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.
In the 1960s and 70s, Route 128 outside of Boston was the center of technology, but by the 1990s Silicon Valley had taken over and never looked back. As AnnaLee Saxenian explained in Regional Advantage, the key difference was that while Route 128 was a collection of value chains, Silicon Valley built an ecosystem.
Clearly, ecosystems are even more important today than they were back then. In fact, a recent study by Accenture Strategy found that ecosystems are a “cornerstone” of future growth and that 60% of executives surveyed viewed ecosystems as a way to disrupt their industry. A similar number saw them as key to increasing revenue.
The problem is that competing in an ecosystem environment is vastly different than a traditional value chain strategy. While a value chain is driven by efficiencies, an ecosystem is driven by connections in a network. So we need to do more than adapt our strategy and tactics, we need to learn how to play a whole new game. The first step is to learn the rules.
In 1970, a scientist at IBM Research named Edgar F. Codd make a remarkable discovery that would truly change the world. Though few realized it at the time, including IBM, which neglected to commercialize it. It was called the relational model for the database and it would spawn an entire industry.
Yet while today few have heard of relational databases, everybody seems to be talking about blockchain. Much like Codd’s idea nearly a half century ago, blockchain represents the opportunity to create a new data infrastructure, which in turn, is likely to help power business for another half century.
Still — and very contrary to the current hype — few of us will ever work with a blockchain or even know it is there. The real revolution will come not from the technology itself, but from its secondary effects in the form of new business models. To leverage these though, you will first need to understand how Edgar Codd created the data economy in the first place.
This time last year I had just published my first book, Mapping Innovation. Having never published one before, I really didn’t know how it was going to go, so I was excited, but also a little bit nervous. Would people like it, or would I be exposed as a fraud and a hack? I hoped for the best, but prepared for the worst.
What a difference a year makes! Mapping Innovation has been a runaway success. Sales are great and it was even nominated for the prestigious Business Book of the Year Award. Even better, this past week I signed a new contract with McGraw-Hill to publish my next book, Cascades, about how to create transformational change.
I had no idea that all this was ahead when I started Digital Tonto in my apartment in Kyiv, nine years ago this week. It’s been a wild ride! I can’t tell you how much I’ve appreciated your support, especially those of you who have stuck with me since those early years. So Happy Birthday Digital Tonto! Here are some of my favorite posts from the last year.
In 1997 Clayton Christensen published The Innovator’s Dilemma and it sparked a revolution. It seemed that all anyone could talk about was disruptive innovation. That was until Henry Chesbrough published Open Innovation in 2003 and that got hot. Then Stanford launched its d.school and design thinking was where it was at.
Yet go to an innovation conference these days and chances are that most people will be talking about “the Lean Startup.” That’s a sure sign that a good idea is about to go bad. When any process or practice descends into mindless conformity, especially an innovation practice, we should begin to be skeptical.
As I explain in my book, Mapping Innovation, there is no one “true” path to innovation. So a process that works great for some types of problems is bound to fail when applied to others. No tool or method is a panacea. That’s why although the Lean Startup model continues to offer tremendous value, we should be increasingly skeptical about it and keep our eyes open.
Very few businesses last. While we like to think we live in a particularly disruptive era, this has always been true. Entrepreneurs start businesses because they see opportunity and build skills, practices and processes to leverage it. Yet as the world changes, these strengths often become vulnerabilities.
The problem is that the past is not always a good guide to the future. Business models, even the successful ones, are designed for inertia. They are great for leveraging past insights, but are often resistant to change. Success does not, in fact, always breed more success, sometimes it breeds failure.
That’s why every business needs to innovate. Yet innovation is not, as some would have us believe, just about moving fast and breaking things. It’s about solving the problems you need to create a better future. What most fail to grasp is that a key factor of success is how you source problems, build a pipeline and, ultimately, choose which ones you will work on.
A recent survey by Deloitte of “aggressive adopters” of cognitive technologies found that 76% believe that they will “substantially transform” their companies within the next three years. There probably hasn’t been this much excitement about a new technology since the dotcom boom years in the late 1990s.
The possibilities would seem to justify the hype. AI isn’t just one technology, but a wide array of tools, including a number of different algorithmic approaches, an abundance of new data sources and advancement in hardware. In the future, we will see new computing architectures, like quantum computing and neuromorphic chips, propel capabilities even further.
Still, there remains a large gap between aspiration and reality. Gartner estimated that 85% of big data projects fail. There have also been embarrassing snafus, such as when Dow Jones reported that Google was buying Apple for $9 billion and the bots fell for it or Microsoft’s Tay chatbot went berserk on Twitter. Here’s how to transform the potential of AI into real results.
In a famous scene in the 1967 movie The Graduate, a family friend takes aside Dustin Hoffman’s character, Benjamin Braddock, and whispers in a conspiratorial tone “Plastics. There’s a great future in plastics.” It’s seems quaint today, but back then plastics really were new and exciting.
If the movie had been set in another age, the advice to young Braddock would have been different. He might have been counseled to go into railroads or electronics or simply to “Go west young man!” Every age has things that seem novel and wonderful at the time, but tepid and banal to future generations.
Today, digital technology is all the rage because after decades of development it has become incredibly useful. Still, if you look closely, you can already see the contours of its inevitable descent into the mundane. We need to start preparing for a new era of innovation in which different technologies, like genomics, materials science and robotics rise to the fore.
Since the turn of the century, the US Military has been continually disrupted. Traditionally it was designed to fight conventional wars against great adversaries. Yet since 9/11 it has found itself fighting networks of loosely connected small groups that are able to continually evolve their tactics. That’s a very different kind of battle.
It’s a situation that most executives at major companies will recognize. Today, as the tempo and pace of technological change seems to be ever-increasing, a competitive threat can come from anywhere. As you ponder the competitive landscape, somebody at a kitchen table somewhere may be getting ready to eat your lunch.
Colonel Pete Newell is at the nexus of both worlds. As Director of the Rapid Equipping Force, he transformed how the Army was able to collaborate with external resources and reduce development time from years to months. He then helped develop the highly acclaimed Hacking 4 Defense program. Now, he’s bringing his innovation program to private industry.
Go to just about any conference today and you will hear a familiar tale of woe. A once great corporation, which had dominated its industry, fails to adapt and descends into irrelevance. The protagonists of these stories always come out looking more than a little bit silly, failing to recognize business trends that seem obvious.
The problem with these stories is that they are rarely true. Make no mistake, it takes a considerable amount of intelligence, ambition and drive to manage a large organization. The notion that these people overlooked what was obvious to everyone else is overly facile and simplistic. It’s also misleading.
Great companies do not fail because of a single decision or trend. The roots of disruption are always more complex than that. So by imagining CEOs to be morons, we neglect to look more closely at their demise and learn valuable lessons. The truth is that every business model fails eventually. We need to learn the true sources of failure if we are to overcome them.
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