Is Your Customer Experience Costing You Customers?

A Free 12-Point Diagnostic

by Braden Kelley and Art Inteligencia

Most organizations don’t know they have a customer experience problem until it shows up as churn they can’t explain, growth that’s stalled despite strong acquisition investment, or a competitor quietly pulling ahead in a market they thought they owned.

By the time those signals are visible in the numbers, the experience failures causing them have usually been accumulating for months — sometimes years. The customers who left didn’t file complaints. They just left. The friction that drove them away wasn’t measured because nobody thought to measure it. The competitive gaps weren’t visible because nobody had walked the competitor’s journey recently enough to know they existed.

This is the fundamental challenge of customer experience management: the experiences that cost organizations the most are almost never the ones they’re already measuring.

How Do You Know If You Need an Experience Audit?

That’s the question I hear most often from leaders who are considering an Experience Audit — and it’s exactly the right question to ask before committing to any significant diagnostic investment.

The honest answer is that many organizations don’t need a full Experience Audit right now. Some have genuinely strong experience fundamentals, solid visibility into their journey gaps, and active improvement programs already addressing the right things. For those organizations, an audit would confirm what they already know — valuable, but not urgent.

Other organizations are flying blind — relying on satisfaction scores that measure the wrong touchpoints, competitive assumptions that haven’t been tested in years, and internal perspectives that have long since lost the ability to see what new customers and employees actually experience. For those organizations, an audit isn’t a nice-to-have. It’s the prerequisite for every other improvement investment they’re considering.

The challenge is that it’s genuinely difficult to know which situation you’re in — from the inside.

Introducing the Free Experience Audit Readiness Checklist

I’ve developed a simple 12-point diagnostic — the Experience Audit Readiness Checklist — that helps leaders answer the “do we need an audit?” question honestly, in about five minutes, without any outside perspective required.

The checklist covers four areas:

  • Visibility & Awareness — Do you actually know what customers or employees experience, or are you relying on internal assumptions? When did anyone on your leadership team last go through your own journey end-to-end?
  • Performance Signals — Are churn, attrition, or satisfaction scores moving in the wrong direction despite investments meant to improve them?
  • Organizational Readiness — Do different departments have conflicting views of what the experience looks like? Have improvement initiatives failed to move the numbers you expected?
  • Strategic Stakes — Is a competitor improving their experience in ways starting to affect your market position? Are you considering a major investment and want to know where it will have the most impact?

A few questions that tend to generate the most honest conversation:

“Nobody on our leadership team has personally gone through our own customer or employee journey end-to-end in the last 12 months.”

“We’ve launched improvement initiatives before that didn’t move the numbers we expected them to move.”

“We’ve never formally compared our experience, touchpoint by touchpoint, against our top competitors.”

In my experience, leadership teams that read those statements and immediately think of one or two colleagues who would answer them differently have found some of their most useful conversations.

What Your Score Means

The checklist produces a simple score based on how many of the 12 items apply to your organization:

  • 0–2 checked — Strong foundation. Keep monitoring proactively.
  • 3–5 checked — Early warning signs worth a closer look.
  • 6–8 checked — Meaningful blind spots likely costing you revenue.
  • 9–12 checked — High risk. An audit should be a near-term priority.

Download the Free Checklist

Experience Audit Readiness ChecklistThe Experience Audit Readiness Checklist is available as a free PDF download — two pages, five minutes, and a clearer picture of whether your experience gaps are a background concern or a front-burner priority.

Download the free checklist on the Experience Audit page →

If you check six or more boxes and want to talk through what an Experience Audit would look like for your specific situation,
contact me directly or call (206) 349-8931. I’m happy to have a no-obligation conversation about whether an audit makes sense for where you are right now.

Image Credit: Gemini

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

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After the Chasm – Scaling Beyond the Beachhead

After the Chasm - Scaling Beyond the Beachhead

GUEST POST from Geoffrey A. Moore

Crossing the chasm is the single most important goal for a B2B application that seeks to disrupt the status quo. The playbook has held up for more than 30 years because it continues to just work. That said, it does not say anything about what to do if you’re stuck in the mud on the other side. So, let’s suppose your enterprise has successfully crossed the chasm, achieved tens of millions of dollars in ARR, but is no longer growing at a rate to keep pace with the Rule of 40 percent (the sum of your profit and your growth rate). Your investors are getting antsy. Now what?

First of all, know your place. You are still sub-scale for a customer CFO to consider you desirable as a go-to vendor. Same goes for a CIO who is trying to consolidate rather than expand the list of vendors they are working with. So, as with crossing the chasm, your only ally will be a process owner with a problem process that is not getting the IT support they need. This time, however, you are looking for an adjacent process owner, someone for whom your chasm-crossing sponsor would make a good reference. This lowers the bar for how problematic the use case may be because there is already some proof that the solution will work.

Note that we are still at the departmental level, still a point-product app, not a platform, not a suite. Those are all worthy ambitions for the future, but if you try to activate them now, the CFO and the CIO will get involved, and you will get bogged down in proof-of-concept exercises that will take forever to scale.

That said, it is not too early to recruit ecosystem partners to help secure your beachhead and expand your reach. The key here is to engage with companies that are big enough to help but small enough to give you their full attention—not Tier 1 systems integrators, more like outsourced service providers to small and medium businesses or specific departmental functions. You don’t need a lot of these, but the ones you do recruit have to lean in, so make sure that there is enough trapped value in the target use case to pay both you and them a premium for resolving it. To accelerate this effort, ask your professional services team to package up their hard-won knowledge and make it available to the partners who can expand your beachhead market. You want your team to be plowing in the adjacent field, not harvesting in the initial one.

On the go-to-market side, you still need to be disciplined in deploying most of your resources into the target market segment and not letting them get distracted by chasing one-off opportunities elsewhere. That said, you can relax a bit from the laser focus of chasm-crossing as long as, say, two-thirds of the marketing and sales resources are directly aligned with your current goal. Remember at this point that marketing is still a territory capture game, so you want to go after targets that are big enough to matter but small enough to lead, and as always, a good fit with your crown jewels.

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

Image Credit: Geoffrey Moore

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The Personal AI Renaissance

Finding the Human Premium in an Automated World – An AI Soft Landing Scenario

LAST UPDATED: July 5, 2026 at 11:58 AM

The Personal AI Renaissance

by Braden Kelley and Art Inteligencia


The Death of the “Average” Knowledge Worker

We are living through a profound transition in the nature of work, yet we continue to measure productivity with the yardsticks of the past. The greatest inequality of the AI era may not be access to information — the internet solved that decades ago — but rather access to intelligence amplification. We are witnessing the arrival of a new, distinct class of augmented individuals, and the divide between those who embrace this evolution and those who resist it is widening by the day.

For a brief moment, we viewed AI merely as a better search engine — a way to get faster, slightly more polished answers. That was the “chatbot” phase. We have now moved into the era of the Personal AI Renaissance. This is not about a tool that generates text; it is about the integration of a persistent, personalized intelligence layer into our daily cognitive workflows. This layer knows your strategic priorities, understands your communication style, and tracks your long-term goals.

The implications for the labor market are seismic. The traditional dichotomy of “human versus AI” is a false framing that distracts from the real competitive shift. The true divide in the coming years will not be between machines and people, but between the unaugmented human and the AI-amplified human. In this new landscape, professional obsolescence is no longer a function of your education level or your years of experience, but of your capacity to effectively manage and leverage your personal intelligence layer. The era of the “average” knowledge worker has ended; the era of the amplified individual has begun.

Beyond “Better Answers”: The Shift to Personalization

To grasp the true power of this shift, we must abandon the notion of AI as a generalized utility. The primary value of the latest generation of models is not merely their ability to generate faster responses; it is their capacity for deep, persistent personalization. When AI moves from being a standalone tool to an integrated intelligence layer, it fundamentally transforms from a search engine into a multifaceted collaborator.

In this Personal AI Renaissance, the individual is supported by a dynamic system that evolves alongside them. We see this intelligence layer manifesting in several key, high-value roles:

  • The Strategist: Beyond simple task management, the AI functions as a partner that aligns your daily decisions with your long-term strategic objectives, helping you maintain focus amidst complexity.
  • The Coach: By providing personalized feedback loops and constructive friction, the AI pushes you to refine your thinking, challenge your biases, and improve your cognitive performance over time.
  • The Researcher/Assistant: This role involves offloading the heavy cognitive load of data synthesis and information retrieval, allowing the human to focus on higher-order decision-making.
  • The Teacher: The AI acts as a bespoke educator, translating complex, dense information into the specific mental models and language that make the most sense for your unique perspective.

By delegating these varied roles to a personalized AI layer, the worker gains a form of cognitive leverage previously unavailable. This isn’t about replacing human input; it is about delegating the friction of execution so that the human can devote more energy to creativity, empathy, and the nuanced judgment required for meaningful innovation.

Personal AI Renaissance Infographic

The Productivity Gap: Capability Over Credentials

We are entering a period where the traditional signals of professional worth — degrees, job titles, and years of tenure — are being rapidly decoupled from actual output. As the AI-amplified human becomes the new standard for high-performance, the competitive landscape is shifting from what you know to how you augment your intelligence.

The productivity gap is no longer dictated by education level, but by augmentation capability — your fluency in integrating AI into your specific workflow to solve problems faster and more creatively. An employee with a strong command of their personalized intelligence layer can now outperform peers who, by conventional standards, might be more “qualified” but remain unaugmented.

This represents a true “soft landing” for human potential. Rather than being replaced, the worker who learns to harness these tools is liberated from the drudgery of rote cognitive tasks. This allows them to pivot their focus toward the activities that require fundamentally human traits: empathy, complex system orchestration, and the high-level judgment required to navigate ambiguity in a digital transformation journey.

However, we must also acknowledge the inherent risk for those who remain static. The danger is not that AI will take your job; the danger is that an AI-amplified human — someone who has learned to partner with this intelligence layer to increase their speed, quality, and strategic focus — will become the new baseline for organizational success. In this high-velocity environment, the ability to rapidly integrate and adapt to new augmentation capabilities is the ultimate professional skill.

The Human-Centered Implication: Agency in the Age of Amplification

The transition to an integrated intelligence layer invites a necessary introspection regarding our own agency. When we delegate synthesis, research, and strategic sparring to an AI partner, the fundamental nature of our cognitive work changes. The risk is not that we lose control, but that we become overly reliant on the convenience of the tool, potentially allowing our critical thinking muscles to atrophy if we treat the output as gospel rather than a starting point for deeper investigation.

True agency in this new era requires a shift in mindset: we must view the AI not as an oracle, but as a mirror — a tool that reflects and expands our own intellectual curiosity. We remain the architects of intent, the ones who define the “why” and the “what,” while the AI provides the “how” and the “how fast.” Maintaining this distinction is essential for preserving the human-centered elements of our work, such as ethical reasoning and the intuitive leaps that often drive true innovation.

For leaders and organizations, this requires a fundamental shift in the management mandate. The focus must move away from top-down efforts to “automate processes” or eliminate roles, and toward the deliberate nurturing of amplified talent. The most successful organizations of the future will be those that foster an ecosystem where human judgment is elevated, not replaced, by these new intelligence layers. It is about creating a culture where the combination of human empathy and machine-augmented speed becomes a source of sustainable, long-term competitive advantage.

Conclusion: Embracing the Renaissance

We are standing at the threshold of a new way of working, one where the boundaries of individual capability are being fundamentally redrawn. Viewing the adoption of a personal AI layer merely as a “tech upgrade” misses the broader, more critical reality: this is a strategic professional imperative. Those who integrate these capabilities into their daily lives are not just working differently; they are working at a velocity and depth that was previously impossible for a single individual to sustain.

The future does not belong to the AI, nor does it belong to the unaugmented human. It belongs to the amplified human — the professional who masters the synergy between human intuition and machine-driven speed. This Renaissance is an invitation to offload the cognitive friction that has historically slowed our most important work, leaving us more space to do what humans do best: ideate, empathize, and lead.

As you step into this new era, ask yourself: How will you curate your own intelligence layer, and where will you focus the newfound capacity you gain? The revolution is already here, and the choice to participate is yours. Choose to amplify.

Frequently Asked Questions

What is the primary difference between a chatbot and a personal AI intelligence layer?

While a chatbot typically provides isolated, one-off answers to queries, a personal AI intelligence layer maintains deep context, understands your unique strategic priorities, and tracks your long-term goals to function as an integrated, persistent collaborator.

Why is “augmentation capability” more important than education level in the AI era?

In the current professional landscape, the productivity gap is driven by an individual’s ability to effectively integrate and leverage AI to enhance their output. Augmentation capability allows professionals to transcend traditional education-based limitations by dramatically increasing their speed, quality, and capacity for complex work.

Does the rise of AI-amplified humans mean the end of human-centered work?

No. The rise of AI-amplified humans actually shifts the focus of work toward inherently human traits. By delegating rote cognitive tasks and information synthesis to the AI, humans are freed to devote more energy to empathy, complex system orchestration, and the high-level judgment required for innovation.


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.

Explore the AI Soft Landing Series

This article is part of a broader exploration into architecting optimistic socioeconomic transitions for the AI era. Dive deeper into the series below:

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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Storytelling Determines the Change We Can Achieve

Storytelling Determines the Change We Can Achieve

GUEST POST from Greg Satell

At some level, change is always about the stories we tell. We like to think that we humans are objective arbiters of the facts, but that’s not really true. We think in narratives. In one study, juries considered experts that shared stories far more credible than those that merely offered an analysis of the relevant facts.

Every organization tells stories, some deliberate, some not. General Electric was able to tell successful stories for decades, yet it was a false narrative and, when the facts caught up, the firm collapsed. On the other hand, Satya Nadella was able to change the narrative at Microsoft even though, objectively, the company was already doing well.

Hollywood mogul Peter Guber describes stories as “emotional transport” and that’s why we need to be purposeful about the ones we tell if we are to bring about genuine transformation. Stakeholders need to be able to see themselves as heroes in the stories we tell, working within shared values to achieve a common purpose. Our story needs to be their story.

What Makes A Story?

The first element of any story is its exposition, which is the world you build around the story and includes the setting, the characters and other background information. This often comes at the beginning of the story, but it doesn’t have to. Sometimes, elements of the setting or details about the characters are leaked out as the plot develops.

The most important aspect of any story is the tension or conflict to be resolved. That’s what keeps the audience’s interest. Will the hero survive? Does the boy end up with the girl? Will justice prevail? It is the uncertainty surrounding the tension that makes a story interesting. A preordained story is a bore.

Another way to look at a story is an intention or ambition and an obstacle. If you can identify an ambition that people actually have—even if they aren’t aware of their intention—and then show that you can overcome the obstacles preventing them from achieving that ambition, you have a powerful narrative. Steve Jobs was a master at telling those kinds of stories (and then adding “one more thing.”)

There are, essentially, two ways to start a story. The first and less effective way is with the exposition, laying out the setting and characters, like when your mom tells the tale of meeting someone at the drug store and 10 minutes later you’re still hearing about their grandchildren. The other is to start with the tension, like when a James Bond movie begins with him hanging off a helicopter and you only find out why only later.

Start with the tension. Identify a problem to be solved. Learning how the obstacles to solving the problem will be overcome is what makes a story interesting.

The Hero’s Journey

One of the most common narrative devices is the “hero’s journey“, which involves different variations of a departure, an initiation, and a return. For example, in the Star Wars trilogy, we met Luke Skywalker as a restless boy on Tatooine. The hologram he unlocked in R2D2 kicked off his departure on a journey, in which he learned about “The Force.”

In a hero’s journey, the primary struggle is internal and the righteousness of your cause is your salvation. In order for Luke to prevail against Darth Vader and the evil empire, he first needed to conquer himself. Once he was able to do that, victory became, in some sense, inevitable.

We often like to think about change in this way because, in some sense, it’s easy. After all, who can oppose us when we are so diligently working for the greater good? If we are just good and pure and true, then success should become inevitable. We just need to keep the faith, endure any hardship that comes our way and we will be rewarded in the end.

Unfortunately, like Star Wars, this story is a fantasy and the stubborn belief in it will almost guarantee failure. Some people mistakenly see nobility in this type of defeat, because they can tell themselves that they fought “the good fight” and the deck was simply stacked against them. That way, they can blame everyone else instead of their “heroic” selves.

Change As A Strategic Conflict

The true story of change is that of strategic conflict between a future vision and the status quo. There are sources of power keeping the status quo in place and that’s where the battle lies. If you can remove—or even mitigate—those sources of power the status quo cannot survive and transformation can take place. As long as they remain, nothing will change.

Once you understand this story, you can begin to build an effective strategy. Power always lies in institutions and you can begin to identify which ones support the status quo, which support the future vision and which are on the fence. Those institutional targets will determine how you develop tactics.

Notice how differently the two stories affect actions. If you believe that you’re on a hero’s journey, then your primary goal is to communicate your virtue. You assume that once everyone understands your idea, they will embrace it. So you spend your time coming up with slogans, convinced that the right message will help others see the light.

When you begin to internalize the story of change as a strategic conflict it becomes clear that it’s more important to make a difference than to make a point. Demonstrating the righteousness of your cause is not nearly as important as letting others see their place in your story, how they too can be heroes in it and how they will be better off for it.

Creating A Shared Journey

Change always begins with a grievance. There’s something people don’t like and they want it to be different. We like to see ourselves as heroes fighting for everything that’s right and we question the motivations of those who oppose our cause. When we believe in something passionately, it’s hard to see how anyone, in good conscience, can see it another way.

Yet consider recent research that finds our conceptions of even something so simple as a penguin vary so widely that the mention of the word evokes very different associations in all of us. Clearly, more emotionally-laden content, such as a policy issue or a business strategy is going to spark vigorous debates.

The stories we tell need to create a sense of safety around transformation, to emphasize a shared future. Yet all too often we begin our stories with silly talk about “disruption” or burning platforms. The storytellers seek to ennoble themselves as champions and demonize others who see things differently.

Yet if we truly care about change, we need to hold ourselves accountable to be effective messengers. That’s why the narratives we build about change need to focus on shared values and establish common ground upon which we can build a shared future.

The stories we tell are important. We need to choose them wisely and tell them well.

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

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Innovation Framework Examples: 7 Real-World Cases That Show How They Work

Innovation Framework Examples: 7 Real-World Cases That Show How They Work

by Braden Kelley and Art Inteligencia

The most common question I get after presenting on innovation frameworks is not “which framework is best?” — it’s “can you show me what this actually looks like inside a real organization?” That question is exactly right. Frameworks are only valuable when you can see how they translate from theory to practice, and the translation is rarely as clean or obvious as the textbook version suggests.

What follows are real examples of organizations applying specific innovation frameworks — what the framework gave them, what it required of them, and what the outcomes looked like. For a complete guide to the major frameworks themselves, see our comprehensive innovation frameworks reference guide.

Design Thinking: IDEO and Bank of America’s “Keep the Change”

Bank of America’s “Keep the Change” savings program is one of the most cited design thinking success stories for good reason — it demonstrates what happens when you apply genuine customer empathy rather than product-feature thinking to a business problem.

The challenge: Bank of America wanted to help customers save more money, but conventional savings products were failing to attract adoption among their target segment of working-age adults. IDEO was brought in to apply design thinking to the problem.

The empathy research revealed something that no amount of market data had surfaced: people found saving difficult not because they lacked discipline, but because saving felt like a deliberate sacrifice that required conscious decision-making every time. The insight was behavioral, not financial.

The solution that emerged from this insight was counterintuitive: make saving automatic and invisible. Every time a customer made a debit card purchase, the amount was rounded up to the nearest dollar, and the difference was automatically transferred to savings. No decision required. No sacrifice felt.

The result: 2.5 million new customers enrolled in the first year, and Bank of America customers saved more than $1 billion through the program in its first year of operation. The program succeeded because the design thinking process surfaced a genuine behavioral insight — that the friction to saving was psychological, not financial — that product-focused thinking had systematically missed.

The framework lesson: Design thinking’s empathy stage is not market research. It surfaces the behavioral and emotional dimensions of a problem that quantitative data can’t see. The “Keep the Change” insight — that automatic saving removes the psychological friction that makes conscious saving feel like sacrifice — was only discoverable through direct human observation.

Jobs to Be Done: McDonald’s Milkshake Story

Clayton Christensen’s milkshake story is the most famous example of Jobs to Be Done thinking in practice — and it’s worth revisiting in detail because it illustrates exactly how differently JTBD reframes a business problem.

McDonald’s wanted to increase milkshake sales. Conventional market research asked customers what they wanted in a milkshake — thicker? sweeter? more flavors? The answers were inconclusive and the improvements they prompted didn’t move the sales needle.

A JTBD researcher took a different approach: instead of asking customers what they wanted in the product, he asked what job they were hiring the milkshake to do. The finding was completely unexpected. The majority of morning milkshake purchasers were buying for the commute — they needed something that would keep them full through a long, boring drive, that they could consume one-handed without making a mess, and that would last long enough to feel like an event rather than a transaction. The milkshake — thick, slow to consume, and easy to hold — was uniquely suited for this job. The alternatives (a banana, a bagel, a coffee) all failed on at least one dimension of the commute job.

The implication was immediately actionable: make the morning commute milkshake even better at its actual job — thicker, available faster at the drive-through, with a thinner straw to make it last longer. Don’t change the flavor. The job, not the product attribute, was the unit of analysis.

The framework lesson: JTBD reframes the competitive set entirely. McDonald’s wasn’t competing with Burger King for milkshake customers — it was competing with bananas and bagels for the morning commute job. That reframe opens completely different improvement directions than conventional competitive analysis would ever produce.

Lean Startup: Dropbox’s Minimum Viable Product

Dropbox’s founding story is the canonical example of Lean Startup’s MVP principle applied to its fullest effect — and what makes it particularly instructive is that the MVP wasn’t even a product. It was a video.

In 2007, Drew Houston had built a working prototype of Dropbox but faced a fundamental challenge: file synchronization is a problem that requires a significant user base to be meaningful, and building that base requires persuading investors and early users that the problem is real and the solution works. The conventional path — build, launch, market, iterate — would require substantial capital for a product whose value proposition was genuinely hard to communicate without experiencing it.

The Lean Startup approach: before investing further in the product, validate that people actually wanted it. Houston created a simple three-minute demo video explaining what Dropbox would do. No working product. No technical demonstration. Just a clear explanation of the problem and how Dropbox would solve it. He posted it on Hacker News.

The waitlist went from 5,000 to 75,000 overnight. The demand signal was unambiguous. The MVP — in this case, a video rather than a product — had validated the core assumption (that people wanted effortless file synchronization across devices) at a cost of hours rather than months of development.

The framework lesson: The point of an MVP is to test the most important assumption at the lowest possible cost, not to build the simplest functional version of the product. In Dropbox’s case, the most important assumption was demand, not technical feasibility — so the MVP was a demand test, not a product prototype.

Three Horizons Framework: Amazon Web Services

Amazon’s development of AWS is the most instructive example of McKinsey’s Three Horizons Framework in practice — partly because Amazon’s leaders almost certainly weren’t thinking about Three Horizons when they built it, but the strategic logic maps perfectly onto the framework.

Amazon’s Horizon 1 business in the early 2000s was e-commerce — the core retail operation that was generating revenue and requiring continuous improvement. The challenge every e-commerce business faces is infrastructure: you need enormous computing capacity to handle peak periods (holiday shopping), but that capacity sits idle for most of the year. Amazon had solved this problem for itself through massive internal infrastructure investment.

The Horizon 2 insight — building an adjacent business from existing capabilities — came from recognizing that the infrastructure Amazon had built to run its own e-commerce operation was itself a valuable product that other companies needed. The capability was already built. The extension was to offer it externally.

The Horizon 3 bet was that computing infrastructure as a service would become a foundational utility — that the long-term market was enormous and that Amazon’s early investment would produce compounding advantages as the market developed. In 2024, AWS generated over $100 billion in annual revenue and represented the majority of Amazon’s operating profit.

The framework lesson: The Three Horizons Framework is most valuable not as a planning tool but as a diagnostic: it forces explicit conversations about whether the organization is investing appropriately across all three time horizons, and whether Horizon 1 pressures are crowding out the Horizon 2 and 3 investments that produce long-term competitive advantage. Amazon’s willingness to invest in and protect Horizon 3 bets — including AWS, Prime, and Alexa — while competitors focused primarily on Horizon 1 optimization is a significant part of why it has compounded value so effectively.

Open Innovation: Procter & Gamble’s Connect + Develop

Procter & Gamble’s Connect + Develop program, launched in 2000 under CEO A.G. Lafley, is the most cited example of open innovation at enterprise scale. Lafley set an ambitious and specific goal: source 50% of P&G’s innovations from outside the company. This was not aspirational language — it was a specific, measurable target that required fundamentally restructuring how P&G approached innovation.

The program built explicit infrastructure for external idea sourcing: a dedicated team for identifying and evaluating external innovations, partnerships with universities and research institutions, a public submission portal for independent inventors, and acquisition strategies that brought external technologies inside P&G’s commercialization machinery.

The results were significant. Spin-off toothbrush innovations, the Swiffer product line, and the Pringles printing technology all came through open innovation channels. By 2006, P&G reported that more than 35% of its new products had elements that originated from outside the company, up from about 15% in 2000. Productivity in R&D improved substantially.

What made Connect + Develop work where most open innovation programs fail was the investment in internal absorption capability — the processes, relationships, and organizational structures that allowed P&G to actually use external ideas rather than just collect them. The “not invented here” syndrome that kills most open innovation programs was addressed through deliberate cultural and process design, not just aspiration.

The framework lesson: Open innovation requires two-sided capability development — not just the ability to attract external ideas, but the organizational capacity to evaluate, integrate, and commercialize them. P&G’s investment in internal absorption capability was as important as its investment in external sourcing.

The Value Innovation Framework: Apple iPad Launch

The Apple iPad launch in 2010 illustrates the Value Innovation Framework’s three components — Value Creation, Value Access, and Value Translation — and specifically demonstrates what happens when Value Translation fails even when the other two are strong.

The iPad’s Value Creation was genuinely significant: a device that made web browsing, email, media consumption, and light content creation dramatically more convenient than a laptop for a large set of use cases. Value Access was strong: the price point was lower than expected, distribution through Apple Stores and carriers was immediate, and the device worked out of the box without configuration.

The initial launch, however, struggled with Value Translation — helping people understand what job the device was actually for. The early marketing positioned it as a larger iPhone or a smaller laptop, both framings that made it seem like a compromise rather than a genuine innovation. Reviews were mixed. The initial sales trajectory was uncertain.

The Value Translation breakthrough came not from a product change but from a single advertising image: a person relaxing on a couch with an iPad in their lap. That image communicated in seconds what no amount of specification comparison could: this is the device for the relaxed, casual computing moment — not the desk, not the commute, but the couch. Sales accelerated dramatically after that visual translation clicked.

The framework lesson: Innovation = Value Creation × Value Access × Value Translation is multiplicative, not additive. The iPad had strong Value Creation and Value Access from day one. The Value Translation gap almost cost Apple the launch. Fixing the translation — not the product — unlocked the market.

Disruptive Innovation: Netflix vs Blockbuster

The Netflix/Blockbuster story has become the defining example of disruptive innovation theory in practice — perhaps because it is unusually clean as a case study, with a visible incumbent, a clear disruption pattern, and a decisive outcome.

Netflix’s initial DVD-by-mail service in 1998 entered the video rental market from exactly the position Christensen’s theory predicts: serving an overlooked segment (frequent renters who resented late fees and found the trip to the store inconvenient) with a simpler, different model that the incumbent (Blockbuster) had no interest in responding to. Blockbuster’s most profitable customers were the casual renters who came into stores and paid late fees — the customers Netflix was serving were not Blockbuster’s priority.

As Netflix improved, it moved upmarket — expanding its library, improving delivery speed, and eventually transitioning to streaming. By the time the threat was obvious to Blockbuster, the incumbent’s response was structurally constrained: its entire business model (physical stores, late fees, walk-in customers) was incompatible with the direction the market was moving. Blockbuster filed for bankruptcy in 2010. Netflix is now a global media company with over 300 million subscribers.

The framework lesson: Disruptive innovation theory’s most valuable practical application is identifying threats that conventional competitive analysis will dismiss. Blockbuster’s leadership could see Netflix’s numbers for years and rationally conclude that the threat was manageable. The framework reveals why that rational conclusion was wrong: the disruption was coming from a direction Blockbuster’s financial incentives prevented it from defending.

Frequently Asked Questions

What are some real-world examples of innovation frameworks in action?

Real-world innovation framework examples include: Bank of America’s “Keep the Change” savings program (design thinking applied to behavioral finance); McDonald’s milkshake insight (Jobs to Be Done reframing the competitive set); Dropbox’s video MVP (Lean Startup demand validation before product development); Amazon Web Services (Three Horizons Framework applied to infrastructure-as-a-service); Procter & Gamble’s Connect + Develop (open innovation at enterprise scale); the Apple iPad launch (Value Innovation Framework showing the importance of Value Translation); and Netflix’s disruption of Blockbuster (Disruptive Innovation theory playing out over a decade). Each example illustrates how frameworks translate from theory to specific, actionable decisions in real organizations.

Which innovation framework is most widely used by large companies?

McKinsey’s Three Horizons Framework and Design Thinking are the most widely adopted innovation frameworks among large organizations. Three Horizons is particularly prevalent in corporate strategy and portfolio management contexts because it provides a common language for conversations about innovation investment allocation. Design Thinking has been widely adopted across industries — from product development to healthcare to public policy — because its human-centered, iterative approach applies to virtually any type of complex problem. In practice, most sophisticated innovation programs use multiple frameworks in combination rather than selecting one exclusively.

How do you choose the right innovation framework for your organization?

Choosing the right innovation framework depends on your primary challenge: if you need to allocate innovation investment across time horizons, use Three Horizons; if you need to identify unmet customer needs, use Jobs to Be Done; if you need to validate a new concept quickly, use Lean Startup; if you need to understand competitive disruption threats, use Disruptive Innovation theory; if you need to access external capabilities, use Open Innovation; if you need to solve a complex human-centered problem, use Design Thinking. Most organizations benefit from using multiple frameworks in combination — each addresses a different dimension of the innovation challenge. For a complete framework selection guide, see our comprehensive innovation frameworks guide.

Want to go deeper on any of these frameworks? Our complete guide to innovation frameworks covers each one in detail — what it does well, where it falls short, and how to choose the right approach for your specific situation.




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Braden Kelley is a LinkedIn Top Voice, bestselling author, and innovation keynote speaker who helps organizations get to the future first and build sustainable innovation cultures.

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Image Credit: Gemini

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

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Five Keys to Building Trust on Teams

Five Keys to Building Trust on Teams

GUEST POST from David Burkus

One of the easiest ways to predict how successful or not a given team will be, is to first measure how much trust exists on the team as a whole. When members of the team trust each other, they’re more likely to succeed because they’re more likely to share information.

They’re more likely to share feedback.

They’re more likely to take risks.

They’re more likely to support each other.

They’re more likely to express crazy ideas that lead to the brilliant ideas they need.

They’re more likely to admit failures and get the help that they need.

And they’re more likely to grow together and reach new levels of performance.

But how do you build trust on a team? How do you get people to trust each other? And how do you get people to trust the team and you as a leader?

In this article, we’ll outline five ways to build trust on teams. Trust is not built overnight, but these five simple actions will start the process of trust-building on your team.

Build Real Bonds

The first way to build trust on teams is to build real bonds. Specifically, build bonds between teammates that form for reasons beyond shared work or collaborative roles. In other words, build friendships. Research suggests those who report having friends at work are more productive, more committed, and yes more trusting (and trustworthy). And while you can’t force two people on your team to be friends, you can create opportunities for your team to have socialization and non-work conversations that will lead to the discovery of mutual interests. These “uncommon commonalities” make people more likely to become friends—and make it more likely they develop mutual trust.

Encourage Candor

The second way to build trust on teams is to encourage candor. Encourage the team to speak freely and even to disagree. While that might seem counterintuitive, respectful dissent and collaborative disagreement are signs of trust on a team. It’s inevitable that members of your team will disagree, if no one is speaking up that’s a sign that there is not yet enough trust built up. As a leader, you can fix this by encouraging dissent and disagreement with you, and then modelling what respectful behavior and civil disagreement look like. This not only demonstrates to the team how to behave when they disagree, it also demonstrates that they can trust that their ideas are heard.

Spotlight Wins

The third way to build trust on teams is to spotlight wins. Whenever members of the team have small wins—work related or not—make sure you take the time to let the whole team know. This is good for the overall culture and camaraderie of the team, but it also tells the individual members that you care and that you notice what matters to them. In addition, it makes it more likely they’ll trust you and come to you with successes and failures—and come to the whole team with successes and failures—because they know that you care.

Accept Failures

The fourth way to build trust on teams is to accept failures—and in some ways this is the opposite side of spotlighting wins. Failures happen. No one wins all of the time and no team is able to deliver on time and under budget every time. Mistakes get made. And situations outside of the team’s control happen. But how leaders and teams respond to those failures is what determines future success, and future trust. Leaders who seek to find blame, and teammates who offer quick excuses, undermine trust, and prevent the team from improving. But leaders who seek to find learning opportunities inside of failure make the team more trusting and, in the long run, much more successful.

Model Vulnerability

The final way to build trust on teams is to model vulnerability. Sometimes, all it takes for a team to start trusting each other is for the team leader to stop pretending to be perfect. When leaders admit their mistakes and own up to their biases, they send a strong message to the rest of the team that they can be trusted. And often that vulnerability is met with vulnerability from others. It’s impossible to build trust on a team without creating the opportunity to be trusted—and that opportunity comes from vulnerability.

While these five methods are not an exhaustive list of the ways trust develops on teams, they all have something in common. Each of these methods is a leader-initiated action that kick starts a cycle of trust. Each method creates space for team members to act on trust and feel trusted. And we know from research that trust is not given, and trust is not earned, trust is reciprocated. It’s a virtuous cycle that starts with one person — usually the leader—demonstrating trust and modeling what trustworthiness looks like. Over time that trust compounds and creates an environment where everyone on the team can do their best work ever.

Image credit: Gemini

This article originally appeared on DavidBurkus.com

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The Pulse of Experience

Multimodal Affective Computing via Remote Photoplethysmography (rPPG)

LAST UPDATED: July 10, 2026 at 5:50 PM

The Pulse of Experience

GUEST POST from Art Inteligencia


Beyond the “Mask” of Traditional Sentiment Analysis

For too long, the design and innovation communities have relied on the “performance” of emotion. Traditional sentiment analysis and basic facial expression tracking are inherently flawed — they capture superficial, easily masked, or culturally misinterpreted reactions. We have spent decades designing experiences based on what people say they feel, or what they choose to show us, rather than the raw reality of their experience.

In our quest for true, human-centered innovation, we must move beyond these superficial layers. We are entering an era of Affective Computing that allows us to bypass the conscious “mask” and read the underlying biology of the user directly, from a distance, and without friction.

The Shift to Biological Truth

The core thesis of this shift is profound: we are transitioning from measuring post-experience reflection to measuring real-time physiological reality. By leveraging Remote Photoplethysmography (rPPG), we stop treating emotion as a subjective opinion and start treating it as observable, quantifiable biological data. This shift fundamentally changes how we understand human interaction by allowing us to:

  • Identify micro-moments of cognitive load and frustration that a user might completely omit in a post-interaction survey.
  • Validate moments of true delight by observing authentic autonomic nervous system responses.
  • Establish a continuous, objective feedback loop that captures the real emotional temperature of a human-system interaction.

Demystifying rPPG: The Invisible Data Stream

To understand the power of this shift, we have to look at the underlying technology. Remote Photoplethysmography (rPPG) sounds complex, but its premise is brilliantly elegant. Every time your heart beats, blood pumps into your face, changing the volume of blood vessels in the skin. While these micro-fluctuations are completely invisible to the human eye, advanced algorithms paired with standard, high-resolution camera sensors can detect them with incredible precision.

This isn’t about scanning a face for a forced smile; it is about extracting pure, unadulterated physiological metrics from a distance without any physical contact or invasive wearables. By analyzing the ambient light reflecting off a user’s skin, rPPG provides an objective window into the human autonomic nervous system.

The Triad of Physiological Metrics

By shifting our focus from outer expressions to inner biology, rPPG allows us to capture three critical dimensions of the human state in real time:

  • Heart Rate Variability (HRV): The gold standard for measuring stress, focus, and emotional resilience. High volatility or sharp drops in HRV give us immediate insight into a user’s cognitive load and anxiety levels.
  • Respiration Rate: Changes in breathing patterns are immediate, involuntary responses to stimuli. Sudden, shallow breathing flags moments of friction, confusion, or sudden panic during an interaction.
  • Autonomic Nervous System (ANS) Response: By aggregating these metrics, we move from interpreting a user’s subjective feedback to observing their direct biological reality — separating what they say happened from how their body actually processed it.

The Evolution of Metrics: From SLAs to XLMs

For decades, organizations have managed operations using Service Level Agreements (SLAs). We track server uptime, average handle times, and page load speeds. But as I have long argued, SLAs are lagging indicators of operational efficiency, not leading indicators of human satisfaction. A system can meet every technical SLA perfectly while still delivering an experience that leaves the user feeling completely alienated, exhausted, or frustrated.

To design a future that honors the human element, we must transition to Experience Level Measures (XLMs). While SLAs measure the mechanics of a transaction, XLMs measure the quality of the interaction. This is exactly where rPPG becomes a game-changer: it bridges the gap between mechanical performance and biological reality, providing the objective, real-time data stream that true XLMs require.

Quantifiable Empathy in Action

The true value of integrating rPPG into an XLM framework is the creation of continuous, objective feedback loops. Instead of relying on a lagging, retrospective Net Promoter Score (NPS) or a post-interaction survey — which are warped by recency bias and emotional fatigue — we can map physiological data directly to specific touchpoints. This gives design and innovation teams access to a whole new class of experience data:

  • Real-Time vs. Retrospective Data: Capturing a physiological stress spike exactly when a user encounters a confusing form field, rather than trying to reconstruct that frustration through a survey twenty minutes later.
  • Continuous Feedback Loops: Establishing a dynamic understanding of user sentiment throughout an entire digital or physical journey, allowing systems to adapt fluidly to the user’s real-time emotional state.
  • Quantifiable Empathy: Moving empathy out of the realm of abstract design thinking principles and transforming it into hard, validating metrics. We no longer have to guess if an experience causes genuine delight or hidden frustration — the data shows us.

Strategic Applications: Where Human-Centered Innovation Meets Biology

The strategic value of rPPG lies in its ability to be deployed passively and non-invasively in real-world scenarios. We no longer need to strap sensors onto a user’s wrists or place them in artificial lab environments to understand their physiological reality. By integrating rPPG into our innovation toolkits, we can elevate several core pillars of experience design and product development.

From Lab Testing to Living Journeys

By bringing objective biological data into the wild, design and innovation teams can radically transform how products and ecosystems are evaluated across three primary frontiers:

  • Next-Generation UX Testing: Traditional usability tests heavily rely on “think-aloud” protocols, which force users to consciously articulate their actions — frequently disrupting their natural cognitive flow. By pairing screen interactions with rPPG data, we transition to physiological-validation protocols. We can pinpoint the exact microsecond an interface element creates an involuntary cognitive spike, validating friction points without asking the user to say a word.
  • Customer Journey Touchpoint Evaluation: In physical environments — like a retail showroom, a bank branch, or an airport terminal — understanding how a customer navigates a physical space has historically been based on observation or post-trip interviews. Deploying rPPG through ambient, high-resolution camera networks allows brands to safely map the “emotional temperature” of a space. We can visually correlate design choices, waiting times, or staff interactions directly with aggregate, anonymized stress or comfort metrics.
  • High-Stress Training and Simulations: For workforce development in high-stakes fields—such as healthcare, aviation, or emergency response — performance isn’t just about technical accuracy; it is about emotional regulation. Utilizing rPPG during simulation training allows coaches to monitor a trainee’s stress threshold and recovery rates in real time. This ensures that learners are pushed into the optimal “stretch zone” for neuroplasticity and retention without crossing over into debilitating anxiety.

The rPPG Ecosystem: Pioneers and Startups to Watch

The transition toward physiological Experience Level Measures (XLMs) is no longer a theoretical exercise. A sophisticated ecosystem of established tech giants, niche health-tech innovators, and agile software startups is actively commercializing Remote Photoplethysmography. For experience designers and corporate strategists, these are the key market players driving the infrastructure of affective computing:

Established Pioneers and Enterprise Platforms

  • Philips Biosensing (by rPPG): As an undisputed heavyweight in HealthTech, Philips has leveraged its massive IP portfolio in optics and signal processing to offer robust, motion-resistant rPPG licensing. Their enterprise-ready algorithms are explicitly targeted at automotive tracking (detecting driver fatigue and stress) and large-scale consumer applications.
  • Blue Spark Technologies (VitalTraq™): Known for clinical-grade wearables, Blue Spark’s VitalTraq platform blends continuous temperature patches with rapid 30-to-60-second rPPG facial scans. They are a prime example of how contactless biometrics are modernizing decentralized clinical trials and consumer experience checkpoints.

Emerging Startups and Core SDK Innovators

  • Circadify (A.Y. Health Technologies): Operating out of Palo Alto, Circadify is aggressively democratizing contactless vitals. Crucially for experience designers, their deep learning models heavily over-sample diverse skin tones across the full Fitzpatrick scale — directly solving the algorithmic blind spots and demographic bias that plague first-generation emotion AI.
  • Darwin Edge: Based in Switzerland, this startup provides highly optimized Software Development Kits (SDKs) that run rPPG processing locally on the edge (including mobile browsers and Raspberry Pi). Their approach is vital for human-centered design because it eliminates cloud dependency, protecting user privacy out of the box.
  • IntelliProve: Hailing from Belgium, this startup is heavily engaged in academic and clinical validation, proving that camera-based physiological biomarkers can hold up in real-world environments without expensive laboratory equipment.

The Human-Centered Imperative: Ethics and the “AI Soft Landing”

As an advocate for human-centered innovation, I must emphasize that the power to read a person’s inner biological state carries profound ethical responsibility. This technology must never be used to build a corporate surveillance state or to manipulate consumer behavior. If we weaponize physiological data for hyper-targeted emotional exploitation, we destroy the fundamental trust required for meaningful human-device collaboration.

To achieve what I call an “AI Soft Landing” — where emerging technologies elevate human potential rather than automate away human dignity — the deployment of rPPG must be governed by strict ethical guardrails. The focus must always remain on designing systems that adapt to support the human, not systems that exploit human vulnerability.

Architecting a Trust-Based Infrastructure

To successfully integrate affective computing into our organizations without compromising our values, leaders must anchor their strategies in three critical pillars:

  • Absolute Privacy and Consent: Physiological data is deeply personal. Users must have explicit, transparent control over when their metrics are gathered, how they are anonymized, and complete assurance that this data is processed locally at the edge rather than stored in a permanent cloud registry.
  • Designing for Intent Orchestration: As labor transitions from manual execution to intent orchestration — where humans direct AI agents to do the heavy lifting — machines must understand our capacity. rPPG acts as a cognitive thermostat, signaling to an AI assistant when to step in, when to simplify an interface, or when to back off based on the user’s real-time stress levels.
  • The AI Apprenticeship Economy: By pairing rPPG with our experience design, we allow AI systems to learn from our biological feedback loops. This transforms the technology into a true apprentice — one that becomes deeply attuned to human cadence, proactively smoothing out friction, and cultivating an environment where humans can thrive in flow states.

Conclusion: Closing the Gap Between System and Soul

The convergence of computer vision, advanced algorithms, and human physiology represents a monumental shift in the design landscape. For decades, we have been forced to design for a caricature of the user — one built from incomplete survey data, delayed analytics, and superficial emotional masks. With Remote Photoplethysmography (rPPG), we finally have the tools to design for the authentic, unfiltered human reality.

This technological milestone is ultimately an evolution in how we define and honor the human experience. By transforming passive observations into deep, quantifiable empathy metrics, we can firmly move away from rigid, lagging operational agreements and step into a future powered by real-time Experience Level Measures (XLMs).

The Path Forward for Experience Leaders

As we look to navigate the complexities of digital transformation and the emerging AI economy, our mandate as innovation strategists and experience designers is clear:

  • Shift the Paradigm: Challenge your organization to stop evaluating experiences solely based on task completion, and start measuring the literal, physiological impact your ecosystem has on human beings.
  • Design for Wellbeing: Treat biometric transparency not as a novel data pipeline, but as an opportunity to actively reduce friction, alleviate cognitive fatigue, and foster digital environments that respect the human nervous system.
  • Lead with Purpose: Ensure that your application of affective computing remains fiercely human-centered, grounded in trust, and explicitly engineered to support an intentional, elegant soft landing for both your customers and your workforce.

Frequently Asked Questions: Understanding rPPG and XLMs

What is rPPG and how does it detect emotions?

Remote Photoplethysmography (rPPG) is a non-invasive technology that uses standard, high-resolution camera sensors and advanced computer vision algorithms to track blood volume pulses. Every time the heart beats, it causes micro-fluctuations in skin color that are completely invisible to the human eye. By analyzing these subtle changes from a distance, rPPG measures real-time physiological metrics like heart rate variability (HRV) and respiration rate, giving us an objective, biological look at cognitive load, stress, and genuine engagement without requiring any physical contact or wearable sensors.

How do rPPG metrics integrate into Experience Level Measures (XLMs)?

Traditional Service Level Agreements (SLAs) only track technical mechanics, like page load speeds or uptime. Experience Level Measures (XLMs) focus entirely on the quality of the human experience. rPPG provides the continuous, real-time data layer that makes XLMs actionable. Instead of relying on lagging, retrospective surveys that suffer from memory bias, rPPG acts as a tool for quantifiable empathy. It maps exact physiological spikes — such as sudden stress or relaxed engagement — directly to specific touchpoints along a digital or physical customer journey.

What are the ethical guardrails for using biometric data in experience design?

Because physiological data is deeply personal, it must never be used for employee surveillance or predatory behavioral manipulation. To achieve an ethical “AI Soft Landing,” organizations must follow three core pillars: absolute transparency and informed user consent, local edge processing to ensure biometric data is never stored or transmitted to a permanent cloud registry, and an explicit focus on intent orchestration — using the data solely to help systems adaptively support and reduce friction for the human user.

FutureHacking™ Is Coming

FutureHacking™ is Braden Kelley’s strategic foresight methodology — and a paid download and training program is launching soon. Register your interest now to be the first to know when it’s available, and get early access pricing.

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

Image credits: Gemini

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How to Diagnose Your Change Type Before You Plan Your Approach

How to Diagnose Your Change Type Before You Plan Your Approach

by Braden Kelley and Art Inteligencia

Organizations that struggle with change almost always share one critical blind spot: they treat all change as the same. They apply the same planning process, the same communication strategy, the same timeline expectations, and the same leadership approach to a technology rollout as they would to a cultural transformation — and they wonder why results are so unpredictable.

The reality is that different types of organizational change require fundamentally different approaches. The change management process that works brilliantly for a planned, incremental process improvement will fail almost completely when applied to an unplanned structural disruption. Understanding which type of change you are actually managing — before you decide how to manage it — is one of the highest-leverage decisions a change leader can make.

This article explores how to diagnose your change type, why the diagnosis matters, and how it should shape your planning approach. For a complete treatment of all the major types of organizational change and their specific characteristics, see our definitive guide to the different types of organizational change.

The Two Dimensions That Define Change Type

Every organizational change can be positioned on two dimensions that together determine how it should be managed:

Dimension 1: Scope — Incremental vs Transformational
Incremental change improves or extends what already exists — a process becomes more efficient, a product gains a new feature, a team adds a new member. The underlying model stays intact; execution quality improves. Transformational change creates something genuinely new — a different business model, a fundamentally restructured organization, a cultural shift that requires people to behave differently in their daily work. The existing model doesn’t just improve; it changes in ways that make the past a less useful guide to the future.

Dimension 2: Origin — Planned vs Unplanned
Planned change is deliberately initiated — a leadership decision to restructure, a strategic choice to implement new technology, a deliberate effort to shift culture. Unplanned change is imposed by external events — a competitor disrupts the market, a regulation changes, a crisis forces rapid response. Planned change allows preparation; unplanned change requires adaptation.

These two dimensions create a 2×2 matrix of change types that most organizations encounter at some point — and each quadrant requires a meaningfully different management approach.

Why Most Change Programs Misdiagnose Their Change Type

The most common misdiagnosis is treating transformational change as if it were incremental — assuming that because you have a clear destination, the path there will look like a more intense version of what you’ve done before. It won’t. Transformational change requires different leadership behaviors, different communication strategies, different timelines, and a fundamentally different relationship with uncertainty than incremental change does.

The second most common misdiagnosis is treating unplanned change as if it were planned — spending time on elaborate planning processes and detailed roadmaps when the situation is actually demanding rapid adaptation. Rigorous planning is valuable. But when circumstances are changing faster than plans can track, the discipline of rapid diagnosis and agile response matters more than the discipline of comprehensive planning.

Three diagnostic questions help leaders identify which type of change they’re actually managing:

  1. Does this change require people to give up something they value — a role, a skill, a process, an identity — or just learn something new while keeping what they have? If people are losing something, you’re in transformational territory regardless of how the initiative is framed.
  2. Do we know what success looks like in enough detail to plan toward it, or are we navigating genuine uncertainty about both the destination and the path? If the answer is the latter, incremental project management tools will frustrate more than they help.
  3. How much time do we have to prepare? Planned change allows the luxury of impact assessment, stakeholder engagement, and communication planning before implementation begins. Unplanned change compresses or eliminates that preparation window — which changes what’s possible and what’s necessary.

How Change Type Should Shape Your Change Management Approach

Incremental Planned Change

This is the home territory of most formal change management methodologies. Structured planning, phased implementation, training programs, and progress metrics all work well here because the destination is known, the timeline is manageable, and the resistance — while real — is generally about disruption to habit rather than threat to identity. The risk to avoid: over-engineering the change management process for what is actually a relatively contained improvement initiative.

Transformational Planned Change

This is where most major change programs live — and where most fail. The planning feels similar to incremental change (there is a destination, there is a timeline, there is a project plan), but the human experience is categorically different. People are not just learning new skills or adjusting to new processes; they are being asked to give up aspects of how they work, what they value, and sometimes who they are professionally. This requires the full toolkit of change management — Bridges’ transition model for understanding the emotional journey, deep resistance management planning, extensive leadership modeling of the new behaviors, and sustained investment well past the technical “go live” date.

Incremental Unplanned Change

A competitive move requires a tactical response, a supplier fails and processes need adjusting, a team member departs unexpectedly. These situations require quick mobilization and clear decision-making, but the scope is contained enough that structured response is possible. The key discipline: resist the temptation to treat every unplanned change as a crisis requiring heroic leadership, which creates change fatigue and undermines the organizational resilience you need for genuinely serious disruptions.

Transformational Unplanned Change

This is the hardest category — fundamental change that arrives without the preparation window that planned transformation allows. Organizational crises, industry disruptions, regulatory upheavals. The change management principles that apply to planned transformation still matter here, but they must be compressed: faster diagnosis, faster stakeholder alignment, faster communication, and higher tolerance for making consequential decisions under genuine uncertainty. Leaders who have built strong organizational change capability through earlier planned change investments handle this category significantly better than those who haven’t.

The Role of the Change Planning Canvas™ in Diagnosing Change Type

One of the most valuable uses of the Change Planning Canvas™ — the central tool of the Human-Centered Change™ methodology — is in the earliest stages of change planning, before any tactical decisions have been made. The Canvas forces the change team to explicitly characterize the change they are managing across multiple dimensions — including scope and origin — which surfaces the diagnostic clarity that most change programs skip in the rush to action.

Teams that spend time on this diagnosis consistently make better downstream decisions: they select the right change management models, they calibrate their communication approaches to the actual emotional journey their people will experience, and they build realistic timelines that account for the full complexity of the change type they’re actually managing rather than the simpler change type they wish they were managing.

For a complete guide to the different types of organizational change and their specific characteristics, impacts, and management requirements, see our comprehensive resource: Organizational Change: The Different Types and Their Impact.

Frequently Asked Questions

How do you identify the type of organizational change you’re dealing with?

Identifying your change type starts with two diagnostic dimensions: scope (is this change incremental — improving what exists — or transformational — creating something genuinely new?) and origin (is this planned — deliberately initiated — or unplanned — imposed by external events?). Three key questions help clarify: Does this change require people to give up something they value, or just learn something new? Do we know what success looks like clearly enough to plan toward it? How much time do we have to prepare? The answers position the change in one of four quadrants — incremental planned, transformational planned, incremental unplanned, or transformational unplanned — each of which requires a meaningfully different management approach.

Why does change type matter for change management?

Change type matters because different types of organizational change require fundamentally different management approaches. The most common and costly change management mistake is treating transformational change as if it were incremental — applying structured project management and training program approaches to situations that actually require deep stakeholder engagement, leadership behavior modeling, resistance management, and sustained investment well past the technical implementation date. Misdiagnosing change type leads to under-resourcing the human dimensions of change, applying the wrong models, and building unrealistic timelines — all of which increase the probability of implementation failure.

What is the difference between incremental and transformational organizational change?

Incremental change improves or extends what already exists — processes become more efficient, products gain new features, teams add capabilities. The underlying organizational model stays intact. Transformational change creates something genuinely new that requires people to work, think, and behave differently in fundamental ways. The distinction matters practically because incremental change primarily requires skill development and habit adjustment, while transformational change also requires people to let go of something they valued — a role, an identity, a way of working — which triggers a different and more emotionally complex human response that standard project management approaches don’t address.

Image Credit: Pexels

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

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Framework • 2×2 Diagnostic

How to Diagnose
Your Change Type

Two dimensions shape your approach: Scope × Origin. Locate your initiative to choose the right method, not just a bigger version of the wrong one.

Incremental: Improves what exists Transformational: Creates new Unplanned: Imposed, adapt fast
Planned Deliberately initiated, allows preparation
←   ORIGIN   →
Unplanned Imposed by external events, requires adaptation

Transformational Planned Change

Major Programs That Most Often Fail

Feels plannable but human experience is categorically different. People give up roles, identity, and ways of working.

✓ What Works
  • Bridges’ transition model
  • Deep resistance management
  • Leadership modeling
  • Sustained investment past go-live
⚠ Risk to Avoid
Treating transformational as just bigger incremental

Transformational Unplanned Change

Hardest Category

Crises, industry disruption, regulatory upheaval. Fundamental change without a preparation window.

✓ What Works
  • Compressed transformation principles
  • Faster diagnosis and alignment
  • Relentless communication
  • High tolerance for uncertainty
⚠ Risk to Avoid
Slow planning when adaptation is needed

Incremental Planned Change

Structured Improvement

Home territory for most formal methodologies. Destination known, timeline manageable.

✓ What Works
  • Phased implementation
  • Training programs
  • Progress metrics
⚠ Risk to Avoid
Over-engineering for a contained initiative

Incremental Unplanned Change

Tactical Response

Competitive move, supplier failure, unexpected departure. Contained scope but needs speed.

✓ What Works
  • Quick mobilization
  • Clear decision-making
⚠ Risk to Avoid
Treating every surprise as a crisis, creates change fatigue
Incremental Improves, model stays intact Scope ↑ Transformational Creates new, past less useful

Sometimes with Novelty, Less Can Be More

The Ghost Pepper Rule

GUEST POST from Mike Shipulski

When it’s time to create something new, most people try to imagine the future and then put a plan together to make it happen. There’s lots of talk about the idealize future state, cries for a clean slate design or an edict for a greenfield solution. Truth is, that’s a recipe for disaster. Truth is, there is no such thing as a clean slate or green field. And because there are an infinite number of future states, it’s highly improbable your idealized future state is the one the universe will choose to make real.

To create something new, don’t look to the future. Instead, sit in the present and understand the system as it is. Define the major elements and what they do. Define connections among the elements. Create a functional diagram using blocks for the major elements, using a noun to name each block, and use arrows to define the interactions between the elements, using a verb to label each arrow. This sounds like a complete waste of time because it’s assumed that everyone knows how the current state system behaves. The system has been the backbone of our success, of course everyone knows the inputs, the outputs, who does what and why they do it.

I have created countless functional models of as-is systems and never has everyone agreed on how it works. More strongly, most of the time the group of experts can’t even create a complete model of the as-is system without doing some digging. And even after three iterations of the model, some think it’s complete, some think it’s incomplete and others think it’s wrong. And, sometimes, the team must run experiments to determine how things work. How can you imagine an idealized future state when you don’t understand the system as it is? The short answer – you can’t.

And once there’s a common understanding of the system as it is, if there’s a call for a clean sheet design, run away. A call for a clean sheet design is sure fire sign that company leadership doesn’t know what they’re doing. When creating something new it’s best to inject the minimum level of novelty and reuse the rest (of the system as it is). If you can get away with 1% novelty and 99% reuse, do it. Novelty, by definition, hasn’t been done before. And things that have never been done before don’t happen quickly, if they happen at all. There’s no extra credit for maximizing novelty. Think of novelty like ghost pepper sauce – a little goes a long way. If you want to know how to handle novelty, imagine a clean sheet design and do the opposite.

Greenfield designs should be avoided like the plague. The existing system has coevolved with its end users so that the system satisfies the right needs, the users know how to use the system and they know what to expect from it. In a hand-in-glove way, the as-is system is comfortable for end users because it fits them. And that’s a big deal. Any deviation from baseline design (novelty) will create discomfort and stress for end users, even if that novelty is responsible for the enhancement you’re trying to deliver. Novelty violates customer expectations and violating customer expectations is a dangerous game. Again, when you think novelty, think ghost peppers. If you want to know how to handle novelty, imagine a green field and do the opposite.

This approach is not incrementalism. Where you need novelty, inject it. And where you don’t need it, reuse. Design the system to maximize new value but do it with minimum novelty. Or, better still, offer less with far less. Think 90% of the value with 10% of the cost.

Image credits: Pexels

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5 Ways IKEA Creates A Luxury Experience

How IKEA Creates A Luxury Experience

GUEST POST from Shep Hyken

IKEA is a retailer known for furniture that the customers have to assemble. Its showrooms feature ongoing displays of its products, and delicious Swedish meatballs are sold in restaurants located inside its stores. It is also known as affordable, which may make you ask, “How can an affordable brand, like IKEA, create a luxury experience?”

That’s the question answered by Neen James, the author of Exceptional Experiences: Five Luxury Levers to Elevate Every Aspect of Your Business, who uses IKEA as a case study to prove that even brands known for low prices can create a luxury experience.

In my interview with James on Amazing Business Radio, she makes it clear that luxury experiences aren’t always about high-end and high cost, although they can be. An evening at the Ritz-Carlton or Four Seasons will cost significantly more than an inexpensive roadside hotel, and the experience will be distinctly different. However, the experience at an inexpensive hotel can be elevated by triggering the five luxury levels in James’ book.

Luxury Is About Experiences, Not Things

This is the title of Part One in the book and makes the case for my hotel comment above. James says, “You don’t have to have a luxury product to provide a luxury level of service.” Luxury experiences don’t have to be costly or limited to fancy products like expensive handbags or high-end cars (or fancy hotels). Even a small business or budget hotel can deliver what feels like a luxury experience if it focuses on how it treats its customers. It’s more important to make people feel special, valued and appreciated than to give them material things. True luxury comes from the way you make someone feel, not just from the price tag.

The Five Characteristics of Luxury

According to James, “In the Luxury Mindset Study, we learned that luxury is defined with five words: high quality, long lasting, authentic, unique and indulgent. These characteristics apply whether you’re at Motel 6 or the Ritz, except maybe the word indulgent.” These words can shape the way your company or team interacts with customers, making every experience feel exceptional. By focusing on these qualities, even basic products and services can seem luxurious. It’s the way you make customers feel. James says, “Always look for ways to make your service authentic and memorable.”

The Five Luxury Levers

James talks about “champagne moments,” about elevating the ordinary and making it extraordinary. Any company can have these types of moments. It’s about elevating these moments and creating a human connection. The experience elevation model has five levers:

  1. Entice: Create the experience that will captivate your customers’ interest and make them pay attention to you.
  2. Invite: Communicate your offerings in a way that makes them feel exclusive and desirable. Make your customers feel special by making them feel as if they have been “invited” to do business with you. When possible, make it feel personal.
  3. Excite: The experience should be exciting enough to be share-worthy. If your customers are talking about you, you’ve triggered this level. James writes in her book, “When clients think of your brand, you want them to ask, with awe and wonder, ‘What else will they do?’”
  4. Delight: This lever comes from making a customer feel unique and special, offering excellent customer service and anticipating your customers’ needs.
  5. Ignite: This is where you create advocates. The experience is so good that customers want to tell others about you.

How IKEA Creates a Luxury Experience

While not traditionally associated with luxury, according to James, IKEA hits a number of luxury triggers. First, they engage all five senses—even taste and smell, thanks to the brand’s delicious Swedish meatballs. The in-store experience allows customers to touch fabrics and see how easy products are to assemble. Its use of “sensory elements” (touch, taste, smell, sight) makes shopping at an IKEA store feel special and memorable.

Additionally, there is the incredible experience of the IKEA effect, in which customers feel a sense of accomplishment when they assemble furniture themselves, creating more satisfaction than simply receiving pre-assembled furniture.

And to emphasize that luxury is about experiences, not things, James points out that luxury is not about the price tag. IKEA offers the luxury experience in a way that makes customers feel special, not just through expensive items. In short, it’s all about the experience.

Final Words

Don’t be fooled by the simplicity of the five characteristics of luxury or James’ luxury levers. They may seem like common sense, but common sense isn’t so common.

Dig into these ideas and strategize around how you can activate them throughout your customers’ journey. Ask yourself questions like, “What do we do to entice our customers?” “Do we make customers feel special, like they are invited guests?,” or “Are we creating the type of experience that our customers would want to tell others about?”

Questions like these will get you into a luxury mindset. Remember, the luxury experience is tied to the customer experience more than it is to fancy and expensive products.

This article was originally published on Forbes.com.

Image Credit: Shep Hyken

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