Category Archives: Change

FLASH SALE — 50% OFF the Key to Human-Centered Change

How to Ensure a Successful Digital Transformation (Charting Change)

Why do over 70% of digital transformations and change initiatives fail? Most organizations focus purely on the technology or the project timeline, while completely neglecting the human element and business architecture required to sustain it.

To successfully drive organizational agility, leadership must treat digital transformation, portfolio management, and human-centered design as a single, unified framework.


Celebrating America’s 250th with a 48-Hour Flash Sale!

The Human-Centered Change Guidebook - Charting Change

To help you master these frameworks and power your latest initiatives to success, the publisher of my second book — Charting Change (Second Edition) — is running an exclusive 48-hour flash sale.

You can get the hardcover, softcover, or the eBook for 50% off the list price using CODE: FLSH50 until July 4, 2026, at 11:59 PM EDT.

The newly expanded second edition is specifically updated to address modern transformation challenges, featuring loads of new content, additional guest expert sections, and dedicated chapters on:

  • Business Architecture: Aligning strategy with operational execution.
  • Project and Portfolio Management (PPM): Prioritizing the right initiatives.
  • Digital & Business Transformations: Overcoming cultural resistance to tech adoption.

I stumbled across this price drop and wanted to share it immediately. If you haven’t already secured your copy to power your organization’s strategy, now you have no excuse!

Click here to get your copy of Charting Change for 50% off using CODE: FLSH50


💡 Exclusive July 4th Bonus Offer:
You can always get 10 free tools here from the book. However, if you buy the book during this flash sale and contact me with your receipt, I will personally send you 26 premium tools from the 70+ tools inside the full Change Planning Toolkit™ — including the Change Planning Canvas™!


*If discount is not applied automatically, please use this code: FLSH50. The discount is available through July 4, 2026 until 23:59 EST. This offer is valid for English-language Springer, Palgrave & Apress Books & eBooks. The discount is redeemable on link.springer.com only. Titles affected by fixed book price laws, forthcoming titles, and titles temporarily not available on link.springer.com are excluded from this promotion, as are reference works, handbooks, encyclopedias, subscriptions, or bulk purchases. The currency in which your order will be invoiced depends on the billing address associated with the payment method used, not necessarily your home currency. Regional VAT/tax may apply. Promotional prices may change due to exchange rates. This offer is valid for individual customers only. Booksellers, book distributors, and institutions such as libraries and corporations, please visit springernature.com/contact-us. This promotion does not work in combination with other discounts or gift cards.

This offer is valid for individual customers only. Booksellers, book distributors, and institutions such as libraries and corporations, please visit springernature.com/contact-us. This promotion does not work in combination with other discounts or gift cards.

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Use Failure as Rocket Fuel for Success Like SpaceX

Use Failure as Rocket Fuel for Success Like SpaceX

GUEST POST from Robert B. Tucker

SUMMARY: SpaceX’s early success, despite three rocket failures, exemplifies how embracing setbacks as learning opportunities drives innovation. Elon Musk fostered a culture of rapid “test, learn, redesign,” where organizational risks, not individual blame, fueled progress. This approach, contrasting with the common fear of mistakes, allowed SpaceX to overcome near collapse and achieve orbit. The article argues that true failure isn’t making errors, but failing to learn from them. Leaders must create environments encouraging prudent risk-taking, where post-mortems focus on lessons, not culprits. Adopting “fail fast and fail cheap” through small experiments helps organizations learn quickly, transforming setbacks into wisdom and better decisions for ultimate success.

Before SpaceX became one of the most valuable companies in the world, it suffered three consecutive rocket failures. By 2008, Elon Musk had invested nearly everything he had. The fourth launch wasn’t merely important — it was a matter of survival.

After Falcon 1 failed three consecutive times between 2006 and 2008, Musk did not conduct a witch hunt. Heads did not roll. Instead, he assembled his engineers, dissected the technical causes, and focused relentlessly on fixing problems and building morale before the next launch. The emphasis was always on emphasizing rapid learning and pushing ahead.

The successful fourth Falcon 1 launch took place on September 28, 2008. On that flight, Falcon 1 became the first privately developed liquid-fueled rocket to reach Earth’s orbit, a milestone many experts had considered nearly impossible for a startup company.

Had Falcon 1 failed a fourth time there might be no SpaceX today. Instead, that launch succeeded, NASA came calling, and a company that was weeks from collapse began its ascent toward bending history.

The lesson for leaders is profound: if you want people to innovate, you must create an environment where failure is an option, and where prudent risk-taking and rapid learning pervade your culture. SpaceX routinely tested rockets knowing they might explode because Musk believed real-world learning happened faster than endless analysis.

Early on, the fledgling start-up adopted a rapid “test, learn, redesign” cycle rather than trying to eliminate every possible risk before launch. Each unsuccessful launch produced engineering insights that were incorporated into the next design. In that sense, the first three launches were not really failures at all. They were expensive tuition payments on the road to success.

Take Away the Safety Net

Another of Elon Musk’s most important innovations wasn’t technological at all. It was organizational. In an industry long dominated by cost-plus contracts, where the federal government pays defense contractors for effort and expenses, plus a guaranteed margin of profit, regardless of results. Instead, Musk embraced milestone-based agreements with the government that essentially said, “Only pay us when we succeed.”

Taking away the safety net created enormous pressure on SpaceX. But it also unleashed extraordinary creativity and drive. Engineers were encouraged to think boldly, challenge “that’s the way we’ve always done it” thinking, and test ideas rapidly. The risks were borne by the organization, not by individual engineers. As a result, failure became rocket fuel rather than stigma.

One of the defining challenges facing young people today is an exaggerated fear of failure. Research shows that today’s students are significantly more anxious about making mistakes than previous generations. Many have come to believe that one wrong decision can derail a career, a reputation, or a future.

In today’s organizations, failure has become a taboo topic. We fear it. We hide it. We spend enormous amounts of energy trying to avoid it. Employees learn quickly which mistakes are acceptable and which ones can damage careers. As a result, people become cautious. They play defense instead of offense. They stop experimenting and growing in their careers. Obsolescence sets in.

Yet history tells us a different story. Almost every meaningful achievement — whether in business, innovation, politics, science, or personal growth — has been preceded by setbacks, disappointments, and outright failures.

Thomas Edison famously tested thousands of materials before finding a workable filament for his electric light bulb. When asked about his failures, he replied that he hadn’t failed at all. He had simply discovered thousands of ways that didn’t work.

Abraham Lincoln’s early career reads like a catalog of disappointments. He lost elections, suffered business failures, endured personal tragedies, and faced repeated public setbacks. Yet those experiences shaped the resilience and wisdom that ultimately carried him to the presidency during one of the most difficult periods in American history.

The lesson is not that failure is desirable. The lesson is that failure is often the price of admission for meaningful success.

The first step toward building a healthier attitude toward failure is being able to talk about them. I was fired from a dead-end corporate job early in my career and for years I hid my shame. Nowadays I realize I wasn’t fired but fired up! I realized that if I was ever going to become a self-supporting independent journalist, that I should seize that moment and dive in. I went on to become an expert in innovation, and a lucrative career that has taken me all over the world.

What I’ve found in teaching managers how to drive growth through innovation is that when mistakes are hidden, their value is lost. Others cannot learn from them. Valuable insights remain trapped inside individuals or departments. The organization pays the cost of the mistake but receives none of the educational benefit.

What I teach is that when there is a “failure,” that’s a good time to conduct a post-mortem after unsuccessful projects. Ask simple questions: What happened and why? What assumptions proved wrong? What can we learn? Most importantly, objective in-depth debriefs remove blame from the discussion. The goal is not to identify a culprit. The goal is to uncover lessons.

Organizations that openly discuss failures build institutional wisdom. Organizations that conceal failures repeat them.

True failure, therefore, is not making a mistake. True failure occurs when we fail to learn from mistakes — either our own or those of others.

Every industry is littered with examples of organizations that ignored warning signs that should have been visible to management. Kodak invented much of the technology behind digital photography yet failed to act on what it had learned. Blockbuster dismissed the significance of streaming. Nokia allowed a top down, risk adverse culture to congeal such that, when the iPhone hit the market, they were unable to pivot fast enough. Countless companies have repeated mistakes that competitors had already paid dearly to discover.

The most successful professionals cultivate the opposite habit. They become students of failure. They study what went wrong, why it went wrong, and how similar mistakes can be avoided in the future.

The risks associated with failure must be borne by the organization, not by individuals within the organization. When employees feel that every unsuccessful initiative could become a career-limiting event, innovation dies. Fear becomes the dominant operating system.

Leaders must create environments where people know that responsible experimentation is encouraged and protected. That does not mean tolerating carelessness or repeated mistakes. Accountability still matters. Preparation still matters. Execution still matters.

But when a well-conceived initiative fails despite thoughtful planning and diligent effort, the organization should absorb the risk and harvest the lessons.

People should not have to choose between innovation and job security.

This brings us to one of the most useful principles in modern business: fail fast and fail cheap.

Rather than investing years and millions of dollars pursuing untested assumptions, successful organizations run small experiments. They test ideas early. They gather feedback quickly. They adjust before costs escalate.

A small failure today can prevent a catastrophic failure tomorrow.

Think of it as buying information. Every experiment produces data. Some experiments confirm assumptions. Others disprove them. Both outcomes are valuable because they reduce uncertainty and improve future decisions.

The organizations that learn the fastest often outperform those with the greatest resources.

Ultimately, success is not achieved by avoiding failure. Success is achieved by creating systems that transform failure into learning, learning into wisdom, and wisdom into better decisions.

Edison understood this. Lincoln understood this. Musk understood this. Every accomplished entrepreneur, inventor, executive, and leader eventually learns the same lesson. Failure itself is rarely fatal. Refusing to learn from it often is.

The organizations that thrive in the future will not be those that make the fewest mistakes. They will be the ones that learn the fastest, adapt the quickest, and create cultures where intelligent risk-taking is not feared but encouraged.

After all, the opposite of failure is not success. The opposite of failure is learning.

This article originally appeared in Forbes

Image credit: Wikimedia Commons

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Illuminate to Innovate

Illuminate to Innovate

GUEST POST from Janet Sernack

Being consciously innovative involves expanding your awareness and opening your heart and mind to disrupt habitual feelings and thinking, allowing for deeper, more holistic decision-making and innovative problem-solving. It allows us to play in the space of possibility by cultivating consciousness – illuminating the state of being aware of your surroundings, internal thoughts, and subjective experiences. This encompasses everything you perceive, feel, and think, ranging from basic sensory awareness to complex self-reflection, decision-making and problem-solving.  Developing people’s consciousness involves strengthening a person’s ability to sense and connect with awareness-based systems and respond appropriately to achieve desired outcomes. Conscious innovation is a mandatory way of being, thinking, and acting that makes people matter and enables them to survive and thrive in the emerging, uncertain and disruptive world of AI, where leaders must know how to illuminate to innovate.

What is consciousness?

According to Dr Dan Seigal[1], consciousness has two elements that shape a person’s inner state or interior condition. There is the knowing, which is awareness itself. And there are the knowns, which are everything that enters awareness. To integrate consciousness means to differentiate these two elements from each other, and then to differentiate the knowns from one another.

Knowns consist of people’s thoughts, feelings, and memories, while sights, sounds, smells, tastes, and touch bring the outside world in as a constant stream of sensation. They also include intuition, inner wisdom, and awareness of mental and emotional processes, such as memories, beliefs, intentions, and hopes. As well as the relational self, the awareness of connection to other people, to living beings, and to something larger than the individual self.

What is conscious innovation?

Our approach to conscious innovation creates the conditions for individuals and teams to move and focus their attention, develop conscious awareness, and become intentional and passionately purposeful in solving challenging problems. People illuminate to innovate by advancing through the three levels of self to make the world a better place by balancing people, profit, and the planet. 

Conscious innovation integrates the key principles and methodologies of emergence, systems thinking, human-centered design, sustainability and technology to empower people to realize their potential at the intersection of human possibility and technological innovation.

Conscious innovation includes being able to understand and improve a person’s inner state or interior condition, and illuminate to innovate by:

  • Focusing on expanding who they are as human beings by creating the conditions to develop people’s metacognition[2] and brain health[3], enabling them to experience what it means to be responsible, passionately purposeful, and agile, and to build an adaptive capacity to flourish in an uncertain world.
  • Developing an awareness of the potential of cognitive dissonance and harnessing creative tension that enables people to safely learn and grow as humans who act in ways that build their capability to be creative, inventive, innovative and resilient in the face of chaos and disruption.
  • Creating the conditions by clarifying an aligned strategy and developing a safe, trusted, and aligned culture that enables and supports people and teams to collaborate, experiment, and innovate by willingly partnering human potential with AI.

These invisible elements of conscious innovation affect how people interact with, relate to, and lead people and teams; how they communicate, learn, make decisions, solve problems, manage, implement, and embed change; and how they execute innovation or transformational projects and initiatives.

Illuminate to Innovate – The three levels of self

The three levels of self-illustrate the deep learning and change journey involved in illuminating and harnessing human potential on the people side of innovation. At a time when companies are required to rethink the very nature of the corporation, especially how to integrate human accountability with virtual and physical AI agents.

  1. Self-regulation involves developing awareness of one’s automatic responses, understanding their sources and effects on one’s physiology and neurology, owning one’s responses, and ensuring they have a positive impact on oneself and those with whom one interacts.
  2. Self-management involves close observation and management of people’s knowns: being attentively present to neurological and physiological factors, including emotional states, traits, thoughts, feelings, mindsets, behaviours, and skills in how people use time to make decisions, communicate, and resolve business challenges.
  3. Self-leadership involves deepening and illuminating known skills: open awareness, knowledge, and the ability to intentionally master one’s own neurology and physiology, as well as others’, in interactions and challenging situations, to mindfully evaluate and successfully create, invent, deliver, and execute innovative solutions.

The intent is to create strategic and cultural alignment that delivers execution excellence by enabling leaders and engaging people to solve problems in generative ways, consciously prioritizing human relationships through collaboration and experimentation in partnership with AI, and steadily moving towards goals in deliberate, focused, systemic, kind and honorable ways.

What are the benefits of being consciously innovative?

Being consciously innovative involves learning to be, think, and act differently; people learn to stop trying to solve a problem with the same thinking that created it and to stop reproducing the same results they no longer want.

At the same time, the emergence of AI requires a major brain shift to maximize human potential by building foundational cognitive, interpersonal, self-leadership, and technological literacy abilities that enable people to adapt, relate, and contribute meaningfully, integrating an awareness-based systems approach and a holistic focus.

The benefits of being consciously innovative include improving leaders’ and people’s abilities to:

  • Replace short-term, reactive, and conventional linear thinking processes that initially created and now sustain problems, and embrace change as a circular, creative, continuous, and systemic process.
  • Courageously adopt long-term, sustainable strategies for the organization’s growth and the impact it seeks to have on clients or customers and wider communities.
  • Make better-informed decisions by considering potential scenarios, anticipating risks, identifying interdependencies, and making decisions that meet needs while keeping the bigger picture in view.
  • Cease overlaying new structures onto people’s unchanged ways of perceiving and experiencing their world by creating the conditions for people to help people make sense of new structures and processes, show up differently, and take new and right actions.
  • Combine futures thinking and systems thinking, emphasizing ethical considerations, social responsibility, and sustainability.
  • Be empathetic and compassionate by discerning, understanding, and considering the needs, values, and perspectives of all stakeholders involved in a problem or a system, not just those present in a room.
  • Improve people’s capacity to attend, observe, inquire, listen to each other, and differ in generative ways, and to feel empowered to think independently and act differently.
  • Embrace AI strategically, using AI and new technologies to assist, help, and empower human agency, to partner, collaborate, and experiment with AI to rebuild engagement and deliver execution excellence.  

Illuminate to innovate

Being consciously innovative requires actively illuminating and integrating the ways leaders and coaches bring clarity, creativity, compassion, courage, and meaning to their decisions, roles, and teams. This involves expanding your awareness and opening your hearts and minds to disrupt habitual thinking, allowing for deeper, more holistic decision-making and innovative problem-solving. It involves cultivating consciousness – illuminating the state of being aware of your surroundings, internal thoughts, and subjective experiences and encompasses everything you perceive, feel, and think, ranging from basic sensory awareness to complex self-reflection, decision-making and problem-solving.


[1]The Developing Mind (The foundation of Interpersonal Neurobiology) [1]

[2] Metacognition is “thinking about thinking”—the awareness, understanding, and control of one’s own cognitive processes, like learning and problem-solving, to improve performance.

[3]https://www.mckinsey.com/mhi/our-insights/the-human-advantage-stronger-brains-in-the-age-of-ai?cid=mgp_opr-eml-nsl-ofl-mgp-glb–&hlkid=507fe91b220d4915bbcd198daaeb857a&hctky=1766168&hdpid=bfbfe441-95e5-45b4-9dc7-c32cd1789c2f#/

Image Credit: Pexels

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Why Students Are Booing Silicon Valley’s AI Vision

Why Students Are Booing Silicon Valley's AI Vision

GUEST POST from Robert B. Tucker

A curious thing happened at the University of Arizona’s commencement ceremony.

The speaker was former Google CEO Eric Schmidt, one of the most influential figures in the development of the digital economy. Addressing thousands of graduates, Schmidt spoke enthusiastically about artificial intelligence and the transformative role it will play in their lives and careers.

Then something unexpected happened. Students began to boo.

For many observers, the moment was jarring. Why would graduates reject a future of technological abundance, economic growth, and unprecedented innovation? Aren’t young people supposed to be technology’s biggest boosters?

Not anymore, apparently. As a futurist who has spent more than three decades advising leaders on adapting to change and innovation, I see this moment as an inflection point. I think what they were rejecting was a vision of the future being jammed down their throats. Looking at a bleak employment market, these young people were saying en masse, “Your vision of our future is not our vision of our future, and we don’t feel you really have our interest at heart.”

The question at this juncture is: What kind of future are we rushing headlong to build, and who will benefit?

The tech industrial complex spins an appealing vision. But it’s beginning to wear thin. Students and other segments of society are pushing back. They are asking tough questions: Will AI really solve humanity’s greatest challenges? Will it cure diseases, eliminate drudgery, unlock extraordinary productivity gains, and usher in a new era of prosperity, as the so-called tech visionaries proudly claim?

Or could it be that the underlying premise is faulty: that the more intelligence we can automate, the better off society will become. The young people are waking up to the possibility that this is hot air.

Across college campuses, among young professionals, and increasingly among the broader public, there is another narrative taking shape. It is one that many technology leaders seem to want to dismiss: growing unease about where all of this is headed.

Many Americans view AI through the lens of issues much closer to home: skyrocketing electricity bills caused in part by data center proliferation; teen chatbot addiction, and looming job displacement. A recent Stanford study, Canaries in the Coal Mine?, found that young workers in the most AI-exposed occupations saw a 16% relative decline in employment from late 2022 through September 2025.

Over the past several years, I have spoken with educators, business leaders, and students around the world. Increasingly, I hear variations of the emerging narrative. I hear people questioning the tech industry’s vision more sharply. Are we building tools that expand human potential, or tools that gradually replace us? The concern isn’t that AI will become more capable. The concern is that humans will become less so.

Scot Rabe has taught design at Ventura College for decades. He recently described his growing frustration with students. Attendance remains high, but engagement is declining. There is little evidence that students are wrestling deeply with ideas. In his words, “the lights are on, but nobody’s home.”

That observation aligns with broader concerns about what I call human agency—the capacity to act intentionally, make decisions, solve problems, and shape one’s own future.

A 2023 survey by the Pew Research Center explored the future of human agency in an increasingly digital world. Experts were deeply divided. Many predicted that emerging technologies would weaken individual autonomy rather than strengthen it.

Their concern deserves attention.

The challenge facing young people today is not simply learning how to use AI. It is learning how to remain fully human in a world increasingly designed to automate thinking, decision-making, and even creativity.

Tim Wu, author of The Age of Extraction, argues that many of today’s largest technology firms operate by extracting value from our attention, data, and behavior. The more time we spend scrolling, clicking, and consuming, the more profitable the system becomes.

But what happens when the same incentives are applied to intelligence itself? What happens when convenience becomes the highest value? What happens when every difficult task can be delegated to a machine? What happens to the development of judgment, wisdom, resilience, and imagination?

These are not anti-technology questions. They are profoundly human questions.

History suggests that societies thrive not when technology advances alone, but when human capability advances alongside it.

The printing press transformed civilization. Electricity transformed civilization. The internet transformed civilization. Yet none of these innovations eliminated the need for human initiative, purpose, or responsibility. If anything, they increased it.

The danger today is not that AI becomes more powerful. The danger is that we gradually surrender the very qualities that make us uniquely human. That may be what those students were trying to express.

Perhaps they were saying that they do not want a future in which every challenge is solved for them. Perhaps they do not want to become passive consumers of machine-generated answers. Perhaps they are pushing back against a worldview that sees efficiency as life’s highest goal.

And perhaps they are asking a deeper question: What role will humans play in the future being built around us?

One vision imagines a future that is increasingly automated, optimized, digitized, and controlled by a small number of powerful technology platforms. Another envisions a future where technology augments rather than replaces human capability. A future where innovation strengthens creativity, deepens relationships, expands opportunity, and reinforces human dignity.

The choice between these futures is being made right now. Every generation inherits a set of technologies. But every generation must also decide how those technologies will shape our lives.

The students who are booing Silicon Valley’s assumptions were doing more than expressing frustration at yet another out-of-touch billionaire. They were reminding us that progress is not simply about building smarter machines. Rather, it is about building a future worth inhabiting.

This article originally appeared in Forbes

Image credit: Wikimedia Commons

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Take an Evidence-Based Approach for Transformation and Change

Take an Evidence-Based Approach for Transformation and Change

GUEST POST from Greg Satell

In The Knowing Doing Gap by Jeffrey Pfeffer and Bob Sutton, the two Stanford professors show, in painstaking detail, that most enterprises fail to act on what they know. They point out that many are set up to reinforce the status quo, because mastering conventional wisdom is key to advancement.

There is a similar gap when it comes to transformation and change, but for somewhat different reasons. Decades of research and insights are largely ignored. Transformational initiatives are seen as exercises in persuasion, with practitioners designing slogans to “create a sense of urgency around change” and shift attitudes, assuming that will change behaviors.

Today we are in a change crisis. Businesses need to internalize new technologies like AI and adapt to new realities like hybrid work, but still struggle to adopt decades old skills related to lean manufacturing, agile development and cultural competency. If we are going to drive the transformations we need to compete, we need to take an evidence based approach.

The Diffusion Of Innovations

In 1962, Everett Rogers published the first edition of his now-famous book, The Diffusion of Innovations, which contained hundreds of studies of how change spreads. These ranged from the seminal study of the adoption of hybrid corn and the spread of hate crime laws in the US, to the doctors use of the antibiotic tetracycline and the uptake of mobile phones in Europe.

In some instances the same subject was studied in a number of different places. The spread of family planning methods was researched in a number of developing nations, including Taiwan, Korea and Egypt, among others. In others, the same effect was observed in very different contexts, like the importance of social ties in both recruiting civil rights activists during “Freedom Summer” and the spread of air conditioners in the 1950s.

The difference between this type of research and the case studies that underlie much change management thinking is that they are much more rigorous and transparent. In a typical case study, researchers interview a limited number of participants and interpret what they see and hear. These sometimes lead to genuine insights, but people often interpret events differently.

In the diffusion studies, there are typically hundreds of people surveyed, sometimes over a number of years. The questionnaires and data are published along with the findings, so that others can re-examine conclusions. Studies can be compared side by side. In some cases, such as this one, data from earlier work is made available to colleagues to see if they can come up with alternative insights.

There is a remarkable consensus on the basic principles of diffusion. Overwhelmingly, these studies find that new ideas come from outside the community and incur resistance; that there is a common and persistent KAP-gap, in which a shift in knowledge and attitudes do not result in changes in practice; that change follows an s-curve pattern (meaning it starts slow, hits a tipping point and accelerates) and ideas are transmitted socially.

Clearly, any change program needs to take these principles into account.

Changing Societies As Well As Organizations

In the early 1960s, around the time that Rogers began publishing his writings about the diffusion of innovations, Gene Sharp began to formulate his theories about changing societies. Sharp saw change as a strategic conflict in which the weapons weren’t military, but psychological, social, economic and political.

Sharp’s key insight was that the status quo isn’t monolithic, but derives its power from specific sources, such as legitimacy, popular support and institutional support. If you can undermine those sources of power, he reasoned, you can bring change about. To do that, however, you need focus strategically on bringing down what supports the current regime.

While there’s no evidence that Sharp and Rogers ever met or were aware of each other’s work, there are striking similarities. For example, the Spectrum of Allies framework that is central to nonviolent conflict is eerily similar to the adoption groups in Rogers’ diffusion curve. Like Rogers, Sharp found that change was transmitted through social bonds.

The main difference is that Sharp and his revolutionary disciples focus, perhaps not surprisingly, on overcoming resistance, which isn’t emphasized in the diffusion research. For example, the global activist Srdja Popović developed the concept of a dilemma action, which has been the subject of increasing interest by researchers.

While Sharp’s legacy doesn’t have the intense academic rigor of the diffusion research, it has proven itself through the work of practitioners. Movements such as the color revolutions in Eastern Europe and the Arab Spring in the Middle East were based on Sharp’s work and his ideas continue to be developed at his Albert Einstein Institution as well as the Centre for Applied Nonviolent Action and Strategies (CANVAS).

A Network Mechanism For Spreading Change

In the late 1990s, a young graduate student named Duncan Watts began to study coupled oscillation, how certain things, such as crickets, pacemaker cells in our hearts and electrical power grids can, under certain conditions, synchronize their collective behavior. That work led to his discovery of small world networks, a concept so important that in 2018 the prestigious journal Nature published a 20-year retrospective on it.

Where Rogers and Sharp both found that change spreads through social ties, Watts discovered the mechanism through which an idea travels. Many assumed that there were special “opinion leaders” that propagated change. Yet Watts found that it was the structure of the network that determined how far an idea could travel. In effect, it is small groups, loosely connected and united by a shared purpose that drive transformational change.

We know that people tend to conform to the opinions of those around them. The best indicator of what we think and do is what the people around us think and do. This effect extends out to three degrees of influence, so it’s not just people we know personally, but the friends of our friends’ friends that shape how we see things.

Practically speaking, the emergence of small-world networks means that change leaders need to focus more on shaping networks than shaping opinions. It is by empowering small groups, helping them to connect with and inspiring them with a sense of common endeavor that you can bring a change initiative to the exponential part of the s-curve and break out.

Acting On What We Know

The biggest misconception about change is that once people understand it, they will embrace it. That’s almost never true. If you intend to influence an entire organization, you have to assume the deck is stacked against you. The status quo always has inertia on its side and never yields its power gracefully.

The good news is that we have over a half-century of research and practice that can inform our efforts. Yet to be effective, we have to put that learning to work. It makes no sense, for example, to “create a sense of urgency” around change when we know that transformation follows an s-shaped curve, starting slowly and then accelerating after a tipping point. Doing so is more likely to trigger resistance than to move things forward.

In much the same way, if we know that shifts in knowledge and attitudes don’t necessarily result in changes in practice and that ideas about change are transmitted socially, we should focus our efforts on empowering enthusiasts rather than wordsmithing and broadcasting slogans. People tend to adopt the ideas and actions of those around them.

We need to think about change as a strategic conflict between the present state and an alternative vision. The truth is that change isn’t about persuasion, but power. To bring about transformation we need to undermine the sources of power that underlie the present state while strengthening the forces that favor a different future.

— Article courtesy of the Digital Tonto blog
— Image credit: 1 of 1,300+ FREE quotes available for presentations from http://misterinnovation.com

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Thinking From No to Yes for Top Line Growth

Top line growth strategies and product applicability frameworks

GUEST POST from Mike Shipulski

Bottom line growth is good, but top line growth is better. But if you want to grow the bottom line, ignore labor costs and reduce material costs. Labor cost is only 5-10% of product cost. Stop chasing it, and, instead, teach your design community to simplify the product so it uses fewer parts and design out the highest cost elements.

Where the factory creates bottom line growth, top line growth is generated in the market/customer domain. The best way I know to grow the top line is to broaden the applicability of your products and services. But, before you can broaden applicability, you’ve got to define applicability as it is. Define the limits of what your product can do – how much it can lift, how fast it can run a calculation and where it can be used. And for your service, define who can use it, where it can be used and what elements without customer involvement. And with the limits defined, you know where top line growth won’t come from.

Radical top line growth comes only when your products and services can be used in new applications. Sure, you can train your sales force to sell more of what you already have, but that runs out of gas soon enough. But, real top line growth comes when your services serve new customers in new ways. By definition, if you’re not trying to make your product work in new ways, you’re not going to achieve meaningful top line growth. And by definition, if you’re not creating new functionality for your services, you might as well be focusing on bottom line growth.

If your product couldn’t do it and now it can, you’re doing it right. If your service couldn’t be used by people that speak Chinese and now it can, you’re on your way. If your product couldn’t be used in applications without electricity and now it can, you’re on to something. If your service couldn’t run on a smartphone and now it can, well, you get the idea.

For the acid test, think no-to-yes.

If your product can’t work in application A, you can’t sell it to people who do that work. If your service can’t be used by visually impaired people, you’re not delivering value to them and they won’t buy it. Turning can’t into can is a big deal. But you’ve got to define can’t before you can turn it into can. If you want top line growth, take the time to define the limits of applicability.

No-to-yes is powerful because it creates clarity. It’s easy to know when a project will create no-to-yes functionality and when it won’t. And that makes it easy to stop projects that don’t deliver no-to-yes value and start projects that do.

No-to-yes is the key element of a compete-with-no-one approach to business.

Image credits: Pixabay

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Managing the Change When Your New Team Member is an AI Agent

Managing the Change When Your New Team Member Is an AI Agent

by Braden Kelley and Art Inteligencia

Every organization rushing to deploy AI agents is making the same mistake: they are treating this as a technology rollout. It isn’t. It is a change management event — possibly the strangest one most of your employees will ever live through — and almost nobody is managing it as one.

I have spent two decades helping organizations navigate change. New systems, new structures, new leadership, new strategy — I have seen the patterns, and I have built frameworks to help people through them. What’s happening right now with AI agents doesn’t fit neatly into any of those patterns, because for the first time, the “new hire” your team has to adjust to isn’t a person. It has no face to read, no body language to interpret, no shared lunch break to build rapport over. And yet your people are being asked to trust it, collaborate with it, and in some cases defer to its output — all without the social mechanisms humans have relied on for millennia to build trust with someone new.

If you are rolling out AI agents into your teams this year — and if you aren’t already, you will be soon — you need a change management approach built for this specific situation. Here is what that requires.

This Is Not a Software Rollout

When organizations introduce new software, the change management playbook is well understood: communicate the why, train people on the how, support them through the learning curve, and reinforce the new behavior until it sticks. That playbook assumes the new thing is a tool. You pick it up, you put it down, you use it when it’s useful.

An AI agent is not a tool in that sense. It takes initiative. It makes judgment calls. It shows up in meetings, in workflows, in decisions — sometimes proactively, without being asked. The closest analog isn’t a new piece of software. It’s a new colleague. And we already have decades of organizational psychology telling us how disruptive a new colleague can be to team dynamics, let alone one that doesn’t operate like any colleague your team has ever had.

This distinction matters because it changes which change management tools actually apply. ADKAR’s emphasis on individual awareness and desire is still relevant. But the resistance you’ll encounter isn’t really about learning a new interface. It’s about something closer to what happens when any new team member joins: uncertainty about role boundaries, anxiety about being replaced or overshadowed, and an unconscious assessment of whether this new “person” can be trusted.

Why People Resist AI Coworkers Differently Than They Resist New Software

I wrote recently about the neuroscience of creativity and the role the amygdala plays in detecting social threat. The same mechanism is firing right now in your organization, and most leaders have no idea it’s happening.

When a new piece of software arrives, the brain files it under “tool” and moves on. When something that behaves like a colleague arrives — something that talks, decides, and acts with a kind of agency — the brain files it under “social actor” and starts running the same threat assessments it runs on any new person: is this safe? Is this going to take something from me? Can I trust what it tells me?

The catch is that an AI agent gives almost none of the signals humans use to answer those questions. There’s no tone of voice to read for sincerity. No facial expression to gauge intent. No shared history to draw on. Your people are being asked to extend trust to something that offers none of the usual evidence trust is normally built on — and then we’re surprised when adoption stalls or quiet resistance shows up as workarounds, double-checking everything the agent produces, or simply not using it at all.

This is not a training problem. You cannot train your way past a threat response. It has to be addressed the way any well-designed change effort addresses resistance: by understanding what’s actually driving it and designing for that, not for the resistance you assumed you’d see.

Applying the Change Management Process to AI Agent Adoption

I’ve written before about the five process groups that make up a disciplined change management process. Here’s how they apply when the change you’re managing is the introduction of an AI teammate:

Evaluate impact and readiness honestly. Most organizations evaluate AI agent impact in terms of tasks automated and hours saved. Few evaluate it in terms of role identity — what happens to how someone sees their own value when a piece of their job is now done by something that isn’t them? Skipping this assessment is how you end up with technically successful deployments and quietly disengaged teams.

Build a strategy that names the relationship, not just the rollout. Is the agent a tool the team directs, a collaborator the team works alongside, or something closer to a delegate that acts with some independence? Most organizations never decide this explicitly, and the ambiguity is exactly what breeds distrust. Decide it, and say it out loud.

Plan for trust-building, not just training. Traditional training plans teach people how to use something. What you actually need here is closer to onboarding a new team member: transparency about what the agent can and can’t do, visible track record before high-stakes use, and early opportunities for people to verify its output before they’re asked to rely on it.

Execute with visible human oversight, especially early. The fastest way to build trust in a new colleague — human or otherwise — is watching them perform well in front of you, not being told they performed well somewhere else. Early AI agent deployments need visible checkpoints where people can see the agent’s work and verify it, not a black box they’re asked to trust on faith.

Close the loop by naming what changed. Once an AI agent has been integrated into a workflow, say so explicitly, and say what it means for the people whose roles shifted around it. Changes that are never formally acknowledged have a way of generating resentment that outlasts the technical transition by years.

Change Management AI Agent Adoption Infographic

The Real Risk Isn’t the AI. It’s Skipping the Human Part.

I’ll say what I’ve said about AI in customer experience: the key isn’t choosing between AI and humans, it’s knowing when and how to bring each one in well. The organizations that get AI agent adoption right in 2026 will not be the ones with the most advanced agents. They’ll be the ones that treated the human side of this transition with the same discipline they’d apply to any major organizational change — because that is exactly what this is.

Skip that discipline, and you won’t get a failed technology rollout. You’ll get a team that technically has access to an AI agent and quietly refuses to use it, or uses it just enough to look compliant while doing the real work the old way. That is the most expensive kind of failure there is: the one that looks like success on a dashboard somewhere while nothing has actually changed.

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, and the change management framing were decisions made by Braden Kelley, with a little help from Claude to research current trends and clean up the article, and Gemini for images/infographics.

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Crossing the Chasm of Fear

AI Soft Landing scenario — Leading People Through the Anxiety of Transformation and AI

LAST UPDATED: June 14, 2026 at 5:48 PM

Crossing the Chasm of Fear

by Braden Kelley and Art Inteligencia


The Hidden Friction in Modern Transformation

Change doesn’t fail because the technology is broken or the strategy is fundamentally flawed; it fails because organizations consistently underestimate the immense gravity of human fear.

We are living in an era of unprecedented, continuous disruption where the rapid, omnipresent rise of Artificial Intelligence (AI) has magnified workplace anxiety to an all-time high. This paradigm shift has fundamentally altered the conversation from standard operational “inertia” to a deep-seated, existential dread regarding professional relevance, personal autonomy, and long-term job security.

To build an agile, future-ready organization, leaders must stop merely trying to “manage” resistance and start actively dismantling fear. True transformation requires moving past rigid, top-down mandates to embrace genuine co-creation, psychological safety, and a commitment to human-centered design.

I. Mapping the Topography of Fear in the AI Era

To successfully guide an organization through a significant shift, leaders must first understand that the friction they encounter is rarely intellectual; it is emotional. In the wake of the generative AI revolution, traditional change management frameworks are proving insufficient precisely because they treat resistance as a logistical hurdle rather than a psychological defense mechanism.

The Shift from Traditional Resistance to Existential Anxiety

Standard change models were built for linear transitions — such as upgrading an ERP system or relocating an office — where the destination is clear and the skill gap is manageable. AI, however, introduces non-linear disruption. Employees are not just resisting a new tool; they are experiencing existential anxiety. The underlying fear is no longer “How do I use this software?” but rather “Does my expertise still matter?”

The Core Drivers of Workplace Fear

This widespread anxiety is fueled by three distinct, interconnected human dynamics:

  • Loss of Competence & Relevance: Professionals who have spent decades perfecting their craft suddenly face systems that can replicate aspects of their output in seconds. The fear of being rendered obsolete overnight leads to defensive behaviors and a reluctance to engage with new platforms.
  • Loss of Autonomy: Employees worry about losing the human element of decision-making. There is a deep-seated anxiety that their daily workflows will be dictated by black-box algorithms, reducing human agency to mere data entry and validation.
  • The “Black Box” Effect: Because advanced AI models operate behind complex neural layers, the lack of transparency breeds immediate distrust. When people do not understand how a technology arrives at a conclusion, they naturally default to worst-case scenario thinking regarding its intent and accuracy.

The Real Cost of Inaction

When leadership fails to recognize and mitigate these fears, the organization pays a heavy cultural tax. This friction rarely manifests as open defiance. Instead, it operations below the surface as:

  • Quiet Quitting: Disengagement driven by the belief that effort is futile in an automated future.
  • Malicious Compliance: Following instructions to the letter while ignoring obvious system errors, effectively letting the new technology fail to prove a point.
  • Organizational Paralysis: A total stall in innovation, as teams become too risk-averse to experiment with new digital capabilities.

II. Redefining the Approach: Moving from Mandates to Co-Creation

The traditional corporate playbook for technology deployment relies heavily on top-down enforcement. Executives select a platform, managers set a deployment date, and training sessions are scheduled to push the workforce into compliance. While this rigid approach might work for static software updates, it completely fractures when applied to cognitive, disruptive technologies like Artificial Intelligence. To cross the chasm of fear, leadership must fundamentally redefine how change is initiated.

The Failure of Top-Down Dictates

When an disruptive technology is thrust upon an organization from above, it triggers the corporate equivalent of an immune system response. Employees perceive the uninvited change as an existential threat to their routines and livelihoods. Pushing mandates down the organizational chart only hardens resistance, forcing anxiety underground and transforming potential advocates into silent saboteurs.

The Power of Participatory Innovation

The alternative to top-down friction is Participatory Innovation — the deliberate practice of shifting the narrative from “This is being done to you” to “You are building this with us.” True ecosystem agility requires flattening the hierarchy of contribution and inviting the entire workforce into the design process. Rather than treating front-line employees as passive recipients of change, organizations must treat them as active co-creators of their own future workflows.

This approach transforms the deployment strategy by:

  • Engaging front-line staff at the inception stage to identify real, daily friction points that AI can genuinely alleviate, rather than forcing technology where it doesn’t fit.
  • Utilizing cross-functional design sessions that break down legacy silos, allowing technical developers and domain experts to build tools in tandem.
  • Establishing iterative feedback loops that give employees a direct hand in shaping, tweaking, and refining the automated systems they are expected to use.

Lowering Resistance Through Shared Ownership

Human beings rarely destroy what they help build. When an employee looks at a newly integrated AI assistant or a redesigned digital workflow and recognizes their own insights, feedback, and domain expertise baked into the final product, the underlying psychological dynamic shifts instantly. The fear of the unknown is replaced by a powerful sense of pride of authorship, transforming potential resistance into proactive, self-sustaining adoption.

III. The Strategic Blueprint: Crossing the Chasm of Fear

Dismantling fear and establishing a culture of participatory innovation requires more than good intentions; it demands an operationalized, human-centered strategy. To successfully cross the chasm of anxiety and achieve meaningful adoption, leaders must execute a deliberate, multi-layered blueprint that prioritizes human experience alongside technical milestone delivery.

Step 1: Cultivate Psychological Safety First

Before introducing a single algorithmic tool, leadership must anchor the organizational culture in psychological safety. If employees believe that experimenting with AI or voicing skepticism will jeopardize their standing, they will retreat into defensive compliance.

  • Create dedicated, judgment-free forums where teams can openly discuss their anxieties, ask “naive” technical questions, and challenge assumptions without fear of retribution.
  • Frame the early stages of AI adoption as an iterative experiment rather than a high-stakes, zero-fault mandate. Normalize failure as a natural, necessary component of learning to collaborate with intelligent systems.

Step 2: Demystify the “Black Box”

Fear thrives in obscurity. When technology is shrouded in complex, dense jargon, employees default to worst-case scenario thinking. Crossing the chasm requires pulling back the curtain on how automated tools function.

  • Provide transparent, accessible education tailored to non-technical users. Demystify the data sources, logic, and operational boundaries of the AI models being deployed.
  • Shift the corporate narrative away from “automation as a replacement” and explicitly reframe it as “augmentation as a partner.” Clearly demonstrate how these tools can absorb repetitive cognitive drudgery, freeing individuals to focus on high-value, uniquely human tasks.

Step 3: Define New “Experience Level Measures” (XLMs)

Traditional change management focuses almost exclusively on cold Operational Measures—tracking system uptime, deployment timelines, software licenses, and output volume. To manage the human friction of transformation, organizations must measure what actually matters: the human experience of the transition.

  • Implement Experience Level Measures (XLMs) to actively track sentiment, cognitive friction, and confidence levels across the workforce during the rollout.
  • Establish an Experience Management Office (XMO). This cross-functional entity acts as the empathetic heartbeat of the transformation, monitoring XLMs in real time and intervening with support, tailored training, or process redesign when emotional friction spikes.

Step 4: Re-skilling with Dignity and Equity

True fairness in transformation means ensuring that the rewards of technological advancement are relative to the effort invested by the people keeping the organization running. If employees feel that upskilling only leads to their own displacement or unfair workloads, adoption will fail.

  • Demonstrate a visible, legally backed commitment to the long-term value of your human capital through robust, funded re-skilling pathways that dignify the worker’s career trajectory.
  • Align future organizational recognition, bonuses, and growth opportunities with equitable outcomes: ensure that the harder working individuals who lean into the challenge of adapting and mastering new tools receive the tangible rewards of that shared success.

IV. Activating the Ecosystem: Leveraging Multi-Dimensional Roles

Successfully steering an organization away from anxiety and toward sustainable innovation requires a diverse network of human capabilities. Relying solely on technical project managers or traditional IT leaders to drive adoption is a structural mistake; these roles are designed to optimize systems, not to heal a fractured human culture. To operationalize empathy and scale change, leadership must activate a multi-dimensional ecosystem of specialized roles.

Beyond the Project Manager

While project managers excel at tracking timelines, budgets, and deployment milestones, they rarely possess the specialized tools or bandwidth required to navigate deep-seated psychological friction. Orchestrating a human-centered transformation requires shifting the focus from managing tasks to nurturing human relationships. Organizations must look beyond standard job titles and intentionally cultivate specific archetypes designed to bridge the gap between human anxiety and technological capability.

The Right People in the Right Seats

To dismantle fear at every layer of the enterprise, leaders should identify, empower, and deploy three distinct operational archetypes across the transformation ecosystem:

  • The Evangelist: This role is responsible for crafting the overarching human narrative of the transformation. The Evangelist does not merely pitch the features of a new AI tool; they communicate the authentic “Why” behind the change. By generating real, unforced energy and painting a vivid picture of a more fulfilling, augmented future, they inspire teams to lift their heads above immediate anxieties and look toward the long-term horizon.
  • The Connector: Change rarely scales effectively through top-down mandates; it spreads horizontally through social proof and trusted networks. Connectors are the cross-functional linchpins who span legacy departmental boundaries. They excel at identifying grassroots wins in one pocket of the organization, translating those successes for other teams, and ensuring that insights, feedback, and shared resources flow seamlessly across the entire ecosystem.
  • The Coach: While Evangelists inspire groups and Connectors build bridges, the Coach works on the front lines of human emotion. Operating with high emotional intelligence, Coaches provide one-on-one empathy and guidance to individuals experiencing severe friction. They help employees navigate personal technical skill gaps, address specific career anxieties, and safely transition into new ways of working without losing their professional dignity.

Conclusion: The Ultimate Reward of a Human-Centered Future

Technology provides the raw capability, but human adoption provides the actual organizational value. As we navigate the complex, non-linear disruptions of the Artificial Intelligence era, it is becoming increasingly clear that the true competitive advantage does not belong to the enterprise with the largest budget or the most advanced algorithms. The future belongs to the organizations that can move their people past anxiety and into a state of shared purpose.

Crossing the chasm of fear requires leaders to abandon the outdated illusion of top-down control. By anchoring your transformation strategy in radical transparency, psychological safety, and participatory innovation, you transform a potentially threatening disruption into a collective opportunity. Measuring the journey through human-centric lenses like Experience Level Measures (XLMs) and deploying empathetic archetypes ensures that no one is left behind in the wake of progress.

Ultimately, when you design fear out of your corporate culture, you unlock the ultimate reward: an agile, resilient, and infinitely innovative workforce. By treating employees as respected co-creators of their digital future, you don’t just achieve a successful technology rollout — you build a human-centered ecosystem capable of thriving through any disruption the future brings.

Frequently Asked Questions

Why do traditional change management frameworks fail when introducing AI?
Traditional frameworks treat change as a linear, logistical hurdle focused on training and compliance. AI introduces non-linear disruption that triggers deep psychological and existential anxiety regarding job security, relevance, and loss of human autonomy. Overcoming this requires an empathy-driven, human-centered approach rather than top-down mandates.
What is Participatory Innovation and how does it reduce resistance?
Participatory Innovation is the practice of actively involving front-line employees in co-creating and designing their future workflows instead of pushing changes down from the executive level. Because human beings rarely destroy what they help build, this shared ownership transforms fear of the unknown into pride of authorship.
What are Experience Level Measures (XLMs) and why are they necessary?
While traditional operational measures track cold metrics like system uptime or deployment timelines, Experience Level Measures (XLMs) actively quantify human sentiment, cognitive friction, and adoption confidence. They are critical because technology only provides capability; human adoption is what actually unlocks organizational value.


Operationalize Organizational Empathy

Ready to Bridge the Gap Between Technology and Human Experience?

Technology only provides capability; human adoption creates the value. If you want to move past cold operational metrics and design fear out of your transformation, let’s connect. Get expert guidance on architecting impactful Experience Level Measures (XLMs) or establishing a dedicated Experience Management Office (XMO) tailored to your culture.

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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Why VUCA is a Myth

Why VUCA is a Myth

GUEST POST from Greg Satell

“Imagine, if you will, a factory as clean, spacious and continuously operating as a hydroelectric plant. The production floor is barren of men,” Fortune magazine declared in its November 1946 issue. Soon the world entered a new world of mass production and mass retail. Then came a green revolution, a space race, genomics, computers, the Internet and now artificial intelligence.

Today it’s become an article of faith that everything moves faster. Business pundits tell us that we’re living in a VUCA world (Volatile, Uncertain, Complex and Ambiguous). These are taken as basic truths that are beyond questioning or reproach. Yet are things actually moving any faster than in earlier eras? The evidence is surprisingly scarce.

The inescapable truth is that some things move faster today and others move slower. We don’t have—nor should we want—more change today than before. We need to be more thoughtful about change, more deliberate about the ones we undertake and more tenacious in our pursuit of them. We should aim to have less disruption and more progress.

An Era of Industrial Stability

Since Jack Welch took over GE in the 1980s, the management ethos has been taken over by a cult of disruption. Pundits say we must “innovate or die.” Managers feel pressure to launch new initiatives, to pivot and then pivot again, because the competition has become so rabid that “only the paranoid survive.”

The data, however, tell a very different story. A report from the OECD found that markets, especially in the United States, have become more concentrated and less competitive, with less churn among industry leaders. The number of young firms have decreased markedly as well, falling from roughly half of the total number of companies in 1982 to one third in 2013.

A comprehensive 2019 study from the National Bureau of Economic Research found two correlated, but countervailing trends: the rise of “superstar” firms and the fall of labor’s share of GDP. Essentially, the typical industry has fewer, but larger players. Their increased bargaining power leads to more profits, but lower wages.

With all of the hype around things like artificial intelligence, this may seem hard to believe. However, once you start to think about where you actually spend your money, food, shelter healthcare, travel and so on the reality sets in that most of the economy involves atoms and not bits and, if you do a bit more research, you’ll find that those industries are, for the most part, less competitive.

The truth is that we don’t really disrupt industries anymore. We disrupt people. Economic data shows that for most Americans, real wages have hardly budged since 1964. Income and wealth inequality remain at historic highs. Anxiety and depression, already at epidemic levels, worsened during the Covid-19 pandemic.

The Limits Of Digital Dominance

Over the past several decades, innovation has become largely synonymous with digital technology. When the topic of innovation comes up, somebody usually points to a company like Apple, Google or Facebook rather than, say, a car company, a hotel or a restaurant. Today, seven out of the ten most valuable companies in the world are digital firms.

This is largely because of two forces converging. The first is Moore’s Law, the exponential doubling of the number of transistors we have been able to cram onto a silicon wafer. Yet our ability to do that is coming up against the constraints of physics. Advancement in conventional chips has already slowed and, at some point, it will stop altogether.

The other force driving the digital economy has been increasing returns. As the economist W. Brian Arthur explained in a 1996 article in Harvard Business Review, certain conditions, such as high upfront investment, negligible marginal costs and network effects, lead to “winner take all markets where the fastest firm reaps incredible benefits.

Yet consider that information and communication technologies only make up about 6% of GDP value added in advanced economies and you begin to see the problem. The Silicon Valley model simply doesn’t work outside of software and consumer gadgets. In industries that have a low tolerance for failure, such as manufacturing and healthcare, you can’t simply move fast and break things because you’ll likely break something important.

Moving Slow To Go Fast

When Covid hit in the winter of 2020, it was a mysterious disease with no known cure. Yet in a mere matter of months vaccines were developed and being tested. By the end of the year two firms, Pfizer and Moderna received emergency authorization and people started getting their shots. Given that before Covid it took more than a decade to develop and test a vaccine, this was almost unheard of speed.

Yet look a little closer and it becomes clear that the real story is somewhat different. Katalin Karikó, published her first paper on the mRNA technology used to make the vaccines in 1990. She wasn’t able to win grants to fund her work and, in 1995, was told that she could either direct her energies in a different way, or be demoted. She took the demotion, worked through it and, a decade later, began to see some success.

Today, of course, mRNA technology is moving very quickly. Funding is flooding into labs to potentially cure or prevent a wide range of diseases, from cancer to malaria, vastly more efficiently than anything we’ve ever seen before. There are similar slow moving revolutions underway in quantum computing, drug and materials discovery and other things.

There’s nothing usual about any of this. It’s long been known that technology follows an s-curve pattern, starting slowly, then hitting an exponential phase in which it moves very quickly before leveling off again. For example, after penicillin became commercially available in 1945, we entered a golden age of antibiotics and scientists quickly uncovered dozens of compounds that could fight infection, before things slowed to a crawl.

At any given time, there are many s-curves going on at once. Some are just beginning to crawl, others speeding up and still others slowing down. Pointing out the ones that are speeding up and ignoring everything else that’s going on may be exciting, but it’s not the way to get the best results.

Rethinking The Change Gospel

It’s no accident that VUCA is a military term. The ever-present mantra that we are living in a time of volatility, uncertainty, complexity and ambiguity makes corporate executives feel like swashbuckling heroes. The truth is that there is very little evidence that is the case and a veritable mountain to the contrary.

There is also evidence that all the hype around change is doing real damage. Leaders conjure up dramatic images of “burning platforms” to justify launching ambitious initiatives, which rarely succeed. These failures then are given as confirmation for how dire the need for change really is and more initiatives are launched with similar results.

That is the change gospel. Transformation has, all too often, become an end in itself rather than a means to an end. We end up pivoting so much that we end up right where we started. The problem with cheerleading change is that it puts the cart before the horse. People don’t embrace change because you came up with a fancy slogan, they adopt what they find meaningful, that creates genuine value to their lives and their work.

We need to have more reverence for the mundane and ordinary. When you look at previous eras in which more genuine transformation took place and far more economic value was produced, there was much less talk about disruption and much more focus on improving the human condition.

The truth is that we’re not really disrupting industries anymore as much as we are disrupting ourselves and fairy tales and living in a VUCA era will not change those basic facts. We need to think less about disruption and more about tackling grand challenges that will impact the world in significant ways. Innovation should serve people, not the other way around.

— Article courtesy of the Digital Tonto blog
— Image credit: Wikimedia Commons

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

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

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

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

  1. Making Change Stick — by David Burkus
  2. Why You Need to Leverage Shared Values in Change Leadership — by Greg Satell
  3. Why Zero UI Will Redefine Experience Design — by Art Inteligencia
  4. Winning with Artificial Intelligence in 90 Days — Exclusive Interview with Charlene Li
  5. The Micro-Enterprise Explosion — by Braden Kelley
  6. Direction of Fit — by Geoffrey A. Moore
  7. The End of AI Data Centers — by Braden Kelley
  8. Cognitive Enhancement and the Augmented Worker — by Braden Kelley
  9. Leveraging Multi-Agent Orchestration Frameworks for Innovation — by Art Inteligencia
  10. We Must Think Less Like Engineers and More Like Gardeners — by Greg Satell

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

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

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