Category Archives: Change

We Need More Innovators and Scientists in Leadership Roles

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We Need More Innovators and Scientists in Leadership Roles

GUEST POST from Pete Foley

Our world is changing at an unprecedented rate. We are in an innovation driven economy. AI, genetic manipulation, energy innovation, climate, and virtually anything driving change are all highly technical and complex. And all come with high stakes pros and cons.

Scientists and innovators navigating this requires strategic leadership that understands technical complexity, uncertainty and that collectively has some knowledge of basic science and engineering. 

Politics Lacks Scientists: Today, while more than half of US Senators have a law background, only one has a science PhD.  I believe this creates a serious gap in fundamental knowledge between our strategic leaders and the innovators that are driving change.

Experts or Oracles? Of course, our leaders have access to ‘experts’ to help them with complex topics.  But when the fundamental knowledge gap between leaders and experts becomes too big, experts become oracles. They pronounce rather than persuade. When this happens we risk the determining factor in strategy becoming superior communication skills, instead of knowledge or superior ideas.  The ideas (and regulations) that win are not the necessarily best ones, but the ones championed by good communicators, salesmen scientists or smooth talking lobbyists.  It’s dangerous to follow the science blindly, and even riskier to regulate what we don’t understand. That invites dangerous unintended consequences. But increasingly, that is the path we are on.
 

Why We Need More Innovators and Scientists in Leadership Roles

Of course, our leaders don’t need to all be 160 IQ polymaths with PhD’s in quantum mechanics. But to make good decisions they do need to at least be able to understand and apply critical thinking to the inevitably conflicting opinions of experts.

Communicating Science and Technology: Now of course, much of the onus for promoting understanding of complex technology lies with us in the broader innovation and science community.  If we cannot communicate knowledge to people who own resources and executive power, then we risk that knowledge becoming redundant.

But communication is always a two way street. Bridging between leaders and experts requires some common ground.  It’s really hard to have a useful discussion with someone who does even have a basic vocabulary for a topic. As technology and innovation become increasingly important, without more technically savvy leaders we risk a disconnect between strategy, regulation and knowledge. As our leaders get older, and more disconnected from the science driving change they rely less on quality of ideas, and more on appealing framing of ideas, or perhaps familiarity with equally disconnected experts. That is a dangerous path.

Non Scientific Mindsets Facing Technical Challenges. One key danger is the tendency to view choices as binary, another is sunk cost. Binary choices are superficially easy, but in the real world most innovation is not black and white, but instead involves some form of trade off.  Whether it is AI, energy strategy, pharmaceutical development or one of the other ever growing list of emerging technologies, there are benefits, but also costs.  With AI for example, the benefits of gaining and holding global leadership of the technology are likely as economically huge as the opportunity cost of not doing so.  But with big opportunity also comes big risks, including the environmental costs of data centers, risks to societal structure, and even existential risk to humanity itself.  The stakes don’t get much higher.

The Uncertainty Principle: And this is multiplied by the sunk cost fallacy. Over commitment to an incorrect binary choice can be really risky. While we know there are going to be pros and cons to any new technology, we rarely understand them very well in advance.  Innovation is by definition a dive into the unknown, and that makes accurately predicting both upsides and downsides really difficult.  This requires flexible, agile thinking, openness to new data, and a willingness to adjust mid-flight, skills inherent to science and technology . 

But as a society, if anything we seem to be moving away from flexible thinking, and towards more rigid viewpoints that are often heavily pre-primed by affiliations, preconceptions and bizarrely, politics.  People are often passionately for or against AI, but all too often without really knowing why. ‘Green’ energy is polarizing, climate change is divisive.  But while passion and ownership have their place, often the best answer is not cheerleading for a team. Instead it’s beneficial to find a flexible balance that acknowledges the pros and cons, and that ideally identifies non zero sum answers for those contradictions. But that again typically requires nuance, and some level of technical understanding. 

Finding Non Zero Sum Answers: The good news is that once we step away from polarized and binary thinking, non zero sum solutions are sometimes not as hard to find as we think.  Just as an example, with AI, there is potential to have our cake and eat it.   If we cut out digital slop, it’s conceivable that could we achieve and maintain technology leadership, but with much lower environmental cost.  For example, using AI to solve complex medical problems may be a net benefit that is worth some damage to our wilderness, or use of our scarce resources.  But action figures, generic illustrations, mediocre music and often pointless copies of master artists not so much!  I’m sure all of the latter help advance our knowledge to some degree, and help to justify AI investment, but by being more selective, could we achieve the same or similar ends with a superior benefit/cost ratio? 


The Human Advantage: But making smart trade-off decisions like this requires flexible and creative thinking.  Ironically that is one of the things humans still do better than AI.  We just need to embrace our human strengths, but also make sure our leaders also reflect those strengths.

Innovators in Leadership Roles: This means we need a more balanced and scientific approach to leadership if we are navigate the increasingly technology driven future.  Having lawyers making laws is not bad per se, but I passionately believe we need a more diverse set of skills at our upper leadership levels if we are to effectively navigate the coming years. That means the innovation and scientific community needs to step up.  We also need to get much better, and mea culpa, at communicating complex issues.  It’s critical to be clear and simple but not simplistic.

The Tyranny of Simplicity: Simplistic answers, memes, and binary choices have a great deal of superficial appeal.  And politicians and the media exploit this very effectively. In our information overloaded, time constrained world, everybody’s cognitive bandwidth is stretched.  We often seek answers rather than understanding because that’s all we have time for.  But from a leadership perspective, we need to understand that limited cognitive bandwidth is not the same as limited intelligence. People may grasp for simplistic answers, but because they have no commitment to them based on their own knowledge or critical thinking, that grasp is tenuous. This means that being simplistic can be self defeating in the long run.  For example, take the much quoted, ‘globally agreed’ climate target; to not exceed a 1.5 degrees Celsius increase since pre-industrial times. For sure, some people will accept this without question. But other enquiring minds will ask if 1.49C OK? Is this a tipping point? Do we fall of a cliff at 1.51C. Conversely, what happens if we exceed that limit and nothing dramatic happens?  Do we discard that boundary, or move it? Then there are obvious questions around how we address that boundary. What will it take to prevent crossing it?  What are the trade offs?  Who has the sphere of influence to actually make a difference?  It’s OK to have a simplistic position, but it needs to be supported by layered reasoning.


Cry Wolf: I’m not suggesting that climate scientists who promote 1.5C don’t grasp this complexity.  But somewhere in the path from science to politicians and media the real world complexity it often gets lost in translation.  And thats not trivial, as it creates the risk of ‘cry wolf’ effects, and of leaders being perceived as manipulative.   If we overstate the importance of 1.5 C, and it proves to be wrong, or at least a softer limit than previously advertised, we risk people perceiving that they have been mislead or manipulated.  That then feeds skepticism, and even gives support to some of the wilder ‘conspiracy theories’. Once a source has become discredited on one vector, it is typically discredited on everything. 

No easy answers to this.  But I believe innovators and scientists really need to take a bigger leadership role in a world where innovation is increasingly the driving force. Politicians generally don’t get elected because they deeply understand complex issues, but because they understand how to motivate, communicate, simplify and manipulate. They often rely on peoples limited cognitive bandwidth, as this helps them to craft simple slogans, concepts, and sometimes trigger fear and division. Remember that we dislike losing something about twice as much as we like gaining it, which makes fear a very powerful manipulative tool. That brings power, but not necessarily wisdom. But limited cognitive bandwidth is not the same as limited intelligence. And simplistic concepts are vulnerable to challenge, or evolving data.

Of course, we don’t want to make every issue a PhD thesis.  But we do need to acknowledge increasing complexity and uncertainty, and at the very least develop authentic, layered narratives that acknowledge complexity and the inevitable uncertainty of an innovation driven world.  Without that, our strategies become extremely fragile, and easily shattered the first time we are proved wrong. Even if we may start from a position of intense conviction, we must also change paths in the face of compelling evidence. Scientists and innovators tend to be good at this. It’s a skill that maybe needs to be used more broadly

Image credits: Google Gemini

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Change Management Models

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A Practitioner’s Guide to the Most Important Frameworks

Change Management Models

by Braden Kelley and Art Inteligencia

Change management models exist because organizational change fails far more often than it succeeds. Research consistently puts the failure rate of major change initiatives at 60–70% — not because leaders lack intelligence or commitment, but because most organizations attempt change without a structured framework for thinking about what change actually requires of people, processes, and leadership.

After two decades of working with organizations on change and innovation, and developing the Human-Centered Change™ methodology — including the Change Planning Canvas™ and more than 70 visual, collaborative tools that make up the Change Planning Toolkit™ — I’ve come to believe that the right change management model is not the one that is most academically respected or most commonly cited. It’s the one that fits your organization’s specific situation, culture, and change challenge.

This guide covers the most important change management models in use today, what each one does well, where each one falls short, and how to choose the right framework for your change initiative.

What is a Change Management Model?

A change management model is a structured framework that helps leaders plan, implement, and sustain organizational change. Models provide a common language for talking about change, a sequence of steps or activities to follow, and a set of principles that reflect how people and organizations actually respond to change. Without a model, change programs tend to focus on technical deliverables (new systems, new org charts, new processes) while neglecting the human dimensions that determine whether change is actually adopted.

The best change management models for different organizational change types share three characteristics: they are grounded in how people actually experience change (not just how organizations want them to), they provide actionable guidance rather than abstract principles, and they are flexible enough to be adapted to different organizational contexts and change types.

The Most Important Change Management Models

Lewin’s Change Model (Unfreeze-Change-Refreeze)

Developed by social psychologist Kurt Lewin in the 1940s, this is the foundational model that most others build on. Lewin proposed that change occurs in three stages:

  • Unfreeze — Create the motivation and readiness to change by challenging the status quo, communicating the need for change, and reducing the forces that resist it
  • Change — Move toward the new desired state through new behaviors, processes, and ways of thinking
  • Refreeze — Stabilize and sustain the new state by embedding new behaviors in culture, systems, and practices

Strengths: Elegantly simple. Captures the essential insight that change requires deliberate unfreezing of current patterns before new ones can take hold — an insight most organizations ignore by jumping straight to implementation.

Limitations: Too linear for complex modern change environments. The “refreeze” concept is increasingly obsolete in organizations that need to change continuously rather than stabilize between change cycles. Also provides little practical guidance on how to execute each stage.

Best for: Providing a conceptual foundation and common language for thinking about change. Less useful as a practical implementation guide.

Kotter’s 8-Step Change Model

Harvard Business School professor John Kotter developed his 8-step model based on research into why change programs fail. The eight steps are: create urgency, build a guiding coalition, form a strategic vision, communicate the vision, remove obstacles, generate short-term wins, sustain acceleration, and institute change.

Strengths: The most widely used change management model in large organizations. Strong emphasis on building a coalition of change champions and creating visible short-term wins to sustain momentum. The urgency-first approach addresses one of the most common failure modes in change programs.

Limitations: Primarily a leadership model — it tells leaders what to do but provides little guidance on the employee experience of change. Sequential step approach can create rigidity in dynamic environments. Does not adequately address resistance or the emotional dimensions of change. Works better for top-down, well-resourced change programs in large organizations than for the complex, multi-directional change challenges most organizations actually face.

Best for: Large-scale organizational transformation programs with strong executive sponsorship. Less effective for culture change or change initiatives that require significant employee participation in the design process.

ADKAR Model (Prosci)

Developed by Jeff Hiatt at Prosci, ADKAR focuses on the individual experience of change rather than the organizational process. The acronym stands for Awareness (of the need for change), Desire (to support the change), Knowledge (of how to change), Ability (to implement new skills and behaviors), and Reinforcement (to sustain the change).

Strengths: The best model available for diagnosing where individual change adoption is breaking down. Highly practical — if someone isn’t changing, ADKAR helps you identify exactly which building block is missing. Strong focus on the human side of change that Kotter’s model underemphasizes. Excellent for managing large-scale ERP implementations, technology rollouts, and process changes where individual adoption is the critical success factor.

Limitations: Individual-focused model that doesn’t address organizational or systemic dimensions of change. Can create a mechanical, compliance-oriented approach to change if not applied thoughtfully. Doesn’t address the cultural and leadership behavioral changes required for transformation. The reinforcement stage is often underfunded and underexecuted in practice.

Best for: Technology adoption, process change, and any initiative where the primary challenge is getting individuals to change their behavior in specific, defined ways.

McKinsey 7-S Framework

Developed by Tom Peters and Robert Waterman at McKinsey in the late 1970s, the 7-S Framework identifies seven interdependent elements of an organization: Strategy, Structure, Systems, Staff, Style, Skills, and Shared Values. The model proposes that effective change requires alignment across all seven elements.

Strengths: The most comprehensive organizational diagnostic tool of the major models. Excellent for identifying where misalignment is undermining change efforts — especially useful for post-merger integration, where organizational systems and values are often deeply misaligned. Forces leaders to think systemically rather than focusing on one or two visible elements of change.

Limitations: A diagnostic model, not an implementation guide. Tells you what needs to be aligned but not how to align it. Complex enough that it often requires external facilitation to apply effectively. Can become an academic exercise without strong executive engagement.

Best for: Organizational diagnosis, post-merger integration, and large-scale transformation programs where systemic alignment is the primary challenge.

Bridges’ Transition Model

William Bridges distinguished between change (the external event or situation) and transition (the internal psychological process people go through in response to change). His model identifies three phases: Endings (letting go of the old), the Neutral Zone (the in-between state of confusion and possibility), and New Beginnings (embracing the new).

Strengths: The most psychologically sophisticated of the major models. The critical insight — that transition begins with an ending, not a beginning — is consistently underappreciated by change leaders who focus on communicating the new state without acknowledging the loss of the old one. Exceptionally useful for understanding and managing resistance to change.

Limitations: A conceptual model rather than a practical implementation framework. Requires skilled facilitation to apply effectively. Less useful for organizations looking for a step-by-step change management process.

Best for: Culture change, leadership transitions, post-restructuring integration, and any change situation where resistance and emotional response are the primary obstacles.

Kübler-Ross Change Curve

Originally developed to describe the emotional stages of grief, Elisabeth Kübler-Ross’s model was adapted for organizational change to describe the emotional journey individuals experience when facing unwanted change: shock, denial, anger, bargaining, depression, acceptance, and integration.

Strengths: Helps leaders understand that resistance and emotional responses to change are normal, predictable, and temporary — not signs of failure. Creates empathy for the human experience of change. Particularly useful for communicating with leaders who are frustrated by employee resistance.

Limitations: Originally developed for grief, not organizational change — the mapping is imperfect. Implies a linear progression through stages that people actually experience non-linearly and idiosyncratically. Can inadvertently normalize a passive, wait-it-out approach to change resistance rather than proactive engagement.

Best for: Building change leadership empathy and designing communication strategies that acknowledge the emotional journey of change.

The ACMP Standard for Change Management

Before covering the Human-Centered Change™ methodology, it’s worth acknowledging the ACMP Standard for Change Management — the professional standard developed by the Association of Change Management Professionals (ACMP). The ACMP Standard is not a prescriptive model but a competency framework that defines what effective change management practice looks like across five process groups: Evaluating Change Impact and Organizational Readiness, Formulating the Change Management Strategy, Developing the Change Management Plan, Executing the Change Management Plan, and Closing the Change Management Effort.

The ACMP Standard is significant because it represents the profession’s consensus on what change management involves — independent of any proprietary model or methodology. Practitioners who hold the Certified Change Management Professional (CCMP™) designation are assessed against this standard. The Human-Centered Change™ methodology is designed to be fully consistent with the ACMP Standard, giving practitioners a practical visual toolkit that aligns with the professional framework their organizations may require.

The Human-Centered Change™ Methodology — A Practitioner’s Evolution

Every model above has genuine value. But after years of applying them in organizations and observing where they fell short, I wrote Charting Change and developed the Human-Centered Change™ methodology to address the gaps that no single existing model fills.

The core problem with most change management models is that they are either too abstract (Lewin, Bridges) or too prescriptive (Kotter), too individually focused (ADKAR) or too organizationally focused (McKinsey 7-S), and critically — none of them are visual or collaborative. They were designed to be communicated to people, not built with them. In an era of complex, multi-stakeholder change, that is a fundamental limitation.

The Human-Centered Change™ methodology takes a different approach. At its center is the Change Planning Canvas™ — a poster-sized visual planning tool that functions as the anchor of a physical or digital Change Planning Wall. Surrounding the Canvas are 70 additional tools from the Change Planning Toolkit™, printed at 11″ x 17″ (A3) size, that cover every dimension of change planning: stakeholder mapping, resistance analysis, communication planning, readiness assessment, and more.

The entire toolkit is designed for both physical and digital use. Change teams can build a Change Planning Wall in a conference room using printed tools, or work entirely in online whiteboarding platforms such as Miro, Mural, FigJam, Lucidspark, Google Jamboard, or Microsoft Whiteboard. This flexibility means the methodology works equally well for co-located, hybrid, and fully distributed teams.

The Change Planning Canvas™ and elements of the Change Planning Toolkit™ (26 of 70+) are included with every copy of Charting Change. Commercial licenses for organizational use are available at bradenkelley.com. The methodology is also delivered through workshops, masterclasses, and private events for organizations that want facilitated implementation support.

The result is a change planning approach that is more visual, more collaborative, more comprehensive, and more likely to produce change plans that are genuinely owned by the teams executing them — rather than documents developed by consultants and communicated downward.

How to Choose the Right Change Management Model

No single model is right for every change situation. The most effective change leaders are fluent in multiple models and know when to apply which one. Here is a practical guide:

Your primary challenge Best model(s) to use
Building executive alignment and urgency for a large transformation Kotter’s 8-Step Model
Diagnosing why individuals aren’t adopting a new system or process ADKAR
Understanding and managing emotional resistance to change Bridges’ Transition Model, Kübler-Ross Change Curve
Identifying systemic misalignment blocking change McKinsey 7-S Framework
Building a shared, comprehensive change plan with your team Human-Centered Change™ / Change Planning Canvas™
Post-merger integration or cultural transformation McKinsey 7-S + Bridges’ Transition Model
Technology rollout or process change ADKAR + Human-Centered Change™ toolkit
Large-scale organizational transformation Kotter + Human-Centered Change™ toolkit
Aligning with professional change management standards ACMP Standard for Change Management + Human-Centered Change™

The most common mistake change leaders make is selecting a model based on familiarity or organizational convention rather than fit. If your organization has always used Kotter, that doesn’t mean Kotter is right for your current change challenge. Take the time to diagnose what your specific situation requires before selecting your framework.

Frequently Asked Questions About Change Management Models

What is the best change management model?

There is no single best change management model — the right model depends on your specific change situation, organizational culture, and primary challenge. Kotter’s 8-Step Model works well for large-scale transformation with strong executive sponsorship. ADKAR is best for individual behavior change and technology adoption. Bridges’ Transition Model is most effective for managing emotional resistance and cultural change. The Human-Centered Change™ methodology and its Change Planning Canvas™ provide the most comprehensive visual and collaborative planning toolkit for change teams who need to build a shared, actionable change plan. Most experienced change leaders use multiple models in combination rather than relying on any single framework, and align their work with the ACMP Standard for Change Management as the professional baseline.

What is the most widely used change management model?

Kotter’s 8-Step Change Model and Prosci’s ADKAR model are the two most widely used change management frameworks in large organizations. Kotter’s model dominates in leadership development and executive education contexts. ADKAR dominates in change management practitioner communities and is especially prevalent in organizations that have invested in Prosci certification for their change practitioners. Lewin’s Unfreeze-Change-Refreeze model, while less commonly cited by name in organizational contexts, is the conceptual foundation underlying most other models.

What is the difference between Kotter and ADKAR?

Kotter’s model focuses on what leaders need to do to drive organizational change — it is a leadership action model with eight sequential steps. ADKAR focuses on what individuals need to successfully adopt change — it is an individual change readiness model with five building blocks. Kotter is organizational and top-down; ADKAR is individual and diagnostic. They are complementary rather than competing: many organizations use Kotter to structure their overall change program and ADKAR to diagnose and address individual adoption barriers within it.

Why do change management models fail?

Change management models fail most often not because the models themselves are flawed, but because of how they are applied. The most common failure modes are: selecting a model based on familiarity rather than fit; applying models mechanically without adapting them to organizational context; using models as compliance frameworks rather than genuine planning tools; underinvesting in the human dimensions of change (communication, training, emotional support) while overinvesting in technical dimensions; and abandoning the model when resistance arises rather than using it to diagnose and address the resistance. A good model poorly applied will fail. A good model thoughtfully adapted to the specific situation will succeed.

What is the Change Planning Canvas™ and how do I get it?

The Change Planning Canvas™ is a 35″ x 56″ poster-sized visual change planning tool developed by Braden Kelley as the centerpiece of the Human-Centered Change™ methodology. It is designed to be used collaboratively with the teams executing the change — either physically on a wall surrounded by 70 additional tools from the Change Planning Toolkit™ printed at 11″ x 17″ (A3) size, or digitally in online whiteboarding platforms like Miro, Mural, FigJam, Lucidspark, Google Jamboard, or Microsoft Whiteboard. The Change Planning Canvas™ and elements of the Change Planning Toolkit™ (26 of 70+) are included with every copy of Braden Kelley’s book Charting Change. Commercial licenses for organizational use are available at bradenkelley.com. Unlike traditional change management models that are communicated top-down, the Canvas is designed to build genuine shared ownership of the change plan among the people who will execute it.

What is the ACMP Standard for Change Management?

The ACMP Standard for Change Management is the professional standard developed by the Association of Change Management Professionals (ACMP) that defines competent change management practice across five process groups: Evaluating Change Impact and Organizational Readiness, Formulating the Change Management Strategy, Developing the Change Management Plan, Executing the Change Management Plan, and Closing the Change Management Effort. It is the basis for the Certified Change Management Professional (CCMP™) designation. Unlike prescriptive models such as Kotter or ADKAR, the ACMP Standard is a competency framework that describes what effective change management involves without dictating a specific methodology. The Human-Centered Change™ methodology is designed to be fully consistent with the ACMP Standard. For the step-by-step ACMP process of executing change, see our guide to the change management process.

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

Image credits: Google Gemini

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Age of Acceleration Will Be Transformational

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Age of Acceleration Will Be Transformational

GUEST POST from Robert B. Tucker

A familiar narrative has congealed around artificial intelligence and the future: “AI is about to usher in an age of abundance. Machines will do the tedious work. Scientific discovery will accelerate. Medical breakthroughs will multiply. Productivity will soar.”

This is a compelling vision, and conference promoters are using it to put “butts in seats.” But it is also incomplete.

Focusing on the supposed benefits of AI ignores deeper questions:

What will this accelerated age really mean for work, human relationships, for trust, and for the underlying social fabric that allows our civilization to function?

Technologists have an economic incentive to sell AI’s bright vision. I’m convinced some of the finest evangelists on the planet reside in Silicon Valley. As a futurist, I have a duty to forecast the most likely future, without fear or favor, and to alert you to both threats and opportunities that lie ahead.

The “Age of Acceleration” Will Be Unlike Anything We’ve Ever Seen

Having researched these past six years what I call “MegaForces of Change,” I conclude that this new and accelerated age will be a wild ride: breakthroughs and breakdowns happening everywhere all at once. More change in the next 10 years than in the previous 100. Deep-seated and fundamental changes will compound and collide and challenge us as never before. The deeper impact of AI and other changes will not be measured merely in productivity gains or GDP growth. The consequence will be measured in how it reshapes the human experience itself.

Perhaps the least discussed effect of the speeded-up world will be decision overload. AI systems generate content, provide recommendations, point out options, and require our decisions at a scale far beyond anything we have previously encountered. The result is a psychological environment in which we must constantly be on guard in order to adapt, evaluate, and decide, at a pace faster than our cognitive wiring has evolved to handle.

The Questions Techno-optimists Chose to Ignore

There is another question rarely addressed in all the “AI will save the world” hype. If machines can replace human labor across a $50 trillion economy, what happens to everyone else? If machines can write articles, compose music, diagnose disease, and generate strategic plans, where does human uniqueness reside? And what about value creation? If the goal of AI is to replace human labor, what do people do in that world to earn money and find meaning?

Techno-optimists argue that humans will simply shift to more creative pursuits. They assure us that new jobs — and new job categories — will be created when old ones are disrupted: “Always have in the past, always will in the future.” But maybe not this time. In fact, creativity, judgment, storytelling, and design, domains once considered uniquely human, are precisely the areas where AI is advancing most rapidly.

Is the goal of technological civilization to optimize efficiency, or to preserve the richness of human experience? Travel writer Rick Steves has noted a growing trend of “de-staffing” in smaller European hotels. On a recent podcast, he noted that traditional, family-run establishments are replacing front-desk staff with automated check-in systems and digital keys. While this modernization may cut costs, Steves lamented that it often sacrifices the personal charm and local hospitality that define a classic, budget-friendly European travel experience.

Silicon Valley tech-sellers want to make everything a digital transaction, as if involving people is antiquated.

That question came up when a friend of mine was stranded for eight hours at the Dallas-Fort Worth airport. There was “not one human to talk to at the gate, and hundreds of people stranded without any human compassion, comfort, or accurate updates.” When robots or machines take over for humans, something serious and critical is being lost, noted Jennifer Freed in a recent Substack.

As daily life moves online, the incidental interactions that once built community (casual conversations, chance encounters, shared spaces, civic engagement) become rarer. Social isolation rises, and human flourishing becomes harder to achieve.

What Happens with Social Trust?

The decline in social trust is nothing new. A longitudinal study conducted by the University of Chicago shows a long-term decline in social trust in the United States dating back to the early 1970s. The core question asked by surveyors is whether “most people can be trusted” or whether “you can’t be too careful.” In the early 1970s, roughly 45–50% of Americans believed most people could be trusted. In recent years, that number has fallen into the low 30% range, sometimes lower depending on the survey year and subgroup analyzed.

Artificial intelligence seems likely to accelerate this decline even further. Deepfakes make it difficult to know whether a video is authentic. Misinformation and disinformation spew from politician’s social media at all hours, while cyber scams grow more sophisticated by the day. Identity theft, fraud, and online harassment have become routine features of the digital landscape.

Relationships In the Age of Algorithms

Another disquieting transformation is occurring in human relationships. Technology allows us to maintain contact with hundreds, or even thousands, of people. Yet these connections are often shallow and transitory. Social platforms reward visibility, speed, and engagement rather than depth or meaning. Communication becomes faster, thinner and blurred between authentic communication and autonomous.

At the same time, economic incentives increasingly shape digital relationships. Influencers, brand partnerships, subscription models, and algorithmic promotion blur the boundary between friendship and commerce. The result is a strange paradox. We are more networked than ever, yet genuine human connection is becoming rarer.

The Shrinking Attention Span

Communication itself is also evolving. Short-form video, algorithmic feeds, and constant notifications fragment attention into smaller slices. There’s little disagreement that our capacity for sustained undivided attention has sharply decreased in recent years. “By some measures you are lucky to get 47 seconds of focused attention on a discrete task, notes D. Graham Burnett, of the Friends of Attention Collective. “Deep reading, much less deep thinking, is next to impossible on that timeline, as are most forms of human interaction out of which meaningful life is made.”

Attention is not merely a mental habit; it is the foundation of reflection, empathy, and long-term thinking. When attention fragments, so does our ability to grapple with complex problems.

Civilization’s greatest achievements, from scientific discovery to democratic governance, require sustained attention and focus. Yet the digital ecosystem increasingly rewards the opposite.

The coming decade will test us in ways few people fully grasp today. It will challenge not only our industries and institutions, but our attention spans, relationships, sense of meaning, and ultimately our humanity itself.

The people who flourish in the years ahead will not necessarily be the most technologically sophisticated. They will be the most intentional, adaptable, grounded, and resilient. In a world increasingly shaped by intelligent machines, deeply human qualities like wisdom, empathy, creativity, judgment, and connection may become our greatest competitive advantage.

Technology will continue advancing at breathtaking speed. But whether humanity flourishes alongside it remains an open question.

The future will belong to those who prepare for it consciously, courageously, and with a clear sense of what it means to remain fully human.

This article originally appeared in Forbes

Image credit: Pexels

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Wisdom, Wonder, and AI in the ASEAN Future

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The View from Up Here

Wisdom, Wonder, and AI in the ASEAN Future

GUEST POST from Kellee M. Franklin, PhD.

“Sometimes you have to go up really high to understand how small you really are.” — Felix Baumgartner

These words, spoken by Felix Baumgartner from the edge of space, capture more than the physical awe of the stratosphere. They echo a deeper truth about perspective — one that is essential as we navigate the uncharted territory of artificial intelligence (AI) in learning and development.

Just weeks ago, the crew of NASA’s Artemis II mission soared farther from Earth than any humans in over half a century. From 252,756 miles away, they were not just testing spacecraft systems. They were gaining a new vantage point — on our planet, on human collaboration, and on what is possible when preparation, humility, and shared purpose converge.

And as I prepare to engage with PhD scholars at Thailand’s National Institute of Development Administration (NIDA), where “Wisdom for Sustainable Development” is both motto and mission, I am reminded: the same principles that guide astronauts and skydivers can guide us in building ethical, human-centered AI in the workplace.

The View from Above: A New Lens on Learning

Baumgartner’s jump was not about adrenaline. It was about data, safety, and pushing boundaries to protect future pioneers. Similarly, Artemis II was not just a technical milestone — it was a masterclass in systems thinking, psychological resilience, and real-time decision-making under uncertainty.

In our organizations, AI adoption often feels like a race to automate, to optimize, to cut costs. But true innovation begins not with tools, but with mindset.

Like those astronauts, holistic AI adoption asks us to rise above the noise. It challenges us to see beyond isolated chatbots or content generators and view learning as an integrated ecosystem — one where technology amplifies human potential, not replaces it.

When we elevate our thinking — leveraging AI for personalization, insight, and empowerment — we create experiences that are more human, not less.

Wisdom in the ASEAN Context: Ethics as the Compass

At NIDA, the focus is not just on knowledge — it is on wisdom. The PhD program cultivates leaders who can navigate complex development challenges across Southeast Asia with integrity, evidence-based analysis, and a commitment to the public good.

This ethos is vital as ASEAN nations embrace AI. Regional frameworks like the ASEAN Guide on AI Governance and Ethics emphasize transparency, bias mitigation, and culturally relevant safeguards. Singapore’s Model AI Governance Framework and Indonesia’s National AI Strategy reflect a growing consensus: technology must serve people, not the other way around.

In this context, AI in learning is not just about efficiency. It is about equity — ensuring rural institutions have access to digital tools, that curricula foster ethical reasoning, and that AI literacy is woven into leadership development.

The mission?

To build a talent pipeline that can harness AI for climate action, health, agriculture, and inclusive growth — because sustainable development starts with wise leadership.

Three Human-Centered Design Principles for AI-Enhanced L&D

Drawing from space missions and scholarly insight, three core learning objectives emerge for leaders in this new era:

1. Model Continuous Learning and Psychological Safety

Baumgartner did not jump alone. He had a team — engineers, medics, mentors — supporting him every step. That trust, that safety, is what allowed him to take the leap.

In the workplace, leaders must do the same: embrace vulnerability, normalize growth, and make it safe to fail forward. When AI is introduced, curiosity should be rewarded, not punished. Questions like “How does this work?” or “What if it’s wrong?” are not resistance — they are engagement. Create spaces where teams can experiment, reflect, and learn together. Because innovation thrives not in silence — or silos — but in dialogue.

2. Embed Learning into Workflow and Performance Systems

Artemis II did not just test hardware — it tested human systems. How do crew members exercise in microgravity? How do they respond to emergencies? The answers were not found in a manual, but in integrated, real-time practice.

Similarly, AI-powered learning should live “in the flow of work.” Personalized learning paths, virtual coaching, and just-in-time feedback should be woven into daily tasks — not delayed and minimized for training modules.

And when we measure success, let us reward collaboration, effort, effectiveness, and skill growth — not just outcomes. Because how we learn matters as much as what we learn.

3. Foster AI Fluency with a Human-Centric, Growth Mindset

AI is not a replacement. It is a collaborator — one that can amplify empathy, creativity, and critical thinking.

Begin by having employees create the “raw material” — drafts, ideas, problem statements, visions — before using AI to refine, critique, and expand. This preserves ownership and mastery while leveraging AI’s analytical strength.

Provide clear, role-specific guidelines, prompt libraries, and peer-sharing platforms. Support upskilling with dedicated centers, updated certifications, and incentives. And always maintain human oversight — because trust is built when people feel in control. AI adoption succeeds not when systems are flawless, but when individuals retain agency. It is about designing experiences where people guide the technology — not the other way around.

From Insight to Impact: A Changemaker’s Lens on Coherence in ASEAN

As AI reshapes the global landscape, ASEAN stands at a unique inflection point where technology does not just drive efficiency — it fosters coherence. The rise of the coherence-centric organization marks a shift from fragmented hierarchies to integrated, adaptive systems guided by shared purpose. AI, far from replacing leaders, is redefining leadership itself: elevating it from command-and-control to a higher vantage point — one of wisdom, context, and collective alignment.

In this new architecture, leaders become curators of meaning, using AI to synthesize vast flows of data into clarity. They no longer need to know all the answers but must ask the right questions — infused with cultural insight, ethical grounding, and a sense of wonder at what’s possible. Across ASEAN’s diverse economies, this shift enables a uniquely regional form of innovation: one that balances rapid digital transformation with deep-rooted values of harmony, community, and long-term stewardship.

This vision is already taking root. William Malek, a former Stanford University instructor and business thought-leader now residing in Thailand, has emerged as a recognized global change-maker, guiding corporations and government leaders in embracing coherence-centric models. His work, including a recent collaboration at NIDA with me to share insights with PhD executive-scholars, highlights how leadership grounded in coherence can drive transformative change across sectors.

AI becomes the lens through which leaders see patterns, anticipate disruptions, and align teams around a coherent vision. The future belongs not to those who merely adopt AI, but to those who rise above the chaos and confusion — leading from above the clouds, where data meets wisdom, and technology serves humanity.

The Rhythm of Growth: Making Space for Questions

As I work with diverse executives in Bangkok, I am always struck by how often the most powerful moments come not from answers, but from questions.

  • What does ethical AI look like in our context?
  • How might we ensure AI serves the many, not the few?
  • How might we prepare leaders to navigate uncertainty with wisdom?
  • How might we lead with wúwéi — action through non-forcing — so progress flows like water, not against resistance?
  • And in cultivating paññā (wisdom) and mettā (loving-kindness), how might we make certain AI serves human dignity, not just efficiency?

These are not technical questions. They are human ones.

And just as the Artemis II crew returned with data that will shape future missions, our conversations in classrooms and boardrooms today will shape the future of work.

Because the stakes are real. AI could boost ASEAN’s GDP by 10–18% and add around $1 trillion by 2030 — but only if guided by strong, forward-thinking leadership. This is not just about technology. It is about trust. About inclusion. About ensuring AI serves the many, not the few.

That future depends on leaders who are not just digitally fluent, but humancentered — balancing data analytics and AI regulations with emotional intelligence and ethical judgment. It calls for strategic upskilling that blends technical mastery with wise decision-making, and for regional coordination that harmonizes policies across borders — from Singapore’s pioneering frameworks to Thailand’s, Malaysia’s, and Indonesia’s emerging AI agencies.

And above all, it demands collaboration: industry and academia, urban and rural, government and community. Because true progress is not measured in GDP alone, but in equitable access, in resilient ecosystems, and in the wisdom to lead with purpose. Coherence and collaboration.

So let us keep dreaming big — above the clouds, beyond the noise. Let us build learning ecosystems that are not just smart, but wise. That are not just efficient, but equitable.

Because the view from up here?

Absolutely worth it!

Image credits: Kellee M. Franklin

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Why You Need to Leverage Shared Values in Change Leadership

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Why You Need to Leverage Shared Values in Change Efforts

GUEST POST from Greg Satell

When Lou Gerstner took over at IBM in 1993, the century-old tech giant was on its knees. Many thought it should be broken up into smaller, more focused companies. Others had different ideas. So at Gerster’s first press conference, people were curious about his strategy and disappointed when he failed to deliver one.

“The last thing IBM needs right now as a vision,” he said. What he meant was that IBM’s culture was broken. “Culture isn’t just one aspect of the game,” he would later write. “It is the game. What does the culture reward and punish – individual achievement or team play, risk taking or consensus building?”

What Gerstner saw was that IBM had lost sight of the values that had made it successful in the first place. He wasn’t “disrupting.” He was making IBM culture safe to innovate again and, by doing that, he achieved one of the most remarkable turnarounds in corporate history. If you want to achieve truly radical change, you need to start with shared values.

Making The Shift From Differentiating Values To Shared Values

IBM wasn’t Gerstner’s first stint leading a company. He’s been President at American Express and CEO at RJR Nabisco, both of which were very different from technological companies. Yet Gerstner didn’t focus on how his experiences were different, but on how they were the same—each of these businesses have to serve the customer.

“Lou refocused us all on customers and listening to what they wanted and he did it by example,” Irving Wladawsky-Berger, one of Gerstner’s chief lieutenants would later tell me. “We started listening to customers more because he listened to customers.” It was upon that simple principle that he changed the course of IBM’s future.

In a similar vein, when Nelson Mandela wanted to create a new future for South Africa, he organized a Congress of the People, a multi-racial gathering which produced a statement of shared values that came to be known as the Freedom Charter, which is still revered even today. He would later say it would have been very different if his organization, the ANC, had written it by themselves, but it wouldn’t have been nearly as powerful

When we’re passionate about an idea, we want to show how it’s different. We want to explain all its beautiful complexity and nuance, so that people can share our passion and fervor. That’s almost always a mistake. The first step to creating truly transformational change is to anchor it in what people already know and feel comfortable with.

Creating Safety Around The Change Conversation

When an enterprise is in crisis, one of the first things that often gets cut is investments in the future. So when Gerstner scheduled his first non-headquarters visit at IBM to the firm’s legendary research facility at Yorktown Heights, everybody there got nervous. Many expected there to be deep cuts and, possibly, that the entire facility would be shut down.

Actually, quite the opposite. “I saw the pain of IBM’s problems on their faces,” Gerstner remembered. “I talked about how proud I was to be at IBM. I underscored the importance of research to IBM’s future.” It was a wise move. Although few knew it at the time, scientists at IBM had just made a major breakthrough that made quantum computing possible and a few years later the company’s Deep Blue supercomputer would beat Garry Kasparov at chess.

Many change management schemes advise to create a “sense of urgency” and creating a “burning platform” atmosphere. Yet Gerstner understood that employees were perfectly aware of how dire the situation was. What they needed wasn’t more fear, but to see a path forward. Terrified people don’t make good decisions. They’re also more likely to head for the exit than to work for the future.

Don’t get me wrong, you don’t want to sugarcoat things. You need to be frank, honest and paint a clear picture. Gerstner made it plain that day that there would be changes. Yet by rooting his message in shared values, he was able to create a sense of safety around the change conversation. The scientists were able to see that they could, in fact, be heroes in the story of IBM’s future. As it turned out, they would be.

Creating A Dilemma Rather Than A Conflict

Once you start being explicit about your values you will inevitably find that not everyone shares them and that was certainly true at IBM. For example, Wladawsky-Berger told me that “IBM had always valued competitiveness, but we had started to compete with each other internally rather than working together to beat the competition. Lou put a stop to that and even let go of some senior executives who were known for infighting.”

A simple truth is that whenever we set out to make a significant impact, there will always be those who will work to undermine what we are trying to achieve in ways that are dishonest, underhanded and deceptive. Yet when that happens we need to be careful not to get sucked into a conflict, which will likely take us off course and discredit what we’re trying to achieve. Instead, we need to learn to design a dilemma.

Dilemma actions have been used for at least a century—famous examples include Gandhi’s Salt March, King’s Birmingham Campaign and Alice Paul’s Silent Sentinels—but more recently codified by the global activist, Srdja Popović. They are just as effective in an organizational context, using an opponent’s resistance against them.

One of the great things about dilemma actions is that you approach them exactly the same way you approach building allies—by identifying a shared purpose. Once you do that, you can design a constructive act rooted in that shared purpose that advances your agenda. That forces your opponent to make a choice: they can either disrupt the act and violate the shared value or they can let it go forward and allow change to proceed.

For example, I was once running a transformation project that was being impeded by a Sales Director hogging accounts. Although it was agreed that she would distribute her clients, she never got around to it. So I set up a meeting with a key account and one of our salespeople. When she tried to disrupt the meeting, she violated the shared value we had established and was dismissed from her position. Everything fell into place after that.

Forging A Shared Purpose

Change always begins with a grievance—there’s something people don’t like and they want it to change. Yet the status quo always has inertia on its side and never yields its power gracefully. That’s why it’s so important to forge a shared purpose, because people need a common mission they can believe in to see themselves as stakeholders in a shared future.

The reason so many organizations find themselves unable to pursue a purpose isn’t because they don’t want to, but because it is so hard. Purpose doesn’t begin with a single step, but with a diverging path. To honor a value we need to be willing to incur costs and constraints. We must choose one direction at the expense of another, or stay mired and lost, unable to move forward.

That’s why the change conversation needs to focus on what you value. Values are how an enterprise honors its mission. They represent choices of what an organization will and will not do, what it rewards and what it punishes and how it defines success and failure. Perhaps most importantly, values will determine an enterprise’s relationships with other stakeholders, how it collaborates and what it can achieve.

Perhaps most importantly, shared values enable a shared identity, which is what you need for change to last. The goal of a revolution, as Srdja Popović once explained to me, is not a constant state of disruption, but eventually to become mainstream, to be mundane and ordinary. That can only be done if change is built on common ground.

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

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

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Top 10 Human-Centered Change & Innovation Articles of April 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 April’s ten most popular innovation posts:

  1. Why an AI Soft Landing Might Look Like Victorian England — by Braden Kelley
  2. The Four Psychological Disruptions of AI at Work — by Braden Kelley
  3. Liberated to Care – How AI Can Restore Humanity in Healthcare — by Kellee M. Franklin, PhD.
  4. The Consumption Collapse – When the Feedback Loop Bites Back — by Art Inteligencia
  5. Four Steps to the Future – Announcing the Newest FREE Addition to the FutureHacking™ Toolkit — by Braden Kelley
  6. Which of the Nine Innovation Roles do you play? (A Quiz) — by Braden Kelley
  7. How to Consciously Develop More Courage — by Tullio Siragusa
  8. Does Planned Obsolescence Fuel the Fire or Just Burn the House Down? – The Innovation Paradox — by Braden Kelley
  9. Misunderstanding Big Ideas is Very Dangerous — by Greg Satell
  10. Artificial Intelligence Powered Teamwork — by David Burkus

BONUS – Here are five more strong articles published in March 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!

Build a Common Language of Innovation on your team

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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Better to be Careful than Smart

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Better to be Careful than Smart

GUEST POST from Greg Satell

Not too long ago, I had a post about the danger of trusting your feelings go viral on LinkedIn. The reason it was so popular wasn’t necessarily that everyone liked it, but because many wanted to voice their disapproval. A surprising number of people vehemently objected to the idea that they should interrogate their feelings or keep them in check.

Make no mistake. While it is true that our emotions can alert us to dangers that our rational mind fails to recognize, they can also lead us wildly astray. Our hippocampus, where our memories reside, has a bee line to our amygdala, which plays a role in governing our emotions, circumventing our rational brain in the prefrontal corpus.

We tend to assume that good judgment is a function of intelligence and education, but often it’s not. We need to recognize that there are glitches in our neural machinery and that our gut feelings can be triggered by random events as well as by people who seek to manipulate us. That’s why we need to be careful. It’s always the suckers who think they’re playing it smart.

Why Smart People Are So Easily Fooled

For decades, the global elite revered Bernie Madoff as one of the world’s most talented asset managers until it was all exposed to be, in his own words, “one big lie.” Elizabeth Holmes’s prominent board at Theranos were so clueless that they put their reputations behind a product that didn’t exist. Anna Sorokin, the daughter of a Russian truck driver, was able to convince the glitterati that she was, in fact, a fabulously wealthy heiress.

In each case, there was no shortage of opportunities to unmask the fraud. Inconsistencies in Madoff’s records were reported to regulators a number of times, but were ignored. Holmes wasn’t able to produce a single peer-reviewed study during 10 years in business to support her claims and there was no shortage of whistleblowers from inside and outside the company. Anna Sorokin left unpaid bills all over town.

Still, many bought the ruses and would interpret facts to support them. Madoff’s secrecy was seen as confirmation that he had a proprietary method. In Holmes’ case, her eccentricities were taken as evidence that she truly was a genius, in the mold of Steve Jobs or Mark Zuckerberg. Sorokin’s unpaid bills were seen as proof of her wealth. After all, who but the fabulously rich could be so nonchalant with money?

People should have known better. Stock market regulators are trained to recognize fraud. Prominent Theranos board members like George Shultz, David Bois and Henry Kissinger, earned their reputations over decades. Hotels allowed Sorokin to stay in luxury suites for weeks at a time before demanding payment. How could they have been so naive?

But what if smart people get taken in because they’re smart? They have a track record of seeing things others don’t, making good bets and winning big. People give them deference, come to them for advice and laugh at their jokes. They’re used to seeing things others don’t. For them, a lack of discernible evidence isn’t always a warning sign. It can be an opportunity.

Gated Community Elites And TED Talk Elites

Living in a gated community necessarily cuts you off from your surroundings. People outside can’t wander in and you can’t wander out. New businesses don’t sprout up and old ones don’t die. Routines are familiar and protected, you remain in your comfort zone and any random disturbance is immediately removed.

On the other end of the spectrum, when you go to fancy conferences your imagination becomes overstimulated. You are inundated with the new and unfamiliar. The normal human experiences begin to seem passé, a remnant of a lost age, while visions of the future begin to appear more genuine than the present reality.

The truth is that both of these environments are manufactured for the tastes of the well-heeled. Gated communities are built for those who want a simple sanctuary in a messy and complex world that doesn’t always follow a linear and understandable logic. The conference world tends to overemphasize the power of imagination and possibility, ignoring the fact that the status quo exerts a power of its own.

The best indicator of what we think and what we do is what the people around us think and do. We tend to conform to the opinions and behaviors of those around us and this effect extends out to three degrees of relationships. So not only our friends’ friends, influence us deeply, but their friends too—people that we don’t even know—affect what we think.

Confirming Our Priors

Clearly, the way we tend to self-sort ourselves into homophilic, homogeneous groups shapes how we perceive what we see and hear, but it will also affect how we access information. When a team of researchers at MIT looked into how we share information—and misinformation—with those around us. What they found was troubling.

When we’re surrounded by people who think like us, we share information more freely because we don’t expect to be rebuked. We’re also less likely to check our facts, because we know that those we are sharing the item with will be less likely to inspect it themselves. So when we’re in a filter bubble, we not only share more, we’re also more likely to share things that are not true. Greater polarization leads to greater misinformation.

We’re prone to think of our brains as biological forms of computers that take in and analyze data leading to rational conclusions. That’s not true. We tend to seize upon the most easily available information, rather than the most reliable sources. We then seek out information that confirms those beliefs and reject evidence that contradicts existing paradigms.

That’s the glitch in our mental machinery that Madoff, Holmes and Sorokin exploited. The investors in Madoff’s funds felt privileged to be allowed into an exclusive investment. Theranos board members thought they were building a better future. Sorokin made those around her feel like they had access to an aristocracy of sorts.

These weren’t mere notions or passing thoughts, but assertions of identity, which is why the shills were so eager to advocate for — and actively protect — their swindlers.

Making Allowances For The Glitches In Our Mental Machinery

We all like to have opinions and like act on them. When, for instance, people were asked if they supported bombing Agrabah, the fictional hometown of the Disney character Aladdin, 30% of Republicans and 19% of Democrats said yes. Yet our urge to make judgments has nothing to do with our ability to make wise choices.

Humans tend to think in terms of narratives. We like things to fit into neat patterns and fill in the gaps in our knowledge so that everything makes sense. People who are “smart,” have a greater ability to retain and process information than most and can use their imagination to build robust visions, but that’s no guarantee those visions will conform to reality.

We need to be hyper-aware that a track record of success makes us more confident and confidence in our judgments is inversely correlated to their accuracy. That’s why it’s often better to be careful than smart. There are formal processes that can help us do that, such as pre-mortems and red teams, but most of all we need to keep ourselves in check.

Perhaps most important is to appreciate that there are glitches in our mental machinery and we are greatly influenced by our social networks. The people around us tend to have access to similar information as we do and our perceptions are colored by prior judgments we’ve made. We are surrounded by mental minefields and the only way out is to proceed with caution.

There’s a sucker born every minute and they’re usually the ones who think they’re playing it smart.

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

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Making Change Stick

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Making Change Stick

GUEST POST from David Burkus

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

And then…nothing.

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

So what happened?

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

The Flawed Approach to Leading Change

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

And in theory, many of them work.

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

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

Why Good Ideas Don’t Stick

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

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

Lack of Personal Benefit

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

Loss of Ownership

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

Curse of Knowledge

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

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

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

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

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

Making Change Stick

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

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

1. Be Simple: Find the Core Message

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

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

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

2. Make It Concrete: Use Sensory and Tangible Language

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

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

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

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

3. Use Stories: Help People Feel the Message

Data informs. Stories stick.

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

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

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

The Bottom Line

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

That means asking yourself:

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

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

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

Image credit: Google Gemini

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We Must Think Less Like Engineers and More Like Gardeners

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We Must Think Less Like Engineers and More Like Gardeners

GUEST POST from Greg Satell

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

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

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

The Death of the Secular Gods

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

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

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

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

A New Religion

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

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

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

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

The Rise Of Faux Scientists

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

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

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

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

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

We Need To Manage Ecosystems, Not Machines

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

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

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

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

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

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

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Business Leaders Must Learn About Political and Social Movements

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Business Leaders Must Learn About Political and Social Movements

GUEST POST from Greg Satell

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

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

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

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

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

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

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

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

Change Is Always Multifaceted, We Need to Understand Multiple Perspectives

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

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

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

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

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

We Need To Overcome Resistance

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

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

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

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

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

Change Is Too Important Not To Take Seriously

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

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

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

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

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

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

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

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