Top 40 Innovation Authors of 2025

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After a week of torrid voting and much passionate support, along with a lot of gut-wrenching consideration and jostling during the judging round, I am proud to announce your Top 40 Innovation Authors of 2025:

Robyn Bolton
Robyn M. Bolton works with leaders of mid and large sized companies to use innovation to repeatably and sustainably grow their businesses.
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Greg Satell
Greg Satell is a popular speaker and consultant. His first book, Mapping Innovation: A Playbook for Navigating a Disruptive Age, was selected as one of the best business books in 2017. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.

Janet Sernack
Janet Sernack is the Founder and CEO of ImagineNation™ which provides innovation consulting services to help organizations adapt, innovate and grow through disruption by challenging businesses to be, think and act differently to co-create a world where people matter & innovation is the norm.

Mike Shipulski
Mike Shipulski brings together people, culture, and tools to change engineering behavior. He writes daily on Twitter as @MikeShipulski and weekly on his blog Shipulski On Design.

Pete Foley
A twenty-five year Procter & Gamble veteran, Pete has spent the last 8+ years applying insights from psychology and behavioral science to innovation, product design, and brand communication. He spent 17 years as a serial innovator, creating novel products, perfume delivery systems, cleaning technologies, devices and many other consumer-centric innovations, resulting in well over 100 granted or published patents. Find him at pete.mindmatters@gmail.com

Geoffrey A. Moore
Geoffrey A. Moore is an author, speaker and business advisor to many of the leading companies in the high-tech sector, including Cisco, Cognizant, Compuware, HP, Microsoft, SAP, and Yahoo! Best known for Crossing the Chasm and Zone to Win with the latest book being The Infinite Staircase. Partner at Wildcat Venture Partners. Chairman Emeritus Chasm Group & Chasm Institute

Shep Hyken
Shep Hyken is a customer service expert, keynote speaker, and New York Times, bestselling business author. For information on The Customer Focus™ customer service training programs, go to www.thecustomerfocus.com. Follow on Twitter: @Hyken

David Burkus
Dr. David Burkus is an organizational psychologist and best-selling author. Recognized as one of the world’s leading business thinkers, his forward-thinking ideas and books are helping leaders and teams do their best work ever. David is the author of five books about business and leadership and he’s been featured in the Wall Street Journal, Harvard Business Review, CNN, the BBC, NPR, and more. A former business school professor turned sought-after international speaker, he’s worked with organizations of all sizes and across all industries.

John Bessant
John Bessant has been active in research, teaching, and consulting in technology and innovation management for over 25 years. Today, he is Chair in Innovation and Entrepreneurship, and Research Director, at Exeter University. In 2003, he was awarded a Fellowship with the Advanced Institute for Management Research and was also elected a Fellow of the British Academy of Management. He has acted as advisor to various national governments and international bodies including the United Nations, The World Bank, and the OECD. John has authored many books including Managing innovation and High Involvement Innovation (Wiley). Follow @johnbessant

Braden Kelley
Braden Kelley is a Human-Centered Experience, Innovation and Transformation consultant at HCL Technologies, a popular innovation speaker, workshop leader, and creator of the FutureHacking™ methodology. He is the author of Stoking Your Innovation Bonfire from John Wiley & Sons and Charting Change from Palgrave Macmillan. Follow him on Linkedin, Twitter, Facebook, or Instagram.

Art Inteligencia
Art Inteligencia is the lead futurist at Inteligencia Ltd. He is passionate about content creation and thinks about it as more science than art. Art travels the world at the speed of light, over mountains and under oceans. His favorite numbers are one and zero.

Stefan Lindegaard
Stefan Lindegaard is an author, speaker and strategic advisor. His work focuses on corporate transformation based on disruption, digitalization and innovation in large corporations, government organizations and smaller companies. Stefan believes that business today requires an open and global perspective, and his work takes him to Europe, North and South America, Africa and Asia.

Dainora Jociute
Dainora (a.k.a. Dee) creates customer-centric content at Viima. Viima is the most widely used and highest rated innovation management software in the world. Passionate about environmental issues, Dee writes about sustainable innovation hoping to save the world – one article at the time.

Teresa Spangler
Teresa Spangler is the CEO of PlazaBridge Group has been a driving force behind innovation and growth for more than 30 years. Today, she wears multiple hats as a social entrepreneur, innovation expert, growth strategist, author and speaker (not to mention mother, wife, band-leader and so much more). She is especially passionate about helping CEOs understand and value the role human capital plays in innovation, and the impact that innovation has on humanity; in our ever-increasing artificial/cyber world.

Soren Kaplan
Soren Kaplan is the bestselling and award-winning author of Leapfrogging and The Invisible Advantage, an affiliated professor at USC’s Center for Effective Organizations, a former corporate executive, and a co-founder of UpBOARD. He has been recognized by the Thinkers50 as one of the world’s top keynote speakers and thought leaders in business strategy and innovation.

Diana Porumboiu
Diana heads marketing at Viima, the most widely used and highest rated innovation management software in the world, and has a passion for innovation, and for genuine, valuable content that creates long-lasting impact. Her combination of creativity, strategic thinking and curiosity has helped organisations grow their online presence through strategic campaigns, community management and engaging content.

Steve Blank
Steve Blank is an Adjunct Professor at Stanford and Senior Fellow for Innovation at Columbia University. He has been described as the Father of Modern Entrepreneurship, credited with launching the Lean Startup movement that changed how startups are built; how entrepreneurship is taught; how science is commercialized, and how companies and the government innovate.

Jesse Nieminen
Jesse Nieminen is the Co-founder and Chairman at Viima, the best way to collect and develop ideas. Viima’s innovation management software is already loved by thousands of organizations all the way to the Global Fortune 500. He’s passionate about helping leaders drive innovation in their organizations and frequently writes on the topic, usually in Viima’s blog.

Robert B Tucker
Robert B. Tucker is the President of The Innovation Resource Consulting Group. He is a speaker, seminar leader and an expert in the management of innovation and assisting companies in accelerating ideas to market.

Dennis Stauffer
Dennis Stauffer is an author, independent researcher, and expert on personal innovativeness. He is the founder of Innovator Mindset LLC which helps individuals, teams, and organizations enhance and accelerate innovation success. by shifting mindset. Follow @DennisStauffer

Arlen Meyers
Arlen Meyers, MD, MBA is an emeritus professor at the University of Colorado School of Medicine, an instructor at the University of Colorado-Denver Business School and cofounding President and CEO of the Society of Physician Entrepreneurs at www.sopenet.org. Linkedin: https://www.linkedin.com/in/ameyers/

Phil McKinney
Phil McKinney is the Author of “Beyond The Obvious”, Host of the Killer Innovations Podcast and Syndicated Radio Show, a Keynote Speaker, President & CEO CableLabs and an Innovation Mentor and Coach.

Ayelet Baron
Ayelet Baron is a pioneering futurist reminding us we are powerful creators through award winning books, daily blog and thinking of what is possible. Former global tech executive who sees trust, relationships and community as our building blocks to a healthy world.

Scott Anthony
Scott Anthony is a strategic advisor, writer and speaker on topics of growth and innovation. He has been based in Singapore since 2010, and currently serves at the Managing Director of Innosight’s Asia-Pacific operations.

Leo Chan
Leo is the founder of Abound Innovation Inc. He’s a people and heart-first entrepreneur who believes everyone can be an innovator. An innovator himself, with 55 US patents and over 20 years of experience, Leo has come alongside organizations like Chick-fil-A and guided them to unleash the innovative potential of their employees by transforming them into confident innovators.

Rachel Audige
Rachel Audige is an Innovation Architect who helps organisations embed inventive thinking as well as a certified Systematic Inventive Thinking Facilitator, based in Melbourne.

Paul Sloane
Paul Sloane writes, speaks and leads workshops on creativity, innovation and leadership. He is the author of The Innovative Leader and editor of A Guide to Open Innovation and Crowdsourcing, both published by Kogan-Page.

Ralph Christian Ohr
Dr. Ralph-Christian Ohr has extensive experience in product/innovation management for international technology-based companies. His particular interest is targeted at the intersection of organizational and human innovation capabilities. You can follow him on Twitter @Ralph_Ohr.

Dean and Linda Anderson
Dr. Dean Anderson and Dr. Linda Ackerman Anderson lead BeingFirst, a consultancy focused on educating the marketplace about what’s possible in personal, organizational and community transformation and how to achieve them. Each has been advising clients and training professionals for more than 40 years.

Howard Tiersky
Howard Tiersky is an inspiring and passionate speaker, the Founder and CEO of FROM, The Digital Transformation Agency, innovation consultant, serial entrepreneur, and the Wall Street Journal bestselling author of Winning Digital Customers: The Antidote to Irrelevance. IDG named him one of the “10 Digital Transformation Influencers to Follow Today”, and Enterprise Management 360 named Howard “One of the Top 10 Digital Transformation Influencers That Will Change Your World.”

Chateau G Pato
Chateau G Pato is a senior futurist at Inteligencia Ltd. She is passionate about content creation and thinks about it as more science than art. Chateau travels the world at the speed of light, over mountains and under oceans. Her favorite numbers are one and zero.

Shilpi Kumar
Shilpi Kumar an inquisitive researcher, designer, strategist and an educator with over 15 years of experience, who truly believes that we can design a better world by understanding human behavior. I work with organizations to identify strategic opportunities and offer user-centric solutions.

Anthony Mills
Anthony Mills is the Founder & CEO of Legacy Innovation Group (www.legacyinnova.com), a world-leading strategic innovation consulting firm working with organizations all over the world. Anthony is also the Executive Director of GInI – Global Innovation Institute (www.gini.org), the world’s foremost certification, accreditation, and membership organization in the field of innovation. Anthony has advised leaders from around the world on how to successfully drive long-term growth and resilience through new innovation. Learn more at www.anthonymills.com. Anthony can be reached directly at anthony@anthonymills.com.

Paul Hobcraft
Paul Hobcraft runs Agility Innovation, an advisory business that stimulates sound innovation practice, researches topics that relate to innovation for the future, as well as aligning innovation to organizations core capabilities. Follow @paul4innovating

Jorge Barba
Jorge Barba is a strategist and entrepreneur, who helps companies build new puzzles using human skills. He is a global Innovation Insurgent and author of the innovation blog www.Game-Changer.net

Douglas Ferguson
Douglas Ferguson is an entrepreneur and human-centered technologist. He is the founder and president of Voltage Control, an Austin-based change agency that helps enterprises spark, accelerate, and sustain innovation. He specializes in helping teams work better together through participatory decision making and design inspired facilitation techniques.

Jeffrey Phillips
Jeffrey Phillips has over 15 years of experience leading innovation in Fortune 500 companies, federal government agencies and non-profits. He is experienced in innovation strategy, defining and implementing front end processes, tools and teams and leading innovation projects. He is the author of Relentless Innovation and OutManeuver. Jeffrey writes the popular Innovate on Purpose blog. Follow him @ovoinnovation

Alain Thys
As an experience architect, Alain helps leaders craft customer, employee and shareholder experiences for profit, reinvention and transformation. He does this through his personal consultancy Alain Thys & Co as well as the transformative venture studio Agents of A.W.E. Together with his teams, Alain has influenced the experience of over 500 million customers and 350,000 employees. Follow his blog or connect on Linkedin.

Bruce Fairley
Bruce Fairley is the CEO and Founder of The Narrative Group, a firm dedicated to helping C-Suite executives build enterprise value. Through smart, human-powered digital transformation, Bruce optimizes the business-technology relationship. His innovative profit over pitfalls approach and customized programs are part of Bruce’s mission to build sustainable ‘best-future’ outcomes for visionary leaders. Having spearheaded large scale change initiatives across four continents, he and his skilled, diverse team elevate process, culture, and the bottom line for medium to large firms worldwide.

Tom Stafford
Tom Stafford studies learning and decision making. His main focus is the movement system – the idea being that if we can understand the intelligence of simple actions we will have an excellent handle on intelligence more generally. His research looks at simple decision making, and simple skill learning, using measures of behaviour informed by the computational, robotics and neuroscience work done in the wider group.

If your favorite didn’t make the list, then next year try to rally more votes for them or convince them to increase the quality and quantity of their contributions.

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

Download PDF versions of the Top 40 Innovation Bloggers of 2020, 2021, 2022, 2023, 2024 and 2025 lists here:

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Happy New Year everyone!

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Making Empathy Your Secret Weapon

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GUEST POST from Greg Satell

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

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

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

Identifying Shared Values

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

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

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

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

Building Shared Purpose

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

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

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

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

Overcoming Conflict By Designing A Dilemma

Unfortunately, building a shared purpose isn’t always possible. A simple truth is that humans build attachments to people, ideas and things. When those attachments are threatened, they will lash out. That’s why whenever we set out to make a significant impact, there will always be those who will work to undermine what we are trying to achieve in ways that are dishonest, underhanded and deceptive.

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

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

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

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

Empathy Is Not Absolution

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

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

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

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

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

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

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Why Photonic Processors are the Nervous System of the Future

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Illumination as Innovation

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

Why Photonic Processors are the Nervous System of the Future

GUEST POST from Art Inteligencia

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

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

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

The End of the Electronic Friction

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

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

Case Study 1: Transforming Real-Time Healthcare Diagnostics

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

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

Case Study 2: Autonomous Urban Flow in Smart Cities

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

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

Leading Companies and Startups to Watch

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

Designing for the Speed of Light

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

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

Frequently Asked Questions

What is a Photonic Processor?

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

Why does photonic computing matter for AI?

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

Is photonic computing environmentally friendly?

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

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

Image credits: Google Gemini

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It’s Impossible to Innovate When …

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It's Impossible to Innovate When ...

GUEST POST from Mike Shipulski

Your company believes everything should always go as planned.

You still have to do your regular job.

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

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

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

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

You’re afraid to make a mistake.

You’re afraid you’ll be judged negatively.

You’re afraid to share unpleasant facts.

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

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

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

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

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

Project decisions are consensus-based.

Your company has been super profitable for a long time.

The project team does not believe in the project.

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

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How Empowered Are Your Employees?

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How Empowered Are Your Employees?

GUEST POST from Shep Hyken

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

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

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

Nordstrom Tires Story Cartoon from Shep Hyken

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

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

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

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

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

Image credits: Pexels, Shep Hyken

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

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

Is OpenAI About to Go Bankrupt? — by Chateau G Pato
The Rise of Human-AI Teaming Platforms — by Art Inteligencia
11 Reasons Why Teams Struggle to Collaborate — by Stefan Lindegaard
How Knowledge Emerges — by Geoffrey Moore
Getting the Most Out of Quiet Employees in Meetings — by David Burkus
The Wood-Fired Automobile — by Art Inteligencia
Was Your AI Strategy Developed by the Underpants Gnomes? — by Robyn Bolton
Will our opinion still really be our own in an AI Future? — by Pete Foley
Three Reasons Change Efforts Fail — by Greg Satell
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:

Don’t Believe the Courageous Leadership Lie — by Robyn Bolton
Why 4D Printing is the Next Frontier of Human-Centered Change — by Art Inteligencia
Is There a Real Difference Between Leaders and Managers? — by David Burkus
Innovation Requires Defying Success — by Mike Shipulski
Leveraging the Power of Play to Innovate — by Janet Sernack

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!

Have something to contribute?

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

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

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What Are We Going to Do Now with GenAI?

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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:

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.
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.
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

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Can AI Replace the CEO?

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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.

Image credits: Google Gemini

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AI Stands for Accidental Innovation

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

AI Stands for Accidental Innovation

GUEST POST from Art Inteligencia

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

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

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

— Braden Kelley

The Parallel Universe: The Meta/Oculus Near-Miss

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

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

Case Study 1: The Meta/Oculus Intersection

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

The Crypto Catalyst: A Fortuitous Detour

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

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

Case Study 2: From Hashes to Tokens

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

Leading Companies and Startups to Watch

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

The Human-Centered Conclusion

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

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

Frequently Asked Questions

Why is Nvidia’s success considered “accidental”?

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

Did Meta almost buy Nvidia?

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

What is the “CUDA moat”?

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

Image credits: Google Gemini

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The Technology of Tomorrow Requires Ecosystems Today

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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

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