Author Archives: Greg Satell

About Greg Satell

Greg Satell is a popular speaker and consultant. His latest book, Cascades: How to Create a Movement That Drives Transformational Change, is available now. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.

How to Pursue a Grand Innovation Challenge

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How to Pursue a Grand Innovation Challenge

GUEST POST from Greg Satell

All too often, innovation is confused with agility. We’re told to “adapt or die” and encouraged to “move fast and break things.” But the most important innovations take time. Einstein spent ten years on special relativity and then another ten on general relativity. To solve tough, fundamental problems, we have to be able to commit for the long haul.

As John F. Kennedy put it in his moonshot speech, “We choose to go to the moon in this decade and do the other things, not because they are easy, but because they are hard, because that goal will serve to organize and measure the best of our energies and skills.” Every organization should pursue grand challenges for the same reason.

Make no mistake. Innovation needs exploration. If you don’t explore, you won’t discover. If you don’t discover you won’t invent and if you don’t invent you will be disrupted. It’s just a matter of time. Unfortunately, exploration can’t be optimized or iterated. That’s why grand challenges don’t favor the quick and agile, but the patient and the determined.

1. Don’t Bet The Company

Most grand challenges aren’t like the original moonshot, which was, in large part, the result of the space race with the Soviets that began with the Sputnik launch in 1957. That was a no-holds-barred effort that consumed the efforts of the nation, because it was widely seen as a fundamental national security issue that represented a clear and present danger.

For most organizations, those type of “bet-the-company” efforts are to be avoided. You don’t want to bet your company if you can avoid it, for the simple reason that if you lose you are unlikely to survive. Most successful grand challenges don’t involve a material investment. They are designed to be sustainable.

“Grand challenges are not about the amount of money you throw at the problem, Bernard Meyerson, IBM’s Chief Innovation Officer, told me. “To run a successful grand challenge program, failure should not be a material risk to the company, but success will have a monumental impact. That’s what makes grand challenges an asymmetric opportunity.”

Take, for example Google’s X division. While the company doesn’t release its budget, it appeared to cost the company about $3.5 billion in 2018, which is a small fraction of its $23 billion in annual profits at the time. At the same time, just one project, Waymo, may be worth $70 billion (2018). In a similar vein, the $3.8 billion invested in the Human Genome Project generated nearly $800 billion of economic activity as of 2011.

So the first rule of grand challenges is not to bet the company. They are, in fact, what you do to avoid having to bet the company later on.

2. Identify A Fundamental Problem

Every innovation starts out with a specific problem to be solved. The iPod, for example, was Steve Jobs’s way of solving the problem of having “a thousand songs in my pocket.” More generally, technology companies strive to deliver better performance and user experience, drug companies aim to cure disease and retail companies look for better ways to drive transactions. Typically, firms evaluate investment based on metrics rooted in past assumptions

Grand challenges are different because they are focused on solving fundamental problems that will change assumptions about what’s possible. For example, IBM’s Jeopardy Grand Challenge had no clear business application, but transformed artificial intelligence from an obscure field to a major business. Later, Google’s AlphaGo made a similar accomplishment with self-learning. Both have led to business opportunities that were not clear at the time.

Grand challenges are not just for technology companies either. MD Anderson Cancer Center has set up a series of Moonshots, each of which is designed to have far reaching effects. 100Kin10, an education nonprofit, has identified a set of grand challenges it has tasked its network with solving.

Talia Milgrom-Elcott, Executive Director of 100Kin10, told me she uses the 5 Whys as a technique to identify grand challenges. Start with a common problem, keep asking why it keeps occurring and you will eventually get to the root problem. By focusing your efforts on solving that, you can make a fundamental impact of wide-ranging consequence.

3. Commit To A Long Term Effort

Grand challenges aren’t like normal problems. They don’t conform to timelines and can’t effectively be quantified. You can’t justify a grand challenge on the basis of return on investment, because fundamental problems are too pervasive and ingrained to surrender themselves to any conventional form of analysis.

Consider The Cancer Genome Atlas, which eventually sequenced and published over 10,000 tumor genomes When Jean Claude Zenklusen first came up with the idea in 2005, it was highly controversial, because although it wasn’t particularly expensive, it would still take resources away from more conventional research.

Today, however, the project is considered to be a runaway success, which has transformed the field, greatly expanding knowledge and substantially lowering costs to perform genetic research. It has also influenced efforts in other fields, such as the Materials Genome Initiative. None of this would have been possible without commitment to a long-term effort.

And that’s what makes grand challenges so different. They are not business as usual and not immediately relevant to present concerns. They are explorations that expand conventional boundaries, so cannot be understood within them.

An Insurance Policy Against A Future You Can’t Yet See

Typically, we analyze a business by extrapolating current trends and making adjustments for things that we think will be different. So, for example, if we expect the market to pick up, we may invest in more capacity to profit from greater demand. On the other hand, if we expect a softer market, we’d probably start trimming costs to preserve margins.

The problem with this type of analysis is that the future tends to surprise us. Technology changes, customer preferences shift and competitors make unexpected moves. Nobody, no matter how diligent or smart, gets every call right. That’s why every business model fails sooner or later, it’s just a matter of time.

It’s also what makes pursuing grand challenges is so important. They are basically an insurance policy against a future we can’t yet see. By investing sustainably in solving fundamental problems, we can create new businesses to replace the ones that will inevitably falter. Google doesn’t invest in self-driving cars to improve its search business, it invests because it knows that the profits from search won’t last forever.

The problem is that there is a fundamental tradeoff between innovation and optimization, so few organizations have the discipline to invest in exploration today for a uncertain payoff tomorrow. That’s why so few businesses last.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Unsplash

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Four Hidden Secrets of Innovation

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Four Hidden Secrets of Innovation

GUEST POST from Greg Satell

Every enterprise needs to innovate. It doesn’t matter whether you are a profit-seeking business, a nonprofit organization or a government entity, the simple truth is that every business model fails eventually, because things change over time. We have to manage not for stability, but for disruption or face irrelevance.

There is no shortage of advice for how to go about it. In fact, there is far too much advice. Design thinkers will tell you to focus on the end user, but Harvard’s Clayton Christensen says that listening too much to customers is how good business fail. Then there’s open innovation, lean startups and on and on it goes.

The truth is that there is no one path to innovation. Everybody has to find their own way. Just because someone had success with one strategy, doesn’t mean that it’s right for the problem you need to solve. So the best advice is to gather as many tools for your toolbox as you can. Here are four things about innovation you rarely hear, but are crucially important.

1. Your Success Often Works Against You

For the most part, managers aren’t responsible for innovation, but as the name implies, to manage operations. That involves hiring and empowering strong employees, optimizing practices and processes and reducing errors and mistakes. You’re generally not trying to build a better mousetrap, you are trying to run things smoothly and efficiently.

It’s easy for someone to stand up on stage at a conference and paint operational managers as dimwits with their heads in the sand, but the truth is that managing a quality operation is a very tough job and requires a lot of talent, dedication and skill. So unless you’ve actually done the job, don’t be too quick to judge.

However, managers do need to realize that there is a fundamental tradeoff between innovation and optimizing operations. Running efficient operations requires standardization and control to yield predictable outcomes. Innovation, on the other hand requires experimentation. You need to try a lot of new things, most of which are going to fail.

That’s why success so often leads to failure. What makes you successful in one competitive environment will likely be a hindrance when things change. So you need to work to find a healthy balance between squeezing everything you can out of the present, while still leaving room to create and build for the future.

2. Don’t Look For A Large Addressable Market, Look For A Hair-On-Fire Use Case

Good operational managers learn to identify large addressable markets. Bigger markets help you scale your business, drive revenues and allow you invest back into operations to create more efficiency. Greater efficiencies lead to fatter profit margins, which allow you to invest even more on improvements, creating a virtuous cycle.

Yet when you are trying something to do something truly new and different, trying to scale too fast can kill your business even before it’s really gotten started. A truly revolutionary product is unpredictable because, by its very nature, it’s not well understood. Charging boldly into the unknown is a sure way to run into unanticipated problems that are expensive to fix at scale.

A better strategy is to identify a hair on fire use case — someone who needs a problem fixed so badly that they are willing to overlook the inevitable glitches. They will help you identify shortcomings early and correct them. Once you get things ironed out, you can begin to scale for more ordinary use cases.

For example, developing a self-driving car is a risky proposition with a dizzying amount of variables you can’t account for. However, a remote mine in Western Australia, where drivers are scarce and traffic nonexistent, is an ideal place to test and improve the technology. In a similar vein, Google Glass failed utterly as a mass product, but is getting a second life as an industrial tool. Sometimes it’s better to build for the few than the many.

3. Start With The Monkey First

When I work with executives, they often have a breakthrough idea they are excited about. They begin to tell me what a great opportunity it is and how they are perfectly positioned to capitalize on it. However, when I begin to dig a little deeper it appears that there is some big barrier to making it happen. When I try to ask about that, they just shut down.

Make no mistake. Innovation isn’t about ideas, it’s about solving problems. The truth is that nobody cares about what ideas you have, they care about the problems you can solve for them. The reason that most people can’t innovate isn’t because they don’t have ideas, but because they lack the perseverance needed to stick with a really tough problem until it’s cracked.

At Google X, the tech giant’s “moonshot factory,” the mantra is #MonkeyFirst. The idea is that if you want to get a monkey to recite Shakespeare on a pedestal, you start by training the monkey, not building the pedestal, because training the monkey is the hard part. Anyone can build a pedestal.

The problem is that most people start with the pedestal, because it’s what they know and by building it, they can show early progress against a timeline. Unfortunately, building a pedestal gets you nowhere. Unless you can actually train the monkey, working on the pedestal is wasted effort.

4. The Next Big Thing Always Starts Out Looking Like Nothing At All

When Alexander Fleming first published his discovery of penicillin, no one really noticed. When Xerox executives first got a look at the Alto — the machine that would become the model for the Macintosh seven years later — they didn’t see what the big deal was. When Jim Allison first showed pharmaceutical executives his idea for cancer immunotherapy, not one would invest in it.

We always think that when we see the next big thing it will be obvious, but the truth is that it always starts out looking like nothing at all. The problem is that when something truly has the power to change the world, the world isn’t ready for it yet. It needs to build advocacy, gain traction among a particular industry or field and combine with other innovations before it can make an impact.

But no one ever tells you that. We are conditioned to think that someone like Steve Jobs or Elon Musk just stands up on stage, announces that the world has changed and everybody just goes along. It never really happens that way because innovation is never a single event. It is a long process of discovery, engineering and transformation that usually takes about 30 years to fully complete.

Don’t worry about people stealing your ideas,” said the computing pioneer Howard Aiken. “If your ideas are any good, you’ll have to ram them down people’s throats” and never were truer words spoken. Great innovators aren’t just people with ideas, they are people who are willing to stick it out, take the shots from people who ridicule them and, eventually, if they are lucky, they really do change the world.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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What Top Innovators Do Differently

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What Top Innovators Do Differently

GUEST POST from Greg Satell

I’ve never really liked the phrase “innovate or die.” Why not, “finance or die” or “sell or die” or even “manage or die?” Clearly every business function is essential and no organization can survive without building some competency in all of them. In an ultra-competitive business environment, you have to do more than just show up.

What makes great innovators different is that they succeed where most others fail. They not only come up with new ideas, they find ways to make them work and create value for the rest of us. Even more importantly, they are able to do it consistently, year after year, decade after decade.

Over the years, I’ve gotten to know many of these extraordinary people and they are all impressive in their own way, but what has struck me is not their differences, but what they have in common. It seems that there are some things that all great innovators share and, importantly, they are all things that we can do as well. So there is hope for the rest of us.

1. They Seek Out Problems, Not Ideas

Elance launched as a startup in 1999 to do for freelancers what Monster.com did for full-time positions — create a marketplace to match employers with talent that had the skills they were looking for. It seemed like a great idea, but it turned out to be a total bust and the company soon shifted to developing vendor management software, where it had better success.

The company sold its software business in 2006 and decided to return to the original idea, but focused on a different problem. Instead of merely making matches, it would design algorithms to make engagements more successful. This time it began to gain traction and soon saw its business grow.

The team also began to see more problems it could solve. Freelancers needed to update their skills, so it added training and certification programs. Employers needed to track freelancers internally, so it created private talent clouds. Every new problem it identified led to a new solution and more value created. Elance merged with rival oDesk in 2014 to form Upwork, and continues to thrive to this day.

I found that every great innovator I met had a similar stories. To my surprise, most didn’t have a lot of ideas and the ones they did come up with weren’t necessarily any better than anybody elses. What they did have was a passion for solving problems. Some spent years or even decades to solve a single grand challenge. That passion, it seems, is what makes all the difference.

2. They Don’t Shout Eureka!

Another thing I began to notice with the best innovators, those who came up with ideas that truly changed the world, is that when they described their moment of discovery they didn’t recall any excitement. No high-fives. No shouting to the rooftops. No alerting of the media. Nothing like that at all.

Now don’t get me wrong. I’m sure that they felt excited, but it was other thoughts that were dominant. Did they get it right? What could they do to validate their findings? Were their other explanations that could explain the data they were seeing? How could they apply these new insights to a bigger problem?

One conversation in particular I remember is with Jim Allison, who developed cancer immunotherapy. When he described his discovery to me he said he “slowly started to put the pieces together.” He didn’t seem to feel brilliant. In fact, he seemed to feel a bit foolish for not noticing where the data was so clearly leading him.

Suffice it to say, nobody else saw it either until Jim pointed it out. In fact, for three years he had to pound the pavement to get anyone to invest in his idea (and that never makes you feel particularly good about yourself). But he saw that as just another problem to be solved and, through sheer will and perseverance, he prevailed. Untold thousands are alive today because he did.

3. They Are Active Collaborators

One of the people I enjoy talking to most is Bernie Meyerson, the Chief Innovation Officer Emeritus at IBM. Bernie is not only a brilliant scientist in his own right, his position puts him at the nexus of much of the really advanced work being done in a number of fields. If something important is going on, chances are he knows about it. Besides, Bernie is a tremendous amount of fun!

He was also kind enough to write the Foreword to my book, Mapping Innovation in which he recounts how he developed the Silicon-Germanium chips that make WiFi Internet connections possible. He explained how at each stage of the development process, they needed to widen the circle to bring in new people with the expertise to take the invention to the next level.

Innovation is never a single event, but a process of discovery, engineering and transformation and those three things almost never happen in the same place. So creating anything that’s truly new and important involves a series of hand-offs. Your ability to create and manage those hand-offs will, to a large extent, determine your ability to innovate.

The truth is that, when it comes to innovation, collaboration is a key competitive advantage. The lone genius is a myth. No one ever truly creates the future by themselves.
Everyone Can Innovate (Which Means That You Can Too)

G.H. Hardy was undoubtedly one of the great mathematicians of the 20th century, but he considered his greatest discovery not a theory, but a person — Srinivasa Ramanujan, the self-taught Indian prodigy. Ramanujan had sent his theories to three great English mathematicians, but it was Hardy — and only Hardy — who was able to see the breathtaking genius beneath the almost indecipherable scrawl.

That’s not to say that Hardy was the only one capable of recognizing Ramanujan’s genius, but he was the only one who took the time to look closely at the humble correspondence of a destitute Indian amateur mathematician. It was his passion, rather than any innate ability, that led him to greatness. In concluding his memoir, Hardy wrote:

The case for my life, then, or for that of any one else who has been a mathematician in the same sense which I have been one, is this: that I have added something to knowledge, and helped others to add more; and that these somethings have a value which differs in degree only, and not in kind, from that of the creations of the great mathematicians, or of any of the other artists, great or small, who have left some kind of memorial behind them.

The truth is that seeking out problems to solve, rigorously checking your facts and actively collaborating with others who can drive an idea forward are all things that anyone can do, but most don’t. It is those things that set great innovators apart.

What makes the difference is not brilliance or even hard work. Lots of brilliant people work hard and achieve little. It is the passion to contribute something, to add not only knowledge but to the collective well being, that sets great innovators apart.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Unsplash

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Humans Wanted for the Decade’s Biggest Innovation Challenges

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Humans Wanted for the Decade's Biggest Innovation Challenges

GUEST POST from Greg Satell

Every era is defined by the problems it tackles. At the beginning of the 20th century, harnessing the power of internal combustion and electricity shaped society. In the 1960s there was the space race. Since the turn of this century, we’ve learned how to decode the human genome and make machines intelligent.

None of these were achieved by one person or even one organization. In the case of electricity, Faraday and Maxwell established key principles in the early and mid 1800s. Edison, Westinghouse and Tesla came up with the first applications later in that century. Scores of people made contributions for decades after that.

The challenges we face today will be fundamentally different because they won’t be solved by humans alone, but through complex human-machine interactions. That will require a new division of labor in which the highest level skills won’t be things like the ability to retain information or manipulate numbers, but to connect and collaborate with other humans.

Making New Computing Architectures Useful

Technology over the past century has been driven by a long succession of digital devices. First vacuum tubes, then transistors and finally microchips transformed electrical power into something approaching an intelligent control system for machines. That has been the key to the electronic and digital eras.

Yet today that smooth procession is coming to an end. Microchips are hitting their theoretical limits and will need to be replaced by new computing paradigms such as quantum computing and neuromorphic chips. The new technologies will not be digital, but will work fundamentally different than what we’re used to.

They will also have fundamentally different capabilities and will be applied in very different ways. Quantum computing, for example, will be able to simulate physical systems, which may revolutionize sciences like chemistry, materials research and biology. Neuromorphic chips may be thousands of times more energy efficient than conventional chips, opening up new possibilities for edge computing and intelligent materials.

There is still a lot of work to be done to make these technologies useful. To be commercially viable, not only do important applications need to be identified, but much like with classical computers, an entire generation of professionals will need to learn how to use them. That, in truth, may be the most significant hurdle.

Ethics For AI And Genomics

Artificial intelligence, once the stuff of science fiction, has become an everyday technology. We speak into our devices as a matter of course and expect to get back coherent answers. In the near future, we will see autonomous cars and other vehicles regularly deliver products and eventually become an integral part of our transportation system.

This opens up a significant number of ethical dilemmas. If given a choice to protect a passenger or a pedestrian, which should be encoded into the software of a autonomous car? Who gets to decide which factors are encoded into systems that make decisions about our education, whether we get hired or if we go to jail? How will these systems be trained? We all worry about who’s educating our kids, but who’s teaching our algorithms?

Powerful genomics techniques like CRISPR open up further ethical dilemmas. What are the guidelines for editing human genes? What are the risks of a mutation inserted in one species jumping to another? Should we revive extinct species, Jurassic Park style? What are the potential consequences?

What’s striking about the moral and ethical issues of both artificial intelligence and genomics is that they have no precedent, save for science fiction. We are in totally uncharted territory. Nevertheless, it is imperative that we develop a consensus about what principles should be applied, in what contexts and for what purpose.

Closing A Perpetual Skills Gap

Education used to be something that you underwent in preparation for your “real life.” Afterwards, you put away the schoolbooks and got down to work, raised a family and never really looked back. Even today, Pew Research reports that nearly one in four adults in the US did not read a single book last year.

Today technology is making many things we learned obsolete. In fact, a study at Oxford estimated that nearly half of the jobs that exist today will be automated in the next 20 years. That doesn’t mean that there won’t be jobs for humans to do, in fact we are in the midst of an acute labor shortage, especially in manufacturing, where automation is most pervasive.

Yet just as advanced technologies are eliminating the need for skills, they are also increasingly able to help us learn new ones. A number of companies are using virtual reality to train workers and finding that it can boost learning efficiency by as much as 40%. IBM, with the Rensselaer Polytechnic Institute, has recently unveiled a system that help you learn a new language like Mandarin. This video shows how it works.

Perhaps the most important challenge is a shift in mindset. We need to treat education as a lifelong need that extends long past childhood. If we only retrain workers once their industry has become obsolete and they’ve lost their jobs, then we are needlessly squandering human potential, not to mention courting an abundance of misery.

Shifting Value To Humans

The industrial revolution replaced the physical labor of humans with that of machines. The result was often mind-numbing labor in factories. Yet further automation opened up new opportunities for knowledge workers who could design ways to boost the productivity of both humans and machines.

Today, we’re seeing a similar shift from cognitive to social skills. Go into a highly automated Apple Store, to take just one example, and you don’t see a futuristic robot dystopia, but a small army of smiling attendants on hand to help you. The future of technology always seems to be more human.

In much the same way, when I talk to companies implementing advanced technologies like artificial intelligence or cloud computing, the one thing I constantly hear is that the human element is often the most important. Unless you can shift your employees to higher level tasks, you miss out on many of the most important benefits

What’s important to consider is that when a task is automated, it is also democratized and value shifts to another place. So, for example, e-commerce devalues the processing of transactions, but increases the value of things like customer service, expertise and resolving problems with orders, which is why we see all those smiling faces when we walk into an Apple Store.

That’s what we often forget about innovation. It’s essentially a very human endeavor and, to measure as true progress, humans always need to be at the center.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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We Must Reinvent Our Organizations for A New Era of Innovation

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We Must Reinvent Our Organizations for A New Era of Innovation

GUEST POST from Greg Satell

In the first half of the 20th century, Alfred Sloan created the modern corporation at General Motors. In many ways, it was based on the military. Senior leadership at headquarters would make plans, while managers at individual units would be allocated resources and made responsible for achieving mission objectives.

The rise of digital technology made this kind of structure untenable. By the time strategic information was gathered centrally, it was often too old to be effective. In much the same way, by the time information flowed up from operating units, it was too late to alter the plan. It had already failed.

So in recent years, agility and iteration has become the mantra. Due to pressures from the market and from shareholders, long-term planning is often eschewed for the needs of the moment. Yet today the digital era is ending and organizations will need to shift once again. We’re going to need to learn to combine long-range planning with empowered execution.

Shifting From Iteration To Exploration

When Steve Jobs came up with the idea for a device that would hold “a thousand songs in my pocket,” it wasn’t technically feasible. There was simply no hard drive available that could fit that much storage into that little space. Nevertheless, within a few years a supplier developed the necessary technology and the iPod was born.

Notice how the bulk of the profits went to Apple, which designed the application and very little to the supplier that developed the technology that made it possible. That’s because the technology for developing hard drives was very well understood. If it hadn’t been that supplier, another would have developed what Jobs needed in six months or so.

Yet today, we’re on the brink of a new era of innovation. New technologies, such as revolutionary computing architectures, genomics and artificial intelligence are coming to the fore that aren’t nearly as well understood as digital technology. So we will have to spend years learning about them before we can develop applications safely and effectively.

For example, companies ranging from Daimler and Samsung to JP Morgan Chase and Barclays have joined IBM’s Q Network to explore quantum computing, even though that it will be years before that technology has a commercial impact. Leading tech companies have formed the Partnership on AI to better understand the consequences for artificial intelligence. Hundreds of companies have joined manufacturing hubs to learn about next generation technology.

It’s becoming more important to prepare than adapt. By the time you realize the need to adapt, it may already be too late.

Building A Pipeline Of Problems To Be Solved

While the need to explore technologies long before they become commercially viable is increasing, competitive pressures show no signs of abating. Just because digital technology is not advancing the way it once did doesn’t mean that it will disappear. Many aspects of the digital world, such as the speed at which we communicate, will continue.

So it is crucial to build a continuous pipeline of problems to solve. Most will be fairly incremental, either improving on an existing product or developing new ones based on standard technology. Others will be a bit more aspirational, such as applying existing capabilities to a completely new market or adopting exciting new technology to improve service to existing customers.

However, as the value generated from digital technology continues to level off, much like it did for earlier technologies like internal combustion and electricity, there will be an increasing need to pursue grand challenges to solve fundamental problems. That’s how truly new markets are created.

Clearly, this presents some issues with resource allocation. Senior managers will have to combine the need to move fast and keep up with immediate competitive pressures with the long-term thinking it takes to invest in years of exploration with an uncertain payoff. There’s no magic bullet, but it is generally accepted that the 70/20/10 principle for incremental, adjacent and fundamental innovation is a good rule of thumb.

Empowering Connectivity

When Sloan designed the modern corporation, capacity was a key constraint. The core challenge was to design and build products for the mass market. So long-term planning to effectively organize plant, equipment, distribution and other resources was an important, if not decisive, competitive attribute.

Digitization and globalization, however, flipped this model and vertical integration gave way to radical specialization. Because resources were no longer concentrated in large enterprises, but distributed across global networks, integration within global supply chains became increasingly important.

With the rise of cloud technology, this trend became even more decisive in the digital world. Creating proprietary technology that is closed off to the rest of the world has become unacceptable to customers, who expect you to maintain API’s that integrate with open technologies and those of your competitors.

Over the next decade, it will become increasingly important to build similar connection points for innovation. For example, the US military set up the Rapid Equipping Force that was specifically designed to connect new technologies with soldiers in the field who needed them. Many companies are setting up incubators, accelerators and corporate venture funds for the same reason. Others have set up programs to connect to academic research.

What’s clear is that going it alone is no longer an option and we need to set up specific structures that not only connect to new technology, but ensure that it is understood and adopted throughout the enterprise.

The Leadership Challenge

The shift from one era to another doesn’t mean that old challenges are eliminated. Even today, we need to scale businesses to service mass markets and rapidly iterate new applications. The problems we need to take on in this new era of innovation won’t replace the old ones, they will simply add to them.

Still, we can expect value to shift from agility to exploration as fundamental technologies rise to the fore. Organizations that are able to deliver new computing architectures, revolutionary new materials and miracle cures will have a distinct competitive advantage over those who can merely engineer and design new applications.

It is only senior leaders that can empower these shifts and it won’t be easy. Shareholders will continue to demand quarterly profit performance. Customers will continue to demand product performance and service. Yet it is only those that are able to harness the technologies of this new era — which will not contribute to profits or customer satisfaction for years to come — that will survive the next decade.

The one true constant is that success eventually breeds failure. The skills and strategies of one era do not translate to another. To survive, the key organizational attribute will not be speed, agility or even operational excellence, but leadership that understands that when the game is up, you need to learn how to play a new one.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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Department Of Energy Programs Helping to Create an American Manufacturing Future

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Department Of Energy Programs Helping to Create an American Manufacturing Future

GUEST POST from Greg Satell

In the recession that followed the dotcom crash in 2000, the United States lost five million manufacturing jobs and, while there has been an uptick in recent years, all indications are that they may never be coming back. Manufacturing, perhaps more than any other sector, relies on deep networks of skills and assets that tend to be highly regional.

The consequences of this loss are deep and pervasive. Losing a significant portion of our manufacturing base has led not only to economic vulnerability, but to political polarization. Clearly, it is important to rebuild our manufacturing base. But to do that, we need to focus on new, more advanced, technologies

That’s the mission of the Advanced Manufacturing Office (AMO) at the Department of Energy. By providing a crucial link between the cutting edge science done at the National Labs and private industry, it has been able to make considerable progress. As the collaboration between government scientists widen and deepens over time, US manufacturing may well be revived.

Linking Advanced Research To Private Industry

The origins of the Department of Energy date back to the Manhattan Project during World War II. The immense project was, in many respects, the start of “big science.” Hundreds of top researchers, used to working in small labs, traveled to newly established outposts to collaborate at places like Los Alamos, New Mexico and Oak Ridge, Tennessee.

After the war was over, the facilities continued their work and similar research centers were established to expand the effort. These National Labs became the backbone of the US government’s internal research efforts. In 1977, the National Labs, along with a number of other programs, were combined to form the Department of Energy.

One of the core missions of the AMO is to link the research done at the National Labs to private industry and the Lab Embedded Entrepreneurship Programs (LEEP) have been particularly successful in this regard. Currently, there are four such programs, Cyclotron Road, Chain Reaction Innovations, West Gate and Innovation Crossroads.

I was able to visit Innovation Crossroads at Oak Ridge National Laboratory and meet the entrepreneurs in its current cohort. Each is working to transform a breakthrough discovery into a market changing application, yet due to technical risk, would not be able to attract funding in the private sector. The LEEP program offers a small amount of seed money, access to lab facilities and scientific and entrepreneurial mentorship to help them get off the ground.

That’s just one of the ways that the AMO opens up the resources of the National Labs. It also helps business get access to supercomputing resources (5 out of the 10 fastest computers in the world are located in the United States, most of them at the National Labs) and conducts early stage research to benefit private industry.

Leading Public-Private Consortia

Another area in which the AMO supports private industry is through taking a leading role in consortia, such as the Manufacturing Institutes that were set up to to give American companies a leg up in advanced areas such as clean energy, composite materials and chemical process intensification.

The idea behind these consortia is to create hubs that provide a critical link with government labs, top scientists at academic universities and private companies looking to solve real-world problems. It both helps firms advance in key areas and allows researchers to focus their work on where they will have the greatest possible impact.

For example, the Critical Materials Institute (CMI) was set up to develop alternatives to materials that are subject to supply disruptions, such as the rare earth elements that are critical to many high tech products and are largely produced in China. A few years ago it developed, along with several National Labs and Eck Industries, an advanced alloy that can replace more costly materials in components of advanced vehicles and aircraft.

“We went from an idea on a whiteboard to a profitable product in less than two years and turned what was a waste product into a valuable asset,” Robert Ivester, Director of the Advanced Manufacturing Office told me.

Technology Assistance Partnerships

In 2011, the International Organization for Standardization released its ISO 50001 guidelines. Like previous guidelines that focused on quality management and environmental impact, ISO 50001 recommends best practices to reduce energy use. These can benefit businesses through lower costs and result in higher margins.

Still, for harried executives facing cutthroat competition and demanding customers, figuring out how to implement new standards can easily get lost in the mix. So a third key role that the AMO plays is to assist companies who wish to implement new standards by providing tools, guides and access to professional expertise.

The AMO offers similar support for a number of critical areas, such as prototype development and also provides energy assessment centers for firms that want to reduce costs. “Helping American companies adopt new technology and standards helps keep American manufacturers on the cutting edge,” Ivester says.

“Spinning In” Rather Than Spinning Out

Traditionally we think of the role of government in business largely in terms of regulation. Legislatures pass laws and watchdog agencies enforce them so that we can have confidence in the the food we eat, the products we buy and the medicines that are supposed to cure us. While that is clearly important, we often overlook how government can help drive innovation.

Inventions spun out of government labs include the Internet, GPS and laser scanners, just to name a few. Many of our most important drugs were also originally developed with government funding. Still, traditionally the work has mostly been done in isolation and only later offered to private companies through licensing agreements.

What makes the Advanced Manufacturing Office different than most scientific programs is that it is more focused on “spinning in” private industry rather than spinning out technologies. That enables executives and entrepreneurs with innovative ideas to power them with some of the best minds and advanced equipment in the world.

As Ivester put it to me, “Spinning out technologies is something that the Department of Energy has traditionally done. Increasingly, we want to spin ideas from industry into our labs, so that companies and entrepreneurs can benefit from the resources we have here. It also helps keep our scientists in touch with market needs and helps guide their research.”

Make no mistake, innovation needs collaboration. Combining the ideas from the private sector with the cutting edge science from government labs can help American manufacturing compete for the 21st century.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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Innovation the Amazon Way

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Innovation the Amazon Way

GUEST POST from Greg Satell

In 2014, Stephenie Landry was finishing up her one-year stint as Technical Advisor to Jeff Wilke, who oversees Amazon’s worldwide consumer business, which is a mentor program that allows high potential executives to shadow a senior leader and learn first-hand. Her next assignment would define her career.

At most companies, an up-and-comer like Stephenie might be given a division to run or work on a big acquisition deal. Amazon, however, is a different kind of place. Landry wrote a memo outlining plans for a new service she’d been thinking about, Prime Now, which today offers one-hour delivery to customers in over 50 cities across 9 countries.

It’s no secret that Amazon is one of the world’s most innovative companies. Starting out as a niche service selling books online, it’s now not only a dominant retailer, but has pioneered new categories such as cloud computing and smart speakers. The key to its success is not any one process, but how it integrates a customer obsession deep within its culture and practice.

Starting With The Customer And Working Back

At the heart of how Amazon innovates is its six-page memo, which is required at the start of every new initiative. What makes it effective isn’t so much the structure of the document itself, but how it is used to embed a fanatical focus on the customer from the day one. It’s something that Amazon employees have impressed upon them early in their careers.

So the first step in developing Prime Now was to write a press release. Landry’s document was not only a description of the service, but how hypothetical customers would react to it. How did the service affect them? What surprised them about it? What concerns did they want addressed? The exercise forced her to internalize how Amazon customers would think and feel about Prime Now from the very start.

Next she wrote a series of FAQ’s anticipating concerns for both customers and for various stakeholders within the firm, like the CFO, operations people and the leadership of the Prime program. So Landry had to imagine what questions each would have, how any issues would be resolved and then explain things in clear, concise language.

All of this happens before the first meeting is held, a single line of code is written or an early prototype is built, because the company strongly believes that until you internalize the customer’s perspective, nothing else really matters. That’s key to how the company operates.

A Deeply Embedded Writing Culture

It’s no accident that the first step to develop a new product at Amazon is a memo rather than, say, a PowerPoint deck or a kickoff meeting. As Fareed Zakaria once put it, “Thinking and writing are inextricably intertwined. When I begin to write, I realize that my ‘thoughts’ are usually a jumble of half-baked, incoherent impulses strung together with gaping logical holes between them”.

So the company focuses on building writing skills early in an executive’s career. “Writing is a key part of our culture,” Landry told me. “I started writing press releases for smaller features and projects. One of my first was actually about packaging for diamond rings. Over years of practice and coaching, I got better at it.” Being able to write a good memo is also a key factor in advancement at Amazon. If you want to rise, you need to write and write well.

She also stressed to me the importance of brevity. “Keeping things concise and to the point forces you to think things through in a way that you wouldn’t otherwise. You can’t hide behind complexity, you actually have to work through it,” Landry said. Or, as another Amazon leader put it, “Perfection is achieved when there is nothing left to remove.”

Moreover, writing a memo isn’t a solo effort, but a collaborative process. Typically, executives spend a week or more and sharing the document with colleagues, getting feedback, honing and tweaking it until every conceivable facet is deeply thought through.

Reinventing The Office Meeting

Another unique facet of Amazon’s culture is how meetings are run. In recent years, a common complaint throughout the corporate world is how the number of meetings has become so oppressive that it’s hard to get any work done. Research from MIT shows that executives spend an average of nearly 23 hours a week in meetings, up from less than 10 hours in 1960

At Amazon, however, the six-page memo cuts down on the number of meetings that are called. If you have to spend a week writing a memo, you don’t just start sending out invites whenever the fancy strikes you. Similarly, the company’s practice of limiting attendance to roughly the number of people that can share two pizzas also promotes restraint.

Each meeting starts out with a 30-60 minute reading period in which everybody digests the memo. From there, all attendees are asked to share gut reactions — senior leaders typically speak last — and then delve into what might be missing, ask probing questions and drill down into any potential issues that may arise.

Subsequent meetings follow the same pattern to review the financials, hone the concept and review mockups as the team further refines ideas and assumptions. “It’s usually not one big piece of feedback that you get,” Landry stressed. “It is really all about the smaller questions, they help you get to a level of detail that really brings the idea to life.”

All of this may seem terribly cumbersome to fast moving executives accustomed to zinging in and out of meetings all day, but you often need to go slow to move fast. In the case of Prime Now, the service took just 111 days to go from an idea on a piece of paper to a product launch in one zip code in Manhattan and expanded quickly from there.

Co-evolving Culture And Practice

Every company innovates differently. Apple has a fanatical focus on design. IBM’s commitment to deep scientific research has enabled it to stay on the cutting edge and compete long after most of its competitors have fallen by the wayside. Google integrates a number of innovation strategies into a seamless whole

What works for one company would likely not work for another, a fact that Amazon CEO Jeff Bezos highlighted in a recent letter to shareholders. “We never claim that our approach is the right one – just that it’s ours – and over the last two decades, we’ve collected a large group of like-minded people. Folks who find our approach energizing and meaningful,” he wrote.

The truth is that there is no one “true” path to innovation because innovation, at its core, is about solving problems and every enterprise chooses different problems to solve. While IBM might be happy to have its scientists work for decades on some arcane technology and Google gladly allows its employees to pursue pet projects, those things probably wouldn’t fly at Amazon.

However, the one thing that all great innovators have in common is that culture and practice are deeply intertwined. That’s what makes them so hard to copy. Anybody can write a six-page memo or start meetings with a reading period. It’s not those specific practices, but the commitment to the values they reflect, that has driven Amazon’s incredible success.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Unsplash

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Four Principles of Successful Digital Transformation

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Four Principles of Successful Digital Transformation

GUEST POST from Greg Satell

When Steve Jobs and Apple launched the Macintosh with great fanfare in 1984, it was to be only one step in a long journey that began with Douglas Engelbart’s Mother of All Demos and the development of the Alto at Xerox PARC more than a decade before. The Macintosh was, in many ways, the culmination of everything that came before.

Yet it was far from the end of the road. In fact, it wouldn’t be until the late 90s, after the rise of the Internet, that computers began to have a measurable effect on economic productivity. Until then, personal computers were mainly an expensive device to automate secretarial work and for kids to play video games.

The truth is that innovation is never a single event, but a process of discovery, engineering and transformation. Yet what few realize is that it is the last part, transformation, that is often the hardest and the longest. In fact, it usually takes about 30 years to go from an initial discovery to a major impact on the world. Here’s what you can do to move things along.

1. Identify A Keystone Change

About a decade before the Macintosh, Xerox invented the Alto, which had many of the features that the Macintosh later became famous for, such as a graphical user interface, a mouse and a bitmapped screen. Yet while the Macintosh became legendary, the Alto never really got off the ground and is now remembered, if at all, as little more than a footnote.

The difference in outcomes had much less to do with technology than it had to do with vision. While Xerox had grand plans to create the “office of the future,” Steve Jobs and Apple merely wanted to create a cool gadget for middle class kids and enthusiasts. Sure, they were only using it to write term papers and play video games, but they were still buying.

In my book, Cascades, I call this a “keystone change,” based on something my friend Talia Milgrom-Elcott told me about ecosystems. Apparently, every ecosystem has one or two keystone species that it needs to thrive. Innovation works the same way, you first need to identify a keystone change before a transformation can begin.

One common mistake is to immediately seek out the largest addressable market for a new product or service. That’s a good idea for an established technology or product category, but when you have something that’s truly new and different, it’s much better to find a hair on fire use case, a problem that’s someone needs solved so badly that they are willing to put up with early glitches and other shortcomings.

2. Indoctrinate Values, Beliefs And Skills

A technology is more than just a collection of transistors and code or even a set of procedures, but needs specific values and skills to make it successful. For example, to shift your business to the cloud, you need to give up control of your infrastructure, which requires a completely new mindset. That’s why so many digital transformations fail. You can’t create a technology shift without a mind shift as well.

For example, when the Institute for Healthcare Improvement began its quest to save 100,000 lives through evidence-based quality practices, it spent significant time preparing the ground beforehand, so that people understood the ethos of the movement. It also created “change kits” and made sure the new procedures were easy to implement to maximize adoption.

In a similar vein, Facebook requires that all new engineers, regardless of experience or expertise, go through its engineering bootcamp. “Beyond the typical training program, at our Bootcamp new engineers see first-hand, and are able to infer, our unique system of values,” Eddie Ruvinsky, an Engineering Director at the company, told me.

“We don’t do this so much through training manuals and PowerPoint decks,” he continued,”but through allowing them to solve real problems working with real people who are going to be their colleagues. We’re not trying to shovel our existing culture at them, but preparing them to shape our culture for the future.”

Before you can change actions, you must first transform values, beliefs and skills.

3. Break Through Higher Thresholds Of Resistance

Growing up in Iowa in the 1930s, Everett Rogers, noticed something strange in his father’s behavior. Although his father loved electrical gadgets, he was hesitant to adopt hybrid seed corn, even though it had higher yields. In fact, his father only made the switch after he saw his neighbor’s hybrid seen crop thrive during a drought in 1936.

This became the basis for Rogers’ now-familiar diffusion of innovations theory, in which an idea first gets popular with a group of early adopters and then only later spreads to other people. Later, Geoffrey Moore explained that most innovations fail because they never cross the chasm from the early adopters to the mainstream.

Both theories have become popular, but are often misunderstood. Early adopters are not a specific personality type, but people with a low threshold of resistance to a particular idea or technology. Remember that Rogers’s father was an early adopter of electrical gadgets, but was more reticent with seed corn.

As network theory pioneer Duncan Watts explained to me, an idea propagates through “easily influenced people influencing other easily influenced people.” So it’s important to start a transformation with people who are already enthusiastic, work out the inevitable kinks and then move on to people slightly more reticent, once you’ve proved success in that earlier group.

4. Focus On The Network, Not The Nodes

Perhaps the biggest mistake that organizations commit when trying to implement a new technology is to try to push everything from above, either through carrots, like financial incentives, or sticks, like disciplinary action for noncompliance. That may give senior management the satisfaction of “taking action,” but can often backfire.

People are much more willing to adopt something new if they feel like its their idea. The Institute for Healthcare Improvement, for example, designated selected institutions to act as “nodes” to help spread its movement. These weren’t watchdogs, but peers that were early adopters who could help their colleagues adopt the new procedures effectively.

In a similar vein, IBM has already taken significant steps to drive adoption of Quantum computing, a technology that won’t be commercially available for years. First it created the Q Experience, an early version of its technology available through the cloud for anyone to use. It has also set up its Q Network of early adopter companies who are working with IBM to develop practical applications for quantum computing.

To date, tens of thousands have already run hundreds of thousands of experiments on Q Experience and about a dozen companies have joined the Q Network. So while there is still significant discovery and engineering to be done, the transformation is already well underway. It always pays to start early.

The truth is that transformation is always about the network, not the nodes. That’s why you need to identify a keystone change, indoctrinate the values and skills that will help you break through higher thresholds of resistance and continuously connect with a diverse set of stakeholders to drive change forward.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Unsplash

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DNA May Be the Next Frontier of Computing and Data Storage

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DNA May Be the Next Frontier of Computing and Data Storage

GUEST POST from Greg Satell

Data, as many have noted, has become the new oil, meaning that we no longer regard the information we store as merely a cost of doing business, but a valuable asset and a potential source of competitive advantage. It has become the fuel that powers advanced technologies such as machine learning.

A problem that’s emerging, however, is that our ability to produce data is outstripping our ability to store it. In fact, an article in the journal Nature predicts that by 2040, data storage would consume 10–100 times the expected supply of microchip-grade silicon, using current technology. Clearly, we need a data storage breakthrough.

One potential solution is DNA, which is a million times more information dense than today’s flash drives. It also is more stable, more secure and uses minimal energy. The problem is that it is currently prohibitively expensive. However, a startup that has emerged out of MIT, called CATALOG, may have found the breakthrough we’re looking for: low-cost DNA Storage.

The Makings Of A Scientist-Entrepreneur

Growing up in his native Korea, Hyunjun Park never planned on a career in business, much less the technology business, but expected to become a biologist. He graduated with honors from Seoul National University and then went on to earn a PhD from the University of Wisconsin. Later he joined Tim Lu’s lab at MIT, which specializes in synthetic biology.

In an earlier time, he would have followed an established career path, from PhD to post-doc to assistant professor to tenure. These days, however, there is a growing trend for graduate students to get an entrepreneurial education in parallel with the traditional scientific curriculum. Park, for example, participated in both the Wisconsin Entrepreneurial Bootcamp and Start MIT.

He also met a kindred spirit in Nate Roquet, a PhD candidate who, about to finish his thesis, had started thinking about what to do next. Inspired by a talk from given by the Chief Science Officer at a seed fund, IndieBio, the two began to talk in earnest about starting a company together based on their work in synthetic biology.

As they batted around ideas, the subject of DNA storage came up. By this time, the advantages of the technology were well known but it was not considered practical, costing hundreds of thousands of dollars to store just a few hundred megabytes of data. However, the two did some back-of -the-envelope calculations and became convinced they could do it far more cheaply.

Moving From Idea To Product

The basic concept of DNA storage is simple. Essentially, you just encode the ones and zeros of digital code into the T, G, A and C’s of genetic code. However, stringing those genetic molecules together is tedious and expensive. The idea that Park and Roquet came up with was to use enzymes to alter strands of DNA, rather than building them up piece by piece.

Contrary to popular opinion, most traditional venture capital firms, such as those that populate Sand Hill Road in Silicon Valley, don’t invest in ideas. They invest in products. IndieBio, however, isn’t your typical investor. They give only give a small amount of seed capital, but offer other services, such as wet labs, entrepreneurial training and scientific mentorship. Park and Roquet reached out to them and found some interest.

“We invest in problems, not necessarily solutions,” Arvind Gupta, Founder at IndieBio told me. “Here the problem is massive. How do you keep the world’s knowledge safe? We know DNA can last thousands of years and can be replicated very inexpensively. That’s a really big deal and Hyunjun and Nate’s approach was incredibly exciting.”

Once the pair entered IndieBio’s four-month program, they found both promise and disappointment. Their approach could dramatically reduce the cost of storing information in DNA, but not nearly quickly enough to build a commercially viable product. They would need to pivot if they were going to turn their idea into an actual business.

Scaling To Market

One flaw in CATALOG’s approach was that the process was too complex to scale. Yet they found that by starting with just a few different DNA strands and attaching them together, much like a printing press pre-arranges words in a book, they could come up with something that was not only scalable, but commercially viable from a cost perspective.

The second problem was more thorny. Working with enzymes is incredibly labor intensive and, being biologists, Park and Roquet didn’t have the mechanical engineering expertise to make their process feasible. Fortunately, an advisor, Darren Link, connected the pair to Cambridge Consultants, an innovation consultancy that could help them.

“We started looking at the problem and it seemed that, on paper at least, we could make it work,” Richard Hammond, Technology Director and Head of Synthetic Biology at Cambridge Consultants, told me. “Now we’re about halfway through making the first prototype and we believe we can make it work and scale it significantly. We’re increasingly confident that we can solve the core technical challenges.”

In 2018 CATALOG introduced the world to Shannon, its prototype DNA writer. In 2022 CATALOG announced its DNA computation work at the HPC User Forum. But CATALOG isn’t without competition in the space. For example, Western Digital‘s LTO-9 from 2022, can store 18 TB per cartridge. CATALOG for its part is partnering with Seagate “on several initiatives to advance scalable and automated DNA-based storage and computation platforms, including making DNA-based platforms up to 1000 times smaller.” That should make the process competitive for archival storage, such as medical and legal records as well as storing film databases at movie studios.

“I think the fact that we’re inventing a completely new medium for data storage is really exciting,” Park told me. “I don’t think that we know yet what the true potential is because the biggest use cases probably don’t exist yet. What I do know is that our demand for data storage will soon outstrip our supply and we are thrilled about the possibility of solving that problem.”

Going Beyond Digital

A generation ago, the task of improving data storage would have been seen as solely a computer science problem. Yet today, the digital era is ending and we’re going to have to look further and wider for solutions to the problems we face. With the vast improvement in genomics, which is far outpacing Moore’s law these days, we can expect biology to increasingly play a role.

“Traditional, information technology has been strictly the realm of electrical engineers, physicists and coders,” Gupta of IndieBio told me. “What we’re increasingly finding is that biology, which has been honed for millions of years by evolution, can often point the way to solutions that are more robust and potentially, much cheaper and more efficient.”

Yet this phenomenon goes far beyond biology. We’re also seeing similar accelerations in other fields, such as materials science and space-related technologies. We’re also seeing a new breed of investors, like IndieBio, that focus specifically on scientist entrepreneurs. “I consider myself a product of the growing ecosystem for scientific entrepreneurs at universities and in the investor community,” Park told me.

Make no mistake. We are entering a new era of innovation and the traditional Silicon Valley approach will not get us where we need to go. Instead, we need to forge greater collaboration between the scientific community, the investor community and government agencies to solve problems that are increasingly complex and interdisciplinary.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Pixabay

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How Tribalism Can Kill Innovation

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How Tribalism Can Kill Innovation

GUEST POST from Greg Satell

While history tends to single out individuals, the truth is that when you look behind the story of any heroic leader, what you find is a network of loyal supporters, active collaborators and outside facilitators that are behind any great achievement. Nobody accomplishes anything significant alone.

That’s probably why it’s become fashionable for pundits to encourage us to “find our tribe,” a network of like-minded people who share your ambitions. Don’t listen to them. The truth is that great things are achieved not by taking comfort from your tribe, but from going beyond it and reaching out to those who aren’t of like mind.

The problem with focusing too much on your tribe is that those people tend to think the same way you do. They frequent the same places, watch the same TED talks and read the same blogs. That may be great for giving you some comfort and confidence, but it also acts as an echo chamber that will reinforce flawed assumptions and lead you down a false path.

The Problem With Closed Networks

In 2005, a team of researchers decided to study why some Broadway plays become hits and others flop. They looked at all the usual factors, such as production budget, marketing budget and the track record of the director, but what they found was that what was most important factor was the informal networks of relationships among the cast and crew.

If no one had ever worked together before, both financial and creative results tended to be poor. However, if the networks among the cast and crew became too dense—for all intents and purposes, becoming a tribe—performance also suffered. It was the teams that had elements of both, strong ties and new blood, that had the greatest success.

The same effect has been found elsewhere. In studies of star engineers at Bell Labs, the German automotive industry and currency traders it has been shown that tightly clustered groups, combined with long range “weak ties” that allow information to flow freely among disparate clusters of activity, consistently outperform close networks of likeminded people.

Just as we need to invest in building strong, trustful relationships, we also need to go beyond our comfort zone and seek out new connections. It’s far too easy to hide in a tribe.

The Discomfort of Diversity

While studies show that closed networks lead to worse performance, it has long been established that diversity improves performance. Researchers at the University of Michigan found that diverse groups can solve problems better than a more homogeneous team of greater objective ability. Another study that simulated markets showed that ethnic diversity deflated asset bubbles.

While the studies noted above merely simulate diversity in a controlled setting, there is also evidence from the real world that diversity produces better outcomes. A McKinsey report that covered 366 public companies in a variety of countries and industries found that those which were more ethnically and gender diverse performed significantly better than others.

Yet diversity also has a downside. In Political Tribes, Yale Professor Amy Chua notes that we are hardwired to be suspicious of others. For example, in a study where young children were randomly assigned to red or blue groups, they liked pictures of other kids who wore t-shirts that reflected their own group better. A study of adults had similar findings.

So you can see the attraction of tribes. We feel uncomfortable with people who we perceive as different. Surrounding ourselves with people who see things the way we do, on the other hand, makes us feel confident and powerful.

Mixing With The Heathens

This became the inspiration for Rogers’ now-familiar diffusion of innovations theory, in which an idea first gets popular with a group of early adopters and then only later spreads to other people. Geoffrey Moore later pointed out that most innovations fail because they never cross the chasm from the early adopters to the mainstream.

A study done by researchers at Kellogg and Stanford explains why. They put together groups of college students to solve a murder mystery. The groups made up of students from the same sorority or fraternity felt more confident and successful, even though they performed worse on the task than integrated groups that experienced more conflict, uncertainty and doubt.

That’s the problem with staying in your tribe. Sure, it feels great to have your ideas supported and reinforced by people you like and respect, but they are doing so because they already believe the same things that you do. To actually achieve something worthwhile, however, you have to go beyond preaching to the choir and start mixing with the heathens.

Do You Want To Make A Point Or Do You Want To Make A Difference?

In my book, Cascades, I cover a wide range of movements. Some, like the civil rights movement and the campaign to save 100,000 lives, succeeded brilliantly. Others, like Occupy and the technology companies along Boston’s Route 128, failed miserably. Another thing I found is that many movements that ultimately succeeded, failed initially because they failed to go beyond their tribe.

Here’s what Srdja Popović, who helped lead the Otpor movement that overthrew the brutal regime of Slobodan Milošević in 2000, told me about the initial student protests in 1992.

These were very ‘Occupy’ type of protests where we occupied the five biggest universities and lived there in our little islands of common sense with intellectuals and rock bands while the rest of the country was more or less supportive of Milošević’s idea. And this is where we began to understand that staying in your little blurb of common sense was not going to save the country.

In a similar vein, Nelson Mandela started out as an angry nationalist, but eventually learned that to get results, he would have to actively collaborate with others that didn’t quite see things the same way he did. In Poland, Solidarity’s first actions were disastrous, because they only involved workers. It was only through a later alliance between workers, intellectuals and the church that the movement ultimately succeeded.

Today, both America and the world have become increasingly tribal and it’s easy to retreat into what Srdja calls “your little blurb of common sense.” You can state your beliefs, make your point and see the heads nod around you. You can live in comfort, knowing that any voices of dissent will be quickly shouted down, as you self righteously feel they should be.

However, at some point, you will have to decide if you want to make a point or whether you want to make a difference. To achieve anything worthwhile, you have to go beyond your tribe.

— Article courtesy of the Digital Tonto blog and previously appeared on Inc.com
— Image credits: Unsplash

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