people actually work, collaborate, and engage.

Looking forward, the integration of AI and automation with digital exhaust analysis promises even more sophisticated capabilities. Intelligent agents may highlight emerging friction points, suggest targeted interventions, or simulate the potential outcomes of proposed changes before they are executed.

Yet, technology alone is not enough. Adaptive organizations are built on a foundation of human-centered insight, trust, and curiosity. Leaders must listen carefully, interpret thoughtfully, and act with empathy—turning the passive signals of digital exhaust into meaningful transformation.

The ultimate promise of this approach is clear: organizations that learn to sense and respond effectively will not just survive change—they will thrive in it. By transforming digital exhaust from noise into signal, they unlock the ability to innovate continuously, adapt dynamically, and create lasting value for employees, customers, and stakeholders alike.

In a world of accelerating complexity, the question is no longer whether digital exhaust matters. The question is whether your organization is ready to listen—and evolve.

Frequently Asked Questions (FAQ)

What is digital exhaust in an organization?

Digital exhaust is the unintentional trail of data created by employees, customers, and systems as they interact with processes and tools. It includes patterns of behavior, communication flows, process deviations, and other signals that reveal how work actually happens, beyond formal metrics.

How can digital exhaust analysis improve innovation and change initiatives?

Digital exhaust analysis provides real-time insights into actual behavior and process execution. By identifying friction points, informal workarounds, and adoption gaps, organizations can adapt more quickly, design human-centered solutions, and uncover opportunities for innovation that traditional metrics may miss.

What are the ethical considerations when analyzing digital exhaust?

Ethical considerations include ensuring transparency, protecting individual privacy, and using insights to improve systems rather than monitor or penalize people. Organizations should combine quantitative data with qualitative context, communicate clearly about data usage, and maintain trust to preserve the integrity of the analysis.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credits: ChatGPT

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