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 estimates 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
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 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
Over the past few decades, Silicon Valley has been such a powerful engine for entrepreneurship in technology that, all too often, it is considered to be some kind of panacea. Corporate executives seek to inject “Silicon Valley DNA” into their cultures, and policy makers point to venture-funded entrepreneurship as a solution for all manner of problems.
This is a dangerous mindset. The Silicon Valley model, for all of its charms, was developed at a specific time, for a specific industry, which was developing a specific set of technologies. While it can offer valuable lessons for other industries and other problems, the model is not universally applicable.
The myth of Silicon Valley is that venture-funded entrepreneurship is a generalizable model that can be applied to every problem, when in actuality it is a model that was built to commercialize mature technologies for certain markets. We’re now entering a new
One of the most common questions I get asked by senior managers is “How can we find more innovative people?” I know the type they have in mind — someone energetic and dynamic, full of ideas and able to present them powerfully. It seems like everybody these days is looking for an early version of Steve Jobs.
Yet in researching my book, Mapping Innovation, I found that most great innovators were nothing like the mercurial stereotype. In fact, almost all of them were kind, generous, and interested in what I was doing. Many were soft-spoken and modest. You would notice very few of them in a crowded room.
So the simplest answer is that you need to start by empowering the people already in your organization. But to do that, you need to take responsibility for creating an environment in which your people can
A decade ago, Microsoft was considered a dinosaur. It had missed the shift to mobile, was out of step with consumer tastes, and seemed too big and slow to adapt to a digital world that was moving at hyperspeed. Yet today the company is thriving again, largely driven by its growing cloud business.
This is not a new effort. In fact, it began in the early 2000s, but was little noticed until recently. In much the same way, IBM’s Watson project, which is helping the venerable company overcome the disruption of its traditional business, began in 2005. Google has created its own moonshot factory, to pursue game-changing technologies that may take years to pay off.
In recent years, we’ve come to associate the practice of innovation with speed and agility, but accomplishments that truly move the needle can’t be achieved quickly or through mere iteration.
One of the best innovation stories I’ve ever heard came to me from a senior executive at a leading tech firm. Apparently, his company had won a million-dollar contract to design a sensor that could detect pollutants at very small concentrations underwater. It was an unusually complex problem, so the firm set up a team of crack microchip designers, and they started putting their heads together.
About 45 minutes into their first working session, the marine biologist assigned to their team walked in with a bag of clams and set them on the table. Seeing the confused looks of the chip designers, he explained that clams can detect pollutants at just a few parts per million, and when that happens, they open their shells.
As it turned out, they didn’t really need a fancy chip to detect pollutants — just a simple one that could alert the system to clams opening
In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.
In the decades to come, we are likely to see similar shifts. Today, just like then, many people’s jobs will be taken over by machines and many of the jobs of the future haven’t been invented yet. That inspires fear in some, excitement in others, but everybody will need to plan for a future that we can barely comprehend today.
This creates a dilemma for leaders. Clearly, any enterprise that doesn’t embrace automation won’t be able to survive any better than a farmer with a horse-drawn plow. At the same time,
The declaration of surrender was touted as a triumph: “Microsoft Loves Linux,” the headline read, but just a decade earlier, the firm’s then CEO, Steve Ballmer, had called Linux a cancer. The all-powerful tech giant had lost and lost badly — to a ragtag band of revolutionaries, no less — but still seemed strangely upbeat.
Overthrows like these are becoming increasingly common and not just in business. As Moisés Naím observed in his book, The End of Power, institutions of all types, from corporations and governments to traditional churches, charities, and militaries, are being disrupted. “Power has become easier to get, but harder to use or keep,” he writes.
The truth is that it’s no longer enough to capture the trappings of power, because movements made up of small groups are able to synchronize their actions through networks. So if you want to effect lasting change today, it’s
Most companies try to avoid problems. Experian actually goes looking for them. In fact, it has set up a specific unit – Experian DataLabs — to actively seek out unresolved problems its customers are having and use them as a launchpad to seek out new opportunities and create new products. In doing so, it has been able to act more like a startup than a global data giant.
Conventional wisdom says that you need to run a big company differently than a startup and there’s a lot of truth to that. But for large enterprises seeking to grow by exploring new lines of business, thinking more like a startup makes a lot of sense.
Steve Blank, who pioneered the concept of the “lean startup,” has often written that “no business plan survives first contact with the customer.” That’s why he urges startups to “get out of the building” and
Last week I wrote an article about Tribune Publishing’s reincarnation as Tronc and the poorly thought-out video that the company released, describing its efforts. As best I could tell, the article was well received and many people, even those employed at Tronc, seemed to think I got it right.
My basic point was that the notion that you can transform a failing media company — or any company in any industry, for that matter — by infusing it with data and algorithms is terribly misguided. I stand by that analysis, but I realize that rather than tell publishers what they should do, I merely spelled out what won’t work.
I also think my article gave Tronc’s management short shrift. They are trying to revive a storied icon of American journalism and should be given some credit. As a former publishing CEO who managed a number of digital and print brands,
Tribune Publishing, a storied icon of American journalism, recently renamed itself Tronc and released a video to show off a new “content optimization platform,” that Malcolm CasSelle, Tronc’s chief technology officer, claims will be “the key to making our content really valuable to the broadest possible audience” through the use of machine learning.
As a marketing ploy the move clearly failed. Instead of debuting a new, tech-savvy firm that would, in the words of chief digital officer Anne Vasquez, be like “having a tech startup culture meet a legacy corporate culture,” it came off as buzzword-laden and naive. The internet positively erupted with derision.
Yet what I find even more disturbing than the style is the substance. The notion that you can transform a failing media company — or any company in any industry for that matter — by infusing it with data and algorithms is terribly misguided. While technology
By the mid-1980’s, the American semiconductor industry seemed like it was doomed. Although US firms had pioneered and dominated the technology for two decades, they were now getting pummeled by cheaper Japanese imports. Much like cars and electronics, microchips seemed destined to become another symbol of American decline.
The dire outlook had serious ramifications for both US competitiveness and national security. So in 1986, the American government created SEMATECH, a consortium of government agencies, research institutions and private industry. By the mid 1990’s, the US was once again dominating semiconductors.
Today, SEMATECH is a wholly private enterprise, funded by its members, but its original model is being widely deployed by other consortia to solve new problems, such as creating next generation batteries, curing cancer, and reviving American manufacturing. The truth is that some of the problems we face today are simply too big and complex to be solved by
Apple fuses technology with design. IBM invests in research that is often a decade ahead of its time. Facebook “moves fast and maintains a stable infrastructure” (but apparently doesn’t break things anymore).
Each of these companies, in its own way, is a superior innovator. But what makes Google (now officially known as Alphabet) different is that it doesn’t rely on any one innovation strategy, but deploys a number of them to create an intricate — but powerful — innovation ecosystem that seems to roll out innovations by the dozens.
The company is, of course, a massive enterprise, with $75 billion in revenues, over 60,000 employees, and a dizzying array of products, from the core search business to the android operating system to nascent businesses like autonomous cars. So to better understand how Google innovates, I took a close look at what it’s doing in one area: Deep Learning, a
One of the biggest cop-outs in corporate life is to say, “We had a great strategy, but we just couldn’t execute it.” Hogwash. Any strategy that doesn’t consider the ability to execute is a lousy strategy to begin with.
The problem is particularly pervasive when it comes to content. For all of the talk about “brands becoming publishers,” most marketers are simply tacking on publishing functions to their existing operations without implementing any new processes or practices. That is a grave mistake.
As I previously wrote for Harvard Business Review, marketers do need to think more like publishers, but they also need to act more like publishers if they are ever going to be able to hold an audience’s attention. If you can’t create a compelling experience, it doesn’t really matter what your content strategy is: it will fail.
Great publishers operationalize their content strategy by doing the following four