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:

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

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?

Image Credit: Pexels

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

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

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

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

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

The Technology of Tomorrow Requires Ecosystems Today

The Technology Of Tomorrow Requires Ecosystems Today

GUEST POST from Greg Satell

There are a number of stories about what led Hans Lipperhey to submit a patent for the telescope in 1608. Some say that he saw two children playing with lenses in his shop who discovered that when they put one lens in front of each other they could see a weather vane across the street. Others say it was an apprentice that noticed the telescopic effect.

Yet the more interesting question is how such an important discovery could have such prosaic origins. Why was it that it was at that time that somebody noticed that looking through two lenses would magnify objects and not before? How could it have been that the discovery was made in a humble workshop and not by some great personage?

The truth is that history tends to converge and cascade around certain places and times, such as Cambridge before World War I, Vienna in the 1920s or, more recently, in Silicon Valley. In each case, we find that there were ecosystems that led to the inventions that changed the world. If we are going to build a more innovative economy, that’s where we need to focus.

How The Printing Press Led To A New Era Of Science

The mystery surrounding the invention of the telescope in the early 1600s begins to make more sense when you consider that the printing press was invented a little over a century before. By the mid-1500s books were transformed from priceless artifacts rarely seen outside monasteries, to something common enough that people could keep in their homes.

As literacy flourished, the need for spectacles grew exponentially and lens making became a much more common trade. With so many lenses around, it was only a matter of time before someone figured out that combining two lenses would create a compound effect and result in magnification (the microscope was invented around the same time).

From there, things began to move quickly. In 1609, Galileo Galilei first used the telescope to explore the heavens and changed our conception of the universe. He was able to see stars that were invisible to the naked eye, mountains and valleys on the moon and noticed that, similar to the moon, Venus had phases suggesting that it revolved around the sun.

A half century later, Antonie van Leeuwenhoek built himself a microscope and discovered an entirely new world made up of cells and fibers far too small for the human eye to detect. For the first time we became aware of bacteria and protozoa, creating the new field of microbiology. The world began to move away from ancient superstition and into one of observation and deduction.

It’s hard to see how any of this could have been foreseen when Gutenberg printed his first bible. Galileo and van Leeuwenhoek were products of their age as much as they were creators of the future.

How The Light Bulb Helped To Reshape Life, Work And Diets

In 1882, just three years after he had almost literally shocked the world with his revolutionary lighting system, Thomas Edison opened his Pearl Street Station, the first commercial electrical distribution plant in the United States. By 1884 it was already servicing over 500 homes.Yet for the next few decades, electric light remained mostly a curiosity.

As the economist Paul David explains in The Dynamo and the Computer, electricity didn’t have a measurable impact on the economy until the early 1920’s — 40 years after Edison’s plant. The problem wasn’t with electricity itself, Edison quickly expanded his distribution network as did his rival George Westinghouse, but a lack of complementary technologies.

To truly impact productivity, factories had to be redesigned to function not around a single steam turbine, but with smaller electric motors powering each machine. That created the opportunity to reimagine work itself, which led to the study of management. Greater productivity raised living standards and a new consumer culture.

Much like with the printing press, the ecosystem created by electric light led to secondary and tertiary inventions. Radios changed the way people received information and were entertained. Refrigeration meant not only that food could be kept fresh, but sent over large distances, reshaping agriculture and greatly improving diets.

The Automobile And The Category Killer

The internal combustion engine was developed in the late 1870’s and early 1880’s. Two of its primary inventors, Gottlieb Daimler and Karl Benz, began developing cars in the mid-1880’s. Henry Ford came two decades later. By pioneering the assembly line, he transformed cars from an expensive curiosity into a true “product for the masses” and it was this transformation that led to its major impact.

When just a few people have a car, it is merely a mode of transportation. But when everyone has a car, it becomes a force that reshapes society. People move from crowded cities into bedroom communities in the suburbs. Social relationships change, especially for farmers who previously lived their entire lives within a single day’s horse ride of 10 or 12 square miles. Lives opened up. Worlds broadened.

New infrastructure, like roads and gas stations were built. Improved logistics began to reshape supply chains and factories moved from cities in the north—close to customers—to small towns in the south, where labor and land were cheaper. That improved the economics of manufacturing, improved incomes and enriched lives.

With the means to easily carry a week’s worth of groceries, corner stores were replaced by supermarkets. Eventually suburbs formed and shopping malls sprang up. In the US, Little League baseball became popular. With mobility combined with the productivity effects of electricity, almost every facet of life—where we lived, worked and shopped—was reshaped.

Embarking On A New Era Of Innovation

These days, it seems that every time you turn around you see some breakthrough technology that will change our lives. We see media reports about computing breakthroughs, miracle cures, new sources of energy and more. Unfortunately, very few will ever see the outside of a lab and even fewer will prove commercially viable enough to impact our lives.

Don’t get me wrong. Many of these are real discoveries produced by serious scientists and reported by reputable sources. The problem is with how science works. At any given time there are a myriad of exciting possibilities, but very few pan out and even the ones that do usually take decades to make an impact.

Digital technology is a great example of how this happens. As AnnaLee Saxenian explained in Regional Advantage, back in the 1970s and 80s, when Boston was the center of the technology universe, Silicon Valley invested in an ecosystem, which included not just corporations, but scientific labs, universities and community colleges. New England rejected that approach. The results speak for themselves.

If you want to understand the technology of tomorrow, don’t try to imagine an idea no one has ever thought of, but look at the problems people are working on today. You’ll find a vast network working on quantum computing, a significant synthetic biology economy, a large-scale effort in materials science and billions of dollars invested into energy storage startups.

That’s why, if we are to win the future, we need to invest in ecosystems. It’s the nodes that grab attention, but the networks that make things happen.

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

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Rebuilding Trust When You’ve Broken It

Rebuilding Trust When You've Broken It

GUEST POST from David Burkus

Trust is the foundation of every high-performing team. It’s the invisible force that enables collaboration, fuels innovation, and keeps teams resilient in the face of setbacks. But when that trust is broken – leaders need to focus on how to rebuild trust carefully and deliberately. Rebuilding trust isn’t as simple as offering an apology and moving on. In fact, that’s where many leaders go wrong.

They believe a sincere “I’m sorry” is all it takes to make things right again.

But it’s not.

Rebuilding trust takes far more than words—it takes sustained action. And if you’re serious about leading a high-performing team, you need to understand the process of how to truly rebuild trust when it’s been damaged.

Most Leaders Get Rebuilding Trust Wrong

Let’s start with the apology. A real apology – the kind that has the potential to begin the healing process – sounds like this: “I did this. I now know it was wrong. I see the impact it had on you. And I’m going to make it right.” That’s not the same as saying “I didn’t mean it” or “I’m sorry if anyone was offended.” Those aren’t apologies; they’re excuses dressed up in regret.

Even when leaders get the words right, they often assume the work ends there. But rebuilding trust doesn’t happen with a single moment of contrition. Trust isn’t built on words. It’s built on behavior.

What leaders fail to realize is that when they betray trust, they don’t just damage the relationship – they break an emotional loop. I call it the trust loop, and it exists in every relationship you have with your team, both collectively and individually. That loop is a cycle of expectation, action, and consistency. When everything is working well, the loop reinforces itself and trust grows. But when trust is violated, the loop shatters—and rebuilding it takes far more than a one-time gesture.

Why Words Aren’t Enough To Rebuild Trust

When you break trust and then try to move on too quickly, you’re sending an unspoken message to your team: “This wasn’t that big of a deal.” And that message undercuts any sincerity you intended with your apology. Research backs this up. Paul Zak, a neuroscientist who studies trust in organizations, found that employees in high-trust workplaces report 74% less stress and 50% higher productivity. Trust isn’t just a feel-good concept – it’s measurable, and it affects everything from performance to retention. But that kind of trust can’t exist unless leaders take full accountability, even for their mistakes.

Taking accountability isn’t just about admitting the error – it’s about acknowledging the impact. And that’s where a lot of well-meaning leaders go off track. They say, “I made a mistake,” but they don’t take the time to understand or validate how that mistake affected others. The result? Their apology feels hollow. The team sees them as principled, maybe, but detached. Or worse – performative.

To truly rebuild trust, leaders need to demonstrate both responsibility and empathy. Because your team needs to know not just that you’re sorry, but that you get it. That you see the ripple effect your actions had, and that you care enough to do better.

What Rebuilding Trust Actually Takes

So how do you rebuild trust?

It starts with a strong apology, yes. But it doesn’t end there. Here are four steps to guide the process—and none of them can be skipped.

1. Own the Mistake – and Its Impact

Rebuilding trust begins with full accountability. You must take ownership of what happened and openly acknowledge the harm it caused. That might mean calling out specific behaviors, admitting lapses in judgment, or addressing how your decision made the team feel undervalued or vulnerable. This isn’t a time to minimize, justify, or deflect. And it’s not just about your intention – it’s about the impact. The more specifically you can articulate what went wrong and why it mattered, the more credible your apology becomes.

2. Invite The Team Into The Solution

After accountability comes action. But not behind closed doors. Telling your team, “I’ll do better,” isn’t enough. They need to see you doing better. Better yet, they need to be part of the process.

Invite them into the solution. Talk through what happened. Share the thinking behind your original decision—not to excuse it, but to help the team understand where things went wrong. Then ask for input. What would they have done differently? What safeguards could be put in place to avoid a repeat? The more you co-create the fix, the more your team sees that you’re serious about change. Transparency builds credibility. And when your team sees you working on yourself, they’re more likely to work with you to rebuild what was broken.

3. Show Them You’re Changing

The most powerful way to rebuild trust is to demonstrate new behavior in old situations. If you made a decision that sidelined the team last time, then the next time a similar decision comes up, you need to do the opposite. Bring the team in early. Ask for feedback. Show them that the lesson was learned – and internalized.

They don’t need to see everything you’re doing differently. But they do need to see you behaving differently in the kinds of situations that broke trust in the first place. That’s how predictability is restored. And predictability is a cornerstone of trust.

4. Be Consistent—Every Day

This is where most leaders lose momentum. They start strong. They apologize, they make a few changes, they check in. But over time, old habits creep back in and the consistency fades. And when that happens, the message to the team is clear: “That apology wasn’t real.”

Rebuilding trust isn’t about grand gestures. It’s about small, daily actions. It’s about showing up consistently. Following through consistently. Making decisions with integrity—consistently.

The longer you sustain those behaviors, the more the trust loop starts to turn again. Slowly, day by day, your team regains their confidence – not just in your words, but in your ability to lead with integrity.

Always Be Rebuilding Trust

You don’t rebuild trust with a single apology. You rebuild trust by showing that your apology meant something. That you’ve changed. That the behavior that broke trust won’t be repeated.

And while that takes time, it’s worth it. Because trust is what makes teams resilient. Trust is what drives performance. And trust – when rebuilt the right way – can actually come back stronger than before.

So, if you’ve broken trust with your team, don’t aim for forgiveness. Aim for consistency. Start by owning your mistake. Involve your team in the fix. Show them the change. And then keep showing up – day after day.

That’s how you rebuild trust. And that’s how you restart the trust loop.

This article originally appeared on DavidBurkus.com

Image credit: Pixabay

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.front

The Rise of Human-AI Teaming Platforms

Designing Partnership, Not Replacement

LAST UPDATED: December 26, 2025 at 4:44 PM

Human-AI Teaming Platforms

GUEST POST from Art Inteligencia

In the rush to adopt artificial intelligence, too many organizations are making a fundamental error. They view AI through the lens of 19th-century industrial automation: a tool to replace expensive human labor with cheaper, faster machines. This perspective is not only shortsighted; it is a recipe for failed digital transformation.

As a human-centered change leader, I argue that the true potential of this era lies not in artificial intelligence alone, but in Augmented Intelligence derived from sophisticated collaboration. We are moving past simple chatbots and isolated algorithms toward comprehensive Human-AI Teaming Platforms. These are environments designed not to remove the human from the loop, but to create a symbiotic workflow where humans and synthetic agents operate as cohesive units, leveraging their respective strengths concurrently.

“Organizations don’t fail because AI is too difficult to adopt. They fail because they never designed how humans and AI would think together and work together.”

Braden Kelley

The Cognitive Collaborative Shift

A Human-AI Teaming Platform differs significantly from standard enterprise software. Traditional tools wait for human input. A teaming platform is proactive; it observes context, anticipates needs, and offers suggestions seamlessly within the flow of work.

The challenge for leadership here is less technological and more cultural. How do we foster psychological safety when a team member is an algorithm? How do we redefine accountability when decisions are co-authored by human judgment and machine probability? Success requires a deliberate shift from managing subordinate tools to orchestrating collaborative partners.

“The ultimate goal of Human-AI teaming isn’t just to build faster organizations, but to build smarter, more adaptable ones. It is about creating a symbiotic relationship where the computational velocity of AI amplifies – rather than replaces – the creative, empathetic, and contextual genius of humans.”

Braden Kelley

When designed correctly, these platforms handle the high-volume cognitive load—data pattern recognition, probabilistic forecasting, and information retrieval—freeing human brains for high-value tasks like ethical reasoning, strategic negotiation, and complex emotional intelligence.

Case Studies in Symbiosis

To understand the practical application of these platforms, we must look at sectors where the cost of error is high and data volumes are overwhelming.

Case Study 1: Mastercard and the Decision Management Platform

In the high-stakes world of global finance, fraud detection is a constant battle against increasingly sophisticated bad actors. Mastercard has moved beyond simple automated flags to a genuine human-AI teaming approach with their Decision Intelligence platform.

The Challenge: False positives in fraud detection insult legitimate customers and stop commerce, while false negatives cost billions. No human team can review every transaction in real-time, and rigid rules-based AI often misses nuanced fraud patterns.

The Teaming Solution: Mastercard employs sophisticated AI that analyzes billions of activities in real-time. However, rather than just issuing a binary block/allow decision, the AI acts as an investigative partner to human analysts. It presents a “reasoned” risk score, highlighting why a transaction looks suspicious based on subtle behavioral shifts that a human would miss. The human analyst then applies contextual knowledge—current geopolitical events, specific merchant relationships, or nuanced customer history—to make the final judgment call. The AI learns from this human intervention, constantly refining its future collaborative suggestions.

Case Study 2: Autodesk and Generative Design in Engineering

The field of engineering and manufacturing is transitioning from computer-aided design (CAD) to human-AI co-creation, pioneered by companies like Autodesk.

The Challenge: When designing complex components—like an aerospace bracket to reduce weight while maintaining structural integrity—an engineer is limited by their experience and the time available to iterate on concepts.

The Teaming Solution: Using Autodesk’s generative design platforms, the human engineer doesn’t draw the part. Instead, they define the constraints: materials, weight limits, load-bearing requirements, and manufacturing methods. The AI then acts as an tireless creative partner, generating hundreds or thousands of permutable design solutions that meet those criteria—many utilizing organic shapes no human would instinctively draw. The human engineer then reviews these options, selecting the optimal design based on aesthetics, manufacturability, and cost-effectiveness. The human sets the goal; the AI explores the solution space; the human selects and refines the outcome.

Leading Platforms and Startups to Watch

The market for these platforms is rapidly bifurcating into massive ecosystem players and niche, workflow-specific innovators.

Among the giants, Microsoft is aggressively positioning its Copilot ecosystem across nearly every knowledge worker touchpoint, turning M365 into the default teaming platform for the enterprise. Salesforce is similarly embedding generative AI deep into its CRM, attempting to turn sales and service records into proactive coaching systems.

However, keep an eye on innovators focused on the mechanics of collaboration. Companies like Atlassian are evolving their suite (Jira, Confluence) to use AI not just to summarize text, but to connect disparate project threads and identify team bottlenecks proactively. In the startup space, look for platforms that are trying to solve the “managerial” layer of AI, helping human leaders coordinate mixed teams of synthetic and biological agents, ensuring alignment and mitigating bias in real-time.

Conclusion: The Leadership Imperative

Implementing Human-AI Teaming Platforms is a change management challenge of the highest order. If introduced poorly, these tools will be viewed as surveillance engines or competitors, leading to resistance and sabotage.

Leaders must communicate a clear vision: AI is brought in to handle the drudgery so humans can focus on the artistry of their professions. The organizations that win in the next decade will not be those with the best AI; they will be the ones with the best relationship between their people and their AI.

Frequently Asked Questions regarding Human-AI Teaming

What is the primary difference between traditional automation and Human-AI teaming?

Traditional automation seeks to replace human tasks entirely to cut costs and increase speed, often removing the human from the loop. Human-AI teaming focuses on augmentation, keeping humans in the loop for complex judgment and creative tasks while leveraging AI for data processing and pattern recognition in a collaborative workflow.

What are the biggest cultural barriers to adopting Human-AI teaming platforms?

The significant barriers include a lack of trust in AI outputs, fear of job displacement among the workforce, and the difficulty of redefining roles and accountability when decisions are co-authored by humans and algorithms.

How do Human-AI teaming platforms improve decision-making?

These platforms improve decision-making by combining the AI’s ability to process vast datasets without fatigue or cognitive bias with the human ability to apply ethical considerations, emotional intelligence, and nuanced contextual understanding to the final choice.

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

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

Drive Out Fear for Innovation to Flow

Drive Out Fear for Innovation to Flow

GUEST POST from Mike Shipulski

The primary impediment to innovation is fear, and the prime directive of any innovation system should be to drive out fear.

A culture of accountability, implemented poorly, can inject fear and deter innovation. When the team is accountable to deliver on a project but are constrained to a fixed scope, a fixed launch date and resources, they will be afraid. Because they know that innovation requires new work and new work is inherently unpredictable, they rightly recognize the triple accountability – time, scope and resources – cannot be met. From the very first day of the project, they know they cannot be successful and are afraid of the consequences.

A culture of accountability can be adapted to innovation to reduce fear. Here’s one way. Keep the team small and keep them dedicated to a single innovation project. No resource sharing, no swapping and no double counting. Create tight time blocks with clear work objectives, where the team reports back on a fixed pitch (weekly, monthly). But make it clear that they can flex on scope and level of completeness. They should try to do all the work within the time constraints but they must know that it’s expected the scope will narrow or shift and the level of completeness will be governed by the time constraint. Tell them you believe in them and you trust them to do their best, then praise their good judgement at the review meeting at the end of the time block.

Innovation is about solving new problems, yet fear blocks teams from trying new things. Teams like to solve problems that are familiar because they have seen previous teams judged negatively for missing deadlines. Here’s the logic – we’d rather add too little novelty than be late. The team would love to solve new problems but their afraid, based on past projects, that they’ll be chastised for missing a completion date that’s disrespectful of the work content and level of novelty. If you want the team to solve new problems, give them the tools, time, training and a teacher so they can select different problems and solve them differently. Simply put – create the causes and conditions for fear to quietly slink away so innovation will flow.

Fear is the most powerful inhibitor. But before we can lessen the team’s fear we’ve got to recognize the causes and conditions that create it. Fear’s job is to keep us safe, to keep us away from situations that have been risky or dangerous. To do this, our bodies create deep memories of those dangerous or scary situations and creates fear when it recognizes similarities between the current situation and past dangerous situations. In that way, less fear is created if the current situation feels differently from situations of the past where people were judged negatively.

To understand the causes and conditions that create fear, look back at previous projects. Make a list of the projects where project members were judged negatively for things outside their control such as: arbitrary launch dates not bound by the work content, high risk levels driven by unjustifiable specifications, insufficient resources, inadequate tools, poor training and no teacher. And make a list of projects where team members were praised. For the projects that praised, write down attributes of those projects (e.g., high reuse, low technical risk) and their outcomes (e.g., on time, on cost). To reduce fear, the project team will bend new projects toward those attributes and outcomes. Do the same for projects that judged negatively for things outside the project teams’ control. To reduce fear, the future project teams will bend away from those attributes and outcomes.

Now the difficult parts. As a leader, it’s time to look inside. Make a list of your behaviors that set (or contributed to) causes and conditions that made it easy for the project team to be judged negatively for the wrong reasons. And then make a list of your new behaviors that will create future causes and conditions where people aren’t afraid to solve new problems in new ways.

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

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.

Voting Open – Top 40 Innovation Authors of 2025

Vote for Top 40 Innovation AuthorsHappy Holidays!

For more than a decade I’ve devoted myself to making innovation insights accessible for the greater good, because I truly believe that the better our organizations get at deliveriseng value to their stakeholders the less waste of natural resources and human resources there will be.

As a result, we are eternally grateful to all of you out there who take the time to create and share great innovation articles, presentations, white papers, and videos with Braden Kelley and the Human-Centered Change and Innovation team. As a small thank you to those of you who follow along, we like to make a list of the Top 40 Innovation Authors available each year!

Our lists from the ten previous years have been tremendously popular, including:

Top 40 Innovation Bloggers of 2015
Top 40 Innovation Bloggers of 2016
Top 40 Innovation Bloggers of 2017
Top 40 Innovation Bloggers of 2018
Top 40 Innovation Bloggers of 2019
Top 40 Innovation Bloggers of 2020
Top 40 Innovation Bloggers of 2021
Top 40 Innovation Bloggers of 2022
Top 40 Innovation Bloggers of 2023
Top 40 Innovation Bloggers of 2024

Do you just have someone that you like to read that writes about innovation, or some of the important adjacencies – trends, consumer psychology, change, leadership, strategy, behavioral economics, collaboration, or design thinking?

Human-Centered Change and Innovation is now looking to recognize the Top 40 Innovation Authors of 2025.

It is time to vote and help us narrow things down.

The deadline for submitting votes is December 31, 2025 at midnight GMT.

Build a Common Language of Innovation on your team

The ranking will be done by me with influence from votes and nominations. The quality and quantity of contributions to this web site by an author will be a BIG contributing factor (through the end of the voting period).

You can vote in any of these three ways (and each earns points for them, so please feel free to vote all three ways):

  1. Sending us the name of the author by @reply on twitter to @innovate
  2. Adding the name of the author as a comment to this article’s posting on Facebook
  3. Adding the name of the author as a comment to this article’s posting on our Linkedin Page (Be sure and follow us)

The official Top 40 Innovation Authors of 2025 will then be announced here in early January 2026.

Here are the people who received nominations this year along with some carryover recommendations (in alphabetical order):

Adi Gaskell – @adigaskell
Alain Thys
Alex Goryachev
Andy Heikkila – @AndyO_TheHammer
Annette Franz
Arlen Meyers – @sopeofficial
Art Inteligencia
Ayelet Baron
Braden Kelley – @innovate
Brian Miller
Bruce Fairley
Chad McAllister – @ChadMcAllister
Chateau G Pato
Chris Beswick
Chris Rollins
Dr. Detlef Reis
Dainora Jociute
Dan Blacharski – @Dan_Blacharski
Daniel Burrus – @DanielBurrus
Daniel Lock
David Burkus
Dean and Linda Anderson
Dennis Stauffer
Diana Porumboiu
Douglas Ferguson
Drew Boyd – @DrewBoyd
Frank Mattes – @FrankMattes
Geoffrey A Moore
Gregg Fraley – @greggfraley
Greg Satell – @Digitaltonto
Helen Yu
Howard Tiersky
Janet Sernack – @JanetSernack
Jeffrey Baumgartner – @creativejeffrey
Jeff Freedman – @SmallArmyAgency
Jeffrey Phillips – @ovoinnovation
Jesse Nieminen – @nieminenjesse
John Bessant
Jorge Barba – @JorgeBarba
Julian Birkinshaw – @JBirkinshaw
Julie Anixter – @julieanixter
Kate Hammer – @Kate_Hammer
Kevin McFarthing – @InnovationFixer
Leo Chan
Lou Killeffer – @LKilleffer
Manuel Berdoy

Accelerate your change and transformation success

Mari Anixter- @MariAnixter
Maria Paula Oliveira – @mpaulaoliveira
Matthew E May – @MatthewEMay
Michael Graber – @SouthernGrowth
Mike Brown – @Brainzooming
Mike Shipulski – @MikeShipulski
Mukesh Gupta
Nick Jain
Nick Partridge – @KnewNewNeu
Nicolas Bry – @NicoBry
Nicholas Longrich
Norbert Majerus and George Taninecz
Pamela Soin
Patricia Salamone
Paul Hobcraft – @Paul4innovating
Paul Sloane – @paulsloane
Pete Foley – @foley_pete
Rachel Audige
Ralph Christian Ohr – @ralph_ohr
Randy Pennington
Richard Haasnoot – @Innovate2Grow
Robert B Tucker – @RobertBTucker
Robyn Bolton – @rm_bolton
Saul Kaplan – @skap5
Shep Hyken – @hyken
Shilpi Kumar
Scott Anthony – @ScottDAnthony
Scott Bowden – @scottbowden51
Shelly Greenway – @ChiefDistiller
Soren Kaplan – @SorenKaplan
Stefan Lindegaard – @Lindegaard
Stephen Shapiro – @stephenshapiro
Steve Blank
Steven Forth – @StevenForth
Tamara Kleinberg – @LaunchStreet
Teresa Spangler – @composerspang
Tom Koulopoulos – @TKspeaks
Tullio Siragusa
Yoram Solomon – @yoram

Subscribe to Human-Centered Change & Innovation WeeklySign up here to get Human-Centered Change & Innovation Weekly delivered to your inbox every week.

We’re curious to see who you think is worth reading!

The Paradox of Customer Recovery

The Paradox of Customer Recovery

GUEST POST from Shep Hyken

Here’s something that might surprise you. Some of your most loyal customers may be the ones who have had problems and complaints in the past. For years, I’ve been preaching that when a customer comes to you with a problem or complaint, the goal is not only to resolve the issue, but also to restore their confidence.

I was recently reminded of the concept known as the Service Recovery Paradox. Back in 1992, Michael McCollough and Sundar Bharadwaj coined the phrase to describe, according to Wikipedia, “a situation when the customer thinks more highly of a company after the company has corrected a problem with their service, compared to how they would regard the company if non-faulty service had been provided. The main reason behind this thinking is that the successful recovery of a faulty service increases the assurance and confidence from the customer.”

BOOM! That’s the point. Fix whatever needs to be fixed in such a way that makes things right and restores the customer’s confidence in you so well that they want to continue doing business with you. Furthermore, if done the right way, you not only get the customer to come back, but that confidence can also create loyalty. When the customer says, “I know I can depend on them even when there is a problem,” why would they consider doing business with anyone else?

Customer Service Recovery Shep Hyken Cartoon

When a customer brings a problem or complaint to your attention, they are hoping for you to take care of it. It’s how you go about doing so that will create the Customer Service Recovery Paradox. Three things must happen:

  1. The resolution makes the customer happy. It may be as simple as answering a question. Or it may require a repair, or a replacement of something that can’t be fixed. Regardless, the customer must agree that the resolution is satisfactory. However, that only brings you back to what the customer expected in the first place. Dissatisfaction can linger from the effort and friction they experienced in getting the issue resolved.
  2. It must happen fast. Speed is your friend. The faster to resolution, the better.
  3. Go beyond the fix. The problem is resolved, and you did it quickly and efficiently. That helps restore the customer’s confidence in you, but let’s take it just a bit further with what happens next. While some instances may require a refund or discount, that’s not always necessary. A simple note or email that thanks the customer for letting you help them and reminds them you will always have their back may be all it takes.

When customers know they can depend on you, especially when things go wrong, why would they risk doing business with anyone else? That’s not just customer retention. That’s a foundation for customer loyalty.

Image credits: Pexels, Shep Hyken

Subscribe to Human-Centered Change & Innovation WeeklySign up here to join 17,000+ leaders getting Human-Centered Change & Innovation Weekly delivered to their inbox every week.