Category Archives: Innovation

Asking for the Right Work Product is Key

GUEST POST from Mike Shipulski

We think we have more control than we really have. We imagine an idealized future state and try desperately to push the organization in the direction of our imagination. Add emotional energy, define a rational approach, provide the supporting rationale and everyone will see the light. Pure hubris.

What if we took a different approach? What if we believed people want to do the right thing but there’s something in the way? What if like a log jam in a fast-moving river, we remove the one log blocking them all? What if like a river there’s a fast-moving current of company culture that wants to push through the emotional log jam that is the status quo? What if it’s not a log at all but, rather, a Peter Principled executive that’s threatened by the very thing that will save the company?

The Peter Principled executive is a tough nut to crack. Deeply entrenched in the powerful goings on of the mundane and enabled by the protective badge of seniority, these sticks-in-the-mud need to be helped out of the way without threatening their no-longer-deserved status. Tricky business.

Rule 1: If you get into an argument with a Peter Principled executive, you’ll lose.

Rule 2: Don’t argue with Peter Principled executive.

If we want to make it easy for the right work to happen, we’ve got to learn how to make it easy for the Peter Principled executive to get out of the way. First, ask yourself why the executive is in the way. Why are they blocking progress? What’s keeping them from doing the right thing? Usually it comes down to the fear of change or the fear of losing control. Now it’s time to think of a work product that will help make the case there’s a a better way. Think of a small experiment to demonstrate a new way is possible and then run the experiment. Don’t ask, just run it. But the experiment isn’t the work product. The work product is a short report that makes it clear the new paradigm has been demonstrated, at least at small scale. The report must be clear and dense and provide objective evidence the right work happened by the right people in the right way. It must be written in a way that preempts argument – this is what happened, this is who did it, this is what it looks like and this is the benefit.

It’s critical to choose the right people to run the experiment and create the work product. The work must be done by someone in the chain of command of the in-the-way executive. Once the work product is created, it must be shared with an executive of equal status who is by definition outside the chain of command. From there, that executive must send a gracious email back into the chain of command that praises the work, praises the people who did it and praises the leader within the chain of command who had the foresight to sponsor such wonderful work.

As this public positivity filters through the organization, more people will add their praise of the work and the leaders that sponsored it. And by the time it makes it up the food chain to the executive of interest, the spider web of positivity is anchored across the organization and can’t be unwound by argument. And there you have it. You created the causes and conditions for the log jam to unjam itself. It’s now easy for the executive to get out of the way because they and their organization have already been praised for demonstrating the new paradigm. You’ve built a bridge across the emotional divide and made it easy for the executive and the status quo to cross it.

Asking for the right work product is a powerful skill. Most error on the side of complication and complexity, but the right work product is just the opposite – simple and tight. Think sledgehammer to the forehead in the form of and Excel chart where the approach is beyond reproach; where the chart can be interpreted just one way; where the axes are labeled; and it’s clear the status quo is long dead.

Business model is dead and we’ve got to stop trying to keep it alive. It’s time to break the log jam. Don’t be afraid. Create the right work product that is the dynamite that blows up the status quo and the executives clinging to it.

Image credit: Pexels

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A New Era of Economic Warfare Arrives

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Is Your Company Prepared?

LAST UPDATED: January 9, 2026 at 3:55PM

A New Era of Economic Warfare Arrives

GUEST POST from Art Inteligencia

Economic warfare rarely announces itself. It embeds quietly into systems designed for trust, openness, and speed. By the time damage becomes visible, advantage has already shifted.

This new era of conflict is not defined by tanks or tariffs alone, but by the strategic exploitation of interdependence — where innovation ecosystems, supply chains, data flows, and cultural platforms become contested terrain.

The most effective economic attacks do not destroy systems outright. They drain them slowly enough to avoid response.

Weaponizing Openness

For decades, the United States has benefited from a research and innovation model grounded in openness, collaboration, and academic freedom. Those same qualities, however, have been repeatedly exploited.

Publicly documented prosecutions, investigations, and corporate disclosures describe coordinated efforts to extract intellectual property from American universities, national laboratories, and private companies through undisclosed affiliations, parallel research pipelines, and cyber-enabled theft.

This is not opportunistic theft. It is strategic harvesting.

When innovation can be copied faster than it can be created, openness becomes a liability instead of a strength.

Cyber Persistence as Economic Strategy

Cyber operations today prioritize persistence over spectacle. Continuous access to sensitive systems allows competitors to shortcut development cycles, underprice rivals, and anticipate strategic moves.

The goal is not disruption — it is advantage.

Skydio and Supply Chain Chokepoints

The experience of American drone manufacturer Skydio illustrates how economic pressure can be applied without direct confrontation.

After achieving leadership through autonomy and software-driven innovation rather than low-cost manufacturing, Skydio encountered pressure through access constraints tied to upstream supply chains.

This was a calculated attack on a successful American business. It serves as a stark reminder: if you depend on a potential adversary for your components, your success is only permitted as long as it doesn’t challenge their dominance. We must decouple our innovation from external control, or we will remain permanently vulnerable.

When supply chains are weaponized, markets no longer reward the best ideas — only the most protected ones.

Agricultural and Biological Vulnerabilities

Incidents involving the unauthorized movement of biological materials related to agriculture and bioscience highlight a critical blind spot. Food systems are economic infrastructure.

Crop blight, livestock disease, and agricultural disruption do not need to be dramatic to be devastating. They only need to be targeted, deniable, and difficult to attribute.

Pandemics and Systemic Shock

The origins of COVID-19 remain contested, with investigations examining both natural spillover and laboratory-associated scenarios. From an economic warfare perspective, attribution matters less than exposure.

The pandemic revealed how research opacity, delayed disclosure, and global interdependence can cascade into economic devastation on a scale rivaling major wars.

Resilience must be designed for uncertainty, not certainty.

The Attention Economy as Strategic Terrain and Algorithmic Narcotic

Platforms such as TikTok represent a new form of economic influence: large-scale behavioral shaping.

Regulatory and academic concerns focus on data governance, algorithmic amplification, and the psychological impact on youth attention, agency, and civic engagement.

TikTok is not just a social media app; it is a cognitive weapon. In China, the algorithm pushes “Douyin” users toward educational content, engineering, and national achievement. In America, the algorithm pushes our youth toward mindless consumption, social fragmentation, and addictive cycles that weaken the mental resilience of the next generation. This is an intentional weakening of our human capital. By controlling the narrative and the attention of 170 million Americans, American children are part of a massive experiment in psychological warfare, designed to ensure that the next generation of Americans is too distracted to lead and too divided to innovate.

Whether intentional or emergent, influence over attention increasingly translates into long-term economic leverage.

The Human Cost of Invisible Conflict

Economic warfare succeeds because its consequences unfold slowly: hollowed industries, lost startups, diminished trust, and weakened social cohesion.

True resilience is not built by reacting to attacks, but by redesigning systems so exploitation becomes expensive and contribution becomes the easiest path forward.

Conclusion

This is not a call for isolation or paranoia. It is a call for strategic maturity.

Openness without safeguards is not virtue — it is exposure. Innovation without resilience is not leadership — it is extraction.

The era of complacency must end. We must treat economic security as national security. This means securing our universities, diversifying our supply chains, and demanding transparency in our digital and biological interactions. We have the power to stoke our own innovation bonfire, but only if we are willing to protect it from those who wish to extinguish it.

The next era of competition will reward nations and companies that design systems where trust is earned, reciprocity is enforced, and long-term value creation is protected.

Frequently Asked Questions

What is economic warfare?

Economic warfare refers to the use of non-military tools — such as intellectual property extraction, cyber operations, supply chain control, and influence platforms — to weaken a rival’s economic position and long-term competitiveness.

Is China the only country using these tactics?

No. Many nations engage in forms of economic competition that blur into coercion. The concern highlighted here is about scale, coordination, and the systematic exploitation of open systems.

How should the United States respond?

By strengthening resilience rather than retreating from openness — protecting critical research, diversifying supply chains, aligning innovation policy with national strategy, and designing systems that reward contribution over extraction.

How should your company protect itself?

Companies should identify their critical knowledge assets, limit unnecessary exposure, diversify suppliers, strengthen cybersecurity, enforce disclosure and governance standards, and design partnerships that balance collaboration with protection. Resilience should be treated as a strategic capability, not a compliance exercise.

Image credits: Google Gemini

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Outcome-Driven Innovation in the Age of Agentic AI

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The North Star Shift

LAST UPDATED: January 5, 2026 at 5:29PM

Outcome-Driven Innovation in the Age of Agentic AI

by Braden Kelley

In a world of accelerating change, the rhetoric around Artificial Intelligence often centers on its incredible capacity for optimization. We hear about AI designing new materials, orchestrating complex logistics, and even writing entire software applications. This year, the technology has truly matured into agentic AI, capable of pursuing and achieving defined objectives with unprecedented autonomy. But as a specialist in Human-Centered Innovation™ (which pairs well with Outcome-Driven Innovation), I pose two crucial questions: Who is defining these outcomes, and what impact do they truly have on the human experience?

The real innovation of 2026 will show not just that AI can optimize against defined outcomes, but that we, as leaders, finally have the imperative — and the tools — to master Outcome-Driven Innovation and Outcome-Driven Change. If innovation is change with impact, then our impact is only as profound as the outcomes we choose to pursue. Without thoughtful, human-centered specifications, AI simply becomes the most efficient way to achieve the wrong goals, leading us directly into the Efficiency Trap. This is where organizations must overcome the Corporate Antibody response that resists fundamental shifts in how we measure success.

Revisiting and Applying Outcome-Driven Change in the Age of Agentic AI

As we integrate agentic AI into our organizations, the principles of Outcome-Driven Change (ODC) I first introduced in 2018 are more vital than ever. The core of the ODC framework rests on the alignment of three critical domains: Cognitive (Thinking), Affective (Feeling), and Conative (Doing). Today, AI agents are increasingly assuming the “conative” role, executing tasks and optimizing workflows at superhuman speeds. However, as I have always maintained, true success only arrives when what is being done is in harmony with what the people in the organization and customer base think and feel.

Outcome-Driven Change Framework

If an AI agent’s autonomous actions are misaligned with human psychological readiness or emotional context, it will trigger a Corporate Antibody response that kills innovation. To practice genuine Human-Centered Change™, we must ensure that AI agents are directed to pursue outcomes that are not just numerically efficient, but humanly resonant. When an AI’s “doing” matches the collective thinking and feeling of the workforce, we move beyond the Efficiency Trap and create lasting change with impact.

“In the age of agentic AI, the true scarcity is not computational power; it is the human wisdom to define the right ‘North Star’ outcomes. An AI optimizing for the wrong goal is a digital express train headed in the wrong direction – efficient, but ultimately destructive.” — Braden Kelley

From Feature-Building to Outcome-Harvesting

For decades, many organizations have been stuck in a cycle of “feature-building.” Product teams were rewarded for shipping more features, marketing for launching more campaigns, and R&D for creating more patents. The focus was on output, not ultimate impact. Outcome-Driven Innovation shifts this paradigm. It forces us to ask: What human or business value are we trying to create? What measurable change in behavior or well-being are we seeking?

Agentic AI, when properly directed, becomes an unparalleled accelerant for this shift. Instead of building a new feature and hoping it works, we can now tell an AI agent, “Achieve Outcome X for Persona Y, within Constraints Z,” and it will explore millions of pathways to get there. This frees human teams from the tactical churn and allows them to focus on the truly strategic work: deeply understanding customer needs, identifying ethical guardrails, and defining aspirational outcomes that genuinely drive Human-Centered Innovation™.

Case Study 1: Sustainable Manufacturing and the “Circular Economy” Outcome

The Challenge: A major electronics manufacturer in early 2025 aimed to reduce its carbon footprint but struggled with the complexity of optimizing its global supply chain, product design, and end-of-life recycling simultaneously. Traditional methods led to incremental, siloed improvements.

The Outcome-Driven Approach: They defined a bold outcome: “Achieve a 50% reduction in virgin material usage across all product lines by 2028, while maintaining profitability and product quality.” They then deployed an agentic AI system to explore new material combinations, reverse logistics networks, and redesign possibilities. This AI was explicitly optimized to achieve the circular economy outcome.

The Impact: The AI identified design changes that led to a 35% reduction in material waste within 18 months, far exceeding human predictions. It also found pathways to integrate recycled content into new products without compromising durability. The organization moved from a reactive “greenwashing” approach to proactive, systemic innovation driven by a clear, human-centric environmental outcome.

Case Study 2: Personalized Education and “Mastery Outcomes”

The Challenge: A national education system faced stagnating literacy rates, despite massive investments in new curricula. The focus was on “covering material” rather than ensuring true student understanding and application.

The Outcome-Driven Approach: They shifted their objective to “Ensure 90% of students achieve demonstrable mastery of core literacy skills by age 10.” An AI tutoring system was developed, designed to optimize for individual student mastery outcomes, rather than just quiz scores. The AI dynamically adapted learning paths, identified specific knowledge gaps, and even generated custom exercises based on each child’s learning style.

The Impact: Within two years, participating schools saw a 25% improvement in mastery rates. The AI became a powerful co-pilot for teachers, freeing them from repetitive grading and allowing them to focus on high-touch mentorship. This demonstrated how AI, directed by human-defined learning outcomes, can empower both educators and students, moving beyond the Efficiency Trap of standardized testing.

Leading Companies and Startups to Watch

As 2026 solidifies Outcome-Driven Innovation, several entities are paving the way. Amplitude and Pendo are evolving their product analytics to connect feature usage directly to customer outcomes. In the AI space, Anthropic‘s work on “Constitutional AI” is fascinating, as it seeks to embed human-defined ethical outcomes directly into the AI’s decision-making. Glean and Perplexity AI are creating agentic knowledge systems that help organizations define and track complex outcomes across their internal data. Startups like Metaculus are even democratizing the prediction of outcomes, allowing collective intelligence to forecast the impact of potential innovations, providing invaluable insights for human decision-makers. These players are all contributing to the core goal: helping humans define the right problems for AI to solve.

Conclusion: The Human Art of Defining the Future

The year 2026 is a pivotal moment. Agentic AI gives us unprecedented power to optimize, but with great power comes great responsibility — the responsibility to define truly meaningful outcomes. This is not a technical challenge; it is a human one. It requires deep empathy, strategic foresight, and the courage to challenge old metrics. It demands leaders who understand that the most impactful Human-Centered Innovation™ starts with a clear, ethically grounded North Star.

If you’re an innovation leader trying to navigate this future, remember: the future is not about what AI can do, but about what outcomes we, as humans, choose to pursue with it. Let’s make sure those outcomes serve humanity first.

Frequently Asked Questions

What is “Outcome-Driven Innovation”?

Outcome-Driven Innovation (ODI) is a strategic approach that focuses on defining and achieving specific, measurable human or business outcomes, rather than simply creating new features or products. AI then optimizes for these defined outcomes.

How does agentic AI change the role of human leaders in ODI?

Agentic AI frees human leaders from tactical execution and micro-management, allowing them to focus on the higher-level strategic work of identifying critical problems, understanding human needs, and defining the ethical, impactful outcomes for AI to pursue.

What is the “Efficiency Trap” in the context of AI and outcomes?

The Efficiency Trap occurs when AI is used to optimize for speed or cost without first ensuring that the underlying outcome is meaningful and human-centered. This can lead to highly efficient processes that achieve undesirable or even harmful results, ultimately undermining trust and innovation.

Image credits: Braden Kelley, Google Gemini

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

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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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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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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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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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