On a December day in 1968, an engineer named Douglas Engelbart launched a revolution. The focus was not a Cold War adversary or even a resource-rich banana republic, but rather to “augment human intellect” and avoid innovation mistakes.
Over the past 100 years, just about every business IBM has dominated has hit the skids. It was a pioneer in tabulating machines, mainframe computers, personal computers, and installed IT services, just to name a few. Nevertheless, every 20 years or so, each one of these businesses has been disrupted.
Yet still, IBM remains one of the most valuable companies in the world because it keeps developing new technologies. Today, as its business for installed solutions continues to decline, it’s building completely new businesses based on technologies like artificial intelligence, quantum computing, and neuromorphic chips.
The one absolute certainty about innovation is that you need to constantly feed your pipeline with new ideas or you will inevitably begin to decline.
Examining this work helped us consider mistakes that can kill the process of innovation.
Check out our thoughts on building innovation.
His presentation that day would be so consequential that it is now called The Mother of All Demos. Two of those in attendance, Bob Taylor and Alan Kay would go on to develop Engelbart’s ideas into the Alto, the first truly personal computer.
Later, Steve Jobs would take many elements of the Alto to create the Macintosh.
So who deserves credit? Engelbart for coming up with the idea? Taylor and Kay for engineering solutions around it?
Jobs for creating a marketable product that made an impact on the world? Strong arguments can be made for each, as well as for many others not mentioned here.
The truth is that there were many mistakes that could have killed the efforts of this cool innovation.
Here are nine we need to consider:
Assuming all innovation is the same size
When most people think about innovation, they think about startups. And certainly, new firms like Uber, Airbnb, and Space X can transform markets. But others such as IBM, Procter and Gamble, and 3M have managed to stay on top for decades.
This happens even as competitors rise to challenge them and then, when markets shift, disappear just as quickly into oblivion.
It is true that small, agile firms can move fast, but larger enterprises have the luxury of going slow. They have loyal customers and an abundance of resources.
They can see past the next hot trend and invest for the long term. There’s a huge difference between hitting on the next big thing and developing it consistently, generation after generation.
Assuming innovation is a single event
Alexander Fleming discovered penicillin in 1928, but it wasn’t until 15 years later, in 1943, that the miracle drug came into widespread use. Alan Turing came up with the idea of a universal computer in 1936, but it wasn’t until 1946 that one was built and not until the 1990’s that computers began to impact productivity statistics.
We tend to think of innovation as arising from a single brilliant flash of insight, but the truth is that it is a drawn-out process involving the discoveries of many insights, the many components of the engineering solution and then the transformation of an industry or field.
That’s almost never achieved by one person or even within one organization.
Not asking the right questions
Too often, we treat innovation as a monolith, as if every problem was the same, but that’s clearly not the case.
In laboratories and factory floors, universities and coffee shops, or even over a beer after work, people are checking out better ways to do things. There is no monopoly on creative thought.
But that leads us to a problem: How should we go about innovation? Should we hand it over to the guys with white lab coats? An external partner? A specialist in the field? Crowdsource it? What we need is a clear framework for making decisions.
The best way to start is by asking the right questions: (1) How well is the problem defined? and (2) How well is the domain defined?
Once you’ve asked those framing questions, you can start defining a sensible way to approach the problem using the innovation matrix.
Clearly, no one method can suffice. Look at any great innovator, whether it is Apple, Tesla or Google, and you’ll find a portfolio of strategies.
So the first step toward solving a difficult problem is asking the questions you need to define your approach. To paraphrase Voltaire, if you need to solve a problem, first define your terms.
Process of innovation … not considering idea combinations
The reason that Fleming was unable to bring Penicillin to market was that, as a biologist, he lacked many of the requisite skills.
It wasn’t until a decade later that two chemists, Howard Florey, and Ernst Boris Chain, picked up the problem and were able to synthesize penicillin.
Even then, it took people with additional expertise in fermentation and manufacturing to turn it into the miracle cure we know today.
This isn’t the exception, but the norm. Darwin’s theory of natural selection borrowed ideas from Thomas Malthus, an economist, and Charles Lyell, a geologist.
Watson and Crick’s discovery of DNA was not achieved by simply plowing away at the lab, but by incorporating discoveries in biology, chemistry and x-ray diffraction to inform their model building.
Great innovation almost never occurs within one field of expertise but is almost invariably the product of synthesis across domains.
Not employing open innovation
When Microsoft launched Kinect for the Xbox in 2010, it quickly became the hottest consumer device ever, selling 8 million units in just the first two months. Almost immediately, hackers began altering its capabilities to do things that Microsoft never intended.
Instead of asking them to stop, it embraced the hackers, quickly releasing a software development kit to help them along.
Like Microsoft, many firms today are embracing open innovation to expand capabilities. Cisco outfoxed Lucent not by developing the technology itself, but by smartly acquiring startups.
Procter & Gamble has found great success with its Connect and Develop program, and platforms like Innocentive allow firms to expose thorny problems to a more diverse skill set.
As was the case with Alexander Fleming and penicillin, most firms will find that solving their most important problems will require the skills and expertise they don’t have.
That means that, at some point, they will need to utilize partners and platforms to go beyond their internal capabilities of technology and talent.
Forgetting about new business models
When Chester Carlson perfected his invention in 1938, he tried to market it to more than 20 companies but had no takers. It was simply far too expensive for the market.
Finally, in 1946, Joe Wilson, President of the Haloid Company, came up with the idea of leasing the machines instead of selling them outright.
The idea was a rousing success, and in 1948 the firm changed its name to Xerox.
The tricky thing about disruptive innovations is that they rarely fit into existing business models and so the value they create isn’t immediately clear.
Kodak made money by selling the film, so was slow to adopt the digital cameras that the company had itself invented. Yahoo’s business was focused on keeping users on its site, so passed on the chance to acquire Google.
It’s not just products that we have to innovate, but business models as well
Not using the ‘one-off’ rule
Many people think of innovation as discarding the old to make room for the new, but as Bain & Co.’s Chris Zook points out in Profit From The Core, smart companies realize that the bulk of their profits will come from current lines of business.
Take Google for example. Yes, it pursues radical innovation, like self-driving cars, at itsGoogle X unit, but the continual improvement of its core search business is what made it the world’s most valuable company.
That’s why Google, as well as many other innovative companies, follow the 70/20/10 rule.
The premise of the rule is simple. Focus 70% of your resources in improving existing technology (i.e. search), 20% of adjacent markets (i.e. Gmail, Google Drive, etc.) and 10% on completely new markets (i.e. self-driving cars).
Forgetting about the synergy of numbers
It’s no accident that the people who would make the vision Engelbart presented at “The Mother of All Demos” a reality attended the event and knew Engelbart personally. In those days, it was difficult, if not impossible, to actively collaborate across time and space.
Today, however, we can use platforms to access ecosystems of technology, talent, and information.
Take Apple’s App Store. It is, of course, a highly effective way for Apple’s network of customers to access functionality on their phones, but it also allows the firm to access the talents of literally millions of developers.
It’s hard to imagine any single enterprise, no matter how efficient or well organized, pulling off that kind of scale.
In a networked world, the surest path to success is not acquiring and controlling assets, but widening and deepening connections.
Lacking the best collaboration
When we look back to the great innovations of the past, it hard not to wonder how it could’ve gone differently. What if chemists had picked up on Fleming’s discovery of penicillin in weeks rather than years?
How many lives could have been saved? Was there no one who could have helped develop Engelbart’s vision of the personal computer outside of Northern California?
And now, the problems we seek to solve are significantly more complex than in earlier generations. That’s one reason why the journal Nature recently noted that the average scientific paper today has four times as many authors as one did in 1950.
At the same time, knowledge has been democratized. A teenager with a smartphone today has more access to information than a highly trained specialist a generation ago.
That’s why now collaboration itself is becoming a competitive advantage.
Take a slightly broader view, and it becomes clear that innovation today goes far beyond research labs, Silicon Valley pitch meetings, and large corporate initiatives.
In 1977, a Harvard-trained psychologist named Keith Simonton, developed a theory that he called the Equal Odds Rule. “The Equal Odds Rule says that the average publication of any particular scientist does not have any statistically different chance of having more of an impact than any other scientist’s average publication.” [4]
In other words, any given scientist is equally likely to create a game-changing piece of work as they are to create something average that is quickly forgotten.
Translated to the world-at-large: You can’t predict your own success. Scientists, artists, inventors, writers, entrepreneurs, and workers of all types are equally likely to produce a useless project as they are to produce an important one.
We all have something to offer and can add to the world’s knowledge in a way that may differ in degree, but not in kind, to the giants of the past.
The bottom line
Don’t let what you know … limit what you can imagine or maybe even a dream. Confidence never comes from having all the answers. It comes from being prepared for, and open to, new ideas and questions. Prepare your mind for new ways of thinking.
Only then will you take advantage of all the business lessons learned for the best innovative ideas.
As Robert Weisberg pointed out in his excellent book, Creativity: The Myth of Genius, great creative thinking arises out of rather ordinary thought processes. Genius is, of course, helpful, but not essential and many of those that are considered geniuses have fairly normal IQs.
So the secret to unlocking creativity and innovation is not grabbing for wild ideas or waiting for divine inspiration, but through knowing your field, defining good problems, taking useful ideas from separate domains, and tenaciously seeking out effective solutions. That’s within the reach of all of us.
Both innovation and creativity come from combining ordinary things in extraordinary ways.
