Making Empathy Your Secret Weapon

Making Empathy Your Secret WeaponGUEST POST from Greg Satell

When I first moved to Kyiv about 20 years ago, I met my friend Pavlo, who is from Belarus. Eventually our talk turned to that country’s leader, Alexander Lukashenko, and an incident in which he turned off the utilities at the US Ambassador’s residence, as well as those of other diplomats. It seemed totally outlandish and crazy to me.

“But he won,” Pavlo countered. I was incredulous, until he explained. “Lukashenko knows he’s a bastard and that the world will never accept him. In that situation all you can win is your freedom and that’s what he won.” It was a mode of thinking so outrageous and foreign to me that I could scarcely believe it.

Yet it opened my eyes and made me a more effective operator. We tend to think of empathy as an act of generosity, but it’s far more than that. Learning how to internalize diverse viewpoints is a skill we should learn not only because it helps make others more comfortable, but because it empowers us to successfully navigate an often complex and difficult world.

Identifying Shared Values

We all have ideas we feel passionately about and, naturally, we want others to adopt them. The ideas we believe in make up an important facet of our identity, dignity and sense of self. For me, as an American living in post-communist countries, the ideas embedded in democratic institutions were important and it was difficult for me to see things another way.

My conversation with Pavlo opened my eyes. Where I saw America and “the west” as a more just society, people in other parts of the world saw it as a dominant force that restricted their freedom. My big insight was that I didn’t need to agree with a perspective to understand, internalize, and leverage it as a shared value.

For example, once I was able to understand that some people saw Americans as powerful—something akin to an invading force—I was able to shed the feelings of vulnerability that arose from being in a strange and foreign land and focus on the shared value of safety in my dealings with others.

A great strategy for identifying shared values is to listen closely to what your opposition is saying. People say and do things because they believe they will be effective. Once I was able to stop dismissing Lukashenko as a corrupt thug, I was able to identify the issues surrounding safety and dominance that could be useful to me.

Building Shared Purpose

Using empathy to identify shared values is a crucial first step, but doesn’t achieve anything by itself. To move things forward, we need to build a shared purpose. Consider a famous study called the Robbers Cave Experiment, which involved 22 boys of similar religious, racial and economic backgrounds invited to spend a few weeks at a summer camp.

In the first phase, they were separated into two groups of “Rattlers” and “Eagles” that had little contact with each other. As each group formed its own identity, they began to display hostility on the rare occasions when they were together. During the second phase, the two groups were given competitive tasks and tensions boiled over, with each group name calling, sabotaging each other’s efforts and violently attacking one another.

In the third phase, the researchers attempted to reduce tensions. At first, they merely brought them into friendly contact, with little effect. The boys just sneered at each other. However, when they were tricked into challenging tasks where they were forced to work together in order to be successful, the tenor changed quickly. By the end of the camp the two groups had fallen into a friendly camaraderie.

As Francis Fukuyama writes in his recent book, “Identity can be used to divide, but it can also be used to integrate,” which is exactly what I found in my years working is foreign cultures. Once I was able to leverage shared values to create a shared purpose and began engaging in shared actions, that purpose and those actions became part of a shared identity. Yes, I was still an American, with American values and perspectives, but I became their American.

Overcoming Conflict By Designing A Dilemma

Unfortunately, building a shared purpose isn’t always possible. A simple truth is that humans build attachments to people, ideas and things. When those attachments are threatened, they will lash out. That’s why 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.

When that happens—and it always does eventually—we can get sucked into a conflict, which will likely take us off course and discredit what we’re trying to achieve. Yet, here too, developing empathy skills to identify shared values can be extremely helpful once we learn how to design a dilemma action, which puts the opponents into an impossible position.

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. Your opponent then has a choice: they can disrupt the act and violate the shared value or they can let it go forward and let change progress.

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, was dismissed from her position and everything fell into place after that.

Empathy Is Not Absolution

Empathy, as powerful as it can potentially be, is widely misunderstood. It is often paired with compassion in the context of creating a more beneficial workplace. That is, of course, a reasonable and worthy objective, but the one-dimensional use of the term is misleading and limits its value.

When seen only through the lens of making others more comfortable, empathy can seem like a “nice to have,” trait rather than a valuable competency and an important source of competitive advantage. It’s much easier to see the advantage of imposing your will, rather than internalizing the perspectives of others.

One thing I learned over many years living in foreign cultures is that it’s important to understand how people around you think, especially if you don’t agree with them and, as is sometimes the case, find their point of view morally reprehensible. In fact, learning more about how others think can make you a more effective leader, negotiator and manager.

Empathy is not absolution. You can internalize the ideas of others and still vehemently disagree. There is a reason that Special Forces are trained to understand the cultures in which they will operate and it isn’t because it makes them nicer people. It’s because it makes them more lethal operators.

It is only through empathy that we can understand motivations—for good or ill—and design effective strategies to build shared purpose or, if need be, design a dilemma for an opponent. To operate in an often difficult world, you need to understand your environment. That’s why building empathy skills can be like a secret weapon.

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

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Why Photonic Processors are the Nervous System of the Future

Illumination as Innovation

LAST UPDATED: January 2, 2026 at 4:59 PM

Why Photonic Processors are the Nervous System of the Future

GUEST POST from Art Inteligencia

In the landscape of 2026, we have reached a critical juncture in what I call the Future Present (which you can also think as the close-in future). Our collective appetite for intelligence — specifically the generative, agentic, and predictive kind — has outpaced the physical capabilities of our silicon ancestors. For decades, we have relied on electrons to do our bidding, pushing them through increasingly narrow copper gates. But electrons have a weight, a heat, and a resistance that is now leading us directly into the Efficiency Trap. If we want to move from change to change with impact, we must change the medium of the message itself.

Enter Photonic Processing. This is not merely an incremental speed boost; it is a fundamental shift from the movement of matter to the movement of light. By using photons instead of electrons to perform calculations, we are moving toward a world of near-zero latency and drastically reduced energy consumption. As a specialist in Human-Centered Innovation™, I see this not just as a hardware upgrade, but as a breakthrough for human potential. When computing becomes as fast as thought and as sustainable as sunlight, the barriers between human intent and innovative execution finally begin to dissolve.

“Innovation is not just about moving faster; it is about illuminating the paths that were previously hidden by the friction of our limitations. Photonic computing is the lighthouse that allows us to navigate the vast oceans of data without burning the world to power the voyage.” — Braden Kelley

The End of the Electronic Friction

The core problem with traditional electronic processors is heat. When you move electrons through silicon, they collide, generating thermal energy. This is why data centers now consume a staggering percentage of the world’s electricity. Photons, however, do not have a charge and essentially do not interact with each other in the same way. They can pass through one another, move at the speed of light, and carry data across vast “optical highways” without the parasitic energy loss that plagues copper wiring.

For the modern organization, this means computational abundance. We can finally train the massive models required for true Human-AI Teaming without the ethical burden of a massive carbon footprint. We can move from “batch processing” our insights to “living insights” that evolve at the speed of human conversation.

Case Study 1: Transforming Real-Time Healthcare Diagnostics

The Challenge: A global genomic research institute in early 2025 was struggling with the “analysis lag.” To provide personalized cancer treatment plans, they needed to sequence and analyze terabytes of data in minutes. Using traditional GPU clusters, the process took days and cost thousands of dollars in energy alone.

The Photonic Solution: By integrating a hybrid photonic-electronic accelerator, the institute was able to perform complex matrix multiplications — the backbone of genomic analysis — using light. The impact? Analysis time dropped from 48 hours to 12 minutes. More importantly, the system consumed 90% less power. This allowed doctors to provide life-saving prescriptions while the patient was still in the clinic, transforming a diagnostic process into a human-centered healing experience.

Case Study 2: Autonomous Urban Flow in Smart Cities

The Challenge: A metropolitan pilot program for autonomous traffic management found that traditional electronic sensors were too slow to handle “edge cases” in dense fog and heavy rain. The latency of sending data to the cloud and back created a safety gap that the corporate antibody of public skepticism used to shut down the project.

The Photonic Solution: The city deployed “Optical Edge” processors at major intersections. These photonic chips processed visual data at the speed of light, identifying potential collisions before a human eye or an electronic sensor could even register the movement. The impact? A 60% reduction in traffic incidents and a 20% increase in average transit speed. By removing the latency, they restored public trust — the ultimate currency of Human-Centered Innovation™.

Leading Companies and Startups to Watch

The race to light-speed computing is no longer a laboratory experiment. Lightmatter is currently leading the pack with its Envise and Passage platforms, which provide a bridge between traditional silicon and the photonic future. Celestial AI is making waves with their “Photonic Fabric,” a technology designed to solve the massive data-bottleneck in AI clusters. We must also watch Ayar Labs, whose optical I/O chiplets are being integrated by giants like Intel to replace copper connections with light. Finally, Luminous Computing is quietly building a “supercomputer on a chip” that promises to bring the power of a data center to a desktop-sized device, truly democratizing the useful seeds of invention.

Designing for the Speed of Light

As we integrate these photonic systems, we must be careful not to fall into the Efficiency Trap. Just because we can process data a thousand times faster doesn’t mean we should automate away the human element. The goal of photonic innovation should be to free us from “grunt work” — the heavy lifting of data processing — so we can focus on “soul work” — the empathy, ethics, and creative leaps that no processor, no matter how fast, can replicate.

If you are an innovation speaker or a leader guiding your team through this transition, remember that technology is a tool, but trust is the architect. We use light to see more clearly, not to move so fast that we lose sight of our purpose. The photonic age is here; let us use it to build a future that is as bright as the medium it is built upon.

Frequently Asked Questions

What is a Photonic Processor?

A photonic processor is a type of computer chip that uses light (photons) instead of electricity (electrons) to perform calculations and transmit data. This allows for significantly higher speeds, lower latency, and dramatically reduced energy consumption compared to traditional silicon chips.

Why does photonic computing matter for AI?

AI models rely on massive “matrix multiplications.” Photonic chips can perform these specific mathematical operations using light interference patterns at the speed of light, making them ideally suited for the next generation of Large Language Models and autonomous systems.

Is photonic computing environmentally friendly?

Yes. Because photons do not generate heat through resistance like electrons do, photonic processors require far less cooling and electricity. This makes them a key technology for sustainable innovation and reducing the carbon footprint of global data centers.

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: Google Gemini

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It’s Impossible to Innovate When …

It's Impossible to Innovate When ...

GUEST POST from Mike Shipulski

Your company believes everything should always go as planned.

You still have to do your regular job.

The project’s completion date is disrespectful of the work content.

Your company doesn’t recognize the difference between complex and complicated.

The team is not given the tools, training, time and a teacher.

You’re asked to generate 500 ideas but you’re afraid no one will do anything with them.

You’re afraid to make a mistake.

You’re afraid you’ll be judged negatively.

You’re afraid to share unpleasant facts.

You’re afraid the status quo will be allowed to squash the new ideas, again.

You’re afraid the company’s proven recipe for success will stifle new thinking.

You’re afraid the project team will be staffed with a patchwork of part time resources.

You’re afraid you’ll have to compete for funding against the existing business units.

You’re afraid to build a functional prototype because the value proposition is poorly defined.

Project decisions are consensus-based.

Your company has been super profitable for a long time.

The project team does not believe in the project.

Image credit: 1 of 1,000+ FREE quote slides available at http://misterinnovation.com

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How Empowered Are Your Employees?

How Empowered Are Your Employees?

Editor’s Note — Braden Kelley

What Does It Mean to Empower Employees?

Employee empowerment is the practice of giving employees the authority, information, and resources they need to make decisions and take action without requiring manager approval at every step. True empowerment is not a slogan or a cultural aspiration — it is a structural commitment, backed by policy, training, and leadership behavior that consistently reinforces the message that employees are trusted to act on behalf of the organization and its customers.

The business case for employee empowerment is direct: empowered employees resolve customer issues faster, create more memorable service experiences, and are significantly more engaged in their work. Gallup research consistently shows that organizations with highly engaged, empowered workforces see 21% higher profitability, 17% higher productivity, and dramatically lower turnover than those that manage through control and approval chains.

The most important — and most underappreciated — dimension of employee empowerment is its connection to customer experience. Disempowered employees cannot deliver empowered customer experiences. When frontline staff must escalate every exception, ask permission for every deviation, and enforce policies that obviously harm the customer relationship, customers experience that disempowerment directly. The best customer experience organizations understand that empowering employees is not a separate HR initiative from improving customer experience — it is the same initiative, approached from the inside out.

Shep Hyken’s article below explores what genuine employee empowerment looks like in practice through the lens of legendary customer service organizations — and why the stories organizations tell about employee empowerment become the DNA of their service culture.

GUEST POST from Shep Hyken

Earlier this year, I wrote a Forbes article celebrating the 50th anniversary of the famous Nordstrom story in which a man wanted to return a set of used tires – even though Nordstrom never even sold tires. That fact didn’t stop the employee from giving the customer a refund. Right or wrong, that story is still talked about 50 years later!

I’ve mentioned this story in the past, and the point is that stories like these become legends inside an organization, and if the brand is lucky, they may even get some good press. They are not easy to find, unless you intentionally look for them. Nordstrom had been in business for 75 years before this legendary story was discovered and shared.

I’ve written about many such stories. They are a reminder for every company to find its unique story that exemplifies the importance of customer service. These stories are powerful because they become a “north star” for how a company should treat its customers. Publicity is optional. The real value is cultural.

Nordstrom Tires Story Cartoon from Shep Hyken

For example, there are numerous Ritz-Carlton legendary stories, such as Joshie the Giraffe, in which the hotel staff made a big effort to return a stuffed animal to a child. There are also stories that aren’t so famous. I interviewed Horst Schulze, the first president and co-founder of the Ritz-Carlton, who shared a story about empowering employees to take care of their guests.

The short version is that the Ritz-Carlton allows employees to spend up to $2,000 to resolve a guest issue without seeking manager approval. One day a housekeeper found a guest’s computer. The guest had already checked out and flown from California to Hawaii. She took it upon herself to book an airline ticket and personally delivered the laptop to the guest.

As crazy as this may sound, the housekeeper was not reprimanded but instead was applauded for her efforts. Then, she was coached that next time, overnight shipping would be sufficient. The point is, there’s no risk in taking care of a guest. The story became a teaching moment for both the housekeeper and all Ritz staff, reinforcing the hotel chain’s commitment to empowered, guest-focused service.

Not every company will have a $2,000 empowerment policy like the Ritz-Carlton or a story like Nordstrom that literally defines their customer experience, but that doesn’t mean you can’t enjoy similar benefits.

So, here’s your assignment. Find your company’s legendary customer service story. If you don’t yet have one, start looking for those stories. Use them in training, meetings, and internal communications. Over time, they will become the DNA of your customer service culture. And who knows? Fifty years from now, someone might still be telling your story.

Put This Into Practice With the Change Planning Toolkit™

The frameworks in this article are part of the Human-Centered Change™ methodology — a visual, collaborative system of 70+ tools built around the Change Planning Canvas™. Every copy of Charting Change gives you access to 26 of the 70+ tools.

Image credits: Pexels, Shep Hyken

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Top 10 Human-Centered Change & Innovation Articles of December 2025

Top 10 Human-Centered Change & Innovation Articles of December 2025Drum 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 December’s ten most popular innovation posts:

  1. Is OpenAI About to Go Bankrupt? — by Chateau G Pato
  2. The Rise of Human-AI Teaming Platforms — by Art Inteligencia
  3. 11 Reasons Why Teams Struggle to Collaborate — by Stefan Lindegaard
  4. How Knowledge Emerges — by Geoffrey Moore
  5. Getting the Most Out of Quiet Employees in Meetings — by David Burkus
  6. The Wood-Fired Automobile — by Art Inteligencia
  7. Was Your AI Strategy Developed by the Underpants Gnomes? — by Robyn Bolton
  8. Will our opinion still really be our own in an AI Future? — by Pete Foley
  9. Three Reasons Change Efforts Fail — by Greg Satell
  10. Do You Have the Courage to Speak Up Against Conformity? — by Mike Shipulski

BONUS – Here are five more strong articles published in November 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 four years:

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Why Human Flourishing Decides Who Thrives in the Future

The human flourishing movement in modern leadership

GUEST POST from Robert B. Tucker

As we enter the final stretch of this decade, one reality is becoming impossible to ignore: innovation alone is no longer enough. In an era defined by compounding political, technological, demographic, and environmental disruption, the decisive question is not how fast we can change, but whether human beings can still flourish amidst all that change.

The gap that now matters most is not ideological or economic. It is the widening divide between those who are flourishing and those who are floundering.

For more than three decades, my work as a futurist has focused on helping leaders anticipate what’s next. But in recent years, the focus has shifted — from unleashing innovation to safeguarding something even more fundamental: human flourishing. With the publication of Build a Better Future: 7 Mindsets for Navigating the Age of Acceleration, flourishing has moved from the margins of leadership conversations to the center. It will also be the focus of my presentation at the Lead Where You Stand Conference next June in Santa Barbara.

Why flourishing? Because we are entering a decade in which there will be more change than in the prior 100 years. Gauging how we’re doing — not just financially, but emotionally, socially, and psychologically — will become essential. This isn’t hyperbole. It is the result of compounding mighty Mississippi Rivers of political, technological, generational, social, and environmental MegaForces of Change converging all at once.

Too Much Change, Too Fast

An Ipsos Global Trends Survey confirms what many of us feel intuitively: large segments of the population in advanced democracies are struggling to keep up with the pace of change. Seventy-five percent of respondents in Germany and nearly 90 percent in South Korea report that their world is changing too fast. In the United States, multiple studies suggest that a majority of adults are not truly flourishing. Among Gen Z, roughly 60 percent report high levels of anxiety, depression, or loneliness, with only about 39 percent thriving in recent surveys.

While political movements on the right have learned to weaponize this sense of unease, their proposed solution — reclaiming a mythologized past — has delivered few tangible results. Promises to fix healthcare, affordability, and government dysfunction ring hollow amid recurring shutdowns, widening inequality, and the erosion of basic social supports.

Dutch historian Rutger Bregman, speaking recently in a BBC lecture, described our moment as one of “wild possibilities.” Yet he also chastised today’s elites across the political spectrum for failing to help societies navigate these turbulent times. Drawing parallels to the decline of ancient Rome, Bregman points to cowardice, corruption, and “moral rot”: billionaires dodging taxes, politicians performing instead of governing, and media systems that profit from outrage and division.

“Today it is not the most capable who rise,” Bregman observed, “but the least scrupulous. Not the most virtuous, but the most brazen.”

What people yearn for — across cultures and ideologies — are leaders who deliver solutions, not slogans and insults. Leaders who can help societies navigate volatility while restoring a sense of agency, safety, and hope. The real divide today is not between red and blue or left and right. It is between those who are flourishing and those who feel left behind.

Enter The Human Flourishing Movement

That is where the Human Flourishing Movement comes in. Based out of research initiatives at Harvard and Baylor Universities, this growing effort seeks a broader and more holistic measure of what it means to thrive. As scholars dig deeper, flourishing has emerged as a more complete lens for understanding human potential — one that goes beyond income or productivity metrics.

True flourishing, according to this research, includes mental and physical health, but also meaning and purpose, strong relationships, character, and even spiritual fulfillment. In short, it is about building lives — and societies — that work.

Some of my futurist colleagues see artificial intelligence as a powerful catalyst for human flourishing. Tech visionaries speak enthusiastically about abundance, the end of work, and a New Renaissance driven by AI-enabled creativity. Zack Kass, former Head of Go-To-Market at OpenAI, has articulated visions that go well beyond productivity gains—imagining breakthroughs that elevate human potential itself.

Yet for many people, the darker realities of AI are far more tangible than its promises. In 2025 alone, more than 1.1 million layoffs have been announced. Anxiety has become the new workplace pandemic, as forecasts suggest that up to half of all entry-level jobs could disappear within a few short years. At the same time, digital technologies are weakening our ability to communicate, collaborate, and act in the common good.

Our central challenge is this: our technological prowess — and too often our greed — has outpaced our commitment to human flourishing. We can split atoms, edit genes, and build machines that rival human intelligence. What we have not yet done is articulate a compelling, inclusive vision of a better future for all.

That, ultimately, is the work ahead. And it is work that leaders in business, government, and civil society can no longer afford to postpone. If the Age of Acceleration has taught us anything, it is that the future does not simply happen to us — we help shape it by the mindsets we adopt and the choices we make. Human flourishing is not a soft aspiration; it is a strategic imperative. The leaders who will matter most in the decade ahead will be those who can combine foresight with humanity, innovation with purpose, and speed with wisdom. Building a better future begins not with grand promises, but with the daily practice of thinking — and leading — differently.

This article originally appeared in Forbes

Image credit: Pexels

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What Are We Going to Do Now with GenAI?

What Are We Going to Do Now With GenAI?

GUEST POST from Geoffrey A. Moore

In 2023 we simply could not stop talking about Generative AI. But in 2024 the question for each enterprise became (continuing to today) — and this includes yours as well — is What are we going to do about it? Tough questions call for tough frameworks, so let’s run this one through the Hierarchy of Powers to see if it can shine some light on what might be your company’s best bet.

Category Power

Gen AI can have an impact anywhere in the Category Maturity Life Cycle, but the way it does so differs depending on where your category is, as follows:

  • Early Market. GenAI will almost certainly be a differentiating ingredient that is enabling a disruptive innovation, and you need to be on the bleeding edge. Think ChatGPT.
  • Crossing the chasm. Nailing your target use case is your sole priority, so you would use GenAI if, and only if, it helped you do so, and avoid getting distracted by its other bells and whistles. Think Khan Academy at the school district level.
  • Inside the tornado. Grabbing as much market share as you can is now the game to play, and GenAI-enabled features can help you do so provided they are fully integrated (no “some assembly required”). You cannot afford to slow your adoption down just at the time it needs to be at full speed. Think Microsoft CoPilot.
  • Growth Main Street (category still growing double digits). Market share boundaries are settling in, so the goal now is to grow your patch as fast as you can, solidifying your position and taking as much share as you can from the also-rans. Adding GenAI to the core product can provide a real boost as long as the disruption is minimal. Think Salesforce CRM.
  • Mature Main Street (category stabilized, single-digit growth). You are now marketing primarily to your installed base, secondarily seeking to pick up new logos as they come into play. GenAI can give you a midlife kicker provided you can use it to generate meaningful productivity gains. Think Adobe Photoshop.
  • Late Main Street (category declining, negative growth). The category has never been more profitable, so you are looking to extend its life in as low-cost a way as you can. GenAI can introduce innovative applications that otherwise would never occur to your end users. Think HP home printing.

Company Power

There are two dimensions of company power to consider when analyzing the ROI from a GenAI investment, as follows:

  • Market Share Status. Are you the market share leader, a challenger, or simply a participant? As a challenger, you can use GenAI to disrupt the market pecking order provided you differentiate in a way that is challenging for the leader to copy. On the other hand, as a leader, you can use GenAI to neutralize the innovations coming from challengers provided you can get it to market fast enough to keep the ecosystem in your camp. As a participant, you would add GenAI only if was your single point of differentiation (as a low-share participant, your R&D budget cannot fund more than one).
  • Default Operating Model. Is your core business better served by the complex systems operating model (typical for B2B companies with hundreds to thousands of large enterprises for customers) or the volume operations operating model (typical for B2C companies with hundreds of thousands to millions of consumers)? The complex systems model has sufficient margins to invest professional services across the entire ownership life cycle, from design consulting to installation to expansion. You are going to need deep in-house expertise to win big in this game. By contrast, GenAI deployed via the volume operations model has to work out-of-the-box. Consumers have neither the courage nor the patience to work through any disconnects.

Market Power

Whereas category share leaders benefit most from going broad, market segment leaders win big by going deep. The key tactic is to overdo it on the use cases that mean the most to your target customers, taking your offer beyond anything reasonable for a category leader to copy. GenAI can certainly be a part of this approach, as the two slides below illustrate:

Market Segmentation for Complex Systems

In the complex systems operating model, GenAI should accentuate the differentiation of your whole product, the complete solution to whatever problem you are targeting. That might mean, for example, taking your Large Language Model to a level of specificity that would normally not be warranted. This sets you apart from the incumbent vendor who has nothing like what you offer as well as from other technology vendors who have not embraced your target segment’s specific concerns. Think Crowdstrike’s Charlotte AI for cybersecurity analysis.

Market Segmentation for Volume Operations

In the volume operations operating model, GenAI should accentuate the differentiation of your brand promise by overdelivering on the relevant value discipline. Once again, it is critical not to get distracted by shiny objects—you want to differentiate in one quadrant only, although you can use GenAI in the other three for neutralization purposes. For Performance, think knowledge discovery. For Productivity, think writing letters. For Economy, think tutoring. For Convenience, think gift suggestions.

Offer Power

Everybody wants to “be innovative,” but it is worth stepping back a moment to ask, how do we get a Return on Innovation? Compared to its financial cousin, this kind of ROI is more of a leading indicator and thus of more strategic value. Basically, it comes in three forms:

  1. Differentiation. This creates customer preference, the goal being not just to be different but to create a clear separation from the competition, one that they cannot easily emulate. Think OpenAI.
  2. Neutralization. This closes the gap between you and a competitor who is taking market share away from you, the goal being to get to “good enough, fast enough,” thereby allowing your installed base to stay loyal. Think Google Bard.
  3. Optimization. This reduces the cost while maintaining performance, the goal being to expand the total available market. Think Edge GenAI on PCs and Macs.

For most of us, GenAI will be an added ingredient rather than a core product, which makes the ROI question even more important. The easiest way to waste innovation dollars is to spend them on differentiation that does not go far enough, neutralization that does not go fast enough, or optimization that does not go deep enough. So, the key lesson here is, pick one and only one as your ROI goal, and then go all in to get a positive return.

Execution Power

How best to incorporate GenAI into your existing enterprise depends on which zone of operations you are looking to enhance, as illustrated by the zone management framework below:

Zone Management Framework

If you are unsure exactly what to do, assign the effort to the Incubation Zone and put them on the clock to come up with a good answer as fast as possible. If you can incorporate it directly into your core business’s offerings at relatively low risk, by all means, do so as it is the current hot ticket, and assign it to the Performance Zone. If there is not a good fit, consider using it internally instead to improve your own productivity, assigning it to the Productivity Zone. Finally, although it is awfully early days for this, if you are convinced it is an absolutely essential ingredient in a big bet you feel compelled to make, then assign it to the Transformation Zone and go all in. Again, the overall point is manage your investment in GenAI out of one zone and only one zone, as the success metrics for each zone are incompatible with those of the other three.

One final point. Embracing anything as novel as GenAI has to feel risky. I submit, however, that in 2025 not building upon meaningful GenAI action taken in 2024 is even more so.

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

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Why Everyone’s Future Depends on Strategic Imagination

Why Everyone's Future Depends on Strategic Imagination

GUEST POST from Robert B. Tucker

One of the most valuable practices I encourage my corporate clients to adopt is shifting constantly between the demands of the present state and the possibilities within their future state.

The ability to mentally step out of today’s activities and spend time visualize tomorrow’s opportunities is a discipline shared by the innovators, former prisoners of war, elite athletes, and top performers I’ve interviewed throughout my career. They imagine the future as they want it to unfold, and once that picture is clear, the next essential step is developing a strategy to bring it to life.

Innovators think strategically even when the chips are down. When change was slow and largely linear, strategy was optional. But in an era of accelerating disruption, crafting and executing a personal strategy is not just advisable – it’s necessary. Webster defines strategy as the art of maneuvering forces into the most advantageous position prior to engagement with the enemy. Today the “enemy” is complacency: resistance to change, isolation, a poor information diet, and the false comfort of familiar routines. Your strategy shapes how you respond to the unexpected, but just as importantly, it guides you toward opportunity and helps you make your own luck.

Change Your Narrative with Strategic Imagination

What can you do to ensure your relevance and viability in a post-pandemic, fast-shifting world? The first step is to begin incorporating yourself mentally. Think of yourself as You, Incorporated. A global enterprise with one employee and one mission: your long-term growth. You may currently serve a single client, your employer, but your unique mix of capabilities, experiences, and aspirations belongs to You, Inc. Research suggests most knowledge workers will have five careers in their lifetime. That makes it vital to continually build the skills and aptitudes you’ll need for your next move.

Equally important is becoming a lifelong learner. Not long ago, what you learned in school could carry you for decades. No longer. We now generate 2.5 quintillion bytes of information daily, and the pandemic only accelerated the knowledge explosion. Endless Zoom calls and digital distractions create the illusion of keeping up while pulling our attention toward celebrity dramas and political theater instead of toward meaningful signals of change. The innovators I’ve interviewed learn out of curiosity, not fear. They binge-learn. They discover a topic, plunge into it, consume the books, devour the articles, and seek out experts who stretch their thinking.

Another vital mindset is to consider yourself a student of change. Recent history — from 9/11 to Moore’s Law to the global financial crisis to COVID-19 — reminds us that no one is insulated. Events far beyond your industry or geography can reshape your career, your livelihood, and your future. Innovators are students of the past and the future. They monitor technological, political, demographic, social, and environmental shifts because they understand these forces can upend markets overnight.

The next dimension is managing your mental environment. When IBM surveyed 1500 CEOs about the most essential leadership trait in a fast-changing world, they named creativity. Whether you’re running a company, leading a team, or guiding a household, your effectiveness hinges on the quality of your mental inputs. By curating your information diet — choosing what you read, watch, and think about — you shape the conditions for your own future success.

Finally, unleash your inner visionary. We have never needed visionaries more. Vision isn’t limited to think tanks or ivy-covered institutions; it’s a mindset available to anyone willing to imagine what will be needed next. My hometown of Santa Barbara is praised for its vision because, after a devastating earthquake in 1924, leaders re-imagined the city’s architectural future with intention and aesthetic ambition. Their vision shaped a place now known for its distinctive Spanish-style architecture and rare sense of coherence.

To tap your own visionary potential, reflect on the question: What do you see when someone asks about your next breakthrough idea? The answer reveals not just your imagination, but the future you are preparing to create.

This article originally appeared in Forbes

Image credit: Pixabay

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Can AI Replace the CEO?

A Day in the Life of the Algorithmic Executive

LAST UPDATED: December 28, 2025 at 1:56 PM

Can AI Replace the CEO?

GUEST POST from Art Inteligencia

We are entering an era where the corporate antibody – that natural organizational resistance to disruptive change – is meeting its most formidable challenger yet: the AI CEO. For years, we have discussed the automation of the factory floor and the back office. But what happens when the “useful seeds of invention” are planted in the corner office?

The suggestion that an algorithm could lead a company often triggers an immediate emotional response. Critics argue that leadership requires soul, while proponents point to the staggering inefficiencies, biases, and ego-driven errors that plague human executives. As an advocate for Innovation = Change with Impact, I believe we must look beyond the novelty and analyze the strategic logic of algorithmic leadership.

“Leadership is not merely a collection of decisions; it is the orchestration of human energy toward a shared purpose. An AI can optimize the notes, but it cannot yet compose the symphony or inspire the orchestra to play with passion.”

Braden Kelley

The Efficiency Play: Data Without Drama

The argument for an AI CEO rests on the pursuit of Truly Actionable Data. Humans are limited by cognitive load, sleep requirements, and emotional variance. An AI executive, by contrast, operates in Future Present mode — constantly processing global market shifts, supply chain micro-fluctuations, and internal sentiment analysis in real-time. It doesn’t have a “bad day,” and it doesn’t make decisions based on who it had lunch with.

Case Study 1: NetDragon Websoft and the “Tang Yu” Experiment

The Experiment: A Virtual CEO in a Gaming Giant

In 2022, NetDragon Websoft, a major Chinese gaming and mobile app company, appointed an AI-powered humanoid robot named Tang Yu as the Rotating CEO of its subsidiary. This wasn’t just a marketing stunt; it was a structural integration into the management flow.

The Results

Tang Yu was tasked with streamlining workflows, improving the quality of work tasks, and enhancing the speed of execution. Over the following year, the company reported that Tang Yu helped the subsidiary outperform the broader Hong Kong stock market. By serving as a real-time data hub, the AI signature was required for document approvals and risk assessments. It proved that in data-rich environments where speed of iteration is the primary competitive advantage, an algorithmic leader can significantly reduce operational friction.

Case Study 2: Dictador’s “Mika” and Brand Stewardship

The Challenge: The Face of Innovation

Dictador, a luxury rum producer, took the concept a step further by appointing Mika, a sophisticated female humanoid robot, as their CEO. Unlike Tang Yu, who worked mostly within internal systems, Mika serves as a public-facing brand steward and high-level decision-maker for their DAO (Decentralized Autonomous Organization) projects.

The Insight

Mika’s role highlights a different facet of leadership: Strategic Pattern Recognition. Mika analyzes consumer behavior and market trends to select artists for bottle designs and lead complex blockchain-based initiatives. While Mika lacks human empathy, the company uses her to demonstrate unbiased precision. However, it also exposes the human-AI gap: while Mika can optimize a product launch, she cannot yet navigate the nuanced political and emotional complexities of a global pandemic or a social crisis with the same grace as a seasoned human leader.

Leading Companies and Startups to Watch

The space is rapidly maturing beyond experimental robot figures. Quantive (with StrategyAI) is building the “operating system” for the modern CEO, connecting KPIs to real-work execution. Microsoft is positioning its Copilot ecosystem to act as a “Chief of Staff” to every executive, effectively automating the data-gathering and synthesis parts of the role. Watch startups like Tessl and Vapi, which are focusing on “Agentic AI” — systems that don’t just recommend decisions but have the autonomy to execute them across disparate platforms.

The Verdict: The Hybrid Future

Will AI replace the CEO? My answer is: not the great ones. AI will certainly replace the transactional CEO — the executive whose primary function is to crunch numbers, approve budgets, and monitor performance. These tasks are ripe for automation because they represent 19th-century management techniques.

However, the transformational CEO — the one who builds culture, navigates ethical gray areas, and creates a sense of belonging — will find that AI is their greatest ally. We must move from fearing replacement to mastering Human-AI Teaming. The CEOs of 2030 will be those who use AI to handle the complexity of the business so they can focus on the humanity of the organization.

Frequently Asked Questions

Can an AI legally serve as a CEO?

Currently, most corporate law jurisdictions require a natural person to serve as a director or officer for liability and accountability reasons. AI “CEOs” like Tang Yu or Mika often operate under the legal umbrella of a human board or chairman who retains ultimate responsibility.

What are the biggest risks of an AI CEO?

The primary risks include Algorithmic Bias (reinforcing historical prejudices found in the data), Lack of Crisis Adaptability (AI struggles with “Black Swan” events that have no historical precedent), and the Loss of Employee Trust if leadership feels cold and disconnected.

How should current CEOs prepare for AI leadership?

Leaders must focus on “Up-skilling for Empathy.” They should delegate data-heavy reporting to AI systems and re-invest that time into Culture Architecture and Change Management. The goal is to become an expert at Orchestrating Intelligence — both human and synthetic.

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: Google Gemini

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AI Stands for Accidental Innovation

LAST UPDATED: December 29, 2025 at 12:49 PM

AI Stands for Accidental Innovation

GUEST POST from Art Inteligencia

In the world of corporate strategy, we love to manufacture myths of inevitable visionary genius. We look at the behemoths of today and assume their current dominance was etched in stone a decade ago by a leader who could see through the fog of time. But as someone who has spent a career studying Human-Centered Innovation and the mechanics of innovation, I can tell you that the reality is often much messier. And this is no different when it comes to artificial intelligence (AI), so much so that it could be said that AI stands for Accidental Innovation.

Take, for instance, the meteoric rise of Nvidia. Today, they are the undisputed architects of the intelligence age, a company whose hardware powers the Large Language Models (LLMs) reshaping our world. Yet, if we pull back the curtain, we find a story of survival, near-acquisitions, and a heavy dose of serendipity. Nvidia didn’t build their current empire because they predicted the exact nuances of the generative AI explosion; they built it because they were lucky enough to have developed technology for a completely different purpose that happened to be the perfect fuel for the AI fire.

“True innovation is rarely a straight line drawn by a visionary; it is more often a resilient platform that survives its original intent long enough to meet a future it didn’t expect.”

Braden Kelley

The Parallel Universe: The Meta/Oculus Near-Miss

It is difficult to imagine now, but there was a point in the Future Present where Nvidia was seen as a vulnerable hardware player. In the mid-2010s, as the Virtual Reality (VR) hype began to peak, Nvidia’s focus was heavily tethered to the gaming market. Internal histories and industry whispers suggest that the Oculus division of Meta (then Facebook) explored the idea of acquiring or deeply merging with Nvidia’s core graphics capabilities to secure their own hardware vertical.

At the time, Nvidia’s valuation was a fraction of what it is today. Had that acquisition occurred, the “Corporate Antibodies” of a social media giant would likely have stifled the very modularity that makes Nvidia great today. Instead of becoming the generic compute engine for the world, Nvidia might have been optimized—and narrowed—into a specialized silicon shop for VR headsets. It was a sliding doors moment for the entire tech industry. By not being acquired, Nvidia maintained the autonomy to follow the scent of demand wherever it led next.

Case Study 1: The Meta/Oculus Intersection

Before the “Magnificent Seven” era, Nvidia was struggling to find its next big act beyond PC gaming. When Meta acquired Oculus, there was a desperate need for low-latency, high-performance GPUs to make VR viable. The relationship between the two companies was so symbiotic that some analysts argued a vertical integration was the only logical step. Had Mark Zuckerberg moved more aggressively to bring Nvidia under the Meta umbrella, the GPU might have become a proprietary tool for the Metaverse. Because this deal failed to materialize, Nvidia remained an open ecosystem, allowing researchers at Google and OpenAI to eventually use that same hardware for a little thing called a Transformer model.

The Crypto Catalyst: A Fortuitous Detour

The second major “accident” in Nvidia’s journey was the Cryptocurrency boom. For years, Nvidia’s stock and production cycles were whipped around by the price of Ethereum. To the outside world, this looked like a distraction—a volatile market that Nvidia was chasing to satisfy shareholders. However, the crypto miners demanded exactly what AI would later require: massive, parallel processing power and specialized chips (ASICs and high-end GPUs) that could perform simple calculations millions of times per second.

Nvidia leaned into this demand, refining their CUDA platform and their manufacturing scale. They weren’t building for LLMs yet; they were building for miners. But in doing so, they solved the scalability problem of parallel computing. When the “AI Winter” ended and the industry realized that Deep Learning was the path forward, Nvidia didn’t have to invent a new chip. They just had to rebrand the one they had already perfected for the blockchain. Preparation met opportunity, but the opportunity wasn’t the one they had initially invited to the dance.

Case Study 2: From Hashes to Tokens

In 2021, Nvidia’s primary concern was “Lite Hash Rate” (LHR) cards to deter crypto miners so gamers could finally buy GPUs. This era of forced scaling forced Nvidia to master the art of data-center-grade reliability. When ChatGPT arrived, the transition was seamless. The “Accidental Innovation” here was that the mathematical operations required to verify a block on a chain are fundamentally similar to the vector mathematics required to predict the next word in a sentence. Nvidia had built the world’s best token-prediction machine while thinking they were building the world’s best ledger-validation machine.

Leading Companies and Startups to Watch

While Nvidia currently sits on the throne of Accidental Innovation, the next wave of change-makers is already emerging by attempting to turn that accident into a deliberate architecture. Cerebras Systems is building “wafer-scale” engines that dwarf traditional GPUs, aiming to eliminate the networking bottlenecks that Nvidia’s “accidental” legacy still carries. Groq (not to be confused with the AI model) is focusing on LPU (Language Processing Units) that prioritize the inference speed necessary for real-time human interaction. In the software layer, Modular is working to decouple the AI software stack from specific hardware, potentially neutralizing Nvidia’s CUDA moat. Finally, keep an eye on CoreWeave, which has pivoted from crypto mining to become a specialized “AI cloud,” proving that Nvidia’s accidental path is a blueprint others can follow by design.

The Human-Centered Conclusion

We must stop teaching innovation as a series of deliberate masterstrokes. When we do that, we discourage leaders from experimenting. If you believe you must see the entire future before you act, you will stay paralyzed. Nvidia’s success is a testament to Agile Resilience. They built a powerful, flexible tool, stayed independent during a crucial acquisition window, and were humble enough to let the market show them what their technology was actually good for.

As we move into this next phase of the Future Present, the lesson is clear: don’t just build for the world you see today. Build for the accidents of tomorrow. Because in the end, the most impactful innovations are rarely the ones we planned; they are the ones we were ready for.

Frequently Asked Questions

Why is Nvidia’s success considered “accidental”?

While Nvidia’s leadership was visionary in parallel computing, their current dominance in AI stems from the fact that hardware they optimized for gaming and cryptocurrency mining turned out to be the exact architecture needed for Large Language Models (LLMs), a use case that wasn’t the primary driver of their R&D for most of their history.

Did Meta almost buy Nvidia?

Historical industry analysis suggests that during the early growth of Oculus, there were significant internal discussions within Meta (Facebook) about vertically integrating hardware. While a formal acquisition of the entire Nvidia corporation was never finalized, the close proximity and the potential for such a deal represent a “what if” moment that would have fundamentally changed the AI landscape.

What is the “CUDA moat”?

CUDA is Nvidia’s proprietary software platform that allows developers to use GPUs for general-purpose processing. Because Nvidia spent years refining this for various industries (including crypto), it has become the industry standard. Most AI developers write code specifically for CUDA, making it very difficult for them to switch to competing chips from AMD or Intel.

Image credits: Google Gemini

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