In Eric Ries’s bestselling book, The Startup Way, the Silicon Valley entrepreneur explains how he helped lead General Electric’s transformation from a stodgy industrial era dinosaur into a lean, entrepreneurial enterprise. Yet strangely, as Steve Blank points out in Harvard Business Review, the effort ended up with the ousting of GE’s CEO for underperformance.
Why did such a seemingly promising initiative turn out to be such a tragic failure? To be honest, I have no idea. Maybe the cultural strain of “lean startup” thinking caused the company to take its eye off the ball. Or maybe it was simply a matter of short-term thinking by activist investors. Or maybe something else entirely.
On a deeper level, running a significant enterprise is incredibly complex and, to be successful, a lot of things need to go right. There are no silver bullets. Unfortunately, that doesn’t stop pundits and consultants from offering them. As Blank also points out in his article, Ries’s ideas weren’t the problem at GE, but neither did they address obvious issues.
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By the early 20th century, the world’s great mathematicians knew they had a big problem. The very foundations of logic, held sacred since the time of Aristotle, were under siege after the discovery of troubling paradoxes by Cantor, Frege and Russell. It was unclear whether things could ever be set aright.
It was against this backdrop that David Hilbert, the most prominent mathematician of the age, created a program to get things back on track. Unfortunately, just the opposite happened. First came Kurt Gödel’s famous incompleteness theorems and then the work of Alan Turing. Logic, as everyone knew it, was dead.
Nevertheless, out of the ashes of Aristotle’s logic arose the foundations of modern computing. As it turns out, solving a fundamental problem, even if the solution isn’t what you thought you wanted, can unlock enormous value. What’s more, in every field and industry, there are countless problems just waiting to be solved. Here’s how to go about working them out.
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Every discussion about artificial intelligence seems to alternate between utopia and dystopia. Some believe that the productivity unleashed through automation will lift up all of society, creating a world of superabundance and more meaningful work, while others see robots taking our jobs and an acceleration of trends favoring capital over labor.
In fact, in an article in Harvard Business Review, Accenture’s Mark Knickrehm describes five distinct schools of thought, ranging from both extremes to various shades of gray in between. He suggests that leaders need to reinvent operating models, redefine jobs and include employees in the process of transformation.
Yet that’s easier said than done. Smart leaders know that even small, subtle changes can sometimes result in a backlash. Preparing your organization to leverage artificial intelligence has can be especially problematic because the most profound problems are intensely human. Gandhi, although he was no tech enthusiast, can be a good guide on where to start.
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Elon Musk is often hailed as a visionary, and rightly so. Tesla, not to mention his other ventures, has the mark of a business that can truly change the world. When he co-founded the company in 2003, electric cars seemed like a pipe dream. Today, however, just about every major carmaker is investing in the technology.
The Tesla narrative has become so well accepted that we rarely stop to think how easily it could have all gone wrong. In 2007, Shai Agassi founded Better Place with an even more expansive vision for electric cars. Yet as Brian Blum explains in his new book, Totaled, that vision led to a series of missteps that resulted in a billion dollar bankruptcy.
There are, of course, a number of reasons for a failure so colossal, including Agassi’s mercurial personality, but most of Better Place’s problems stemmed from a single mistake. To truly change the world, you need to build for the few, not the many. Find a customer that wants or needs your product so badly they almost literally have their hair on fire.
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When Steve Jobs first came up with the idea for the iPod, it wasn’t actually a machine he had in mind, but “a thousand songs in my pocket.” It was, at the time, an impossible idea, because hard drives of that capacity and size just didn’t exist. In fairly short order though, the technology caught up to the vision.
That kind of singular focus and drive helps explain Jobs’ incredible success, but what about his failures? The Lisa, a precursor to the Macintosh, flopped. So did his first venture after Apple, NeXT Computer. Even at the height of Apple’s dominance, there were missteps such as iAds. Apple TV still hasn’t really gained traction.
“It’s not what you don’t know that kills you,” Mark Twain famously said, “it’s what you know for sure that ain’t true” and that’s the real innovator’s dilemma. Innovation, necessarily, is about the future, but all we can really know is about the past and some of the present. It’s always a balancing act of staying true to your vision and re-examining your assumptions.
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Today’s problems can sometimes seem almost insurmountable. Although we’ve made strong progress over the last decade in many areas such as violence, extreme poverty and global health, issues like climate change, income inequality and a rise in populist authoritarianism still loom large.
Education could be a possible bulwark against many of these problems, providing a greater supply of skilled people to solve them, but here again we have issues. The US routinely scores poorly in international PISA tests and the OECD recently found that the US is falling behind other countries in college graduates.
100Kin10 is a non-profit trying to help turn things around. It realized that it’s not enough to simply deal with surface problems, you need to get at root causes, but those can be devilishly hard to identify. Nevertheless, they found that a productivity hack called the 5 Whys from the famed Toyota Production System was able to help point the way forward.
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It often seems easy to know when the next big thing is upon us. Someone like Steve Jobs or Elon Musk stands on stage and tells us what is being launched next. The business press gets excited, pundits swoon and a thousand imitators are created. Before long an ecosystem develops and the world is forever changed.
In reality though, things are much murkier than that. Innovation is a process of discovery, engineering and transformation and it is only the last part that is visible to most of us. The seeds of a revolution start long before, in obscure labs and at conferences with high priests presenting papers written in arcane vernacular.
Since the 1950s, the engine that’s driven new knowledge to, as Vannevar Bush put it, “turn the wheels of private and public enterprise,” has been the US government. Unfortunately, moving new discoveries out of federal labs has often been a slow and cumbersome process, but a new model holds promise for greatly accelerating breakthrough innovation.
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IPsoft is, in many ways, an unusual entrant into the crowded, but burgeoning, artificial intelligence industry. First of all, it is not a startup, but a 20-year-old company and its leader isn’t some millennial savant, but a fashionable former NYU professor named Chetan Dube. It bills its cognitive agent, Amelia, as the “world’s most human AI.”
It got its start building and selling autonomic IT solutions and its years of experience providing business solutions give it a leg up on many of its competitors. It can offer not only technological solutions, but insights it gained helping businesses to streamline operations with automation.
Ever since IBM’s Watson defeated human champions on the game show Jeopardy!, the initial excitement about AI has led to inflated expectations and often given way to disappointment. So I met recently with top executives at IPsoft to get a better understanding of how leaders can successfully implement AI solutions. Here are four things you should keep in mind:
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At Google’s X division, the company’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’d better 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.
The truth is that most places are designed to produce pedestals and not to train monkeys. They work within existing frameworks and hone operations to improve performance against established metrics. That works great as long as people want pedestals, but unless you learn how to do something fundamentally new, you’re bound to be disrupted eventually.
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Has America lost its fascination with the tech industry? An article that went viral on CNBC suggests that the bloom is very much off the rose. Fashioned as a letter from a disappointed dad to an misguided son, it blasts the tech world for its miscues over the past year, from frat-boy antics to a sometimes appalling lack of transparency.
In a sense, this shouldn’t be surprising. Silicon Valley is no longer a collection of swashbuckling upstarts battling corporate behemoths. Today, Apple, Alphabet, Amazon and Microsoft are the most valuable companies in the world. Younger firms, such as Facebook and Uber, have already become powerful forces in our lives.
Perhaps most of all, the digital revolution is now two decades old and it has become very hard to move the needle. Compared with the personal computer, the Internet and the smartphone, smart watches and talking assistants don’t add that much value. To be truly useful, digital technology can’t stand alone, but must learn to empower industries in the physical world.
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