Category Archives: Technology

Innovation or Not – The VR Path to the Super Bowl

Innovation or Not - The VR Path to the Super Bowl

GUEST POST from Art Inteligencia

In the competitive arena of sports, athletes and coaches are perpetually seeking the next edge, the innovative stroke of genius that will propel them towards success. Enter Jayden Daniels, a pioneer quarterback who has embraced one of the most cutting-edge tools in sports performance enhancement: Virtual Reality (VR) training. Is this a true innovation or just another gimmick? Let’s journey through the lens of Jayden’s experience and see how this technology is reshaping the sporting world.

The Virtual Reality Revolution in Sports

For decades, athletes have relied on traditional training regimes, focusing on physical conditioning and repetitive skill drills. However, VR has transformed the landscape by introducing immersive environments where athletes can practice without the physical constraints of time, space, or risk of injury. Through VR headsets and meticulously simulated environments, players like Jayden Daniels are able to visualize and rehearse plays and strategies, improve their decision-making, and enhance their mental resilience.

“VR training is like a playbook come to life—it gives players the opportunity to be in the game without being on the field.”

Realizing this potential, Daniels incorporated VR training into his routine, and the results have been phenomenal. His ability to read defenses and execute plays has been augmented by this technology, helping him transition from mere player to game-changer.

Here is a video that tells the in depth story with commentary, but it won’t let me embed it here so just click the link in the box to watch it on YouTube:

EDITOR’S NOTE: Key takeaways include the technology’s ability to run at 1.75x speed so that on game day things slow down for the quarterback and he is able to engage in extra preparation without the entire team having to be present, and even to familiarize himself with away stadium nuances like where the play clocks are, etc.

Case Study #1: The Championship Turnaround

One of the most striking illustrations of VR’s impact occurred during a pivotal championship game. Daniels’ team was facing a formidable opponent known for their complex defensive schemes. The team’s traditional preparation methods were proving inadequate against such a sophisticated defense.

In the weeks leading to the game, Daniels immersed himself in VR simulations of the opponent’s defense. He studied every blitz, every zone coverage, and every adaptive quirk under the close guidance of his coaches, who were able to create a virtual replica of the team they were facing. By the time the championship game arrived, Daniels was not only prepared—he was several steps ahead.

During the game, his performance was near flawless. He anticipated defensive movements with uncanny accuracy, leading his team to a come-from-behind victory that analysts credited in large part to his innovative use of VR.

The MVP Moment

This VR-driven insight culminated in one memorable play: a perfectly executed fake pass that caught the opposing defense entirely off-guard, leading to the game-winning touchdown. This wasn’t just victory—it was an unveiling of how technology and sport can harmonize to create extraordinary outcomes.

Case Study #2: The Rival Rumble

In another celebrated match-up, Daniels faced his long-time rivals—a team that had bested his own in recent seasons. Known for their reactive plays and dynamic shifts, this opponent posed a considerable mental challenge that extended beyond physical prowess.

Once again, VR training became Daniels’ secret weapon. By simulating hundreds of scenarios, his VR regimen enabled him to practice responses to the rival’s play-calling tendencies, helping him build a memory bank of potential outcomes and counter-strategies.

When faced with crucial decisions on the field, Daniels was markedly less stressed and more composed. He deftly outmaneuvered the rival’s defense, leading his team to a decisive victory, and doing so with an air of confidence that captivated spectators and silenced skeptics.

The VR Vision

By the end of the season, Daniels had not only improved his own performance but had also inspired a wave of interest and investment in VR training across the league. Teams began revisiting their training paradigms, nudging the sports industry towards a more tech-savvy future.

Innovation or Not?

Jayden Daniels’ success with VR training may invite debates about whether this is innovation or merely a novel tool in an athlete’s repertoire. Regardless of where you stand, what cannot be denied is the transformative impact VR has had on enhancing an athlete’s strategic prowess and mental fortitude.

Beyond just quick optical improvements, VR training stands at the intersection of cognitive science and performance enhancement, offering a paradigm where mental sharpness is honed in tandem with physical capabilities. For Daniels, and countless athletes following in his footsteps, VR presents a formidable new teammate in their quest for greatness.

As we stand at the threshold of a technologically enhanced sports era, the question still lingers in the locker room and boardrooms: Is VR the future of sports training, or just another fleeting fad? For Jayden Daniels, it’s clear that VR is more than just a tool—it’s a revelation.

Image credit: Wikimedia Commons – All-Pro Reels of District of Columbia, USA

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The Breakthrough Lifecycle

The Breakthrough Lifecycle

GUEST POST from Greg Satell

Many experts suspect that the COVID crisis is receding into the background. It is, of course, hard to know for sure. There will continue to be debate and we will still need to have some mitigating measures in place. Still, for the most part, people are back at work, kids are in school, and relatively normal routines have returned.

Generations from now, historians will most likely still question what lessons are to be gleaned from the past few years. Should we strengthen our multilateral institutions or have they become so sclerotic that they need to be dismantled? Is the rise of populist nationalism a harbinger for the future or a flash in the pan?

One thing I don’t expect to be hotly debated, in fact seems perfectly clear even now, is that science saved us. Untold thousands, working mostly anonymously in labs around the world, created a vaccine of astonishing efficacy in record time. It is these types of breakthroughs that change the course of history and, if we can embrace their power, lead us to a better future.

A Seemingly Useless Idea

The MRNA technology that led to the Moderna and Pfizer-BioNTech vaccines have the potential to revolutionize medical science. It can rapidly reprogram the machinery in our cells to manufacture things that can potentially cure or prevent a wide range of diseases, from cancer to malaria, vastly more efficiently than anything we’ve ever seen before.

Yet while revolutionary, it is not at all a new idea. In fact Katalin Karikó, who pioneered the approach, published her first paper on mRNA-based therapy way back in 1990. Unfortunately, she wasn’t able to win grants to fund her work and, by 1995, things came to a head. She was told that she could either direct her energies in a different way, or be demoted.

This type of thing is not unusual. Jim Allison, who won the Nobel Prize for his work on cancer immunotherapy, had a very similar experience when he had his breakthrough, despite having already become a prominent leader in the field. “It was depressing,” he told me. “I knew this discovery could make a difference, but nobody wanted to invest in it.”

The truth is that the next big thing always starts out looking like nothing at all. Things that really change the world always arrive out of context for the simple reason that the world hasn’t changed yet.

Overcoming Resistance

Humans tend to see things in a linear fashion. It is easier for us to imagine a clear line of cause and effect, like a row of dominoes falling into each other, rather than a series of complex interactions and feedback loops. So it shouldn’t be surprising that, in hindsight, breakthrough ideas seem so obvious that only the most dim-witted would deny their utility.

When we think of something like, say, electricity, we often just assume that it was immediately adopted and the world simply changed overnight. After all, who could deny the superiority of an efficient electric motor over a big, noisy steam engine? Yet as the economist Paul David explained in a famous paper, it took 40 years for it to really take hold.

There are a few reasons why this is the case. The first is switching costs. A new technology almost always has to replace something that already does the job. Another problem involves establishing a learning curve. People need to figure out how to unlock the potential of the new technology. To bring about any significant change you first have to overcome resistance.

With electricity, the transition happened slowly. It wouldn’t have made sense to immediately tear down steam-powered factories and replace them. At first, only new plants used the electricity. Yet it wasn’t so much the technology itself, but how people learned to use it to re-imagine how factories functioned that unlocked a revolution in productivity gains.

In the case of mRNA technology, no one had seen a mRNA vaccine work, so many favored more traditional methods. Johnson & Johnson and AstraZeneca, for example, used a more traditional DNA-based approach using adenoviruses that was much better understood, rather than take a chance on a newer, unproven approach.

We seem to be at a similar point now with mRNA and other technologies, such as CRISPR. They’ve been proven to be viable, but we really don’t understand them well enough yet to unlock their full potential.

Building Out The Ecosystem

When we look back through history, we see a series of inventions. It seems obvious to us that things like the internal combustion engine and electricity would change the world. Still, as late as 1920, roughly 40 years after they were invented, most American’s lives remained unchanged. For practical purposes, the impact of those two breakthroughs were negligible.

What made the difference wasn’t so much the inventions themselves, but the ecosystems that form around them. For internal combustion engines it took a separate networks to supply oil, to build roads, manufacture cars and ships and so on. For electricity, entire industries based on secondary inventions, such as household appliances and radios, needed to form to fully realize the potential of the underlying technology.

Much of what came after could scarcely have been dreamed of. Who could have seen how transportation would transform retail? Or how communications technologies would revolutionize warfare? Do you really think anybody looked at an IBM mainframe in the 1960s and said, “Gee, this will be a real problem for newspapers some day?”

We can expect something similar to happen with mRNA technology. Once penicillin hit the market in 1946, a “golden age” of antibiotics ensued, resulting in revolutionary new drugs being introduced every year between 1950 and 1970. We’ve seen a similar bonanza in cancer immunotherapies since Jim Allison’s breakthrough.

In marked contrast to Katalin Karikó’s earlier difficulty in winning grants for her work, the floodgates have now opened as pharma companies are now racing to develop mRNA approaches for a myriad of diseases and maladies.

The Paradox Of New Paradigms

The global activist Srdja Popović once told me that when a revolution is successful, it’s difficult to explain the previous order, because it comes to be seen as unbelievable. Just as it’s hard to imagine a world without electricity, internal combustion or antibiotics today, it will be difficult to explain our lives today to future generations.

In much the same way, we cannot understand the future through linear extrapolation. We can, of course, look at today’s breakthroughs in things like artificial intelligence, synthetic biology and quantum computing, but what we don’t see is the second or third order effects, how they will shape societies and how societies will choose to shape them.

Looking at Edison’s lightbulb would tell you nothing about radios, rock music and the counterculture of the 60s, much like taking a ride in Ford’s “Model T” would offer little insight into the suburbs and shopping malls his machine would make possible. Ecosystems are, by definition, chaotic and non-linear.

What is important is that we allow for the unexpected. It was not obvious to anyone that Katalin Karikó could ever get her idea to work, but she shouldn’t have had to risk her career to make a go of it. We’re enormously lucky that she didn’t, as so many others would have, taken an easier path. It is, in the final analysis, that one brave decision that we have to thank for what promises to be brighter days ahead.

All who wander are not lost.

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

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Startups, Companies, Acquisitions and Hurricanes

Startups, Companies, Acquisitions and Hurricanes

GUEST POST from Mike Shipulski

If you run a company, the most important thing you can control is how you allocate your resources. You can’t control how the people in your company will respond to input, but you can choose the projects they work on. You can’t control which features and functions your customers will like, but you can choose which features and functions become part of the next product. And you can’t control if a new technology will work, but you can choose the design space to investigate. The open question – How to choose in a way that increases your probability of success?

If you want to buy a company, the most important thing you can control is how you allocate your resources. In this case, the resources are your hard-earned money and your choice is which company to buy. The open question – How to choose in a way that increases your probability of success?

If you want to invest in a startup company, the most important thing you can control is how you allocate your resources. This case is the same as the previous one – your money is the resource and the company you choose defines how you allocate your resources. This one is a little different in that the uncertainty is greater, but so is the potential reward. Again, the same open question – How to choose in a way that increases your probability of success?

Taking a step back, the three scenarios can be generalized into a category called a “system.” And the question becomes – how to understand the system in a way that improves resource allocation and increases your probability of success?

These people systems aren’t predictable in an if-A-then-B way. But they do have personalities or dispositions. They’ve got characteristics similar to hurricanes. A hurricane’s exact path cannot be forecasted, the meteorologist can use history and environmental conditions to broadly define regions where the probability of danger is higher. The meteorologist continually monitors the current state of the hurricane (the system as it is) and tracks its position over time to get an idea of its trajectory (a system’s momentum). The key to understanding where the hurricane could go next: where it is right now (current state), how it got there (how it has behaved over time), and how have other hurricanes tracked under similar conditions (its disposition). And it’s the same for systems.

To improve your understanding of how your system may respond, understand it as it is. Define the elements and how those elements interact. Then, work backward in time to understand previous generations of the system. Which elements were improved? Which ones were added? Then, like the meteorologist, start at the system’s genesis and move forward to the present to understand its path. Use the knowledge of its path and the knowledge of systems (it’s important to be the one that improves the immature elements of the system and systems follow S-curves until the S-curve flattens) to broadly define regions where the probability of success is higher.

These methods won’t guarantee success. But, they will help you choose projects, choose acquisitions, choose technologies, and choose startups in a way that increases your probability of success.

Image credits: Pexels

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Top 100 Innovation and Transformation Articles of 2024

Top 100 Innovation and Transformation Articles of 2024

2021 marked the re-birth of my original Blogging Innovation blog as a new blog called Human-Centered Change and Innovation.

Many of you may know that Blogging Innovation grew into the world’s most popular global innovation community before being re-branded as Innovation Excellence and being ultimately sold to DisruptorLeague.com.

Thanks to an outpouring of support I’ve ignited the fuse of this new multiple author blog around the topics of human-centered change, innovation, transformation and design.

I feel blessed that the global innovation and change professional communities have responded with a growing roster of contributing authors and more than 17,000 newsletter subscribers.

To celebrate we’ve pulled together the Top 100 Innovation and Transformation Articles of 2024 from our archive of over 2,500 articles on these topics.

We do some other rankings too.

We just published the Top 40 Innovation Bloggers of 2024 and as the volume of this blog has grown we have brought back our monthly article ranking to complement this annual one.

But enough delay, here are the 100 most popular innovation and transformation posts of 2024.

Did your favorite make the cut?

1. Organizational Debt Syndrome Poses a Threat – by Stefan Lindegaard

2. FREE Innovation Maturity Assessment – by Braden Kelley

3. The Education Business Model Canvas – by Arlen Meyers, M.D.

4. The Role of Stakeholder Analysis in Change Management – by Art Inteligencia

5. Act Like an Owner – Revisited! – by Shep Hyken

6. Iterate Your Thinking – by Dennis Stauffer

7. SpaceX is a Masterclass in Innovation Simplification – by Pete Foley

8. What is Human-Centered Change? – by Braden Kelley

9. A 90% Project Failure Rate Means You’re Doing it Wrong – by Mike Shipulski

10. Should a Bad Grade in Organic Chemistry be a Doctor Killer? – by Arlen Meyers, M.D.

11. How Netflix Built a Culture of Innovation – by Art Inteligencia

12. Fear is a Leading Indicator of Personal Growth – by Mike Shipulski

13. Sustaining Imagination is Hard – by Braden Kelley

14. No Regret Decisions: The First Steps of Leading through Hyper-Change – by Phil Buckley

15. The Art of Adaptability: How to Respond to Changing Market Conditions – by Art Inteligencia

16. Sprint Toward the Innovation Action – by Mike Shipulski

17. Marriott’s Approach to Customer Service – by Shep Hyken

18. Top 5 Future Studies Programs – by Art Inteligencia

19. Reversible versus Irreversible Decisions – by Farnham Street

20. 50 Cognitive Biases Reference – Free Download – Courtesy of TitleMax

21. Free Human-Centered Change Tools – by Braden Kelley

22. Designing an Innovation Lab: A Step-by-Step Guide – by Art Inteligencia

23. Why More Women Are Needed in Innovation – by Greg Satell

24. How to Defeat Corporate Antibodies – by Stefan Lindegaard

25. The Nine Innovation Roles – by Braden Kelley

26. Top 40 Innovation Bloggers of 2023 – Curated by Braden Kelley

27. Human-Centered Change – by Braden Kelley

28. Visual Project Charter™ – 35″ x 56″ (Poster Size) and JPG for Online Whiteboarding – by Braden Kelley

29. FutureHacking – Be Your Own Futurist – by Braden Kelley

30. ACMP Standard for Change Management® Visualization – 35″ x 56″ (Poster Size) – Association of Change Management Professionals – by Braden Kelley


Build a common language of innovation on your team


31. Overcoming Resistance to Change – by Chateau G Pato

32. Are We Abandoning Science? – by Greg Satell

33. How Networks Power Transformation – by Greg Satell

34. What Differentiates High Performing Teams – by David Burkus

35. The 6 Building Blocks of Great Teams – by David Burkus

36. Unintended Consequences. The Hidden Risk of Fast-Paced Innovation – by Pete Foley

37. The Role of Employee Training and Development in Enhancing Customer Experience – by Art Inteligencia

38. The Pyramid of Results, Motivation and Ability – by Braden Kelley

39. Your Strategy Must Reach Beyond Markets to Ecosystems – by Greg Satell

40. What is the difference between signals and trends? – by Art Inteligencia

41. Next Generation Leadership Traits and Characteristics – by Stefan Lindegaard

42. Latest Interview with the What’s Next? Podcast – Featuring Braden Kelley

43. A Tipping Point for Organizational Culture – by Janet Sernack

44. Accountability and Empowerment in Team Dynamics – by Stefan Lindegaard

45. Design Thinking for Non-Designers – by Chateau G Pato

46. The Innovation Enthusiasm Gap – by Howard Tiersky

47. The One Movie All Electric Car Designers Should Watch – by Braden Kelley

48. The Ultimate Guide to the Phase-Gate Process – by Dainora Jociute

49. Innovation Management ISO 56000 Series Explained – by Diana Porumboiu

50. How to Create an Effective Innovation Hub – by Chateau G Pato


Accelerate your change and transformation success


51. Imagination versus Knowledge – Is imagination really more important? – by Janet Sernack

52. Stoking Your Innovation Bonfire – by Braden Kelley

53. A Shortcut to Making Strategic Trade-Offs – by Geoffrey A. Moore

54. How to Make Navigating Ambiguity a Super Power – by Robyn Bolton

55. Three HOW MIGHT WE Alternatives That Actually Spark Creative Ideas – by Robyn Bolton

56. Problems vs. Solutions vs. Complaints – by Mike Shipulski

57. Innovation or Not – Liquid Trees – by Art Inteligencia

58. Everyone Clear Now on What ChatGPT is Doing? – by Geoffrey A. Moore

59. Leadership Best Quacktices from Oregon’s Dan Lanning – by Braden Kelley

60. Will Innovation Management Leverage AI in the Future? – by Jesse Nieminen

61. The Power of Position Innovation – by John Bessant

62. Creating Organizational Agility – by Howard Tiersky

63. A Case Study on High Performance Teams – by Stefan Lindegaard

64. Secrets to Overcoming Resistance to Change – by David Burkus

65. How to Write a Failure Resume – by Arlen Meyers, M.D.

66. 9 of 10 Companies Requiring Employees to Return to the Office in 2024 – by Shep Hyken

67. The Five Keys to Successful Change – by Braden Kelley

68. What is Social Analysis? – by Art Inteligencia

69. Dare to Think Differently – by Janet Sernack

70. Parallels Between the 1920’s and Today Are Frightening – by Greg Satell

71. What is Trend Spotting? – by Art Inteligencia

72. Driving Change is Not Enough – You Also Have To Survive Victory – by Greg Satell

73. 5 Simple Steps to Team Alignment – by David Burkus

74. Building a Better Change Communication Plan – by Braden Kelley

75. The Role of Leadership in Fostering a Culture of Innovation – by Art Inteligencia

76. 4 Simple Steps to Becoming Your Own Futurist – An Introduction to the FutureHacking™ methodology – by Braden Kelley

77. Four Hidden Secrets of Innovation – by Greg Satell

78. Why Organizations Struggle with Innovation – by Howard Tiersky

79. An Introduction to Strategic Foresight – by Stefan Lindegaard

80. Learning About Innovation – From a Skateboard? – by John Bessant


Get the Change Planning Toolkit


81. 800+ FREE Quote Posters – by Braden Kelley

82. Do you have a fixed or growth mindset? – by Stefan Lindegaard

83. Generation AI Replacing Generation Z – by Braden Kelley

84. The End of the Digital Revolution – by Greg Satell

85. Is AI Saving Corporate Innovation or Killing It? – by Robyn Bolton

86. The Experiment Canvas™ – 35″ x 56″ (Poster Size) – by Braden Kelley

87. America Drops Out of the Ten Most Innovative Countries – by Braden Kelley

88. 5 Essential Customer Experience Tools to Master – by Braden Kelley

89. AI as an Innovation Tool – How to Work with a Deeply Flawed Genius! – by Pete Foley

90. Four Ways To Empower Change In Your Organization – by Greg Satell

91. Agile Innovation Management – by Diana Porumboiu

92. Do Nothing More Often – by Robyn Bolton

93. Five Things Most Managers Don’t Know About Innovation – by Greg Satell

94. The Fail Fast Fallacy – by Rachel Audige

95. Top Six Trends for Innovation Management in 2025 – by Jesse Nieminen

96. How to Re-engineer the Incubation Zone – by Geoffrey A. Moore

97. Flaws in the Crawl Walk Run Methodology – by Braden Kelley

98. Master the Customer Hierarchy of Needs – by Shep Hyken

99. Rise of the Atomic Consultant – Or the Making of a Superhero – by Braden Kelley

100. A Shared Language for Radical Change – by Greg Satell

Curious which article just missed the cut? Well, here it is just for fun:

101. Is Disruption About to Claim a New Victim? – by Robyn Bolton

These are the Top 100 innovation and transformation articles of 2024 based on the number of page views. If your favorite Human-Centered Change & Innovation article didn’t make the cut, then send a tweet to @innovate and maybe we’ll consider doing a People’s Choice List for 2024.

If you’re not familiar with Human-Centered Change & Innovation, we publish 1-6 new articles every week focused on human-centered change, innovation, transformation and design insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook feed or on Twitter or LinkedIn too!

Editor’s Note: Human-Centered Change & Innovation is open to contributions from any and all the innovation & transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have a valuable insight to share with everyone for the greater good. If you’d like to contribute, contact us.

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Uber Economy is Killing Innovation, Prosperity and Entrepreneurship

Uber Economy is Killing Innovation, Prosperity and Entrepreneurship

GUEST POST from Greg Satell

Today, it seems that almost everyone wants to be the “Uber” of something, and why not? With very little capital investment, the company has completely disrupted the taxicab industry and attained a market value of over $100 billion. In an earlier era, it would have taken decades to have created that kind of impact on a global scale.

Still, we’re not exactly talking about Henry Ford and his Model T here. Or even the Boeing 707 or the IBM 360. Like Uber, those innovations quickly grew to dominance, but also unleashed incredible productivity. Uber, on the other hand, gushed red ink for more than a decade despite $25 billion invested. In 2021 it lost more than $6 billion, the company made progress in 2022 but still lost money, and it was only in 2023 that they finally made a profit.

The truth is that we have a major problem and, while Uber didn’t cause it, the company is emblematic of it. Put simply, a market economy runs on innovation. It is only through consistent gains in productivity that we can create real prosperity. The data and evidence strongly suggests that we have failed to do that for the past 50 years. We need to do better.

The Productivity Paradox Writ Large

The 20th century was, for the most part, an era of unprecedented prosperity. The emergence of electricity and internal combustion kicked off a 50-year productivity boom between 1920 and 1970. Yet after that, gains in productivity mysteriously disappeared even as business investment in computing technology increased, causing economist Robert Solow to observe that “You can see the computer age everywhere but in the productivity statistics.”

When the internet emerged in the mid-90’s things improved and everybody assumed that the mystery of the productivity paradox had been resolved. However, after 2004 productivity growth disappeared once again. Today, despite the hype surrounding things such as Web 2.0, the mobile Internet and, most recently, artificial intelligence, productivity continues to slump.

Take a closer look at Uber and you can begin to see why. Compare the $25 billion invested in the ride-sharing company with the $5 billion (worth about $45 billion today) IBM invested to build its System 360 in the early 1960s. The System 360 was considered revolutionary, changed computing forever and dominated the industry for decades.

Uber, on the other hand, launched with no hardware or software that was particularly new or revolutionary. In fact, the company used fairly ordinary technology to dis-intermediate relatively low-paid taxi dispatchers. The money invested was largely used to fend off would-be competitors through promoting the service and discounting rides.

Maybe the “productivity paradox” isn’t so mysterious after all.

Two Paths To Profitability

Anybody who’s ever taken an Economics 101 course knows that, under conditions of perfect competition, the forces of supply and demand are supposed to drive markets toward equilibrium. It is at this magical point that prices are high enough to attract supply sufficient to satisfy demand, but not any higher.

Unfortunately for anyone running a business, that equilibrium point is the same point at which economic profit disappears. So to make a profit over the long-term, managers need to alter market dynamics either through limiting competition, often through strategies such as rent seeking and regulatory capture, or by creating new markets through innovation.

As should be clear by now, the digital revolution has been relatively ineffective at creating meaningful innovation. Economists Daron Acemoglu and Pascual Restrepo refer to technologies like Uber, as well as things like automated customer service, as “so-so technologies,” because they displace workers without significantly increasing productivity.

Joseph Schumpeter pointed out long ago, market economies need innovation to fuel prosperity. Without meaningful innovation, managers are left with only strategies that limit innovation, undermine markets and impoverish society, which is what largely seems to have happened over the past few decades.

The Silicon Valley Doomsday Machine

The arrogance of Silicon Valley entrepreneurs seems so outrageous—and so childishly naive— that it is scarcely hard to believe. How could an industry that has produced so little in terms of productivity seem so sure that they’ve been “changing the world” for the better. And how have they made so much money?

The answer lies in something called increasing returns. As it turns out, under certain conditions, namely high up-front investment, negligible marginal costs, network effects and “winner-take-all markets,” the normal laws of economics can be somewhat suspended. In these conditions, it makes sense to pump as much money as possible into an early Amazon, Google or Facebook.

However this seemingly happy story has a few important downsides. First, to a large extent these technologies do not create new markets as much as they disrupt or displace old ones, which is one reason why productivity gains are so meager. Second, the conditions apply to a small set of products, namely software and consumer gadgets, which makes the Silicon Valley model a bad fit for many groundbreaking technologies.

Still, if the perception is that you can make a business viable by pumping a lot of cash into it, you can actually crowd-out a lot of good businesses with bad, albeit well-funded ones. In fact, there is increasing evidence that is exactly what is happening. Rather than an engine of prosperity, Silicon Valley is increasingly looking like a doomsday machine.

Returning To An Innovation Economy

Clearly, we cannot continue “Ubering” ourselves to death. We must return to an economy fueled by innovation, rather than disruption, which produces the kind of prosperity that lifts all boats, rather than outsized profits for a meager few. It is clearly in our power to do that, but we must begin to make better choices.

First, we need to recognize that innovation is something that people do, but instead of investing in human capital, we are actively undermining it. In the US, food insecurity has become an epidemic on college campuses. To make matters worse, the cost of college has created a student debt crisis, essentially condemning our best and brightest to decades of indentured servitude. To add insult to injury, healthcare costs continue to soar. Should we be at all surprised that entrepreneurship is in decline?

Second, we need to rebuild scientific capital. As Vannevar Bush once put it, “There must be a stream of new scientific knowledge to turn the wheels of private and public enterprise.” To take just one example, it is estimated that the $3.8 billion invested in the Human Genome Project generated nearly $800 billion of economic activity as of 2011. Clearly, we need to renew our commitment to basic research.

Finally, we need to rededicate ourselves to free and fair markets. In the United States, by almost every metric imaginable, whether it is industry concentration, occupational licensing, higher prices, lower wages or whatever else you want to look at capitalism has been weakened by poor regulation and oversight. Not surprisingly, innovation has suffered.

Perhaps most importantly, we need to shift our focus from disrupting markets to creating them, from “The Hacker Way”, to tackling grand challenges and from a reductionist approach to an economy based on dignity and well being. Make no mistake: The “Uber Economy” is not the solution, it’s the problem.

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

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Innovation or Not – AI Birdwatching

Innovation or Not - AI Birdwatching

GUEST POST from Art Inteligencia

Welcome to another installment of the “Innovation or Not” series, where we dissect intriguing products and services to determine whether they truly represent ingenuity, or if they’re just another notch on the belt of incremental progress. Today, we’re venturing into the realm of birdwatching — a niche hobby that surprisingly intersects with cutting-edge artificial intelligence and automated insights into our avian neighbors.

Introducing FeatherSnap

The product up for review is the FeatherSnap bird feeder camera. At its core, FeatherSnap is a bird feeder equipped with a camera that not only captures images of our feathery friends but also uses AI to identify species and offer insights to the user. The idea itself blends the tranquility of birdwatching with the technological advancements of AI and machine learning. It also has a smart design to integrate the food storage into the structure itself to save space, and has solar panels to power the onboard technology. But the question remains: is this a pleasant convenience or a groundbreaking innovation?

The Tech Behind FeatherSnap

FeatherSnap integrates a high-quality camera with AI capabilities to recognize and catalog bird species visiting your garden or backyard. It allows the user to receive real-time alerts on their smartphone, providing information about the birds that stop by for a snack. This records data such as the species, time of day, and frequency of visits, creating a rich, personalized avian database over time.

“AI birdwatching may be niche, but it bridges a gap between nature enthusiasts and technology, making the act of observation more engaging and informed.”

Innovation Analysis

When assessing FeatherSnap through the lens of innovation, we explore several key criteria:

  • Originality: AI-augmented birdwatching is a fresh take on a traditional hobby, significantly enhancing the user experience.
  • Technology Application: The application of AI in identifying bird species represents an advancement in both hobbyist technology and AI’s practical capabilities.
  • Value Creation: FeatherSnap adds substantial value to the birdwatching experience by providing educational insights and personalized interaction with nature.
  • Market Impact: While its potential market may seem limited to bird enthusiasts, the push towards automated, intelligent environmental engagement could have broader applications.

Final Verdict: Innovation or Not?

So, is FeatherSnap an innovation or not? Taking all factors into consideration, I would argue that FeatherSnap qualifies as an innovation. Despite its niche market, it presents a clever integration of AI with everyday life that could inspire further applications across different domains. The product encourages a deeper interaction with nature and presents a template for utilizing technology to enrich leisure activities.

In the broader context of our tech-driven world, FeatherSnap’s introduction to the market both exemplifies ingenuity in leisure tech and challenges developers to think creatively about AI’s scope and potential in nature-based contexts.

As we reflect on this product, it reminds us that innovation isn’t always about life-changing inventions but also about elevating the simple joys of life with smart adaptations.

I encourage you to share your thoughts and opinions on FeatherSnap and whether you consider it groundbreaking or just another incremental product in the tech landscape. Until next time, keep questioning and exploring the ever-changing facets of innovation around you.

Image credit: FeatherSnap

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Make the Planet and Your Bottom Line Smile

Make the Planet and Your Bottom Line Smile

GUEST POST from Mike Shipulski

What if the most profitable thing you could do was work that reduced the rise in the earth’s temperature? What if it was most profitable to reduce CO2 emissions, improve water quality or generate renewable energy? Or what if it was most profitable to do work that indirectly made the planet smile?

What if while your competitors greenwashed their products and you radically reduced the environmental impacts of yours? And what if the market would pay more for your greener product? And what if your competitors saw this and disregarded the early warning signs of their demise? This is what I call a compete-with-no-one condition. This is where your competitors eat each other’s ankles in a race to the bottom while you raise prices and sell more on a different line of goodness – environmental goodness. This is where you compete against no one because you’re the only one with products that make the planet smile.

The problem with an environmentally-centric, compete-with-no-one approach is you have to put yourself out there and design and commercialize new products based on this “unproven” goodness. In a world of profits through cost, quality and speed, you’ve got to choose profits through reduced CO2, improved water quality and renewable energy. Why would anyone pay more for a more environmentally responsible product when its price is higher than the ones that work well and pollute just as much as they did last year?

When the Toyota Prius hybrid first arrived on the market, it cost more than traditional cars and its performance was nothing special. Yet it sold. Yes, it had radically improved fuel economy, but the fuel savings didn’t justify the higher price, yet it sold. Competitors advertised that the Prius hybrid didn’t make financial sense, yet it sold. With the Prius hybrid, Toyota took an environmentally-centric, compete-with-no-one approach. They made little on each vehicle or even lost money, but they did it anyway. They did the most important thing. They started.

The Toyota Prius hybrid wasn’t a logical purchase, it was an emotional one. People bought them to make a statement about themselves – I drive a funny-shaped car that gets great gas mileage, I’m environmentally responsible, and I want you to know that. And as other companies scoffed, Toyota created a new category and owned the whole thing.

And, slowly, as Toyota improved the technology and reduced their costs, the price of the Prius dropped and they sold more. And then all the other manufacturers jumped into the race and tried to catch up. And while everyone else cut their teeth on high volume manufacturing a hybrid vehicle, Toyota accelerated.

Below is a chart of hybrid electric vehicles (hev) sold in the US from 2000 to 2017. Each color represents a different model and the Toyota Prius hybrid is represented by the tall blue segment of each year’s stacked bar. In 2000, Toyota sold 5,562 Prius hybrids (60% of all hevs). In 2005, they sold 107,897 Prius hybrids, 17,989 Highlander hybrids and 20,674 Lexus hybrids for a total of 209,711 hybrids (69% of all hevs). In 2007, they sold 181,221 Prius and five other hybrid models for a total of 228,593 (65% of all hevs). In 2017, sold 15 hybrid models and the nearest competitor sold four models. The reduction from 2008 to 2011 is due to reduced gas prices. (Here’s a link to the chart.)

United States Hybrid Electric Vehicle Sales

The success of the Prius vehicle set off the battery wars which set the stage for the plug-in hybrids (larger batteries) and all-electric vehicles (still larger batteries). At the start, the Prius didn’t make sense in a race-to-the-bottom way, but it made sense to people that wanted to make the planet smile. It cost more, and it sold. And that was enough for Toyota to make profits with a more environmentally friendly product. No, Prius didn’t save the planet, but it showed companies that it’s possible to make profits while making the planet smile (a bit). And it made it safe for companies to pursue the next generation of environmentally-friendly vehicles.

The only way to guarantee you won’t make more profits with environmentally responsible products is to believe you won’t. And that may be okay unless one of your companies believes it is possible.

Here’s a thought experiment. Put yourself ten years into the future. There is more CO2 in the atmosphere, the earth is warmer, sea levels are higher, water is more polluted and renewable energy is far cheaper. Are your sales higher if your product creates more CO2, or less? Are your sales higher if your product heats the earth, or cools it? Are your sales higher if your product pollutes water, or makes it cleaner? Are your sales higher because you bet against renewable energy, or because you embraced it? Are your sales higher because you made the planet frown, or smile?

Now, with your new perspective, bring yourself back to the present and do what it takes to increase sales ten years from now. Your future self, your children, their children, and the planet will thank you.

Image credits: Google Gemini

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Artificial Innovation

Artificial Innovation

by Braden Kelley

Recently several people have asked me whether or not artificial intelligence (AI) has a role to play innovation. One of the ways I’ve answered this question is by speaking about how artificial intelligence can be used to help test/disprove assumptions. Innovation always makes assumptions and often the success or failure of any innovation effort is determined by how well the team identifies the critical assumptions to test, those that if incorrectly assumed to be true could later derail the pursuit of innovation or waste limited innovation investment dollars.

But I thought it could be interesting to use AI to answer this question in more detail, leveraging my Eight I’s of Infinite Innovation framework to highlight how artificial intelligence could be used at each step of the continuous innovation journey.

Below you will find a detailed explanation of the Eight I’s of Infinite Innovation framework along with clearly called out contextual responses generated by Microsoft CoPilot detailing how AI could be used productively during that specific phase of the continuous innovation journey from prompts generated by me after uploading a PDF version of the original Eight I’s of Infinite Innovation article (see the link at the bottom).

Eight I's of Infinite Innovation

Creating a Continuous Innovation Capability

To achieve sustainable success at innovation, you must work to embed a repeatable process and way of thinking within your organization, and this is why it is important to have a simple common language and guiding framework of infinite innovation that all employees can easily grasp. If innovation becomes too complex, or seems too difficult then people will stop pursuing it, or supporting it.

Some organizations try to achieve this simplicity, or to make the pursuit of innovation seem more attainable, by viewing innovation as a project-driven activity. But, a project approach to innovation will prevent it from ever becoming a way of life in your organization. Instead you must work to position innovation as something infinite, a pillar of the organization, something with its own quest for excellence – a professional practice to be committed to.

So, if we take a lot of the best practices of innovation excellence and mix them together with a few new ingredients, the result is a simple framework organizations can use to guide their pursuit of continuous innovation – the Eight I’s of Infinite Innovation. This framework anchors what is a very collaborative process. Here is the framework and some of the many points organizations must consider during each stage of the continuous process:

1. Inspiration

  • Employees are constantly navigating an ever changing world both in their home context, and as they travel the world for business or pleasure, or even across various web pages in the browser of their PC, tablet, or smartphone.
  • What do they see as they move through the world that inspires them and possibly the innovation efforts of the company?
  • What do they see technology making possible soon that wasn’t possible before?
  • The first time through we are looking for inspiration around what to do, the second time through we are looking to be inspired around how to do it.
  • What inspiration do we find in the ideas that are selected for their implementation, illumination and/or installation?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help employees find inspiration by analyzing vast amounts of data from various sources, such as social media, news articles, and industry reports. By identifying emerging trends and patterns, AI can provide insights into what is possible and inspire new ideas for innovation. Additionally, AI-powered tools can help employees visualize potential solutions and explore creative possibilities.

2. Investigation

  • What can we learn from the various pieces of inspiration that employees come across?
  • How do the isolated elements of inspiration collect and connect? Or do they?
  • What customer insights are hidden in these pieces of inspiration?
  • What jobs-to-be-done are most underserved and are worth digging deeper on?
  • Which unmet customer needs that we see are worth trying to address?
  • Which are the most promising opportunities, and which might be the most profitable?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can assist in the investigation phase by processing and analyzing large datasets to uncover hidden insights and customer needs. Machine learning algorithms can identify patterns and correlations that may not be immediately apparent to humans, helping organizations understand which opportunities are most promising and worth pursuing. AI can also automate the process of gathering and organizing information, making it easier for employees to focus on deeper analysis.

3. Ideation

  • We don’t want to just get lots of ideas, we want to get lots of good ideas
  • Insights and inspiration from first two stages increase relevance and depth of the ideas
  • We must give people a way of sharing their ideas in a way that feels safe for them
  • How can we best integrate online and offline ideation methods?
  • How well have we communicated the kinds of innovation we seek?
  • Have we trained our employees in a variety of creativity methods?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the ideation process by generating a wide range of ideas based on input from employees and external sources. Natural language processing (NLP) algorithms can analyze and categorize ideas, making it easier to identify the most relevant and promising ones. AI-powered collaboration tools can also facilitate brainstorming sessions, allowing employees to share and build on each other’s ideas in real-time, regardless of their physical location.

4. Iteration

  • No idea emerges fully formed, so we must give people a tool that allows them to contribute ideas in a way that others can build on them and help uncover the potential fatal flaws of ideas so that they can be overcome
  • We must prototype ideas and conduct experiments to validate assumptions and test potential stumbling blocks or unknowns to get learnings that we can use to make the idea and its prototype stronger
  • Are we instrumenting for learning as we conduct each experiment?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can support the iteration phase by providing tools for rapid prototyping and experimentation. Machine learning models can simulate different scenarios and predict potential outcomes, helping teams identify and address potential flaws in their ideas. AI can also automate the process of collecting and analyzing feedback from experiments, enabling continuous improvement and refinement of prototypes.

Eight I's of Infinite Innovation

5. Identification

  • In what ways do we make it difficult for customers to unlock the potential value from this potentially innovative solution?
  • What are the biggest potential barriers to adoption?
  • What changes do we need to make from a financing, marketing, design, or sales perspective to make it easier for customers to access the value of this new solution?
  • Which ideas are we best positioned to develop and bring to market?
  • What resources do we lack to realize the promise of each idea?
  • Based on all of the experiments, data, and markets, which ideas should we select?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can help organizations identify the most viable ideas by analyzing data from experiments, market research, and customer feedback. Predictive analytics can assess the potential success of different ideas and prioritize those with the highest likelihood of success. AI can also identify potential barriers to adoption and suggest strategies to overcome them, ensuring that innovative solutions are accessible and valuable to customers.

You’ll see in the framework that things loop back through inspiration again before proceeding to implementation. There are two main reasons why. First, if employees aren’t inspired by the ideas that you’ve selected to commercialize and some of the potential implementation issues you’ve identified, then you either have selected the wrong ideas or you’ve got the wrong employees. Second, at this intersection you might want to loop back through the first five stages though an implementation lens before actually starting to implement your ideas OR you may unlock a lot of inspiration and input from a wider internal audience to bring into the implementation stage.

6. Implementation

  • What are the most effective and efficient ways to make, market, and sell this new solution?
  • How long will it take us to develop the solution?
  • Do we have access to the resources we will need to produce the solution?
  • Are we strong in the channels of distribution that are most suitable for delivering this solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can streamline the implementation process by optimizing production, marketing, and sales strategies. AI-powered project management tools can help teams plan and execute tasks more efficiently, while machine learning algorithms can optimize supply chain and distribution processes. AI can also personalize marketing campaigns and sales approaches, ensuring that new solutions reach the right customers at the right time.

7. Illumination

  • Is the need for the solution obvious to potential customers?
  • Are we launching a new solution into an existing product or service category or are we creating a new category?
  • Does this new solution fit under our existing brand umbrella and represent something that potential customers will trust us to sell to them?
  • How much value translation do we need to do for potential customers to help them understand how this new solution fits into their lives and is a must-have?
  • Do we need to merely explain this potential innovation to customers because it anchors to something that they already understand, or do we need to educate them on the value that it will add to their lives?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can enhance the illumination phase by helping organizations communicate the value of their innovations to potential customers. NLP algorithms can generate compelling marketing content and product descriptions, while sentiment analysis can gauge customer reactions and adjust messaging accordingly. AI can also identify key influencers and target them with personalized messages to amplify the reach of new solutions.

8. Installation

  • How do we best make this new solution an accepted part of everyday life for a large number of people?
  • How do we remove access barriers to make it easy as possible for people to adopt this new solution, and even tell their friends about it?
  • How do we instrument for learning during the installation process to feedback new customer learnings back into the process for potential updates to the solution?

How to leverage artificial innovation during the Inspiration phase (according to AI):

  • AI can facilitate the installation of new solutions by removing barriers to adoption and ensuring a seamless customer experience. AI-powered customer support tools can provide instant assistance and troubleshooting, while machine learning algorithms can personalize onboarding processes to meet individual customer needs. AI can also monitor usage patterns and gather feedback, enabling continuous improvement and updates to the solution.

Conclusion

The Eight I’s of Infinite Innovation framework is designed to be a continuous learning process, one without end as the outputs of one round become inputs for the next round. It’s also a relatively new guiding framework for organizations to use, so if you have thoughts on how to make it even better, please let me know in the comments. The framework is also ideally suited to power a wave of new organizational transformations that are coming as an increasing number of organizations (including Hallmark) begin to move from a product-centered organizational structure to a customer needs-centered organizational structure. The power of this new approach is that it focuses the organization on delivering the solutions that customers need as their needs continue to change, instead of focusing only on how to make a particular product (or set of products) better.

By leveraging AI at each stage of the innovation process, organizations can enhance their ability to generate, develop, and implement successful innovations.

So, as you move from the project approach that is preventing innovation from ever becoming a way of life in your organization, consider using the Eight I’s of Infinite Innovation to influence your organization’s mindset and to anchor your common language of innovation. The framework is great for guiding conversations, making your innovation outputs that much stronger, and will contribute to your quest for innovation excellence – it is even more powerful when you combine it with my Value Innovation Framework (found here). The two are like chocolate and peanut butter. They’re powerful tools when used separately, but even more powerful when used together.

Click to access this framework as a FREE scalable 11″x17″ PDF download

Click to download the PDF version of this article

People who upgrade to the Bronze Version of the Change Planning Toolkit™ will get access to my Innovation Planning Canvas™ which combines the Value Innovation Framework together with the Eight I’s of Infinite Innovation, allowing you to track the progress of each potential innovation on the three value innovation measures as you evolve any individual idea through this eight step process.

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The State of Customer Experience and the Contact Center

The State of Customer Experience and the Contact Center

GUEST POST from Shep Hyken

Oh, what a difference a year makes. A few months ago I traveled to Las Vegas to attend the Customer Contact Week (CCW), the largest conference and trade show in the contact center industry. For the past several years, the big discussion has centered on artificial intelligence (AI), and that continues, but Customer Experience (CX) is also moving into the spotlight. AI and natural language models can give customers an almost human-like experience when they have a question or complaint. However, no surprise, some companies do it better than others.

First, all the hype around AI is not new. AI has been in our lives for decades, just at a much simpler level. How do you think Outlook and other email companies recognize that an email is spam and belongs in the junk/spam folder? Of course, it’s not 100% perfect, and neither are today’s best AI programs.

Many of us use Siri and Alexa. That’s AI. And as simple as that is, it’s obviously more sophisticated when you apply it to customer support and CX.

Let’s go back 10 years ago when I attended the IBM Watson conference in Las Vegas. The big hype then was around AI. There were some incredible cases of AI changing customer service, sales and marketing, not to mention automated processes. One of the demonstrations during the general session showcased AI’s stunning capability. Here’s what I saw:

A customer called the contact center. While the customer service agent listened to the customer, the computer (fueled by AI) listened to the conversation and fed the agent answers without the agent typing the questions. In addition, the computer informed the agent how long the customer had been doing business with the company, how often they made purchases, what products they had bought and more. The computer also compared this customer to others who had the same questions and suggested the agent answer those questions. Even though the customer didn’t yet know to ask them, at some point in the future, they would surely be calling back to do so.

That demonstration was a preview of what we have today. One big difference is that implementing that type of solution back then could have cost hundreds of thousands of dollars, if not more than a million. Today, that technology is affordable to almost any company, costing a fraction of what it cost back then (as in just a few thousand dollars).

Voice Technology Gets Better

Less than two years ago, ChatGPT was introduced to the world. Similar technologies have been developed. The capability continues to improve at an incredibly rapid pace. The response from an AI-fueled chatbot is lightning fast. Now, the technology is moving to voice. Rather than type a question for the chatbot, you talk, and it responds in a human-like voice. While voice technology has existed for years, it’s never been this good. Google introduced voice technology that seemed almost human-like. The operative word here is almost. As good as it was, people could still sense they weren’t talking to a human. Today, the best systems are human-like, not almost human-like. Think Alexa and Siri on steroids.

Foreign Accents Are Disappearing

We’ve all experienced calling customer support, and an offshore customer service agent with a heavy accent answers the call. Sometimes, it’s nearly impossible to understand the agent. New technologies are neutralizing accents. A year ago, the software sounded a little “digital.” Today, it sounds almost perfect.

Why Customers Struggle with AI and Other Self-Service Solutions

As far as these technologies have come, customers still struggle to accept them. Our customer service research (sponsored by RingCentral) found that 63% of customers are frustrated by self-service options, such as ChatGPT and similar technologies. Furthermore, 56% of customers admit to being scared of these technologies. Even though 32% of the customers surveyed said they had successfully resolved a customer service issue using AI or ChatGPT-type technologies, it’s not their top preference as 70% still choose the phone as their first level of support. Inconsistency is part of the problem. Some companies still use old technology. The result is that the customer experience varies from company to company. In other words, customers don’t know whether the next time they experience an AI solution if it will be good or not. Inconsistency destroys trust and confidence.

Companies Are Investing in Creating a Better CX

I’ve never been more excited about customer service, CX and the contact center. The main reason is that almost everything about this conference was focused on creating a better experience for the customer. The above examples are just the tip of the iceberg. Companies and brands know what customers want and expect. They know the only way to keep customers is to give them a product that works with an experience they can count on. Price is no longer a barrier as the cost of some of these technologies has dropped to a level that even small companies can afford.

Customer Service Goes Beyond Technology: We Still Need People!

This article focused on the digital experience rather than the traditional human experience. But to nail it for customers, a company can’t invest in just tech. It must also invest in its employees. Even the best technology doesn’t always get the customer what they need, which means the customer will be transferred to a live agent. That agent must be properly trained to deliver the experience that gets customers to say, “I’ll be back.”

Image Credits: Pexels, Shep Hyken

This article originally appeared on Forbes.com

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Success is a Hardship Too

Success is a Hardship Too

GUEST POST from Mike Shipulski

Everything has a half-life, but we don’t behave that way. Especially when it comes to success. The thinking goes – if it was successful last time, it will be successful next time. So, do it again. And again. It is an efficient strategy – the heavy resources to bring it to life have already been spent. And it is predictable – the same customers, the same value proposition, the same supply base, the same distribution channel, and the same technology. And it is dangerous.

Success is successful right up until it isn’t. It will go away. But it will take time. A successful product line will not fall off the face of the earth overnight. It will deliver profits year-over-year and your company will come to expect them. And your company will get hooked on the lifestyle enabled by those profits. And because of the addiction, when they start to drop off the company will do whatever it takes to convince itself all is well. No need to change. If anything, it is time to double-down on the successful formula.

Here’s a rule: When your successful recipe no longer brings success, it’s not time to double-down.

Success’ decline will be slow, so you have time. But creating a new recipe takes a long time, so it is time to declare that the decline has already started. And it is time to learn how to start work on the new recipe.

Hardship 1 – Allocate resources differently. The whole company wants to spend resources on the same old recipes, even when told not to. It is time to create a funding stream that is independent of the normal yearly planning cycle. Simply put, the people at the top have to reallocate a part of the operating budget to projects that will create the next successful platform.

Hardship 2 – Work differently. The company is used to polishing the old products and they don’t know how to create new ones. You need to hire someone who can partner with outside companies (likely startups), build internal teams with a healthy disrespect for previous success, create mechanisms to support those teams and teach them how to work in domains of high uncertainty.

Hardship 3 – See value differently. How do you provide value today? How will you provide value when you cannot do it that way? What is your business model? Are you sure that’s your business model? Which elements of your business model are immature? Are you sure? What is the next logical evolution of how you go about your business? Hire someone to help you answer those questions and create projects to bring the solutions to life.

Hardship 4 – Measure differently. When there is no customer, no technology and no product, there is no revenue. You must learn how to measure the value of the work (and the progress) with something other than revenue. Good luck with that.

Hardship 5 – Compensate differently. People that create something from nothing want different compensation than people that do continuous improvement. And you want to move quickly, violate the status quo, push through constraints and create whole new markets. Figure out the compensation schemes that give them what they want and helps them deliver what you want.

This work is hard, but it’s not impossible. But your company doesn’t have all the pieces to make it happen. Don’t be afraid to look outside your company for help and partnership.

Image credit: Pixabay

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