Motivating the Unmotivated

Motivating the Unmotivated

GUEST POST from David Burkus

Motivation can vary wildly on a team. At any given time, a few people might be highly motivated, while others are totally unmotivated. Ideally, there are times where everyone is motivated at once, but sadly there may be times when everyone is demotivated or burnt out. All this means that an inescapable part of a leader’s job is to motivate the unmotivated.

The good news is that leaders don’t have to rely on raw charisma or the inspirational words of a halftime speech from insert-your-favorite-sports-movie-here. Instead, motivation is less about the qualities of the leader and more about understanding the needs of the team and of each individual on the team.

In this article, we’ll outline five ways to motivate the unmotivated.

Change Up Tasks

The first way to motivate the unmotivated is to change up tasks. Novelty can be a powerful motivator, and the lack of novelty in a job can be demotivating. Few people get excited about coming to work and repeating the same few tasks over and over again. People want new experiences and new challenges. They want to feel that they’re making progress and they often judge that progress based on the projects they’re being given and whether those projects require them to learn new skills or merely execute the same routine functions.

As a leader, this means examining the task list of your motivated team members. Are they doing the same old over again or are they being given new, growth-inducing tasks and projects to work on. You may not be able to change their job description, but you can help them find new learning opportunities or ask them to sit in on meetings they’re not regularly a part of. Even a little novelty can go a long way toward restoring motivation.

Build New Bonds

The second way to motivate the unmotivated is to build new bonds. Over four decades of research have made a compelling case that relatedness is an essential element of intrinsic motivation. People want to feel cared for and feel that their work cares for others. They want to feel connected to the people their work serves and the people they work alongside. And if they feel disconnected or isolated from the team or the customers/stakeholders of an organization, they can become unmotivated.

As a leader, there are two ways to utilize relatedness to motivate the unmotivated. The first is to make sure team members feel connected to each other, most often by making time for socialization and connection through non-work discussions. (The second we’ll cover in a moment.) It may seem like a waste of time, but social functions, icebreakers, or any other activities where people talk about their lives outside of work create opportunities for stronger connections to form. And there’s a strong connection between social connection and motivation.

Re-frame The Work

The third way to motivate the unmotivated is to re-frame the work. As discussed above, knowing how your work serves others can be a powerful motivator. But for many jobs, teams are so far removed from the end customers or even from other teams who benefit from their work that they lose sight of how their work makes a difference. Their work loses task significance, and their motivation quickly follows. And the larger the organization, the harder it is to keep task significance.

As a leader, restoring task significance and relatedness requires re-framing the work or rebuilding connections to those who benefit directly from your team’s work. This could be by bringing customers in to meet your team, or by sharing thank you notes or stories of how the team’s tasks enabled others to work or live better. The test for whether your team needs a re-frame is how quickly they can answer the question “Who is served by the work that we do?” And if they can’t find an answer fast, they likely can’t find their motivation either.

Provide More Feedback

The fourth way to motivate the unmotivated is to provide more feedback. Ken Blanchard was right, “feedback is the breakfast of champions.” We already covered how a feeling of growth and development contributes to motivation. But without regular feedback, your people don’t know what to improve upon—or if they’re improving. As well-intentioned as annual reviews are, they are not a sufficient source of feedback to keep people motivated to improve. Instead, try regular check-ins and feedback sessions both individually and as a team.

As a leader, it’s important to note the distinction between providing sufficient feedback and becoming a micromanager. As people grow and develop in their role, feedback should shift from telling people how to do specific tasks and towards coaching them to solve problems they’re already equipped to solve. In the beginning, provide feedback to help them grow. But as they develop, provide feedback that helps them notice their growth.

Watch The Stress

The fifth way to motivate the unmotivated is to watch the stress. Most leaders know that too much stress can demotivate anyone. But too little stress can be demotivating as well. Psychologist have long known about a concept called eustress—the sweet spot of stress where the demands of the moment match their ability and capacity. Too much demand leads to distress and burnout, but too little demand leads to boredom and…burnout.

As a leader, watching the stress means monitoring your team’s capacity so that they don’t get overloaded. In which case, you’ll want to find ways to offload certain projects or otherwise reduce the workload. But it also means watching each individual on your team for signs that they’re not being challenged enough. In which case, you’ll want to consider other methods in this article for ways to help them feel more growth and challenge in their work. In either case, the goal is to continue to make adjustments and continue to watch the stress, to bring it back to that eustress level.

And monitoring and making adjustments is really the ideal for each of these methods to motivate the unmotivated. Because motivation is individual. It’s felt on an individual level. Which means increasing motivation requires knowing each person individually and continuing to monitor their motivation levels for individual adjustments that need to be made. But when you do, it will raise the overall motivation on your team, and raise the level of performance until everyone on the team can do their best work ever.

Image credit: 1,300+ free quotes for your presentations at http://misterinnovation.com

Originally published at https://davidburkus.com on January 2, 2023.

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The Energy Grid Revolt

FCEVs, and the Pragmatic Pivot in Eco-Conscious Mobility

LAST UPDATED: June 19, 2026 at 4:11 PM

Honda CR-V e:FCEV plug-in hybrid charging next to a stressed electrical grid utility tower

GUEST POST from Art Inteligencia


The Great Grid Contraction and the Consumer Revolt

A perfect storm is hitting the aging American energy grid. On one side, residential electricity costs are hitting historic highs as utilities scramble to fund infrastructure upgrades. On the other, the nation faces a massive, unprecedented surge in energy demand driven by the expansion of AI data centers — a technological race America must win to maintain global economic leadership.

For the everyday consumer, this collision is creating massive experience friction. The original economic promise of electric vehicles — the idea of “fueling up for cheap at home” — is rapidly eroding when charging a high-capacity battery overnight becomes a glaring, high-impact line item on a strained household budget. Forcing millions of new vehicles onto the grid while simultaneously enacting localized natural gas bans creates a single point of failure that stresses both family finances and municipal infrastructure.

The Strategic Pivot: A Case for Pragmatic Change Management

True innovation never forces people into an unstable, single-source bottleneck. Instead of top-down mandates that ignore current physical and economic realities, a human-centered approach to mobility demands a strategic pause. We must allow power generation infrastructure to catch up to our digital ambitions while diversifying our energy portfolio to keep the economy resilient.

By hitting the brakes on aggressive EV sales timelines and restoring energy choice through the repeal of natural gas restrictions, we can protect the grid for vital computing infrastructure. This pragmatic pivot shifts the spotlight back to highly efficient internal combustion hybrids and adaptive, forward-looking alternatives like the plug-in hydrogen fuel cell hybrid. It is time to design for the world we actually inhabit, ensuring a stable foundation for both physical mobility and digital transformation.

Case Study: Is the Honda CR-V e:FCEV a True Innovation?

The traditional fuel cell electric vehicle (FCEV) market has long suffered from a classic chicken-and-egg dilemma: consumers won’t buy hydrogen cars without a refueling network, and stakeholders won’t build stations without cars on the road. Past pioneers forced an rigid, all-or-nothing infrastructure choice onto the driver. The Honda CR-V e:FCEV represents a true paradigm shift because it introduces a human-centered, adaptive approach — the co-creation of convenience.

Hand-assembled at Honda’s Performance Manufacturing Center in Marysville, Ohio, the vehicle represents a major technological leap by combining two distinct zero-emission engineering principles into a single, cohesive customer experience.

The Twin-Engine Topology: Designing for Real-World Ecosystems

Instead of forcing the driver to rely solely on public hydrogen networks, the CR-V e:FCEV integrates a dual-energy architecture that adapts directly to the user’s daily habits and local infrastructure constraints:

  • The 17.7-kWh Plug-In On-Board Battery: This lithium-ion system grants approximately 29 miles of pure electric, battery-powered range on a full charge. For the eco-conscious consumer, this handles the vast majority of local, daily commuting entirely on household electricity. Because the battery capacity is modest compared to a massive 100-kWh pure electric vehicle, it charges rapidly on standard Level 1 or Level 2 equipment without triggering expensive panel upgrades or severe local grid stress.
  • The Next-Generation Fuel Cell Stack: Co-developed through a landmark engineering joint venture with General Motors, this advanced proton-exchange membrane system represents a massive manufacturing milestone. Built at Fuel Cell System Manufacturing (FCSM) in Michigan, the co-developed stack achieves double the durability while reducing production costs by two-thirds compared to previous generations. Feeding from dual 10,000 psi high-pressure tanks holding 4.3 kilograms of compressed hydrogen gas, it delivers an overall 270-mile EPA range rating and refuels completely in just 3 to 5 minutes.

The Verdict from an Experience Design Perspective

From an innovation management standpoint, the CR-V e:FCEV is a brilliant bridge architecture. It systematically mitigates “range anxiety” and “charging-station downtime friction” simultaneously. True human-centered design acknowledges the messiness of the world as it exists today rather than designing for an idealized, frictionless future. By treating the consumer as an active partner and offering energy flexibility, Honda has created a blueprint for resilient, adaptive mobility.

The Macro Outlook: The Global and American Infrastructure Split

An innovation is only as powerful as the ecosystem that supports it. While the Honda CR-V e:FCEV represents a masterful piece of human-centered engineering, its market viability is completely dependent on regional infrastructure architecture. When we analyze the landscape through a global lens, we see a stark divergence in how different societies are structuring the future of clean mobility.

The American Landscape: Severe Regional Fragmentation

In the United States, the deployment of consumer hydrogen infrastructure remains highly fractured and localized. Outside of California—where early public-private investments attempted to establish initial hydrogen corridors—the vast majority of the American continent remains a complete refueling desert for retail hydrogen consumers. Because of this stark geographical limitation, Honda is rolling out the CR-V e:FCEV as a regional, lease-only vehicle, targeted primarily at markets with established hydrogen ecosystems.

This dynamic illustrates the critical importance of systemic change management: a technological breakthrough cannot scale if the surrounding infrastructure remains trapped in a localized silo. Until federal and state initiatives prioritize comprehensive midstream hydrogen logistics and production, fuel cell vehicles in America will largely serve as specialized, pilot-program solutions rather than mainstream alternatives.

The Global Matrix: Strategic Infrastructure Realignment

Beyond American borders, the strategic playbook changes rapidly, driven by unique geographic, economic, and geopolitical imperatives:

  • Europe: The European strategy leans heavily on high-traffic, industrial, and heavy commercial transport corridors. Rather than deploying sparse consumer networks, European nations are prioritizing high-capacity hydrogen refueling hubs along primary freight routes, recognizing that fuel cell technology provides the rapid turnaround times and high-payload capabilities required to decarbonize commercial logistics and public transit networks.
  • Asia-Pacific (Japan, South Korea, China): In these high-density urban economies, hydrogen is viewed as a pillar of long-term energy security and a necessary alternative to widespread battery electrification. In cities characterized by massive, multi-tenant residential high-rises, overnight at-home charging for millions of individual battery-electric vehicles is structurally and logistically impossible. Consequently, national policy initiatives are aggressively subsidizing high-pressure hydrogen distribution networks to power both consumer fleets and regional distributed energy grids.

The Strategic Takeaway: Mobility is Not a Monolith

The global divergence in hydrogen adoption proves that the “Future of Mobility” will not be a singular, globally standardized platform. True innovation leaders do not design for a fictional, universally uniform market. They recognize that physical, economic, and geographic constraints dictate technology adoption, requiring diverse, localized innovation architectures to successfully bridge the transition toward a more resilient energy ecosystem.

The Strategic Pause: Aligning Grid Capacity with Sovereign AI Leadership

Forcing a premature, top-down transition to heavy battery-electric vehicles (BEVs) before a stable, affordable, and robust electrical grid exists is an administrative mandate lacking empathy for real-world economic conditions. True innovation requires us to zoom out and analyze the broader macro-ecosystem. Today, a profound industrial conflict is brewing: the rapid, exponential computing requirements of the artificial intelligence revolution are colliding directly with consumer grid capacity.

Winning the global race to lead the AI industry demands unprecedented amounts of stable, high-density, uninterrupted baseload power for next-generation data centers. This computational infrastructure is the primary engine of our future economy. We cannot afford to compromise this critical digital runway by overloading the grid with artificial peak demands from enforced vehicle electrification and short-sighted municipal mandates.

The Policy Recalibration: Pausing Mandates and Restoring Portfolio Diversity

To ensure American economic resilience and technological sovereignty, we must implement a pragmatic change management strategy at the civic, county, and state levels:

  • Implementing a Strategic EV Sales Mandate Pause: Policymakers must temporarily halt aggressive timelines and purchasing mandates for pure electric vehicles. This strategic pause buys critical time for public utilities and independent power producers to build out modern, high-capacity generation infrastructure, transition to safer nuclear or advanced clean energy options, and stabilize regional distribution lines.
  • Repealing Punitive Natural Gas Bans: Restoring balance requires immediately dismantling localized municipal and state bans on residential and commercial natural gas infrastructure. Forcing space heating, water heating, and cooking completely onto an already strained electrical grid creates a precarious single point of failure. Reinstating natural gas options ensures a diversified energy portfolio and protects citizens from catastrophic grid failures during peak seasonal demand.

The Eco-Conscious Portfolio Approach

From an experience design perspective, innovation should be participatory, not enforced through economic scarcity or utility rate shocks. While the power grid catches up to our digital ambitions, eco-conscious consumers should be empowered to direct their attention toward a highly efficient, diverse mobility portfolio:

  1. Ultra-Efficient ICE and Traditional Hybrids: Highly optimized internal combustion and standard hybrid technologies deliver exceptional fuel economy (often exceeding 40 to 50 MPG) and immediate carbon reduction today, entirely utilizing existing refueling infrastructure without placing a single watt of additional demand on a fragile electrical grid.
  2. Plug-In Hydrogen Hybrids (FCEV/BEV Blends): Vehicles engineered with the topology of the Honda CR-V e:FCEV offer an ideal blueprint. By utilizing a small, easily managed battery for local trips and a high-pressure fuel cell stack for extended range, they demonstrate how we can transition toward zero-emission transportation without demanding massive, system-wide grid overhauls.

The path forward requires a shift in focus from subsidizing individual vehicle purchases to fundamentally upgrading our systemic infrastructure. By stabilizing our foundational power generation first, we protect the consumer’s economic reality, maintain grid reliability, and fuel the computational power required to lead the next century of technological innovation.

Conclusion: Designing for the World We Have, Not the One We Want

True change management requires the harmonious alignment of economics, technology, and human behavior. When top-down administrative mandates outpace the physical realities of infrastructure, the system breaks down. Today, as skyrocketing utility costs trigger a widespread consumer revolt and the computational demands of the AI revolution reshape our energy landscape, the primary survival mechanism for both households and economies is flexibility.

The path forward cannot be dictated by rigid, single-source mandates that ignore regional grid limitations. Instead, we must embrace an ecosystem-wide perspective that balances our digital ambitions with physical constraints. By implementing a pragmatic pause on aggressive vehicle electrification, restoring energy choice through the repeal of short-sighted natural gas bans, and allowing power generation infrastructure the runway it needs to catch up, we ensure a more stable and resilient economy.

The Blueprint for Adaptive Mobility

The Honda CR-V e:FCEV serves as a profound beacon of this necessary transition. It stands as an explicit engineering reminder to automakers, regulators, and policy architects alike: the most elegant technology is fundamentally useless if it ignores the economic, geographic, and systemic realities of the environment it inhabits.

By offering a dual-energy paradigm—combining local plug-in convenience with long-range hydrogen capability—it demonstrates how true human-centered innovation can co-create convenience with the consumer. As we look toward the future direction of mobility in America and across the globe, our success will not be measured by how quickly we can force a single solution, but by how skillfully we design diverse, adaptive, and resilient portfolios that empower human progress.

Frequently Asked Questions (FAQ)

What is a plug-in hydrogen fuel cell hybrid vehicle (FCEV)?

Unlike standard fuel cell vehicles that rely exclusively on hydrogen gas, a plug-in fuel cell hybrid integrates a modest, rechargeable lithium-ion battery package with a hydrogen fuel cell stack. This dual-energy architecture allows drivers to plug into standard electrical outlets for short, everyday trips while utilizing high-pressure hydrogen for extended range and rapid 3-to-5-minute refueling on longer journeys.

Can the Honda CR-V e:FCEV run purely on electricity without hydrogen?

Yes. The vehicle features a 17.7-kWh onboard battery that delivers an EPA-rated 29 miles of pure electric driving. For daily, local commuting, you can operate the vehicle entirely as a battery-electric vehicle (BEV), charging it at home overnight without using a single gram of hydrogen gas.

Why are some experts advocating for a strategic pause on absolute EV sales mandates?

The transition to massive, pure-battery electric vehicles is placing extreme stress on an aging electrical grid, contributing to skyrocketing utility rates for consumers. Simultaneously, the explosive growth of artificial intelligence requires massive, uninterrupted baseload power for regional data centers. A strategic pause on vehicle mandates allows public utilities critical time to build out modern power generation infrastructure without triggering grid failures or economic instability.

How does repealing natural gas bans protect the consumer energy experience?

Forcing space heating, water heating, and cooking completely onto the electrical grid creates a precarious single point of failure and drastically increases residential peak loads. Repealing natural gas bans restores energy choice and portfolio diversity, ensuring households remain resilient during extreme weather events while reducing the immediate, artificial demand on regional power grids.

Where can the Honda CR-V e:FCEV be driven today?

Because consumer high-pressure hydrogen refueling infrastructure is highly fractured and primarily localized in California, Honda is rolling out the CR-V e:FCEV through a specialized, regional lease program. It is specifically designed as a bridge innovation, maximizing its utility in regions with established hydrogen ecosystems while offering plug-in electrical flexibility anywhere standard charging equipment is available.


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: Gemini

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Thinking From No to Yes for Top Line Growth

Top line growth strategies and product applicability frameworks

GUEST POST from Mike Shipulski

Bottom line growth is good, but top line growth is better. But if you want to grow the bottom line, ignore labor costs and reduce material costs. Labor cost is only 5-10% of product cost. Stop chasing it, and, instead, teach your design community to simplify the product so it uses fewer parts and design out the highest cost elements.

Where the factory creates bottom line growth, top line growth is generated in the market/customer domain. The best way I know to grow the top line is to broaden the applicability of your products and services. But, before you can broaden applicability, you’ve got to define applicability as it is. Define the limits of what your product can do – how much it can lift, how fast it can run a calculation and where it can be used. And for your service, define who can use it, where it can be used and what elements without customer involvement. And with the limits defined, you know where top line growth won’t come from.

Radical top line growth comes only when your products and services can be used in new applications. Sure, you can train your sales force to sell more of what you already have, but that runs out of gas soon enough. But, real top line growth comes when your services serve new customers in new ways. By definition, if you’re not trying to make your product work in new ways, you’re not going to achieve meaningful top line growth. And by definition, if you’re not creating new functionality for your services, you might as well be focusing on bottom line growth.

If your product couldn’t do it and now it can, you’re doing it right. If your service couldn’t be used by people that speak Chinese and now it can, you’re on your way. If your product couldn’t be used in applications without electricity and now it can, you’re on to something. If your service couldn’t run on a smartphone and now it can, well, you get the idea.

For the acid test, think no-to-yes.

If your product can’t work in application A, you can’t sell it to people who do that work. If your service can’t be used by visually impaired people, you’re not delivering value to them and they won’t buy it. Turning can’t into can is a big deal. But you’ve got to define can’t before you can turn it into can. If you want top line growth, take the time to define the limits of applicability.

No-to-yes is powerful because it creates clarity. It’s easy to know when a project will create no-to-yes functionality and when it won’t. And that makes it easy to stop projects that don’t deliver no-to-yes value and start projects that do.

No-to-yes is the key element of a compete-with-no-one approach to business.

Image credits: Pixabay

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Is it Possible to be Incorruptible?

Is it Possible to be Incorruptible?

Exclusive Interview with Eric Ries

This candid, wide-ranging Q&A dives deep into what Eric Ries calls the “physics of organizations” — the hidden structural and financial forces that dictate whether a company thrives or decays over time. Moving past superficial business trends, the conversation tackles the intense psychological toll of entrepreneurship, the systemic flaws of shareholder primacy, and the historical reality of alternative corporate governance.

Over the last two decades, Eric Ries’ ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader’s Guide; and The Startup Way.

Eric RiesAs a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; Virgil, a legal services startup; and IMVU. On The Eric Ries Show, he talks with world-class technologists, thought leaders, and executives building for the long-term. He lives in the San Francisco Bay Area with his wife and three children. He is excited to announce his latest book Incorruptible: Why Good Companies Go Bad… and How Great Companies Stay Great.

Ries offers a provocative look at how truly resilient, mission-driven institutions can protect themselves from the gravitational pull of short-term financial systems to prioritize long-term human flourishing.

Below is the text of my interview with Eric and a preview of the kinds of insights you’ll find in Incorruptible presented in a Q&A format:

1. Why do purpose-driven companies create so much value for society?

The evidence shows that purpose driven companies outperform conventional companies financially as well as in almost any other dimension you care to measure, including the social dimension. Intuitively, this makes a lot of sense, because entrepreneurship is very difficult. Everyone says they know this, but I don’t think we really grapple with this fact nearly enough. If you just want to make money, there simply are better, more convenient ways than entrepreneurship. So to get not just the founder, but the early team, the early investors, all these people to take a risk to do this crazy thing generally requires some kind of extra-financial purpose or goal. Sometimes we call that vision, sometimes we call that, in a more demeaning way, strategy. But it’s also fine to call it purpose, which is really what intuitively makes the most sense to people that do this. This is one of those cases where intuition and the evidence agree, yet it is somehow still considered a controversial fact.

2. What were some of the most important lessons you absorbed during your time on the bathroom floor?

As I’ve been going around talking about the book, this is one of the stories that actually gets a very different reaction depending on whether I’m talking to an entrepreneur or somebody else. Entrepreneurs all recognize this moment, where I really thought my company was going to fail and I couldn’t handle it. A lot of non-entrepreneurs don’t get it. They’re like, “Why? It seems like a bit of an overreaction. Okay, you had a business setback. We’ve all had career setbacks — what’s the big deal?” But what you don’t realize until you’re in it is how much, especially if you’re doing something out of a sense of purpose or passion that’s personally meaningful to you, you start to identify with it and start to become inseparable from it. So that story is very important in the book because I learned a lot of important business lessons. I thought the company was going to die, but it didn’t. It survived precisely because of its mission, not in spite of it. I learned, in a very visceral way, about the forces, that prevent reform from coming to fruition in so many areas of our life, not just financial. And of course I learned a personal lesson about the importance of equanimity and the need to tackle the psychological and even spiritual dimensions of entrepreneurship if we’re going to create real change in the world.

3. How much chance is there of us getting companies to more broadly redefine profit to include elements of maximizing human flourishing?

This question reminds me of a of an incredible video of the great Steve Jobs before he died. He’s being interviewed at an industry conference at the time of the launch of the iPhone, when the Blackberry was the dominant smartphone in the world. It had something like 80 or 90% market share. A journalist asks this question something like, “Do you really think realistically you can take share from this dominant player?” And you can tell Steve is irked by this question, and I’m expecting because we all know his famous temper, that he’s going to lash out at the person. But he doesn’t. Instead, he says, “You know, that’s not really up to me. My job, our job at Apple, is to make the best phone we can, the one that we’re proud of. Market share is up to the customer. That’s their decision, their choice. We don’t think about that, we don’t know, and we don’t need to know in order to do our best work.” I’m paraphrasing because I haven’t seen this video in a long time, but that’s how I feel about this, too. I get this question a lot because people want to feel like, if I’m going to jump on the bandwagon, I want to know that it’s going to work. But the truth is none of us know what’s going to work, even those of us who advocate for these ideas. You, who’s reading this, are the only one who gets to decide if this is likely or unlikely. This is not what the economist John Maynard Keyes called a beauty contest. You don’t have to worry about what everyone else is going to do. You only have to decide for yourself if you think this makes sense to you. And if it does, well, like I said — like Steve said — it’s up to you.

4. As America becomes more capitalist and less of a free market economy, what steps can we take to reverse the regulatory capture, lawfare and other methods that degrade competition, purchasing power, class mobility and the American dream? Do we need a pCombinator? (purpose-driven company accelerator)

You’re asking questions about words that we no longer have consensus about what they mean. What is a free market economy? What is capitalism? What is regulatory capture? The very definition of these words is what’s under threat. If you look at the broader media landscape, the political landscape, in many, many pockets of our society now the very idea of a for-profit company is being attacked as inherently exploitative or extractive. The consensus that we used to have that we can be working commercially to improve the world and make it a better place, that used to be seen as quite obvious and now that whole idea is under threat. I don’t blame the people doing the attacking, especially the young people who have, after all, lived their whole lives, under this regime of a very extractive flavor of capitalism that goes by the anodyne-sounding name “shareholder primacy”. This is the simple idea that customers, employees, communities all exist as resources to be mined for the benefit of shareholders. But this question is also loaded with so many other political issues of our time that we are going to have to tackle if we’re going to come out of this darkness, as our grandparents who battled fascism once had to do. So, I don’t think it’s going to be as simple as fixing one thing. But I think that one of the things we have to do, among many, is build a power base, an economic gravity pulling towards the values aligned with human flourishing. And many of the political, economic, and social challenges of our time are downstream of this action in the same way that the catastrophes that we’re currently living through are downstream of what seem like very simple and relatively benign policy changes from the past century.

5. What should purpose-driven companies look for in a CEO as the company outgrows or outlives the founder(s)?

IncorruptibleThis is a really important part of the architecture of institutional longevity. Most companies fail the test of succession. The evidence seems to suggest that people who train and hire from within have a big advantage here. I think that is something we don’t even really teach anymore as a corporate value, but that is actually super valuable. There’s a reason why that old story of the employee that worked their way up from the mail room was such an important legend in the previous century. Now we hardly tell stories like that anymore. We tend to want the big fancy turnaround, the bold new strategy, the external CEO, which for companies that are in crisis makes sense. And since our modern best practices tend to ruin companies, they tend to be in crisis quite a lot. But what we want to do is we want to find a CEO who combines two really important elements. One, they personally, deeply and profoundly reflect the ethos of the company. This is why a company that doesn’t have an ethos can never pass this test because they don’t even know who to pick. But you don’t want someone who, who apes the values of the past, or is slavishly loyal to the specific things that worked in the past. You need someone who is both deeply aligned to the ethos, and who nonetheless is very performance oriented, meaning they see that when the ethos is working, it should generate long-term performance. They can’t get distracted by short-term blips but they have to have the adaptability to realize when sacred cows need to be challenged. Now, it’s commonly said that only a founder can have the moral authority to do this unique combination of things I’m describing, only they can go into founder mode, as it’s called. But I don’t think that is supported by the evidence. When companies have the right structure, they actually can imbue subsequent generations of managers with this moral authority.

6. Why is magnetic alignment so important for purpose-driven organizations and their survival?

I conceived of this book as a look into the physical forces, the underlying forces, that affect organizations. So not the surface level characteristics that we spill so much ink about, org chart, culture, business model strategy, even vision, things we can touch and taste and control. Those things are important, don’t get me wrong. But there is a deeper layer to this, like a physics of organizations. In the book, I explore very dominant force that I call financial gravity. This is the gravity that pulls companies down into mediocrity or worse and is exacerbated by our heavily financialized economy. So to build an organization that is going to endure and is going to maintain its distinctiveness or its sovereignty over time, we have to have a force that is stronger than gravity with which we can power both the alignment that we need of people, and the structural integrity to resist outside pressure. And I call that the force of magnetic alignment. This is the mechanism by which companies gain that most valuable and underrated asset: trustworthiness. And the evidence shows that companies that have this asset, that activate this force, have numerous superpowers that conventional companies simply cannot touch.

7. Is super voting stock the silver bullet for purpose driven companies or are their other possibly better or complementary ways for purpose-driven companies to protect themselves?

It’s funny because the simple answer to your question is no. And yet I advocate for super voting shares all the time. I may be the most negative advocate of super voting shares! To understand, you have to see it this way: Imagine I went to a political science professor, an expert in political philosophy and I said, “I’m thinking of setting up a new city state, a new polis. I want your advice about what kind of governance it should have.” The professor’s going to be really excited. “Oh, great. What are you considering?” And I’ll say, “Well, I’ve only got two options. Option one is a situation in which whoever borrows the most money gets the most votes. Also, the tourists can vote, and you only have to borrow the money or be a tourist on election day, after which you can release your loans or leave the country and your vote is still binding on the whole polity.” The professor’s going to look at me and be like, “That’s pretty terrible. What else you got?” So, I’ll say, “Okay, option two is despotic emperor for life and my heirs and assigns.” The professor is going to say, “That’s all you got? Those are the only two options you can think of, really? You know, in the political science department, we’ve been working on this problem for a couple hundred years. We could maybe suggest a few other things!” That is the state of corporate governance today. It is such a paucity of thinking and originality. It is so bare of our human birthright, which is to imagine different ways that power can be shared amongst people. Human beings have been experimenting with this question since there have been human beings. So, the fact that companies are choosing despotic emperor for life to me should be read not as an endorsement of autocracy, but rather as an indictment of standard governance. Standard governance is so bad that emperor for life looks like an improvement. So yes, I do think it is an improvement. I do think there are times when that’s the best we can do, but we know from the research that it is not really the best long-term solution. We know that having too much power centralized in too few people leads to what psychologists called hubris syndrome, and many other problems besides. On top of being, ultimately not that long-term, since it’s limited by the human lifespan, this also puts a lot of founders into really an untenable and very undesirable psychological situation, where they are basically indentured servants and can never leave, for fear that their creation will be destroyed. So, maybe it’s the least bad of the current available options. But of course, we can think of far better ideas. In the book I argue for what I call “constitutional governance”, which is a set of concepts that take us beyond this false dichotomy.

8. How do you think we escape the big food doom loop? (healthy food company starts, wins customers, seeks an exit to get paid, big food makes it unhealthy and lower quality – i.e. Naked, Ben ‘n’ Jerry’s, Breyer’s, etc.)

This question is not really about food, so I’m not going to address big food. What does that even mean? Because we have a tendency to want to personalize these dramas, looking for villains. I understand that there are some villains out there. I get it. But this phenomenon that you’re describing, where someone figures out a more enlightened way to create any kind of product — doesn’t matter if it’s a food product or a tech product or a product design to bring a little beauty into people’s lives — it doesn’t matter what it is. The more successful it becomes, the more valuable it is as a target. And the more of a premium someone bigger will pay to acquire it. On this book tour, I have encountered many people who’ve told me their horror stories. They tend to want to tell food stories. That’s why I like this question. They’ll be like, look, private equity took over my favorite restaurant. Now the food is disgusting. Someone said to me a couple of weeks ago about a certain brand, “I hope they’re really successful,” and then they had to amend their statement to “Well, actually, I hope they’re somewhat successful. Successful enough to keep going, but not so successful that they get bought out by private equity.” That’s how much this idea that when things become successful, they get ruined has passed into the mainstream culture. So this is not about food. In the book, I describe this phenomenon, dating back at least two hundred years, and give the mechanics of how it happens and why. Why are we so conditioned to reenact the parable of the killing of the golden goose? And more importantly, what we can do to stop it?

9. Is it time to change the ‘corporations number one duty is to its shareholders’ narrative (aka shareholder primacy)? Is that part of what you’re trying to do with this book?

Yes. I believe that the era of shareholder primacy is actually already over, for two reasons. One is, this is an idea that has proved to be self-defeating. It was originally enacted — not in ancient times, but in the 1980s, at least in Delaware — to be beneficial to shareholders, but that is not how it has proved. We’ve actually metastasized into what I would call “extraction primacy”, in which investors themselves are now locked in a zero sum prisoner’s dilemma struggle where each has to try to squeeze as much out of everything they invest in lest someone else beat them to it. I think even investors are ready for change. The second reason I think it’s already over, and that we’re like the road runner having run off this cliff and haven’t looked down yet, is there’s a massive generational shift underway. As I mentioned before, the younger generation who has lived their whole lives under the hegemony of this idea, increasingly find it absolutely repugnant. They may not know to call it shareholder primacy, they may not realize that this is an idea that, by the way, has never been democratically enacted ever in history and therefore has no democratic legitimacy. But they are hungry for something new. And so I think our energy needs to be spent not on complaining about shareholder privacy anymore. It’s over. The question needs to be, what should the successor idea be? In the book I suggest mission primacy as one alternative.

10. You mention Novo Nordisk and its foundation in the book, which apparently is about to be passed by the OpenAI foundation for the mantle of the largest foundation (much bigger than the Bill & Melinda Gates Foundation) through their 26% ownership of OpenAI shares. Is this a model that we should encourage more startups to embrace from the outset?

I’d be very careful drawing lessons from the OpenAI experience because that company is quite singular and there’s a lot of stuff going on there quite unusual, a lot of big ego people like Elon and Sam. But interestingly, people often claim that the foundation ownership of OpenAI is unusual, and that’s not true. The idea that a for-profit company can be governed by a nonprofit foundation is an old one. The German optics company Zeiss had the structure in the 1880s. And as the question asked, Novo Nordisk has had it since the 1920s. In fact there are so many of these companies in the world that they have been studied and found to be dramatically more stable. Companies that have this structure are simply more likely to invest counter-cyclically. They are more likely to invest more in R&D. They have better financial performance and they are something like five or six times more likely to live to year fifty than conventional companies. Now the key to the structure’s stability is to have a system of checks and balances, which, as far as I understand, OpenAI struggled with for much of its existence. OpenAI had only one board, but what makes companies like Novo Nordisk, Patagonia, and Tony’s Chocolonely distinctive is that they have two entities — a for-profit board of directors who’s held accountable or in some cases even appointed by an outside board of trustees. That checks and balances, two-entity structure seems in the data to the most stable corporate form in the world.

11. As we enter the age of AI and the disruption it is beginning to cause, can the displaced really rely on enlightened capitalism to keep their families from starving?

This is a very grim question, and it presupposes one of the many, many doomsday scenarios about AI that is circulating. In order to think clearly about what it makes sense to do with AI, you have to realize two really interesting facts about this moment. The first is that almost every future scenario about this technology depends on a series of empirical facts that no one on this planet really knows the answer to. And these facts are very strange. Only a few years ago, they would have been considered post-modernist, irrelevant debates in your local philosophy department about questions like, “is there such a thing as reasoning or is it all just language?” And “what is the nature of intelligence and consciousness?” Of course, we as human beings have studied these questions for many generations. But I was on CNBC talking about this the other day — it’s rare that they are of such economic import that stock traders are wondering about them. To give one example, one of the most important questions you have to ask about AI is when or if the scaling laws will ever run out. So far, for quite a number of years,, thanks to pioneering researchers, including many far-sighted ones like my co-founder at Answer.AI Jeremy Howard, have figured out that simply by applying more computation to a very simple learning algorithm, you can create language models that seem quite intelligent, at least at first glance. So far, the more computation we use to train and run these models, the more capable they become. I think most people generally assume that this is some kind of S-curve and that eventually this curve will level off. Some even think that it already has leveled off. Others think we are years, or even decades, away from it leveling off, and of course some people believe it will never level off. This is the law of the universe. Depending on which of those things is true, the future scenarios are almost comically different from each other. A world in which the scaling laws level off next year is almost unimaginably different from one in which we have ten more years of this. And many of the doomsday scenarios, but also many of the utopia scenarios, depend critically on knowing the answer to this fundamental question about the universe that nobody knows. So, back to your question: How do we know what actions to take when the range of possible futures is so wide, so different from each other and so dependent on facts not in evidence. I think there’s only one thing that makes sense, which is to ask ourselves what are actions that would make sense, that you’ll be glad that you did, in a wide variety of potential futures? And I think that takes us out of the job of having to predict the future, which is very difficult, and rather into a more prudence-based mindset of what can be done to prepare for many possible futures. And when you go through that analysis, many of the things that you want to do to protect yourself against future AI scenarios are actually things you probably should be doing anyway. Think about having better mandatory disclosure, hardening our critical infrastructure, making sure that the gains from new technologies are widely distributed, going back to the era of widely shared prosperity. So if people are going to be displaced, should they just sit around and hope that the leaders who do the displacing will wind up being enlightened? Absolutely not. Of course not. In fact, the whole point of this book is to show how unless we make changes, the gravitational field of our financial system will warp and even destroy, turn malignant, any company. But where does the gravitational field come from? I think the most surprising part of the book for many readers is in later chapters when we reveal how the same tools that we’ve been discussing about how to create more resilient companies are also tools that can be wielded by all of us to shape the gravitational field of the future and affect what kinds of companies can and can’t form, how those companies can and cannot behave. And while some of those levers are traditional levers, like policy changes, of course., the book is primarily about the other, more surprising lovers, that I bet most readers have not thought of before.

I hope everyone has enjoyed this peek into the mind of the man behind the insightful new title Incorruptible!

Image credits: Eric Ries, Google Gemini

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How to Hire Like the Richest Man in the World

How to Hire Like the Richest Man in the World

GUEST POST from Shep Hyken

This article answers the question: Is it better to hire employees for attitude or for skill, especially in customer service roles?

The old saying in business, when it comes to hiring people, is this:

Hire for attitude, train for skill.

I’ve shared ideas related to this quote in several articles and videos. So, why bring it up again? First, it’s a concept worth revisiting to remind us of this important truth, especially in the world of customer service and experience. Second, I recently heard a version of this that captures the essence and further emphasizes the importance of attitude versus skill.

As I write, the richest man in the world is Elon Musk, the CEO of Tesla and founder of SpaceX, with an estimated net worth north of $500 billion. Whether you like the way he does business or not, we can’t ignore that he may have ideas worth paying attention to, and his take on this old quote is one of those ideas. The concept of hiring for attitude is driven home when he says, “Skills can be taught, but attitude changes require a brain transplant.”

Another man worth paying attention to is Jim Bush. In my book The Amazement Revolution, I interviewed Bush, who at the time was the executive VP of world service for American Express, responsible for customer support centers around the world. He shared that if he could hire someone with years of experience at a support center or working at the front desk of a hotel, he would choose the person with the hotel front desk experience.

Shep Hyken cartoon illustrating why you should hire for attitude and train for skill

Bush said, “We’re talking about human engagement, and that requires the ability to connect.” That’s why American Express began hiring people with hospitality experience. They had the attitude American Express was looking for. After being hired, they could be trained on the technical skills needed to work the computers at a contact center.

Now, before I go further, some of you might be thinking that certain jobs require specific skills, regardless of employees’ attitudes, and you are correct. A surgeon must graduate from medical school before operating. An electrician must learn the trade before wiring a home. Certain jobs require technical proficiency. However, if you hire someone with those skills who has the wrong attitude, they can harm your culture and potentially drive customers away. So, take this concept in the spirit of its meaning.

So, back to Musk’s line about attitude changes requiring a brain transplant. The comment is a bold way of saying that attitude isn’t something you can download like software. It’s hard-wired. People’s attitudes have been formed over their entire lives, from the time they were babies. Leaders who understand this focus on recruiting people who come to the job with the right mindset, with an attitude that fits the personality of the company. The takeaway is simple. Hire people who care. Then, teach them the specific skills they need to perform their job effectively. You can train for competence, but you can’t train for caring.

Image Credit: Pixabay

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Managing the Change When Your New Team Member is an AI Agent

Managing the Change When Your New Team Member Is an AI Agent

by Braden Kelley and Art Inteligencia

Every organization rushing to deploy AI agents is making the same mistake: they are treating this as a technology rollout. It isn’t. It is a change management event — possibly the strangest one most of your employees will ever live through — and almost nobody is managing it as one.

I have spent two decades helping organizations navigate change. New systems, new structures, new leadership, new strategy — I have seen the patterns, and I have built frameworks to help people through them. What’s happening right now with AI agents doesn’t fit neatly into any of those patterns, because for the first time, the “new hire” your team has to adjust to isn’t a person. It has no face to read, no body language to interpret, no shared lunch break to build rapport over. And yet your people are being asked to trust it, collaborate with it, and in some cases defer to its output — all without the social mechanisms humans have relied on for millennia to build trust with someone new.

If you are rolling out AI agents into your teams this year — and if you aren’t already, you will be soon — you need a change management approach built for this specific situation. Here is what that requires.

This Is Not a Software Rollout

When organizations introduce new software, the change management playbook is well understood: communicate the why, train people on the how, support them through the learning curve, and reinforce the new behavior until it sticks. That playbook assumes the new thing is a tool. You pick it up, you put it down, you use it when it’s useful.

An AI agent is not a tool in that sense. It takes initiative. It makes judgment calls. It shows up in meetings, in workflows, in decisions — sometimes proactively, without being asked. The closest analog isn’t a new piece of software. It’s a new colleague. And we already have decades of organizational psychology telling us how disruptive a new colleague can be to team dynamics, let alone one that doesn’t operate like any colleague your team has ever had.

This distinction matters because it changes which change management tools actually apply. ADKAR’s emphasis on individual awareness and desire is still relevant. But the resistance you’ll encounter isn’t really about learning a new interface. It’s about something closer to what happens when any new team member joins: uncertainty about role boundaries, anxiety about being replaced or overshadowed, and an unconscious assessment of whether this new “person” can be trusted.

Why People Resist AI Coworkers Differently Than They Resist New Software

I wrote recently about the neuroscience of creativity and the role the amygdala plays in detecting social threat. The same mechanism is firing right now in your organization, and most leaders have no idea it’s happening.

When a new piece of software arrives, the brain files it under “tool” and moves on. When something that behaves like a colleague arrives — something that talks, decides, and acts with a kind of agency — the brain files it under “social actor” and starts running the same threat assessments it runs on any new person: is this safe? Is this going to take something from me? Can I trust what it tells me?

The catch is that an AI agent gives almost none of the signals humans use to answer those questions. There’s no tone of voice to read for sincerity. No facial expression to gauge intent. No shared history to draw on. Your people are being asked to extend trust to something that offers none of the usual evidence trust is normally built on — and then we’re surprised when adoption stalls or quiet resistance shows up as workarounds, double-checking everything the agent produces, or simply not using it at all.

This is not a training problem. You cannot train your way past a threat response. It has to be addressed the way any well-designed change effort addresses resistance: by understanding what’s actually driving it and designing for that, not for the resistance you assumed you’d see.

Applying the Change Management Process to AI Agent Adoption

I’ve written before about the five process groups that make up a disciplined change management process. Here’s how they apply when the change you’re managing is the introduction of an AI teammate:

Evaluate impact and readiness honestly. Most organizations evaluate AI agent impact in terms of tasks automated and hours saved. Few evaluate it in terms of role identity — what happens to how someone sees their own value when a piece of their job is now done by something that isn’t them? Skipping this assessment is how you end up with technically successful deployments and quietly disengaged teams.

Build a strategy that names the relationship, not just the rollout. Is the agent a tool the team directs, a collaborator the team works alongside, or something closer to a delegate that acts with some independence? Most organizations never decide this explicitly, and the ambiguity is exactly what breeds distrust. Decide it, and say it out loud.

Plan for trust-building, not just training. Traditional training plans teach people how to use something. What you actually need here is closer to onboarding a new team member: transparency about what the agent can and can’t do, visible track record before high-stakes use, and early opportunities for people to verify its output before they’re asked to rely on it.

Execute with visible human oversight, especially early. The fastest way to build trust in a new colleague — human or otherwise — is watching them perform well in front of you, not being told they performed well somewhere else. Early AI agent deployments need visible checkpoints where people can see the agent’s work and verify it, not a black box they’re asked to trust on faith.

Close the loop by naming what changed. Once an AI agent has been integrated into a workflow, say so explicitly, and say what it means for the people whose roles shifted around it. Changes that are never formally acknowledged have a way of generating resentment that outlasts the technical transition by years.

Change Management AI Agent Adoption Infographic

The Real Risk Isn’t the AI. It’s Skipping the Human Part.

I’ll say what I’ve said about AI in customer experience: the key isn’t choosing between AI and humans, it’s knowing when and how to bring each one in well. The organizations that get AI agent adoption right in 2026 will not be the ones with the most advanced agents. They’ll be the ones that treated the human side of this transition with the same discipline they’d apply to any major organizational change — because that is exactly what this is.

Skip that discipline, and you won’t get a failed technology rollout. You’ll get a team that technically has access to an AI agent and quietly refuses to use it, or uses it just enough to look compliant while doing the real work the old way. That is the most expensive kind of failure there is: the one that looks like success on a dashboard somewhere while nothing has actually changed.

Image credits: Gemini

Content Authenticity Statement: The topic area, key elements to focus on, and the change management framing were decisions made by Braden Kelley, with a little help from Claude to research current trends and clean up the article, and Gemini for images/infographics.

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The Fourth Inning in the Future of Work

The Future of Work Evolution: What Inning Are We In?

Editor’s Note: The State of the Game in 2026

When tech strategist Geoffrey A. Moore penned this piece in the spring of 2024, the “top of the fourth inning” was characterized by the initial, frantic rush toward generative AI adoption and a baseline shift toward customer success. Two years later, as we navigate 2026, the game has rapidly intensified.

We are no longer just talking about shifting toward “outcomes”—we are actively building the infrastructure to measure them. The baseline has evolved from simple subscription tracking to deep Experience Management Offices (XMOs) and Experience Level Measures (XLMs), proving that human-centered value is the ultimate digital metric. Furthermore, the early AI hype has matured into what we call the AI Soft Landing, where organizations are moving past experimental tools to restructure workflows around systemic, human-led collaboration. Read on to explore Geoffrey’s brilliant structural breakdown of how we arrived at this pivotal inning.

GUEST POST from Geoffrey A. Moore

It’s spring of 2024, and as Major League baseball is getting underway, everyone in tech is talking about the future of work. Let me suggest we are in the top of the fourth inning, a couple of runners on base, but still much to be decided (all with the understanding that an inning in tech lasts somewhere between one and two decades—and you thought baseball games were long!). At any rate, here’s how I see it playing out.

The first inning where tech made a definitive impact on work spanned the 1970s and 80s when the dominant paradigm was proprietary mainframe computing and the focus was on management information systems. This was an era of control cultures where the mantra was plan your work and then work your plan. IBM and Oracle were the dominant players, and workflows were organized around reports.

The second inning emerged with the rise of client-server computing in the 1990s, where the focus was on real-time business processes. This was an era of competition cultures where the mantra was give me my objectives, give me my resources, and get the hell out of my way. Microsoft and Cisco were the dominant players, and workflows were organized around documents.

The third inning emerged out of the tech bubble popping at the turn of the century, where the dominant paradigm transitioned to cloud computing combined with mobile applications, and the focus shifted from B2B complex systems to B2C volume operations. This was an era of creativity cultures where the mantra was think different. Google and Apple were the dominant players, and workflows were organized around transactions.

Now we find ourselves at the top of the fourth inning, initiated with the rise of artificial intelligence, where the focus is on as-a-service subscription business models, the economics of churn, and the importance of the customer experience. This is an era of collaboration cultures where the mantra is put customer success before everything else. The dominant players have yet to be determined, but we do know that workflows will be organized around outcomes.

And that’s the point. Information technology that began at the periphery of the business as a back office report generation utility has now migrated to the very core of the enterprise’s mission, vision, and values. That’s why digital transformation is getting so much attention. But how to transform, and how to use digital technology to ensure that customers achieve the outcomes they seek, is very much still a work in progress.

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

Image Credit: Gemini

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Crossing the Chasm of Fear

AI Soft Landing scenario — Leading People Through the Anxiety of Transformation and AI

LAST UPDATED: June 14, 2026 at 5:48 PM

Crossing the Chasm of Fear

by Braden Kelley and Art Inteligencia


The Hidden Friction in Modern Transformation

Change doesn’t fail because the technology is broken or the strategy is fundamentally flawed; it fails because organizations consistently underestimate the immense gravity of human fear.

We are living in an era of unprecedented, continuous disruption where the rapid, omnipresent rise of Artificial Intelligence (AI) has magnified workplace anxiety to an all-time high. This paradigm shift has fundamentally altered the conversation from standard operational “inertia” to a deep-seated, existential dread regarding professional relevance, personal autonomy, and long-term job security.

To build an agile, future-ready organization, leaders must stop merely trying to “manage” resistance and start actively dismantling fear. True transformation requires moving past rigid, top-down mandates to embrace genuine co-creation, psychological safety, and a commitment to human-centered design.

I. Mapping the Topography of Fear in the AI Era

To successfully guide an organization through a significant shift, leaders must first understand that the friction they encounter is rarely intellectual; it is emotional. In the wake of the generative AI revolution, traditional change management frameworks are proving insufficient precisely because they treat resistance as a logistical hurdle rather than a psychological defense mechanism.

The Shift from Traditional Resistance to Existential Anxiety

Standard change models were built for linear transitions — such as upgrading an ERP system or relocating an office — where the destination is clear and the skill gap is manageable. AI, however, introduces non-linear disruption. Employees are not just resisting a new tool; they are experiencing existential anxiety. The underlying fear is no longer “How do I use this software?” but rather “Does my expertise still matter?”

The Core Drivers of Workplace Fear

This widespread anxiety is fueled by three distinct, interconnected human dynamics:

  • Loss of Competence & Relevance: Professionals who have spent decades perfecting their craft suddenly face systems that can replicate aspects of their output in seconds. The fear of being rendered obsolete overnight leads to defensive behaviors and a reluctance to engage with new platforms.
  • Loss of Autonomy: Employees worry about losing the human element of decision-making. There is a deep-seated anxiety that their daily workflows will be dictated by black-box algorithms, reducing human agency to mere data entry and validation.
  • The “Black Box” Effect: Because advanced AI models operate behind complex neural layers, the lack of transparency breeds immediate distrust. When people do not understand how a technology arrives at a conclusion, they naturally default to worst-case scenario thinking regarding its intent and accuracy.

The Real Cost of Inaction

When leadership fails to recognize and mitigate these fears, the organization pays a heavy cultural tax. This friction rarely manifests as open defiance. Instead, it operations below the surface as:

  • Quiet Quitting: Disengagement driven by the belief that effort is futile in an automated future.
  • Malicious Compliance: Following instructions to the letter while ignoring obvious system errors, effectively letting the new technology fail to prove a point.
  • Organizational Paralysis: A total stall in innovation, as teams become too risk-averse to experiment with new digital capabilities.

II. Redefining the Approach: Moving from Mandates to Co-Creation

The traditional corporate playbook for technology deployment relies heavily on top-down enforcement. Executives select a platform, managers set a deployment date, and training sessions are scheduled to push the workforce into compliance. While this rigid approach might work for static software updates, it completely fractures when applied to cognitive, disruptive technologies like Artificial Intelligence. To cross the chasm of fear, leadership must fundamentally redefine how change is initiated.

The Failure of Top-Down Dictates

When an disruptive technology is thrust upon an organization from above, it triggers the corporate equivalent of an immune system response. Employees perceive the uninvited change as an existential threat to their routines and livelihoods. Pushing mandates down the organizational chart only hardens resistance, forcing anxiety underground and transforming potential advocates into silent saboteurs.

The Power of Participatory Innovation

The alternative to top-down friction is Participatory Innovation — the deliberate practice of shifting the narrative from “This is being done to you” to “You are building this with us.” True ecosystem agility requires flattening the hierarchy of contribution and inviting the entire workforce into the design process. Rather than treating front-line employees as passive recipients of change, organizations must treat them as active co-creators of their own future workflows.

This approach transforms the deployment strategy by:

  • Engaging front-line staff at the inception stage to identify real, daily friction points that AI can genuinely alleviate, rather than forcing technology where it doesn’t fit.
  • Utilizing cross-functional design sessions that break down legacy silos, allowing technical developers and domain experts to build tools in tandem.
  • Establishing iterative feedback loops that give employees a direct hand in shaping, tweaking, and refining the automated systems they are expected to use.

Lowering Resistance Through Shared Ownership

Human beings rarely destroy what they help build. When an employee looks at a newly integrated AI assistant or a redesigned digital workflow and recognizes their own insights, feedback, and domain expertise baked into the final product, the underlying psychological dynamic shifts instantly. The fear of the unknown is replaced by a powerful sense of pride of authorship, transforming potential resistance into proactive, self-sustaining adoption.

III. The Strategic Blueprint: Crossing the Chasm of Fear

Dismantling fear and establishing a culture of participatory innovation requires more than good intentions; it demands an operationalized, human-centered strategy. To successfully cross the chasm of anxiety and achieve meaningful adoption, leaders must execute a deliberate, multi-layered blueprint that prioritizes human experience alongside technical milestone delivery.

Step 1: Cultivate Psychological Safety First

Before introducing a single algorithmic tool, leadership must anchor the organizational culture in psychological safety. If employees believe that experimenting with AI or voicing skepticism will jeopardize their standing, they will retreat into defensive compliance.

  • Create dedicated, judgment-free forums where teams can openly discuss their anxieties, ask “naive” technical questions, and challenge assumptions without fear of retribution.
  • Frame the early stages of AI adoption as an iterative experiment rather than a high-stakes, zero-fault mandate. Normalize failure as a natural, necessary component of learning to collaborate with intelligent systems.

Step 2: Demystify the “Black Box”

Fear thrives in obscurity. When technology is shrouded in complex, dense jargon, employees default to worst-case scenario thinking. Crossing the chasm requires pulling back the curtain on how automated tools function.

  • Provide transparent, accessible education tailored to non-technical users. Demystify the data sources, logic, and operational boundaries of the AI models being deployed.
  • Shift the corporate narrative away from “automation as a replacement” and explicitly reframe it as “augmentation as a partner.” Clearly demonstrate how these tools can absorb repetitive cognitive drudgery, freeing individuals to focus on high-value, uniquely human tasks.

Step 3: Define New “Experience Level Measures” (XLMs)

Traditional change management focuses almost exclusively on cold Operational Measures—tracking system uptime, deployment timelines, software licenses, and output volume. To manage the human friction of transformation, organizations must measure what actually matters: the human experience of the transition.

  • Implement Experience Level Measures (XLMs) to actively track sentiment, cognitive friction, and confidence levels across the workforce during the rollout.
  • Establish an Experience Management Office (XMO). This cross-functional entity acts as the empathetic heartbeat of the transformation, monitoring XLMs in real time and intervening with support, tailored training, or process redesign when emotional friction spikes.

Step 4: Re-skilling with Dignity and Equity

True fairness in transformation means ensuring that the rewards of technological advancement are relative to the effort invested by the people keeping the organization running. If employees feel that upskilling only leads to their own displacement or unfair workloads, adoption will fail.

  • Demonstrate a visible, legally backed commitment to the long-term value of your human capital through robust, funded re-skilling pathways that dignify the worker’s career trajectory.
  • Align future organizational recognition, bonuses, and growth opportunities with equitable outcomes: ensure that the harder working individuals who lean into the challenge of adapting and mastering new tools receive the tangible rewards of that shared success.

IV. Activating the Ecosystem: Leveraging Multi-Dimensional Roles

Successfully steering an organization away from anxiety and toward sustainable innovation requires a diverse network of human capabilities. Relying solely on technical project managers or traditional IT leaders to drive adoption is a structural mistake; these roles are designed to optimize systems, not to heal a fractured human culture. To operationalize empathy and scale change, leadership must activate a multi-dimensional ecosystem of specialized roles.

Beyond the Project Manager

While project managers excel at tracking timelines, budgets, and deployment milestones, they rarely possess the specialized tools or bandwidth required to navigate deep-seated psychological friction. Orchestrating a human-centered transformation requires shifting the focus from managing tasks to nurturing human relationships. Organizations must look beyond standard job titles and intentionally cultivate specific archetypes designed to bridge the gap between human anxiety and technological capability.

The Right People in the Right Seats

To dismantle fear at every layer of the enterprise, leaders should identify, empower, and deploy three distinct operational archetypes across the transformation ecosystem:

  • The Evangelist: This role is responsible for crafting the overarching human narrative of the transformation. The Evangelist does not merely pitch the features of a new AI tool; they communicate the authentic “Why” behind the change. By generating real, unforced energy and painting a vivid picture of a more fulfilling, augmented future, they inspire teams to lift their heads above immediate anxieties and look toward the long-term horizon.
  • The Connector: Change rarely scales effectively through top-down mandates; it spreads horizontally through social proof and trusted networks. Connectors are the cross-functional linchpins who span legacy departmental boundaries. They excel at identifying grassroots wins in one pocket of the organization, translating those successes for other teams, and ensuring that insights, feedback, and shared resources flow seamlessly across the entire ecosystem.
  • The Coach: While Evangelists inspire groups and Connectors build bridges, the Coach works on the front lines of human emotion. Operating with high emotional intelligence, Coaches provide one-on-one empathy and guidance to individuals experiencing severe friction. They help employees navigate personal technical skill gaps, address specific career anxieties, and safely transition into new ways of working without losing their professional dignity.

Conclusion: The Ultimate Reward of a Human-Centered Future

Technology provides the raw capability, but human adoption provides the actual organizational value. As we navigate the complex, non-linear disruptions of the Artificial Intelligence era, it is becoming increasingly clear that the true competitive advantage does not belong to the enterprise with the largest budget or the most advanced algorithms. The future belongs to the organizations that can move their people past anxiety and into a state of shared purpose.

Crossing the chasm of fear requires leaders to abandon the outdated illusion of top-down control. By anchoring your transformation strategy in radical transparency, psychological safety, and participatory innovation, you transform a potentially threatening disruption into a collective opportunity. Measuring the journey through human-centric lenses like Experience Level Measures (XLMs) and deploying empathetic archetypes ensures that no one is left behind in the wake of progress.

Ultimately, when you design fear out of your corporate culture, you unlock the ultimate reward: an agile, resilient, and infinitely innovative workforce. By treating employees as respected co-creators of their digital future, you don’t just achieve a successful technology rollout — you build a human-centered ecosystem capable of thriving through any disruption the future brings.

Frequently Asked Questions

Why do traditional change management frameworks fail when introducing AI?
Traditional frameworks treat change as a linear, logistical hurdle focused on training and compliance. AI introduces non-linear disruption that triggers deep psychological and existential anxiety regarding job security, relevance, and loss of human autonomy. Overcoming this requires an empathy-driven, human-centered approach rather than top-down mandates.
What is Participatory Innovation and how does it reduce resistance?
Participatory Innovation is the practice of actively involving front-line employees in co-creating and designing their future workflows instead of pushing changes down from the executive level. Because human beings rarely destroy what they help build, this shared ownership transforms fear of the unknown into pride of authorship.
What are Experience Level Measures (XLMs) and why are they necessary?
While traditional operational measures track cold metrics like system uptime or deployment timelines, Experience Level Measures (XLMs) actively quantify human sentiment, cognitive friction, and adoption confidence. They are critical because technology only provides capability; human adoption is what actually unlocks organizational value.


Operationalize Organizational Empathy

Ready to Bridge the Gap Between Technology and Human Experience?

Technology only provides capability; human adoption creates the value. If you want to move past cold operational metrics and design fear out of your transformation, let’s connect. Get expert guidance on architecting impactful Experience Level Measures (XLMs) or establishing a dedicated Experience Management Office (XMO) tailored to your culture.

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

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

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The Circular Harvest — How Systems Engineering and Design Thinking Are Rewriting the Future of Farming

The Circular Harvest — How Systems Engineering and Design Thinking Are Rewriting the Future of Farming

by Braden Kelley and Art Inteligencia


I. Introduction: The Industrialist in the Mud

For generations, the global imagination has romanticized agriculture. We cling to a nostalgic, cottage-industry myth of farming—one filled with rustic barns, predictable seasons, and manual labor. But as a futurist and innovation strategist, I look at the reality of our current global landscape and see a system under immense friction. Our traditional models of food production are increasingly vulnerable to climate volatility, geopolitical shifts, and severe supply chain disruptions.

Take the United Kingdom’s strawberry market as a prime case study. Historically, during the bleak winter months, the UK has been forced to import roughly 90% of its strawberries. This reliance creates a massive carbon footprint, accumulating thousands of unnecessary air miles just to place fresh fruit on supermarket shelves. It is a textbook example of a broken user experience within our food ecosystem.

The Agri-Tech Paradigm Shift

True innovation occurs when we challenge these deeply entrenched systemic flaws. This is precisely what unfolded when Sir James Dyson turned his attention to the British countryside. His entry into agriculture was not a billionaire’s eccentric hobby; it was a massive, calculated manufacturing scale operation. Today, Dyson Farming spans over 36,000 acres, fundamentally shifting the paradigm of what a modern farm can be.

By treating the field not as a scenic backdrop, but as an advanced production ecosystem, Dyson has proven that high-technology and ecology are entirely symbiotic. He recognized that solving our grandest challenges requires us to ditch nostalgia in favor of relentless, forward-thinking execution.

“Farming is not a cottage-industry, or something quaint and nostalgic; efficient, high-technology agriculture holds many of the keys to our future.”

— Sir James Dyson

II. The Genesis: From Airflow to Agriculture

To understand how a company world-renowned for cyclonic vacuums, digital motors, and hair care ends up producing millions of British strawberries, you have to look past the end product and examine the underlying mindset. True cross-industry innovation happens when we stop defining ourselves by what we make, and start defining ourselves by how we solve problems.

For Sir James Dyson, the connection to the land is deeply personal. Long before he was an industrialist, he grew up in an agricultural community in North Norfolk. His early winters were spent lifting wet potato sacks and hauling brussels sprouts—hard, manual labor that left a lasting impression of the sheer grit required to sustain farming. When he returned to agriculture decades later, he didn’t see a separate world; he saw an industry ripe for the same system optimization principles that drive advanced manufacturing.

The Universal Laws of Engineering

To a systems engineer, a factory floor and an agricultural field are fundamentally governed by the same variables: inputs, throughput, energy transfers, and waste mitigation. Whether you are guiding airflow through a bagless vacuum cleaner or orchestrating the micro-climate around a living organism, the goal is peak operational efficiency.

Dyson looked at traditional farming and spotted classic design friction points: unmitigated environmental dependency, unpredictable yields, high labor inefficiency, and the massive carbon cost of importing out-of-season fruit. It was a broken system screaming for a design thinking intervention.

“Growing things is rather like making things – I am a manufacturer, and I have approached farming from that point of view… A factory should be well designed, well-built and work most efficiently as a machine, using the latest technology for production. The same applies to farming.”

— Sir James Dyson

Solving What Doesn’t Work

The core ethos of Dyson has always been a relentless desire to fix things that are fundamentally broken or inefficient. By exporting core fluiddynamics, automated robotics, and thermodynamic expertise from the laboratory to the greenhouse, Dyson Farming bypassed incremental adjustments. Instead, they designed a predictable, localized agricultural machine capable of operating 365 days a year.

III. The 26-Acre Glasshouse: Bringing Systems Thinking to the Strawberry

In Carrington, Lincolnshire, sits a 26-acre glasshouse that serves as the physical manifestation of Dyson’s systems-led philosophy. This facility is far from a passive greenhouse; it functions as a highly automated, data-driven food laboratory containing upwards of 1.2 million strawberry plants. By controlling every variable—from ambient temperature and humidity to root nutrition and light wavelengths—Dyson has removed the unpredictability of traditional farming, turning strawberry cultivation into a precise, scalable process.

Central to this facility is the implementation of a Hybrid Vertical Growing System (HVGS). Rather than planting traditionally in the ground, rows of strawberries are suspended on advanced, dynamic aluminum rigs that maximize vertical space. These massive structures operate like slow-moving Ferris wheels, rotating the plants to ensure they receive uniform exposure to natural sunlight. By optimizing the three-dimensional footprint of the glasshouse, Dyson Farming generates a 250% increase in yield per square meter compared to traditional flat-field farming methods.

The Integration of Robotics and Automation

Managing over a million plants across a 26-acre footprint requires an entirely new operational framework. Dyson engineers have bridged the gap between agriculture and advanced manufacturing by introducing proprietary automation suites directly to the gutters. Intelligent vision-sensing robots navigate the rows, using machine learning algorithms to calculate the exact color profile and ripeness of individual berries before picking them with absolute precision.

Furthermore, the facility mitigates disease without relying on standard chemical interventions. At night, autonomous rail-guided vehicles traverse the dark aisles, passing targeted ultraviolet (UV-C) light over the foliage to neutralize powdery mildew and mold spores before they can take root. When pests like aphids do emerge, the engineering team deploys biological controls, programmatically releasing predatory insects to establish a natural balance within the micro-climate.

Data-Driven Climate Architecture

Every element of the glasshouse acts as an interconnected sensor node. Advanced climate software dynamically adjusts the glasshouse’s roof vents, internal shading screens, and massive LED growth lamps based on real-time meteorological data. By treating the physical structure as a macro-machine designed to cater to the physiological needs of the plant, Dyson has managed to extend the British strawberry season to a full 12 months, delivering fresh fruit to local markets even in the depths of winter.

IV. The Closed-Loop Ecosystem: The Ultimate Circular Economy

True innovation within complex systems requires us to look beyond immediate outputs and design for industrial symbiosis. A standalone high-tech glasshouse is an engineering achievement; however, if it relies on fossil fuels to maintain its tropical winter temperatures, it fails the test of sustainable experience design. Dyson Farming resolved this challenge by implementing a highly integrated, closed-loop circular economy framework at their Carrington site.

The 26-acre strawberry glasshouse does not burden the local energy grid. Instead, it operates adjacent to a massive, industrial-scale Anaerobic Digestion (AD) plant. This facility processes organic matter—primarily energy crops grown on the surrounding farm alongside organic crop waste from the glasshouse itself—breaking it down using specialized bacteria to produce biogas. This gas is then captured and utilized to drive massive turbines, generating enough clean electricity to power more than 10,000 homes.

The Thermodynamic Cascade

In a standard power plant, the massive amount of heat generated by electricity production is lost to the atmosphere as waste. Dyson’s engineering team viewed this thermal loss as an untapped input. They designed a closed system of insulated subterranean piping to capture this surplus heat from the AD plant’s generators, channeling it directly into the glasshouse structure. This steady, recycled thermal energy maintains the internal climate at an optimal 18–20°C even when outdoor temperatures drop below freezing.

The circularity extends deep into the byproduct architecture of the process:

  • Renewable Heat: The thermal energy from the generator cooling systems replaces fossil-fuel heating, mitigating thousands of tons of carbon emissions.
  • Nutrient Digestion: The solid and liquid organic residue left over after anaerobic digestion—known as digestate—is treated and used as a nutrient-dense organic fertilizer across Dyson’s 36,000 acres of open-field farming, eliminating the need for synthetic, petroleum-derived fertilizers.
  • Carbon Capture: Carbon dioxide emissions from the gas engines are cleaned, cooled, and pumped directly into the glasshouse to accelerate plant photosynthesis during daylight hours.
  • Hydrological Security: The glasshouse roof acts as a massive rain catchment system, funneling water into a 50-million-gallon local lagoon to supply the precise, closed-loop drip irrigation network.

“It might seem odd for an industrialist who makes vacuum cleaners, hairdryers and robotics to be interested in farming but I see it as an extension of that. This is all about machinery, mechanics and science improving things, it’s regenerative and it’s the right way to farm.”

— Sir James Dyson

Designing Out the Concept of Waste

By connecting these disparate operational layers—thermodynamics, microbiology, mechanical engineering, and botany—Dyson Farming has created a highly resilient agricultural machine. This ecosystem model proves that the future of sustainability doesn’t lie in reducing our output, but in optimizing the interconnected loops between our inputs, resources, and environments.

V. Futurology & The Human Element: The Future of the Agronomist

When analyzing the future of labor and automation, my strategic foresight research often highlights a concept I call the AI Soft Landing—the intentional transition where automation doesn’t displace the human workforce, but rather elevates it to perform higher-value, more rewarding roles. Agriculture is on the absolute frontline of this shift. Globally, the farming sector faces a profound demographic crisis; in the UK, the average age of an agricultural worker hovers around 59 years old. By shifting the paradigm from manual labor to high-technology operations, Dyson Farming has effectively dropped their average workforce age to 40, turning farming into a highly attractive destination for the next generation of talent.

The employee experience at a modern agri-tech facility looks completely different than it did a generation ago. The workforce is no longer composed solely of manual pickers working under unpredictable skies; instead, the glasshouse is managed by data analysts, drone operators, software engineers, and advanced agronomists. Humans work alongside machine intelligence, using data dashboards to monitor sap flow, track nutrient profiles, and optimize robotic picking schedules. We are witnessing the birth of a new professional class: the tech-driven land steward.

Biodiversity as an Engineering KPI

A true human-centered innovation framework recognizes that humanity cannot thrive unless the surrounding natural ecosystem thrives with it. In a traditional industrial farming setup, maximizing yield often comes at the direct expense of local biodiversity. Dyson’s systems-engineering approach treats the surrounding environment not as an external variable, but as a critical part of the macro-machine that must be carefully maintained.

Across their expansive holdings, biodiversity metrics are tracked with the same rigor as manufacturing outputs. The operation actively manages over 400 kilometers of native hedgerows, establishes extensive wildflower margins to support wild pollinators, and constructs dedicated nesting boxes for barn owls and birds of prey. By utilizing automated data collection and drone surveying, the engineering teams treat soil health, water purity, and wildlife populations as vital key performance indicators (KPIs) of the farm’s long-term commercial sustainability.

“Dyson Farming is developing new approaches to efficient, high-technology agriculture, which we hope will lead to a commercially sustainable future… Sustainable food production, food security and the environment are vital to the nation’s health and the nation’s economy.”

— Sir James Dyson

The Legacy of Participatory Ecosystems

Ultimately, this model proves that top-down design is obsolete in complex ecological and economic systems. By inviting engineers, biologists, and local communities to co-create a localized food production system, Dyson Farming demonstrates how strategic foresight can be grounded in practical, scalable realities. They are redefining what it means to be a custodian of the land in the twenty-first century.

VI. Conclusion: The Blueprint for Cross-Disciplinary Innovation

The transformation of Dyson Farming from an experimental project into a high-yielding, circular agricultural powerhouse offers a profound lesson for leadership across all sectors: true breakthrough innovation rarely happens by staying safely inside your comfort zone. It occurs at the intersection of disciplines, when a proven methodology from one industry is boldly exported to completely rewrite the rules of another.

Sir James Dyson did not attempt to alter the fundamental biological mechanics of how a strawberry grows. Instead, he and his engineering teams used systems thinking and human-centered experience design to re-engineer the entire macro-environment surrounding the plant. By connecting thermodynamics, robotics, and microbiology into a cohesive, closed-loop engine, they transformed a volatile, seasonal gamble into a predictable, localized, and commercially viable reality.

The Takeaway for Tomorrow’s Leaders

As we look to the future, the grand challenges of our era—whether in food security, healthcare, or energy infrastructure—will not be solved by siloed thinking. They require an expansive, ecosystem-wide view that treats waste as an unutilized input and views automation as a tool to elevate the human workforce. Dyson Farming serves as a brilliant blueprint for this exact ethos. It proves that when you possess a relentless desire to fix what is broken, bring manufacturing precision to the natural world, and design with the wider ecosystem in mind, you can build a sustainable, resilient future—one system, and one harvest, at a time.

Frequently Asked Questions: Systems Thinking in Agriculture

How does an engineering company like Dyson transition successfully into commercial farming?

Dyson approached agriculture not as a traditional farming operation, but as an advanced manufacturing and systems engineering challenge. By treating a greenhouse or a field exactly like a factory floor, they mapped their existing core competencies—such as fluid dynamics, thermal management, automation, and robotics—directly onto agricultural friction points. This systemic mindset allowed them to optimize inputs, design out waste, and create a highly predictable, climate-resilient growing process.

What exactly makes Dyson Farming’s strawberry greenhouse a “closed-loop” ecosystem?

The 26-acre glasshouse achieved circular sustainability by integrating directly with an adjacent Anaerobic Digestion (AD) plant. The AD plant processes energy crops and organic waste to generate clean electricity for the local grid. Dyson engineers capture the natural by-products of this process: the waste heat is piped back to warm the glasshouse in winter, the captured carbon dioxide is used to accelerate plant photosynthesis, and the nutrient-dense digestate residue replaces synthetic chemicals as an organic fertilizer for the open fields.

How does advanced agricultural automation impact the human workforce and employment?

Instead of completely displacing human workers, advanced automation elevates the employee experience and shifts workforce demographics. By integrating automated vision-sensing picking robots and autonomous UV-C disease-control rovers, Dyson Farming eliminates grueling, repetitive manual labor. This transforms the traditional agricultural role into high-value career paths, attracting a younger generation of data analysts, software developers, drone pilots, and tech-driven agronomists.


Image credits: 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, add images and create infographics.

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Why VUCA is a Myth

Why VUCA is a Myth

GUEST POST from Greg Satell

“Imagine, if you will, a factory as clean, spacious and continuously operating as a hydroelectric plant. The production floor is barren of men,” Fortune magazine declared in its November 1946 issue. Soon the world entered a new world of mass production and mass retail. Then came a green revolution, a space race, genomics, computers, the Internet and now artificial intelligence.

Today it’s become an article of faith that everything moves faster. Business pundits tell us that we’re living in a VUCA world (Volatile, Uncertain, Complex and Ambiguous). These are taken as basic truths that are beyond questioning or reproach. Yet are things actually moving any faster than in earlier eras? The evidence is surprisingly scarce.

The inescapable truth is that some things move faster today and others move slower. We don’t have—nor should we want—more change today than before. We need to be more thoughtful about change, more deliberate about the ones we undertake and more tenacious in our pursuit of them. We should aim to have less disruption and more progress.

An Era of Industrial Stability

Since Jack Welch took over GE in the 1980s, the management ethos has been taken over by a cult of disruption. Pundits say we must “innovate or die.” Managers feel pressure to launch new initiatives, to pivot and then pivot again, because the competition has become so rabid that “only the paranoid survive.”

The data, however, tell a very different story. A report from the OECD found that markets, especially in the United States, have become more concentrated and less competitive, with less churn among industry leaders. The number of young firms have decreased markedly as well, falling from roughly half of the total number of companies in 1982 to one third in 2013.

A comprehensive 2019 study from the National Bureau of Economic Research found two correlated, but countervailing trends: the rise of “superstar” firms and the fall of labor’s share of GDP. Essentially, the typical industry has fewer, but larger players. Their increased bargaining power leads to more profits, but lower wages.

With all of the hype around things like artificial intelligence, this may seem hard to believe. However, once you start to think about where you actually spend your money, food, shelter healthcare, travel and so on the reality sets in that most of the economy involves atoms and not bits and, if you do a bit more research, you’ll find that those industries are, for the most part, less competitive.

The truth is that we don’t really disrupt industries anymore. We disrupt people. Economic data shows that for most Americans, real wages have hardly budged since 1964. Income and wealth inequality remain at historic highs. Anxiety and depression, already at epidemic levels, worsened during the Covid-19 pandemic.

The Limits Of Digital Dominance

Over the past several decades, innovation has become largely synonymous with digital technology. When the topic of innovation comes up, somebody usually points to a company like Apple, Google or Facebook rather than, say, a car company, a hotel or a restaurant. Today, seven out of the ten most valuable companies in the world are digital firms.

This is largely because of two forces converging. The first is Moore’s Law, the exponential doubling of the number of transistors we have been able to cram onto a silicon wafer. Yet our ability to do that is coming up against the constraints of physics. Advancement in conventional chips has already slowed and, at some point, it will stop altogether.

The other force driving the digital economy has been increasing returns. As the economist W. Brian Arthur explained in a 1996 article in Harvard Business Review, certain conditions, such as high upfront investment, negligible marginal costs and network effects, lead to “winner take all markets where the fastest firm reaps incredible benefits.

Yet consider that information and communication technologies only make up about 6% of GDP value added in advanced economies and you begin to see the problem. The Silicon Valley model simply doesn’t work outside of software and consumer gadgets. In industries that have a low tolerance for failure, such as manufacturing and healthcare, you can’t simply move fast and break things because you’ll likely break something important.

Moving Slow To Go Fast

When Covid hit in the winter of 2020, it was a mysterious disease with no known cure. Yet in a mere matter of months vaccines were developed and being tested. By the end of the year two firms, Pfizer and Moderna received emergency authorization and people started getting their shots. Given that before Covid it took more than a decade to develop and test a vaccine, this was almost unheard of speed.

Yet look a little closer and it becomes clear that the real story is somewhat different. Katalin Karikó, published her first paper on the mRNA technology used to make the vaccines in 1990. She wasn’t able to win grants to fund her work and, in 1995, was told that she could either direct her energies in a different way, or be demoted. She took the demotion, worked through it and, a decade later, began to see some success.

Today, of course, mRNA technology is moving very quickly. Funding is flooding into labs to potentially cure or prevent a wide range of diseases, from cancer to malaria, vastly more efficiently than anything we’ve ever seen before. There are similar slow moving revolutions underway in quantum computing, drug and materials discovery and other things.

There’s nothing usual about any of this. It’s long been known that technology follows an s-curve pattern, starting slowly, then hitting an exponential phase in which it moves very quickly before leveling off again. For example, after penicillin became commercially available in 1945, we entered a golden age of antibiotics and scientists quickly uncovered dozens of compounds that could fight infection, before things slowed to a crawl.

At any given time, there are many s-curves going on at once. Some are just beginning to crawl, others speeding up and still others slowing down. Pointing out the ones that are speeding up and ignoring everything else that’s going on may be exciting, but it’s not the way to get the best results.

Rethinking The Change Gospel

It’s no accident that VUCA is a military term. The ever-present mantra that we are living in a time of volatility, uncertainty, complexity and ambiguity makes corporate executives feel like swashbuckling heroes. The truth is that there is very little evidence that is the case and a veritable mountain to the contrary.

There is also evidence that all the hype around change is doing real damage. Leaders conjure up dramatic images of “burning platforms” to justify launching ambitious initiatives, which rarely succeed. These failures then are given as confirmation for how dire the need for change really is and more initiatives are launched with similar results.

That is the change gospel. Transformation has, all too often, become an end in itself rather than a means to an end. We end up pivoting so much that we end up right where we started. The problem with cheerleading change is that it puts the cart before the horse. People don’t embrace change because you came up with a fancy slogan, they adopt what they find meaningful, that creates genuine value to their lives and their work.

We need to have more reverence for the mundane and ordinary. When you look at previous eras in which more genuine transformation took place and far more economic value was produced, there was much less talk about disruption and much more focus on improving the human condition.

The truth is that we’re not really disrupting industries anymore as much as we are disrupting ourselves and fairy tales and living in a VUCA era will not change those basic facts. We need to think less about disruption and more about tackling grand challenges that will impact the world in significant ways. Innovation should serve people, not the other way around.

— Article courtesy of the Digital Tonto blog
— Image credit: Wikimedia Commons

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