What is the right time horizon for technology development?

What is the right time horizon for technology development?

GUEST POST from Mike Shipulski

Patents are the currency of technology and profits are the currency of business. And as it turns out, if you focus on creating technology you’ll get technology (and patents) and if you focus on profits you’ll get profits. But if no one buys your technology (in the form of the products or services that use it), you’ll go out of business. And if you focus exclusively on profits you won’t create technology and you’ll go out of business. I’m not sure which path is faster or more dangerous, but I don’t think it matters because either way you’re out of business.

It’s easy to measure the number of patents and easier to measure profits. But there’s a problem. Not all patents (technologies) are equal and not all profits are equal. You can have a stockpile of low-level patents that make small improvements to existing products/services and you can have a stockpile of profits generated by short-term business practices, both of which are far less valuable than they appear. If you measure the number of patents without evaluating the level of inventiveness, you’re running your business without a true understanding of how things really are. And if you’re looking at the pile of profits without evaluating the long-term viability of the engine that created them you’re likely living beyond your means.

In both cases, it’s important to be aware of your time horizon. You can create incremental technologies that create short term wins and consume all your resource so you can’t work on the longer-term technologies that reinvent your industry. And you can implement business practices that eliminate costs and squeeze customers for next-quarter sales at the expense of building trust-based engines of growth. It’s all about opportunity cost.

It’s easy to develop technologies and implement business processes for the short term. And it’s equally easy to invest in the long term at the expense of today’s bottom line and payroll. The trick is to balance short against long.

And for patents, to achieve the right balance rate your patents on the level of inventiveness.

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Five Ways to React to Online Customer Feedback

Five Ways to React to Online Customer Feedback

GUEST POST from Shep Hyken

It’s one thing to listen to what your customers are saying when they reach out to you directly through calls, emails, texts, or direct messages. But many customers prefer to “go social” and comment on social media, review sites, and online forums.

So the question is, “Are you listening?”

By “listening,” I mean social listening, paying attention to what customers are saying about you everywhere except directly to you.

In the past month, I’ve been asked twice about social listening, responding to surveys, and monitoring online comments and reviews. However, let me emphasize that comments and reviews are not limited to the typical review sites, such as Google, Yelp, TripAdvisor, and others. Your customers will also share comments on Facebook, Instagram, and other social media sites.

So, even though we call it social listening, a better name might be social reacting. If you take the time to “listen,” which means reading or watching what customers are saying about you, it is in your best interest to react with an appropriate response.

Negative Reviews Shep Hyken Cartoon

While I believe you should respond to all comments and reviews, it’s especially important to respond to the negative. By the way, negative reviews aren’t so bad. In one of my articles about embracing negative reviews, I mentioned that a perfect five-star rating causes some customers to think, “This is too good to be true.” Perfection is not reality, and customers know this.

With that in mind, here are five social reaction strategies and tactics:

  1. React to Positive Comments: A short thank you is appropriate. If you can personalize it, even better.
  2. React to Negative Comments: As mentioned, it’s especially important to respond to negative reviews and comments, and I’ll add, in a timely fashion. The sooner the better. This adds a sense of urgency and creates credibility. If possible, take the complaint “offline” and deal directly with the customer. Then return to the site where the comment or review was first shared and let the world know you resolved the issue.
  3. React to Unreasonable Comments: Not every comment will be reasonable. Some people will be unreasonable. A simple and professional response is appropriate. Offer a way for the customer to contact you directly. Don’t be defensive, or you’ll add fuel to the fire.
  4. It’s Okay to Use AI and Templates When Reacting: Depending on how many comments you get, AI and templates can save you time. But, make sure to customize them to the situation. Don’t just copy and paste comments. Customers will notice.
  5. Treat Customer Comments as Learning Opportunities: This idea goes beyond social channels and review sites. Any comment that comes your way, positive or negative, is a learning opportunity. If you get negative feedback, find ways to prevent it from happening again. If the feedback is positive, find ways to make sure it always happens.

Companies spend a lot of money to get customers to notice them through marketing and advertising. Don’t waste that investment by not considering social reacting as part of your marketing and customer experience (CX) plan.

Image credits: Pixabay

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Innovation Lessons from the 50 Most Admired Companies of 2026

The Architecture of Admiration

LAST UPDATED: February 18, 2026 at 2:22 PM

Innovation Lessons from the 50 Most Admired Companies of 2026

by Braden Kelley and Art Inteligencia

Every year, the Fortune World’s Most Admired Companies list serves as a masterclass in reputation management. In 2026, the stakes have shifted. We are no longer just looking at who can build a better widget; we are looking at who can navigate the “perpetual pivot.”

“Innovation is no longer a department — it is a survival reflex built on human trust.”

— Braden Kelley

The 2026 All-Star Circle

  1. Apple
  2. Microsoft
  3. Amazon.com
  4. Nvidia
  5. JPMorgan Chase
  6. Berkshire Hathaway
  7. Costco Wholesale
  8. Alphabet
  9. Walmart
  10. American Express
  11. Delta Air Lines
  12. Netflix
  13. Coca-Cola
  14. Marriott International
  15. Walt Disney
  16. Goldman Sachs Group
  17. Eli Lilly
  18. FedEx
  19. Procter & Gamble
  20. Salesforce
  21. Home Depot
  22. BlackRock
  23. Toyota Motor
  24. Singapore Airlines
  25. Nike
  26. BMW
  27. USAA
  28. Starbucks
  29. Johnson & Johnson
  30. Morgan Stanley
  31. Bank of America
  32. IBM
  33. Accenture
  34. Caterpillar
  35. Visa
  36. Taiwan Semiconductor
  37. Samsung Electronics
  38. ServiceNow
  39. Danaher
  40. Mastercard
  41. L’Oréal
  42. Lowe’s Companies, Inc.
  43. UPS
  44. GE Aerospace
  45. Airbus
  46. Pfizer
  47. Lockheed Martin
  48. Advanced Micro Devices (AMD)
  49. Workday
  50. Publix Super Markets

The companies that stay on this list aren’t just “big”; they are masters of Human-Centered Innovation. They create environments where the cost of failure is lower than the cost of standing still.

Case Study: Walmart (No. 9)

The AI-Augmented Associate

Walmart has successfully rewired retail by treating its massive physical footprint as an innovation asset. In 2026, their “Agentic AI” assistant, Sparky, manages everything from grocery budgets to real-time meal planning.

The Human Shift: Rather than replacing staff, Walmart used AI to automate the “drudge work” of inventory scanning. This freed 1.5 million associates to focus on higher-value human interaction, proving that technology works best when it empowers people.

Case Study: Eli Lilly (No. 17)

Manufacturing the Future of Health

Eli Lilly’s rise into the top 20 is a story of manufacturing foresight. By partnering with Nvidia to build a DGX SuperPOD, they created the pharmaceutical industry’s most powerful AI supercomputer.

The Human Shift: Through “LillyDirect,” they bypassed traditional pharmacy friction. Innovation here wasn’t just the molecule; it was the Customer Experience of getting life-changing medication directly to those who need it.

Case Study: Nvidia (No. 4)

The Culture of Radical Openness

Nvidia’s meteoric rise to No. 4 isn’t just about GPUs; it’s about their organizational “operating system.” In 2026, CEO Jensen Huang has operationalized a culture where learning is a “group sport.”

The Human Shift: Nvidia avoids the “manager-as-gatekeeper” model. Feedback is a live, company-wide clinic where errors are dissected openly. By making it safe to fail in public, Nvidia accelerates the collective intelligence of the entire firm, ensuring they out-learn their competition every single day.

Case Study: Singapore Airlines (No. 24)

The Ultra-Long-Haul Experience

Ranking as the top airline and No. 24 overall, SIA has committed $1.1 billion to a massive retrofit of its Airbus A350 fleet, introducing Low Earth Orbit (LEO) satellite internet across all classes.

The Human Shift: SIA understands that in 2026, “luxury” means “continuity.” By providing broadband-speed Wi-Fi that allows for Zoom calls at 30,000 feet, they’ve solved the “digital isolation” problem of long-haul travel. They aren’t just flying planes; they are extending the passenger’s lifestyle into the clouds.

Why These Companies? The Innovation Multiplier

Innovation at the “Most Admired” level is about the Innovation Multiplier: the ability to apply new technology to old problems in a way that creates defensible value. Companies like Apple (No. 1) stay at the top because they wait until they can deliver the most human-centered version of a technology.

How the Rankings are Calculated:
To create the 2026 list, Fortune partnered with Korn Ferry to survey 3,700 executives, directors, and analysts. Starting with 1,500 candidates (the largest U.S. and Global 500 firms), respondents rated companies in their own industry on nine criteria: Innovation, People Management, Use of Corporate Assets, Social Responsibility, Quality of Management, Financial Soundness, Long-Term Investment Value, Quality of Products/Services, and Global Competitiveness. A company must score in the top half of its industry peer group to be listed.

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 and add citations.

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Digital Phenotyping and the Future of Preventative Experience Design

The Silent Pulse

LAST UPDATED: February 16, 2026 at 6:01 PM

Digital Phenotyping and the Future of Preventative Experience Design

GUEST POST from Art Inteligencia


I. Introduction: Beyond the Survey

The Death of “Self-Reporting”

For decades, the gold standard for understanding employee well-being or customer satisfaction has been the survey. We ask people how they feel, and they give us an answer filtered through their own biases, current mood, or what they think we want to hear. In the world of innovation, self-reporting is a lagging indicator — and a flawed one at that.

Defining Digital Phenotyping

We are entering the era of Digital Phenotyping: the moment-by-moment quantification of the individual-level human phenotype in situ using data from personal digital devices. By analyzing the “digital exhaust” from smartphones and wearables — mobility patterns, social interactions, and even typing rhythm — we can infer behavioral, emotional, and cognitive states with unprecedented accuracy.

The Paradigm Shift: From Reactive to Preventative

The true power of this technology lies in its ability to turn experience design from a reactive fix into a preventative strategy. We no longer have to wait for a “burnout crisis” or a drop in productivity to realize our team is under excessive stress. The signals are there, in real-time, hidden in the cadence of our digital lives.

“Innovation is about solving the problems that people haven’t yet found the words to describe. Digital Phenotyping gives us the ears to hear those unspoken needs.”
— Braden Kelley

As we move beyond the survey, we must lead with a human-centered lens. The goal isn’t to monitor; it’s to support. We are shifting from a world that reacts to failure to a world that senses — and sustains — human flourishing.

II. The Mechanics of Passive Sensing

Digital phenotyping relies on passive data — information collected in the background without requiring any active input from the user. This removes the “friction” of participation and provides a continuous stream of objective reality.

The Three Primary Data Streams

1. Mobility and Physical Activity

Using GPS and accelerometers, we can map “life space.” A sudden constriction in a person’s physical movement — fewer locations visited or reduced steps — can be a powerful proxy for depressive states or social withdrawal. Conversely, erratic movement patterns might signal high levels of anxiety or agitation.

2. Social and Communication Meta-data

This isn’t about what is being said, but how the person is interacting. Call frequency, text latency, and social media engagement patterns reveal shifts in social connectivity. A drop in outbound communication often precedes a burnout phase before the employee even feels “tired.”

3. Human-Computer Interaction (HCI)

The way we interact with our screens is a window into our cognitive health. Typing speed, the frequency of “backspacing,” and scrolling patterns can indicate cognitive overload or a lapse in focus. These “digital biomarkers” are the most immediate indicators of whether a task is designed for human success or human failure.

The Synthesis: From Signals to Insights

The magic happens in the AI synthesis layer. By correlating these streams, machine learning models can identify a “baseline” for an individual. When the data deviates from that baseline, the system identifies a “glitch” — a moment where the human-centered design of the environment is no longer supporting the human within it.

“Data is just a signal; insight is the story. In digital phenotyping, we are learning to read the stories written in the rhythm of our daily digital interactions.”
— Braden Kelley

III. Value Creation: Turning Insight into Action

The true ROI of digital phenotyping isn’t found in the data itself, but in the Experience Design it enables. By moving from reactive to preventative models, we can create environments that adapt to the human state in real-time.

Preventative Experience Design in Practice

Real-Time Burnout Mitigation

Imagine a project management tool that senses cognitive overload through typing patterns and erratic screen switching. Instead of pushing another notification, the system “softens” — delaying non-essential alerts and suggesting a recovery break. This is human-centered design in action: protecting the asset (the person) before the damage occurs.

Adaptive User Interfaces (AUI)

In high-stakes environments like healthcare or emergency response, digital phenotyping allows interfaces to simplify themselves when stress markers are detected. By reducing the “information density” during moments of high stress, we prevent human error and improve outcomes.

The Strategic Advantage of “Wellness as a Service”

Organizations that implement these tools as a benefit rather than a monitor will see a massive shift in retention and engagement. When an employee knows the “system” is looking out for their mental health — flagging potential depression signals or isolation patterns early — the relationship between employer and employee evolves from transactional to collaborative.

“Value in the future of work won’t be measured by output alone, but by the sustainability of the human spirit behind that output.”
— Braden Kelley

By leveraging these insights, we aren’t just innovating products; we are innovating the way we treat people.

IV. The Innovation Ethical Frontier

Digital phenotyping sits at the intersection of extreme utility and extreme vulnerability. As innovators, we must acknowledge that data is a surrogate for intimacy. When we measure a person’s gait or typing rhythm, we are entering their private mental space. Without a robust ethical framework, we risk building a “Digital Panopticon” rather than a supportive ecosystem.

The Three Pillars of Ethical Phenotyping

1. Radical Transparency & Consent

Standard “Terms and Conditions” are insufficient. Consent must be active and ongoing. Users should know exactly what biomarkers are being tracked and have the “Right to Disconnect” without penalty. Transparency isn’t just a legal hurdle; it’s a trust-building feature.

2. Purpose-Driven Data Minimization

The temptation to “collect it all” is the enemy of ethics. We must practice data minimalism: collecting only the specific signals required to provide the promised human-centered value. If a signal doesn’t directly contribute to a preventative intervention, it shouldn’t be gathered.

3. The “Benefit Flow” Guarantee

The value derived from the data must flow primarily back to the individual. If the organization is the only one benefiting (through higher productivity), it’s surveillance. If the individual benefits (through better mental health and reduced stress), it’s empowerment.

Leading with Empathy-Led Ethics

We must move beyond “compliance-based” privacy. In a human-centered organization, we ask: “Would our employees feel cared for or watched if they knew how this worked?” If the answer is “watched,” the innovation is flawed at the architectural level.

“Trust is the only currency that matters in the future of innovation. Once you spend it on surveillance, you can never buy it back.”
— Braden Kelley

By establishing these guardrails early, we ensure that digital phenotyping remains a tool for human flourishing rather than a weapon for corporate control.

V. Leading the Human-Centered Change

Implementing digital phenotyping is not a technical deployment; it is a cultural transformation. If leaders treat this like a software update, they will face immediate resistance. To succeed, we must lead with transparency and a clear focus on the “human” in human-centered innovation.

The Role of the “Architect” in Rollout

Leaders must act as the architects of trust. This means the Chief Innovation Officer and the CHRO must work in lockstep to ensure that the purpose of the data is clearly defined and that those definitions are unshakeable.

Strategies for Successful Integration:

  • The “Opt-In” Mandate: Never make passive sensing mandatory. The power of these tools comes from voluntary participation. When people choose to participate, they become stakeholders in their own well-being.
  • Stakeholder Education: We must educate every level of the organization — especially our “Sensors” (the employees) — on what digital biomarkers are and how they are used to trigger supportive interventions.
  • Feedback Loops: Create a mechanism where employees can provide feedback on the interventions. If a system suggests a “burnout break,” was it helpful or annoying? The human must remain the final authority.

Transparency as a Competitive Feature

In the future, the most successful organizations will be those that are radically transparent about their data practices. By being open about the algorithms and the “why” behind the sensing, we remove the mystery and the fear. Transparency turns a “black box” into a “glass box.”

“Change happens at the speed of trust. If you want to innovate at the edge of human behavior, you must first build a foundation of absolute integrity.”
— Braden Kelley

By focusing on the human-centered change, we ensure that digital phenotyping isn’t something done to people, but something done for them.

VI. Conclusion: Designing a More Intuitive World

The transition from reactive to preventative design represents one of the most significant leaps in the history of Human-Centered Innovation. Digital phenotyping allows us to stop guessing and start knowing — not for the sake of control, but for the sake of care.

The Future is Empathetic

We are moving toward a world where our tools understand our limits as well as we do. Imagine a workplace that recognizes your stress before you have a headache, or a digital assistant that knows you’re cognitively overloaded and helps you prioritize. This is the Intuitive World we are designing.

A Leader’s Final Responsibility

As innovators and leaders, our responsibility is to ensure that as our machines become more “human-literate,” we do not become less human in our leadership. Digital phenotyping is a tool of immense power. Used correctly, it can eradicate burnout, foster deep engagement, and support mental health on a global scale.

“The most advanced technology is the one that makes us feel most human. Our job is to ensure digital phenotyping does exactly that.”
— Braden Kelley

The signals are all around us, pulsing through the devices in our pockets and on our wrists. The question is no longer whether we can hear them, but whether we have the innovation leadership and ethical courage to act on what they are telling us.

Deep Dive: Frequently Asked Questions

Does Digital Phenotyping mean my boss is reading my texts?

Absolutely not. Ethical digital phenotyping focuses on metadata and patterns, not content. It looks at the frequency of communication or the speed of your typing, not the words you say. As an innovation leader, I advocate for systems where the content remains private and encrypted.

Why is this better than a monthly wellness survey?

Surveys are “lagging indicators” — they tell us how you felt in the past. By the time a survey is analyzed, burnout has often already occurred. Digital phenotyping provides real-time signals, allowing for immediate, helpful interventions that can prevent a crisis before it starts.

Can I opt-out of this kind of data collection?

In any human-centered organization, the answer must be yes. Trust is the foundation of innovation. For digital phenotyping to work, it must be an opt-in benefit that employees use because they see the value in their own well-being and professional growth.

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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Dualism is Bunk – Emergentism Rules!

Dualism is Bunk - Emergentism Rules!

GUEST POST from Geoffrey A. Moore

Readers of The Infinite Staircase (who are not many but whom I highly esteem) will know that it describes reality as constituted not of two but rather of eleven separate levels. At the bottom of the staircase is physics, all matter, no mind. At the top is theory, all mind, no matter. But there are nine layers in between, and here is the amazing thing. Each one is not only distinctly separable from the one above and below it, it is also defined by what I will call a characteristic attribute.

Now, getting that characteristic attribute right is no small feat, so consider the following a first cut at something that undoubtedly can be improved upon. (Start at the bottom and work your way up the staircase.)

OK, I am not crazy about the word imitational, but setting that aside, this list specifies an amazing amount of structure in a relatively confined space. All the italicized words represent fruitful fields of study in themselves, and taken together might constitute a Masters Degree in Reality.

Needless to say, every one of these claims is debatable, so let me just close by saying,

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

Image Credit: Geoffrey Moore

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

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

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

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

  1. Top 40 Innovation Authors of 2025 — Curated by Braden Kelley
  2. Trust is a Gold Mine for Organizations, but it Takes a Bit of Courage — by Oscar Amundsen
  3. Outcome-Driven Innovation in the Age of Agentic AI — by Braden Kelley
  4. Building Your Dream Organization — by Braden Kelley
  5. Why Photonic Processors are the Nervous System of the Future — by Art Inteligencia
  6. Reimagining Personalization — by Geoffrey Moore
  7. We Must Hold AI Accountable — by Greg Satell
  8. The Keys to Changing Someone’s Mind — by Greg Satell
  9. Concentrated Wealth, Consolidated Markets, and the Collapse of Innovation — by Art Inteligencia
  10. It’s Impossible to Innovate When … — by Mike Shipulski

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

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

Build a Common Language of Innovation on your team

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last five years:

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Your Feelings Are Often Triggers That Mislead You

Your Feelings Are Often Triggers That Mislead You

GUEST POST from Greg Satell

The social psychologist Jonathan Haidt developed the metaphor of the Elephant and the Rider to describe the relationship between our emotional and cognitive brains. While the rider (representing our cognitive brain) may feel in control, it is the elephant (our emotions) that is more likely to determine which direction we will go.

That’s why it feels so good to act on our emotions. Rather than struggling with the reins to get the elephant to go where we want it to, we can just give in and race with abandon towards our destination. It’s usually not until we’ve run off a cliff that we realize that we should have exercised more restraint. By that time, it’s often too late to undo the damage.

The truth is that our brains are wired for survival, not to make rational decisions for a modern, industrialized economy. That’s why we shouldn’t blindly trust our feelings. We should see them as warning signs to proceed with caution because, while they can alert us to unseen dangers, they can also be triggers that others use to manipulate us.

The Thrill Of The Shift & Pivot

As Eric Ries explained in The Startup Way, when General Electric CEO Jeffrey Immelt wanted to implement a more entrepreneurial approach he asked Ries to help him implement “Lean Startup” methods at the company. The resulting program, called Fastworks, trained 80 coaches and launched a hundred projects in its first year. Pretty soon, Immelt was calling his company a 124 year-old startup.

A key ambition was the development of Predix, an industrial software platform. No longer would GE be a boring old manufacturing company, but would make a “pivot” to the digital age. It did not go well. During Immelt’s tenure, the company’s value would fall by 30%, while the broader maker more than doubled. Eventually the firm would collapse altogether.

Pundits love to tout the change gospel, but there’s little evidence that “pivots” are necessarily a good idea. Look at the world’s most valuable companies, Apple still makes most of its money on iPhones, Microsoft’s success is still rooted in business software, Alphabet’s profits come from search and so on. There are exceptions, of course, but most organizations become and stay successful by deepening their capabilities in a few key areas.

But that’s boring. Journalists rarely write cover stories about it. Business school professors don’t get tenure for writing case studies about how Procter & Gamble stuck with soap for more than a century or how Coke continues to make money off of sugary water. “Pivots,” on the other hand, are thrilling and fun. They get people talking. They feel good. That’s why they’re so popular.

The Eden Myth

Watch pundits on cable news or on stage at conferences and you may begin to notice a familiar pattern. They tell us that once there was a period when everything was pure and good, but then we—or the organization we work for—were corrupted in some way and cast out. So to return to the good times, we need to eliminate that corrupting influence.

This Eden myth is as old as history itself and it continues to thrive because it works so well.. We’re constantly inundated with scapegoats— the government, big business, tech giants, the “billionaire” class, immigrants, “woke” society—to blame for our fall from grace. The story feeds our anger and, much like the “thrill of the pivot,” makes us want to act.

Perhaps most importantly, the Eden myth makes us feel good. The outrage it triggers stimulates the release of the neurotransmitter dopamine which affects the pleasure centers in our brain. Our adrenal glands then begin to produce cortisol, which initiates a “fight or flight” response. Our senses get heightened. We feel motivated and alive.

Who wouldn’t want to feel like that? That’s why we can become addicted to the outrage-dopamine response machine and continually look for new opportunities to get our fix. We begin to need it and tune in every night, doom scroll on social media and seek out social connections that promote it. Ultimately, we’re going to want to act on it.

People who seek to manipulate us know all about this and design their approach to trigger an emotional response.

Creating An Echo Chamber

Once our neurons are primed and our senses are tuned to respond to specific stimuli, we will begin to frame what we experience in terms that reinforce those biases. Psychologists have found that we tend to overweight information that is most easily accessible and then look for information to confirm those early impressions and ignore evidence to the contrary.

These effects are multiplied by tribal tendencies. We form group identities easily, and groups tend to develop into echo chambers, which amplify common beliefs and minimize contrary information. We also tend to share more actively with people who agree with us and, without fear of questioning or rebuke, we are less likely to check that information for accuracy.

We are highly affected by what those around us think. In fact, a series of famous experiments first performed in the 1950’s, and confirmed many times since then, showed that we will conform to the opinions of those around us even if they are obviously wrong. More recent research has found that the effect extends to three degrees of social distance.

It’s likely that some version of this is what doomed Jeffrey Immelt at General Electric. When he took over as CEO in 2001, Silicon Valley was in a process of renewal after the dotcom crash. As the startup boom gathered steam, it captured the imagination of business journalists. He brought in Ries to “cast out” the old ways of plodding, industrial firms and surrounded himself with people who believed similar things. Everything must have felt right.

The elephant was in full control and the rider just went along—all the way off the cliff.

Don’t Believe Everything You Feel

The neuroscientist Antonio Damasio believes we encode experiences in our bodies as somatic markers and that our emotions often alert us to things that our brains aren’t aware of. Another researcher, Joseph Ledoux, had similar findings. He pointed out that our body reacts much faster than our mind, such as when we jump out of the way of an oncoming object and only seconds later realize what happened.

Nobel Laureate Daniel Kahneman suggests that we have two modes of thinking. The first is emotive, intuitive and fast. The second is rational, deliberative and slow. Our bodies evolved to make decisions quickly in life or death situations. Our rational minds came much later and don’t automatically engage. It takes effort to bring in the second system.

There are some contexts in which we should favor system one over system two. Certain professions, such as surgeons and pilots, train for years to hone their instincts so that they will be able to react quickly and appropriately in an emergency. When we have a bad feeling about a situation, we should take it seriously and proceed with caution.

However, our feelings need to be interrogated, especially in areas for which we do not have specific training or relevant expertise. We need to gain insight into what exactly our feelings are alerting us to and that requires us to engage our rational brain.

Yes, feelings should be taken seriously. They are often telling us that something is amiss. But they are much more reliable when they are alerting us to danger than when they are pushing us to overlook pertinent facts and proceed with a course of action. When we go with our gut, we need to make sure it’s not just because we had a bad lunch.

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

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Managing B Players in the Pursuit of Excellence

Managing B Players in the Pursuit of Excellence

GUEST POST from David Burkus

When we talk about building high-performing teams, we tend to focus on the stars — the A players. These are the people who turn heads, drive results, and seemingly do the work of ten. They’re the ones we spotlight in meetings, promote quickly, and praise loudly.

But here’s what we often miss: it’s not just the A players that keep teams running. In fact, it’s the B players — yes, the so-called “average performers” — that are often the reason your company is still standing after a crisis and the reason your team is humming along today.

Surprised? Let’s talk about why B players might be the unsung heroes of your team — and what great leaders do to support them.

Why B Players Get Overlooked

We over-glorify A players for a lot of reasons. They’re visible. They’re charismatic. They get results. But they can also be volatile. A players burn out. They job-hop. And if we’re not careful, they create cultures that are high-performance… until they’re not. Because eventually, the instability catches up.

B players, by contrast, are consistent. Reliable. Thoughtful. They’re the ones who quietly get the work done. They don’t seek the spotlight, not because they’re less capable, but because they’re not interested in climbing the ladder just for the sake of it. They value balance. They want to do great work — and then go home and be present for the rest of their life.

And that’s not a weakness. In many ways, it’s wisdom.

The Peter Principle and the Trap of Promotion

Part of the reason we mismanage B players is because most career paths are still built on a single staircase: do good work, get promoted into management. But this structure leads us right into what Dr. Laurence J. Peter famously called the Peter Principle: in any hierarchy, people tend to get promoted to their level of incompetence.

Think about it: a top-performing engineer gets promoted into a managerial role…and suddenly spends all their time in meetings, writing budgets, managing people — and none of it leverages what made them successful in the first place.

It’s not that they’re incompetent. It’s that they’ve been promoted into a role that requires a different skill set — one they may not have, and often, don’t even want.

What makes B players so valuable is that many of them recognize this dynamic early. They choose to stay in the roles where they excel, where they’re engaged, and where they contribute meaningfully. They don’t take the bait of promotion for promotion’s sake. And that self-awareness makes them an asset — not a liability.

The Many Faces of a B Player

B players aren’t one-size-fits-all. Some are former A players who chose to step off the fast track for the sake of family, health, or sanity. Some are deeply mission-driven truth-tellers who care more about doing the right thing than climbing a corporate ladder. Others are the connectors — the people who know how everything (and everyone) fits together in your organization.

Think of the longtime office manager who can navigate the org chart better than anyone else. Or the behind-the-scenes analyst whose work drives key decisions. These aren’t future VPs, but they’re foundational. If they left, your team would feel the loss immediately.

So how do you support B players in a way that helps them thrive?

Step One: Give B Players Permission

Many B players aren’t disengaged — they’re just waiting for a green light. They know what to do. They see the solution. But they’re respectful. They’re not going to go rogue or overstep their role. What they need isn’t more direction — it’s permission.

Sometimes, all it takes is six words: “I trust you. Go for it.”

When leaders make it clear that judgment is trusted, that autonomy is welcomed, and that action is encouraged, B players shine. It’s not about micromanaging less — it’s about actively empowering more.

Step Two: Build B Players a Parallel Path

Most organizations treat advancement as a vertical path. If you want more recognition or compensation, you have to manage people. But what if we built a parallel path — one that rewards deep expertise, not just leadership?

Titles like principal engineer, lead strategist, internal consultant, or senior specialist aren’t consolation prizes. They’re strategic roles that allow people to grow and stay aligned with the work they love.

Not every B player wants to be a people manager. And that’s not just okay — it’s something to design for. Because when we force people up the ladder without giving them options, we risk turning our best contributors into struggling supervisors.

If you can’t create new roles on the org chart, you can still help B players feel like they’re moving forward. Ask them: • “What part of your job do you wish you could do more of?” • “Where do you want to grow this year?” • “If I could redesign your role to be more aligned with your strengths, what would that look like?”

You’ll be surprised what you learn just by asking — and how much more engaged your B players become when they feel seen and supported.

Step Three: Recognize B Players’ Value — Loudly

We tend to celebrate the visible wins: the product launch, the sales deal, the standout presentation. But high-performing teams are built just as much on quiet consistency as they are on flashy achievements.

As a leader, it’s your job to see the whole team — not just the ones shouting the loudest. Make time to recognize the B players, the steady hands, the glue that keeps the group together.

If they’re remote, reach out. If they’re introverted, check in one-on-one. Leadership isn’t about chasing stars. It’s about making sure everyone has the opportunity to do their best work and be recognized for it.

The Bottom Line on B Players

The truth is, you can’t build a high-performing team with A players alone. You build it by assembling the right mix of talent, by understanding what each person brings to the table, and by creating an environment where everyone — including your B players — can thrive.

And here’s the best part: when you lead B players well — when you trust them, invest in them, and help them grow — you may just find that they had A-level talent all along. They just needed a leader who saw it.

Image credit: Pexels

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Causal AI

Moving Beyond Prediction to Purpose

LAST UPDATED: February 13, 2026 at 5:13 PM

Causal AI

GUEST POST from Art Inteligencia

For the last decade, the business world has been obsessed with predictive models. We have spent billions trying to answer the question, “What will happen next?” While these tools have helped us optimize supply chains, they often fail when the world changes. Why? Because prediction is based on correlation, and correlation is not causation. To truly innovate using Human-Centered Innovation™, we must move toward Causal AI.

Causal AI is the next frontier of FutureHacking™. Instead of merely identifying patterns, it seeks to understand the why. It maps the underlying “wiring” of a system to determine how changing one variable will influence another. This shift is vital because innovation isn’t about following a trend; it’s about making a deliberate intervention to create a better future.

“Data can tell you that two things are happening at once, but only Causal AI can tell you which one is the lever and which one is the result. Innovation is the art of pulling the right lever.”
— Braden Kelley

The End of the “Black Box” Strategy

One of the greatest barriers to institutional trust is the “Black Box” nature of traditional machine learning. Causal AI, by its very nature, is explainable. It provides a transparent map of cause and effect, allowing human leaders to maintain autonomy and act as the “gardener” tending to the seeds of technology.

Case Study 1: Personalized Medicine and Healthcare

A leading pharmaceutical institution recently moved beyond predictive patient modeling. By using Causal AI to simulate “What if” scenarios, they identified specific causal drivers for individual patients. This allowed for targeted interventions that actually changed outcomes rather than just predicting a decline. This is the difference between watching a storm and seeding the clouds.

Case Study 2: Retail Pricing and Elasticity

A global retail giant utilized Causal AI to solve why deep discounts led to long-term dips in brand loyalty. Causal models revealed that the discounts were causing a shift in quality perception in specific demographics. By understanding this link, the company pivoted to a human-centered value strategy that maintained price integrity while increasing engagement.

Leading the Causal Frontier

The landscape of Causal AI is rapidly maturing in 2026. causaLens remains a primary pioneer with their Causal AI operating system designed for enterprise decision intelligence. Microsoft Research continues to lead the open-source movement with its DoWhy and EconML libraries, which are now essential tools for data scientists globally. Meanwhile, startups like Geminos Software are revolutionizing industrial intelligence by blending causal reasoning with knowledge graphs to address the high failure rate of traditional models. Causaly is specifically transforming the life sciences sector by mapping over 500 million causal relationships in biomedical data to accelerate drug discovery.

“Causal AI doesn’t just predict the future — it teaches us how to change it.”
— Braden Kelley

From Correlation to Causation

Predictive models operate on correlations. They answer: “Given the patterns in historical data, what will likely happen next?” Causal models ask a deeper question: “If we change this variable, how will the outcome change?” This fundamental difference elevates causal AI from forecasting to strategic influence.

Causal AI leverages counterfactual reasoning — the ability to simulate alternative realities. It makes systems more explainable, robust to context shifts, and aligned with human intentions for impact.

Case Study 3: Healthcare — Reducing Hospital Readmissions

A large health system used predictive analytics to identify patients at high risk of readmission. While accurate, the system did not reveal which interventions would reduce that risk. Nurses and clinicians were left with uncertainty about how to act.

By implementing causal AI techniques, the health system could simulate different combinations of follow-up calls, personalized care plans, and care coordination efforts. The causal model showed which interventions would most reduce readmission likelihood. The organization then prioritized those interventions, achieving a measurable reduction in readmissions and better patient outcomes.

This example illustrates how causal AI moves health leaders from reactive alerts to proactive, evidence-based intervention planning.

Case Study 4: Public Policy — Effective Job Training Programs

A metropolitan region sought to improve employment outcomes through various workforce programs. Traditional analytics identified which neighborhoods had high unemployment, but offered little guidance on which programs would yield the best impact.

Causal AI empowered policymakers to model the effects of expanding job training, childcare support, transportation subsidies, and employer incentives. Rather than piloting each program with limited insight, the city prioritized interventions with the highest projected causal effect. Ultimately, unemployment declined more rapidly than in prior years.

This case demonstrates how causal reasoning can inform public decision-making, directing limited resources toward policies that truly move the needle.

Human-Centered Innovation and Causal AI

Causal AI complements human-centered innovation by prioritizing actionable insight over surface-level pattern recognition. It aligns analytics with stakeholder needs: transparency, explainability, and purpose-driven outcomes.

By embracing causal reasoning, leaders design systems that illuminate why problems occur and how to address them. Instead of deploying technology that automates decisions, causal AI enables decision-makers to retain judgment while accessing deeper insight. This synergy reinforces human agency and enhances trust in AI-driven processes.

Challenges and Ethical Guardrails

Despite its potential, causal AI has challenges. It requires domain expertise to define meaningful variables and valid causal structures. Data quality and context matter. Ethical considerations demand clarity about assumptions, transparency in limitations, and safeguards against misuse.

Causal AI is not a shortcut to certainty. It is a discipline grounded in rigorous reasoning. When applied thoughtfully, it empowers organizations to act with purpose rather than default to correlation-based intuition.

Conclusion: Lead with Causality

In a world of noise, Causal AI provides the signal. It respects human autonomy by providing the evidence needed for a human to make the final call. As you look to your next change management initiative, ask yourself: Are you just predicting the weather, or are you learning how to build a better shelter?

Strategic FAQ

How does Causal AI differ from traditional Machine Learning?

Traditional Machine Learning identifies correlations and patterns in historical data to predict future occurrences. Causal AI identifies the functional relationships between variables, allowing users to understand the impact of specific interventions.

Why is Causal AI better for human-centered innovation?

It provides explainability. Because it maps cause and effect, human leaders can see the logic behind a recommendation, ensuring technology remains a tool for human ingenuity.

Can Causal AI help with bureaucratic corrosion?

Yes. By exposing the “why” behind organizational outcomes, it helps leaders identify which processes (the wiring) are actually producing value and which ones are simply creating friction.

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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Why It’s Important to Help Others

Why It's Important to Help Others

GUEST POST from Mike Shipulski

When someone you care about needs help, help them. Even when you have other things to do, help them anyway.

When people ask you for help, it’s a sign they trust you. And they trust you because you’ve demonstrated over time that your words and behaviors match. You said you’d do A and you did A. You said you’d do B and you did B. And because you’ve made that investment in them over the years, they value you and your time. And because they value you and your time, they don’t want to be a burden to you. And if they think you’ve got a lot on your plate, they may downplay the importance of their need for help and say things like “It’s no big deal.” or “It’s not that important.” or “It’s okay, it can wait.”.

However unforcefully, they asked for help because the need it. It was a big deal for them to ask because they know you are busy. And their willingness to dismiss or delay, is not a sign of unimportance of their need. Rather, it’s a show of their respect for you and your time. They desperately need your help, but care enough about you to give you any opportunity to say no. Those are the telltale signs that it’s time to stop what you’re doing and help them. This is the time when you can make the biggest difference. Stop immediately and help them.

Your helping starts with listening and listening starts with getting ready to listen. Smile and tell them that this little chat deserves a coffee or cold drink and walk with them to get a beverage. This critical step serves several functions. It makes it clear you are willing to make time for them and puts them at ease; it gives you time to let go of what you were working on so you can give them your full attention; and it gives you a little time to put yourself in their shoes so you will be able to hear what really going on.

By making time for them, you’ve already helped them. Someone they trust and respect stopped what they were doing and made time for them. They’re already standing two inches taller. And, with a clear head, you actively listen and understand, they grow another two inches. Often, just telling their story is enough for them to solve their own problem. In that way, your helping starts and ends with listening. And other times, they don’t really want you to solve their problem, they just want you to listen and empathize. And when they’re looking for more, rather than giving them answers, they’d rather you ask clarifying questions and paraphrase to demonstrate understanding.

You can’t do this for everyone, but you can do it for the people you care about most. Sure, you have to scamper to catch up on your own work, but it’s worth it. By helping them you help yourself twice – once from happiness that comes from helping someone you care about and twice from the joy that comes from watching them do the same for people they care about.

Our work is difficult and our lives are busy. But our work gets easier when we get and give help. And even with our always-on, always-connected culture, life is about building meaningful connections. How can your life be too busy for that?

Maybe we have it backwards. What if meaningful connections aren’t something we create so we can do our work better? What if we think of work as nothing more than a mechanism to create meaningful connections?

Image credit: 1 of 1,050+ FREE quotes for your meetings & presentations at http://misterinnovation.com

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