Category Archives: Innovation

Can AI Replace the CEO?

A Day in the Life of the Algorithmic Executive

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

Can AI Replace the CEO?

GUEST POST from Art Inteligencia

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

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

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

— Braden Kelley

The Efficiency Play: Data Without Drama

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

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

The Experiment: A Virtual CEO in a Gaming Giant

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

The Results

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

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

The Challenge: The Face of Innovation

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

The Insight

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

Leading Companies and Startups to Watch

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

The Verdict: The Hybrid Future

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

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

Frequently Asked Questions

Can an AI legally serve as a CEO?

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

What are the biggest risks of an AI CEO?

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

How should current CEOs prepare for AI leadership?

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

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

Image credits: Google Gemini

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AI Stands for Accidental Innovation

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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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The Rise of Human-AI Teaming Platforms

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Designing Partnership, Not Replacement

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

Human-AI Teaming Platforms

GUEST POST from Art Inteligencia

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

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

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

Braden Kelley

The Cognitive Collaborative Shift

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

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

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

Braden Kelley

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

Case Studies in Symbiosis

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

Case Study 1: Mastercard and the Decision Management Platform

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

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

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

Case Study 2: Autodesk and Generative Design in Engineering

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

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

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

Leading Platforms and Startups to Watch

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

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

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

Conclusion: The Leadership Imperative

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

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

Frequently Asked Questions regarding Human-AI Teaming

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

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

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

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

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

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

Image credits: Google Gemini

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Drive Out Fear for Innovation to Flow

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Drive Out Fear for Innovation to Flow

GUEST POST from Mike Shipulski

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

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

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

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

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

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

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

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

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Voting Open – Top 40 Innovation Authors of 2025

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Vote for Top 40 Innovation Authors Happy Holidays!

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Nominations Open – Top 40 Innovation Authors of 2025

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Human-Centered Change and Innovation loves making innovation insights accessible for the greater good, because we truly believe that the better our organizations get at delivering value to their stakeholders the less waste of natural resources and human resources there will be.

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

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

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

The deadline for submitting nominations is December 24, 2025 at midnight GMT.

You can submit a nomination either of these two ways:

Sending us the name of the author and the url of their blog by @reply on twitter to @innovate
Sending the name of the author and the url of their blog and your e-mail address using our contact form

(Note: HUGE bonus points for being a contributing author)

So, think about who you like to read and let us know by midnight GMT on December 24, 2025.

We will then compile a voting list of all the nominations, and publish it on December 25, 2025.

Voting will then be open from December 25, 2025 – January 1, 2026 via comments and twitter @replies to @innovate.

The ranking will be done by me with influence from votes and nominations. The quality and quantity of contributions by an author to this web site will be a contributing factor.

Contact me with writing samples if you’d like to publish your articles on our platform!

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

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

SPECIAL BONUS: From now until December 31, 2025 you can get either the hardcover or softcover of my latest best-selling book Charting Change (free shipping worldwide) for only £/$/€ 23.99 (~36% OFF).

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Will our opinion still really be our own in an AI Future?

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Will our opinion still really be our own in an AI Future?

GUEST POST from Pete Foley

Intuitively we all mostly believe our opinions are our own. After all, they come from that mysterious thing we call consciousness that resides somewhere inside of us.

But we also know that other peoples opinions are influenced by all sorts of external influences. So unless we as individuals are uniquely immune to influence, it begs at the question; ‘how much of what we think, and what we do, is really uniquely us?’ And perhaps even more importantly, as our understanding of behavioral modification techniques evolves, and the power of the tools at our disposal grows, how much mental autonomy will any of us truly have in the future?

AI Manipulation of Political Opinion: A recent study from the Oxford Internet Institute (OII) and the UK AI Security Institute (AISI) showed how conversational AI can meaningfully influence peoples political beliefs. https://www.ox.ac.uk/news/2025-12-11-study-reveals-how-conversational-ai-can-exert-influence-over-political-beliefs . Leveraging AI in this way potentially opens the door to a step-change in behavioral and opinion manipulation inn general. And that’s quite sobering on a couple of fronts. Firstly, for many today their political beliefs are deeply tied to our value system and deep sense of self, so this manipulation is potentially profound. Secondly, if AI can do this today, how much more will it be able to do in the future?

A long History of Manipulation: Of course, manipulation of opinion or behavior is not new. We are all overwhelmed by political marketing during election season. We accept that media has manipulated public opinion for decades, and that social media has amplified this over the last few decades. Similarly we’ve all grown up immersed in marketing and advertising designed to influence our decisions, opinions and actions. Meanwhile the rise in prominence of the behavioral sciences in recent decades has provided more structure and efficiency to behavioral influence, literally turning an art into a science. Framing, priming, pre-suasion, nudging and a host of other techniques can have a profound impact on what we believe and what we actually do. And not only do we accept it, but many, if not most of the people reading this will have used one or more of these channels or techniques.

An Art and a Science: And behavioral manipulation is a highly diverse field, and can be deployed as an art or a science. Whether it’s influencers, content creators, politicians, lawyers, marketers, advertisers, movie directors, magicians, artists, comedians, even physicians or financial advisors, our lives are full of people who influence us, often using implicit cues that operate below our awareness.

And it’s the largely implicit nature of these processes that explains why we tend to intuitively think this is something that happens to other people. By definition we are largely unaware of implicit influence on ourselves, although we can often see it in others. And even in hindsight, it’s very difficult to introspect implicit manipulation of our own actions and opinions, because there is often no obvious conscious causal event.

So what does this mean? As with a lot of discussion around how an AI future, or any future for that matter, will unfold, informed speculation is pretty much all we have. Futurism is far from an exact science. But there are a couple of things we can make pretty decent guesses around.

1. The ability to manipulate how people think creates power and wealth.

2. Some will use this for good, some not, but given the nature of humanity, it’s unlikely that it will be used exclusively for either.

3. AI is going to amplify our ability to manipulate how people think.

The Good news: Benevolent behavioral and opinion manipulation has the power to do enormous good. Whether it’s mental health and happiness (an increasingly challenging area as we as a species face unprecedented technology driven disruption), health, wellness, job satisfaction, social engagement, important for many of us, adoption of beneficial technology and innovation and so many other areas can benefit from this. And given the power of the brain, there is even potential for conceptual manipulation to replace significant numbers of pharmaceuticals, by, for example, managing depression, or via preventative behavioral health interventions. Will this be authentic? It’s probably a little Huxley dystopian, but will we care? It’s one of the many ethical connundrums AI will pose us with.

The Bad News. Did I mention wealth and power? As humans, we don’t have a great record of doing the right thing when wealth and power come into the equation. And AI and AI empowered social, conceptual and behavioral manipulation has potential to concentrate meaningful power even more so than today’s tech driven society. Will this be used exclusively for good, or will some seek to leverage for their personal benefit at the expense of the border community? Answers on a postcard (or AI generated DM if you prefer).

What can and should we do? Realistically, as individuals we can self police, but we obviously also face limits in self awareness of implicit manipulations. That said, we can to some degree still audit ourselves. We’ve probably all felt ourselves at some point being riled up by a well constructed meme designed to amplify our beliefs. Sometimes we recognize this quickly, other times we may be a little slower. But just simple awareness of the potential to be manipulated, and the symptoms of manipulation, such as intense or disproportionate emotional responses, can help us mitigate and even correct some of the worst effects.

Collectively, there are more opportunities. We are better at seeing others being manipulated than ourselves. We can use that as a mirror, and/or call it out to others when we see it. And many of us will find ourselves somewhere in the deployment chain, especially as AI is still in it’s early stages. For those of us that this applies to, we have the opportunity to collectively nudge this emerging technology in the right direction. I still recall a conversation with Dan Ariely when I first started exploring behavioral science, perhaps 15-20 years ago. It’s so long ago I have to paraphrase, but the essence of the conversation was to never manipulate people to do something that was not in there best interest.

There is a pretty obvious and compelling moral framework behind this. But there is also an element of enlightened self interest. As a marketer working for a consumer goods company at the time, even if I could have nudged somebody into buying something they really didn’t want, it might have offered initial success, but would likely come back to bite me in the long-term. They certainly wouldn’t become repeat customers, and a mixture of buyers remorse, loss aversion and revenge could turn them into active opponents. This potential for critical thinking in hindsight exists for virtually every situation where outcomes damage the individual.

The bottom line is that even today, we already ave to continually ask ourselves if what we see is real, if our beliefs are truly our own, or have they been manipulated? Media and social media memes already play the manipulation game. AI may already be better, and if not, it’s only a matter of time before it is. If you think we are politically polarized now, hang onto your hat!!! But awareness is key. We all need to stay aware, be conscious of manipulation in ourselves and others, and counter it when we see it occurring for the wrong reasons.

Image credits: Google Gemini

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Why Are We Forcing People Back into Cubicles?

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Why Are We Forcing People Back into Cubicles?

GUEST POST from Mike Shipulski

Whether it’s placing machine tools on the factory floor or designing work spaces for people that work at the company, the number one guiding metric is resources per square foot. If you’re placing machine tools, this metric causes the machines to be stacked closely together, where the space between them is minimized, access to the machines is minimized, and the aisles are the smallest they can be. The result – the number of machines per square foot is maximized.

And though there has been talk of workplaces that promote effective interactions and creativity, the primary metric is still people per square foot. Don’t believe me? I have one word for you – cubicles. Cubicles are the design solution of choice when you want to pack the most people into the smallest area.

Here’s a test. At your next team meeting, ask people to raise their hand if they hate working in a cubicle. I rest my case.

With cubicles, it’s the worst of both worlds. There is none of the benefit of an office and none of the benefit of collaborative environment. They are half of neither.

What is one of Dilbert’s favorite topic? Cubicles.

If no one likes them, why do we still have them? If you want quiet, cubicles are the wrong answer. If you want effective collaboration, cubicles are the wrong answer. If everyone hates them, why do we still have them?

When people need to do deep work, they stay home so they can have peace and quiet. When people they want to concentrate, they avoid cubicles at all costs. When you need to focus, you need quiet. And the best way to get quiet is with four walls and a door. Some would call that and office, but those are passe. And in some cases, they are outlawed. In either case, they are the best way to get some quiet time. And, as a side benefit, they also block interruptions.

Best way for people to interact is face-to-face. And in order to interact at way, they’ve got to be in the same place at the same time. Sure spontaneous interactions are good, but it’s far better to facilitate interactions with a fixed schedule. Like with a bus stop schedule, people know where to be and when. In that way, many people can come together efficiently and effectively and the number of interactions increases dramatically. So why not set up planned interactions at ten in the morning and two in the afternoon?

I propose a new metric for facilities design – number of good ideas per square foot. Good ideas require deep thought, so quiet is important. And good ideas require respectful interaction with others, so interactions are important.

I’m not exactly sure what a facility must look like to maximize the number of good ideas per square foot, but I do know it has no cubicles.

Image credit: Unsplash

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Do What 91% of Executives Will Not

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Winning in Times of Uncertainty

Do What 91% of Executives Will Not

GUEST POST from Robyn Bolton

In times of great uncertainty, we seek safety. But what does “safety” look like?

What We Say: Safety = Data

We tend to believe that we are rational beings and, as a result, we rely on data to make decisions.

Great! We’ve got lots of data from lots of uncertain periods. HBR examined 4,700 public companies during three global recessions (1980, 1990, and 2000). They found that the companies that emerged “outperforming rivals in their industry by at least 10% in terms of sales and profits growth” had one thing in common: They aggressively made cuts to improve operational efficiency and ruthlessly invested in marketing, R&D, and building new assets to better serve customers have the highest probability of emerging as markets leaders post-recession.

This research was backed up in 2020 in a McKinsey study that found that “Organizations that maintained their innovation focus through the 2009 financial crisis, for example, emerged stronger, outperforming the market average by more than 30 percent and continuing to deliver accelerated growth over the subsequent three to five years.”

What We Do: Safety = Hoarding

The reality is that we are human beings and, as a result, make decisions based on how we feel and the use data to justify those decisions.

How else do you explain that despite the data, only 9% of companies took the balanced approach recommended in the HBR study and, ten years later, only 25% of the companies studied by McKinsey stated that “capturing new growth” was a top priority coming out of the COVID-19 pandemic.

Uncertainty is scary so, as individuals and as organizations, we scramble to secure scarce resources, cut anything that feels extraneous, and shift or focus to survival.

What now? AND, not OR

What was true in 2010 is still true today and new research from Bain offers practical advice for how leaders can follow both their hearts and their heads.

Implement systems to protect you from yourself. Bain studied Fast Company’s 50 Most Innovative Companies and found that 79% use two different operating models for innovation to combat executives’ natural risk aversion. The first, for sustaining innovation uses traditional stage-gate models, seeks input from experts and existing customers, and is evaluated on ROI-driven metrics.

The second, for breakthrough innovations, is designed to embrace and manage uncertainty by learning from new customers and emerging trends, working with speed and agility, engaging non-traditional collaborators, and evaluating projects based on their long-term potential and strategic option value.

Don’t outspend. Out-allocate. Supporting the two-system approach, nearly half of the companies studied send less on R&D than their peers overall and spend it differently: 39% of their R&D budgets to sustaining innovations and 61% to expanding into new categories or business models.

Use AI to accelerate, not create. Companies integrating AI into innovation processes have seen design-to-launch timelines shrink by 20% or more. The key word there is “integrate,” not outsource. They use AI for data and trend analysis, rapid prototyping, and automating repetitive tasks. But they still rely on humans for original thinking, intuition-based decisions, and genuine customer empathy.

Prioritize humans above all else. Even though all the information in the world is at our fingerprints, humans remain unknowable, unpredictable, and wonderfully weird. That’s why successful companies use AI to enhance, not replace, direct engagement with customers. They use synthetic personas as a rehearsal space for brainstorming, designing research, and concept testing. But they also know there is no replacement (yet) for human-to-human interaction, especially when creating new offerings and business models.

In times of create uncertainty, we seek safety. But safety doesn’t guarantee certainty. Nothing does. So, the safest thing we can do is learn from the past, prepare (not plan) for the future, make the best decisions possible based on what we know and feel today, and stay open to changing them tomorrow.

Image credit: Pexels

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