GUEST POST by Greg Satell

Throughout America’s history, technological innovation has been key to security and prosperity. Whether it was through entrepreneurs like Thomas Edison, Henry Ford and Thomas Watson, or government programs like the Manhattan Project, the Apollo Program and the Human Genome Project, The United States has been on the cutting edge.

Today, as we enter a new era of innovation, America remains at the forefront of scientific discoveries in advanced areas such as artificial intelligence, synthetic biology, new computing architectures and materials science. Continued investment in science, both public and private, provides the “seed corn” for continued dominance in the 21st century.

Still, scientific advancement is not enough. We need entrepreneurs to start companies and mid-level technicians and engineers to implement technologies. The truth is that America’s human capital is being hollowed out and that’s becoming a serious problem that we need to address. Once we lose our competitive edge, we might never get it back.

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GUEST POST by Greg Satell

Today, technology has become central to how every business competes. Futuristic advancements like artificial intelligence, big data and cloud computing are no longer pie-in-the-sky propositions, but mission critical initiatives that leaders are racing to implement within their organizations.

Unfortunately, most  of these initiatives fail. In fact, McKinsey found that fewer than a third of organizational transformations succeed. That’s incredibly sobering. Imagine any other initiative with that type of expected return not only getting consistently funded, but enthusiastically viewed as a smart bet on the future.

Yet digital transformation doesn’t have to be a sucker’s bet. The truth is that digital transformation is human transformation and that’s where you need to start. Initiatives fail because organizations habitually get blinded by the “gee-whiz” aspects of technology, don’t focus on clear business objectives, scale too fast and then declare victory way too early.

1. Focus on People First, Technology Second

At first, digital transformation seems fairly straightforward. There are many capable vendors that can competently implement cloud technology, automation, artificial intelligence applications or whatever else you’re looking for. So, theoretically at least, a standard procurement process should be effective in sourcing and executing a project.

Yet consider how the the nature of work has changed has changed over the last few decades, due to technological shifts. We spend far less time quietly working away at our desks and far more interacting with others. Much of the value has shifted from cognitive skills to social skills and collaboration has increasingly become a competitive advantage. New technologies such as the cloud and AI will only strengthen and accelerate these trends.

The truth is that value never disappears it just shifts to another place. Consider the case of bank tellers. There are more than twice the number of bank tellers today than there were before ATM machines, but the work they do is vastly different. They are no longer there to execute transactions, but to advise, solve problems and up-sell. That takes very different skills.

So the first step towards a successful digital transformation is not the technology itself, but thinking about how you can empower your people through it. Where do you expect value to shift to? What new skills will your people need to learn in order to succeed? How can technology help them get where they need to be to serve your customers well?

2. Establish Clear Business Outcomes 

Another common mistake executives make when implementing new technology is to focus on the capabilities of the technology itself, rather than the business outcome you hope to achieve. Are you trying to drive transactions, improve service and customer experience or something else entirely? You need to determine that before you can even think about a technical approach.

That’s why every transformational effort should involve operational managers, partners and front-line workers from the start. You need also to talk to customers and see what they actually value, rather than what would simply help operations to run smoother. From there, you can begin to develop a vision for how your business can function differently.

For example, when Barry Libenson first arrived at the data giant Experian as Global CIO in 2015, he spent his first few months talking to customers and the business units that served them. Everywhere he went, he found the same thing: what customers valued most was access to real-time data, which his company’s existing infrastructure could not provide.

From there, the path forward was fairly simple, but not easy. He needed to shift  his company from a traditional on-site server architecture to the cloud. That took him three years to accomplish, but it transformed Experian’s business, empowered new business models and led to new revenue streams.

3. Identify A Keystone Change 

Once the vision is in place, the tendency, all too often, is to embark on what becomes a “five-year death march” to achieve it. In the end, everybody ends up frustrated, angry and, inevitably, it turns out that by the time the vision is achieved, the technology is out of date.

So instead of trying to swallow the entire vision whole, it’s best to start out with a keystone change. Think about a clear and tangible goal you can achieve in the near term that would require the involvement of multiple stakeholders and pave the way for future, more complex initiatives in the future.

One way to do this is to choose a solution that will help people with tedious, mundane tasks rather than create a new capability. It’s much easier to get people excited by reducing the time and effort they have to expend on something they hate then it is to push them to adopt something new. You always want to attract and empower, rather than bribe or coerce.

For example, in Experian’s case, Libenson started out by creating internal API’s rather than building customer facing features. These didn’t create an enormous impact, but they showed what was possible and built momentum for the larger vision.

4. Treat Transformation As A Journey, Not A Destination

Perhaps the most dangerous part of any transformation is when the initial objectives have been achieved. That’s when motivation begins to weaken and complacency sets in. In my book, Cascades, I call this problem surviving victory and it is a crucial element of every transformational effort.

The key to surviving victory is to plan for it from the start. In Experian’s case, the journey was never about the cloud. That was merely a destination. The vision was always to serve customers better and to develop new business models. That’s why Libenson focused not only on implementing technology, but indoctrinating new values and beliefs.

“Having gone through this transformational process over the past three years and seeing concrete business results, we are much better positioned to adopt those technologies,” he told me. “We’ve made the changes in culture, our organizational structure and skills to be able to adopt new technologies quickly, completely and with better collaboration with our customers.”

That emphasis on values is key, because to change fundamental behaviors you first have to change fundamental beliefs and digital transformation is always about empowering action. Keep your eye on that and you will be likely to succeed where most others fail.

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GUEST POST by Greg Satell

In 1980, a young Harvard Business School professor named Michael Porter published Competitive Strategy, which drove thinking on the subject for the next 30 years. In essence, he argued that you build sustainable competitive advantage by maximizing bargaining power throughout a value chain.

Yet more recently, that kind of single firm level analysis has been called into question and leaders have learned to look more broadly at ecosystems. In fact, a recent report by Accenture Strategy found that because business models are being constantly disrupted, ecosystems have become a “cornerstone” of future growth.”

While value chains are strictly defined by “primary activities” such as “inbound logistics” and “support activities” like technology, ecosystems have mostly been a nebulous term. Clearly that’s not good enough. If we are going to compete in an ecosystem-driven world, we need to understand how they function and how we can leverage them to drive a business forward.

What Is An Ecosystem?

Ecosystem is a term that gets thrown around quite a bit, but people never seem to define what one is. All too often, it alludes to some indescribable ether that surrounds an enterprise. When you can’t define how an action would impact a customer or partner, you simply invoke “the ecosystem” and that’s supposed to make it all make sense.

Yet it’s important that we define terms that have meaning, because if we don’t they just become a catchall for things that we can’t describe. That’s a problem. As Wittgenstein pointed out long ago, if we can’t define something we don’t really understand it and if we don’t understand something we can’t hope to manage it very well.

Ecosystems are best understood as networks of networks and that tells us a lot. In fact, there is a whole science of networks to guide us. What’s most important about networks is that they are driven by links not nodes, so the most important network activity is connection. Networks are dynamic, always evolving, not static.

That’s where focusing on value chains runs into problems. Maximizing bargaining power within a value chain almost compels us to see things as a static, “winner take all” type of challenge in which you play one partner off against another. When you see things as an ecosystem, however, there is clear value in investing in connections and building up the nodes around you to improve your position.

It Is Ecosystems, Not Inventions That Drive The Future

We tend to think of history as a series of “great men” driving events. So electricity conjures up visions of Edison and his light bulb and automobiles remind us of Henry Ford creating the Model T. Yet the truth is that the impact from those inventions didn’t come till decades after those men brought those inventions to life.

In both cases, it was secondary inventions that drove the impact. Electricity allowed businesses to redesign factories to optimize workflow and drive productivity. Home appliances replaced backbreaking work and freed up energy for other tasks. Roads and gas stations revolutionized product distribution and led to the modern retail industry.

Computers followed a similar path. Digital technology had been around for decades when IBM launched the PC in 1981, yet it wouldn’t be till the late 90s that we first started to see an impact on productivity. The truth is that computers don’t do much by themselves. Applications need to be designed and people need to figure out how to put them to good use.

Notice that it’s impossible to point to any one thing that tipped the scale, because what drove impact was an ecosystem of connections between partners, suppliers and customers who needed to learn how to collaborate effectively. That has far less to do with technology than it does with forging meaningful human relationships and it takes time.

Power Today Lies In The Center Of Ecosystems, Rather Than At The Top Of Hierarchies

Traditionally we’ve seen the world as a driven by hierarchies. Kings and queens ruled the world through through aristocracies that carried out their orders. Corporate CEO’s outlined strategies that underlings would have to execute. Discipline was enforced through a system of punishments and rewards.

In a hierarchy driven world, you progress by climbing your way to the top. So you do your best to drive the performance of those under you to impress those above you. Success is determined by how high you rise. You learn to put great emphasis on signals that you have made it, such as the title on your business card and the size and location of your office.

In an ecosystem driven world, however, power does not lie at the top of hierarchies, but emanates from the center of networks. So an office on the executive floor may, in fact, diminish your ability to shape events if it leads to disconnection. At the same time, being seen as approachable, rather than high status, may enhance your power.

Here’s where Porter’s ideas about value chains can get you into trouble. If you are constantly trying to maximize your bargaining power, you are likely to weaken connections and find yourself at the periphery, rather than at the center, of networks. In an ecosystem driven world, displaying your power can often serve to undermine it.

You Move To The Center By Connecting Out

As I explain in my book Cascades, the best way move to the center of networks is by connecting out. At first, that may seem counterintuitive because it seems simpler to identify a central hub and connect in. Yet those nodes, by definition, already have a lot of links and your connection is less likely to be meaningful.

Once you understand that networks are dynamic and evolving, it becomes clear that a better strategy is to identify emergent nodes and connect to them early on. As the network grows, the center shifts and you are more likely to improve your position. In an ecosystem world, the best strategy is to widen and deepen connections throughout the network.

AnnaLee Saxenian gives an apt description of how this works in Regional Advantage, where she tells the story of how Boston’s “Technology Highway” lost relevance and Silicon Valley moved to the center of the technology universe. The Boston-based companies saw things in terms of value chains and focused on vertical integration to maximize their bargaining power. The Silicon Valley upstarts, on the other hand, saw an ecosystem and thrived on connection.

Today, of course, technology has exponentially increased our ability to make connections. However, what is crucial to understand is that relationships are essentially a very human activity. You don’t build them through gadgets or algorithms, but my investing your most valuable resource — yourself.

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GUEST POST by Greg Satell

Over 50% of startups fail (and that number goes up to 75% for venture backed startups). The same is true of about three quarters of corporate transformations, which is probably why the average lifespan on the S&P 500 continues to shrink. These statistics tell a humbling story: few significant endeavors ever actually succeed.

So it’s probably not surprising that we’ve come to glorify failure. We are urged to “fail fast” and are cheered on when we do. Failure, after all, is hard evidence that you’ve tried something difficult and paid the price. Yet failure, as anyone who actually experienced it knows well, is a horrible, painful thing.

As I explain in Cascades, great transformations are achieved not by glorifying failure, but when we learn from mistakes and begin to do things differently. That’s how great enterprises are transformed, industries are disrupted and then remade a new and seemingly all powerful tyrants are overthrown. Failure is something we should never accept, but rather overcome.

Ask The Hard Questions

Go to just about any innovation conference and you will find some pundit on the stage telling the story of some corporate giant, usually Blockbuster, Kodak or Xerox, that stumbled and failed. It is then explained that these firms were run by silly, foolish people who simply didn’t want to see the signs of disruption around them.

These stories are almost never true and, in fact, should be seen as ridiculous on their face. It takes no small amount of intelligence, drive and ambition to run a significant enterprise so to suggest that executives managing highly successful businesses were utter dopes beggars belief. The truth is that smart, hard working people fail all the time.

Once you realize that it forces you to ask some hard questions. Why did these smart, successful people fail? Why weren’t the dangers lurking more obvious? What hidden forces were working against them? Why did they think that they actions they undertook, after no small amount of deliberation, were the best of the available options?

Consider the case of Mahatma Gandhi and his Himalayan miscalculation. In 1919, he organized a series of demonstrations to protest against unjust laws passed by the British Raj. These were successful at first, but soon got out of hand and eventually led to the massacre at Amritsar, in which British soldiers left hundreds dead and more than a thousand wounded.

Most people would have simply concluded that the British were far too cruel and brutal to be dealt with peacefully. Gandhi, however, looked for the error in his own actions and learned from his mistakes. A decade later, rather than embark on a wholesale revolt, he identified a keystone change that would break the logjam. Today, both the salt march that resulted, and Gandhi himself, have become icons.

Test Your Hypotheses (Cheaply)

If you want to get a project going in a typical organization, the first thing you do is try to procure a big budget. So you write up an impressive business plan, examine the political tea leaves and work your contacts. If you’re successful, you can build out a great staff, line up tier-one partners and really hit the ground running.

You also can’t make any mistakes. Unless your plan was truly bulletproof from conception (and it never is) or you just get really lucky, you’re going to make some big, well-funded, well-staffed blunder that you’ll have to scramble to recover from. Unless you catch it early or have the political clout within your organization to get more money, you are likely to fail.

Now consider how Nick Swinmurn started his business. As Eric Ries explained in The Lean Startup, instead of spending money on some expensive marketing study to see if people would buy shoes online, he simply built a cheap site. When he got an order, he would go to the store, buy the pair at retail, and ship it out. He lost money on every sale.

That’s a terrible way to run a business, but a great way to test a business hypothesis. Once he knew that people were willing to buy shoes online, he started Zappos, which quickly grew to dominate the market for selling shoes online. It was sold to Amazon in 2009, ten years after Swinmurn started, for $940 million.

Build A Network

We tend to think that success is the result of hard work and talent. Yet look at any category and one brand tends to dominate. There are many search engines, but only one Google, just like there are many smartphone manufacturers, but only one Apple. Both are great products, but they end up taking the vast majority of profits in their industry. Are they really that much better than their competitors?

The truth is, as Albert-László Barabási explains in The Formula, is that performance is bounded, but success isn’t. You can be better than your competitors, but not that much better. On the other hand, there are no limits to success because networks tend to be dominated by a central node.

To understand why, consider the case of Albert Einstein. Until April 3rd, 1921, he was a prominent scientist, but by no means an icon. In fact, much of his press coverage was negative. But on that date, he arrived in America with the Zionist leader Chaim Weizmann. Reporters covering the event mistook the enormous crowds there to meet Weizmann as fans of Einstein and the story made the first page of all major newspapers.

That, along with his brilliance and endearing personality, is what catapulted Einstein to iconic status. In a similar vein, Google launched its product on the techie-dense Stanford computer network and Apple introduced the iPhone to its already expansive fan base. It’s networks, not nodes, that drive success.

Stop Disrupting And Start Solving Problems

Walk down any grocery store aisle and it becomes clear that there is no shortage of ideas. At any given time there are countless opportunities for line extensions, expansions into new categories, partnerships and other things. Executives spend countless hours discussing the merits and demerits of ideas like these.

Yet innovation isn’t about ideas, it’s about solving problems. That’s why most ideas fail, because they don’t address a meaningful problem that people really need solved. Nobody really needs a different flavor of cereal, but Zappos, Google and Apple all met needs that people cared about and that made all the difference.

That’s why companies that last not only look to solve problems for today’s customers, but also take on grand challenges. These are not “bet the company” type of propositions, but long, sustained efforts that seek to fundamentally change the realm of the possible, like Google’s more than decade long quest to create a self-driving car or IBM’s generational pursuit of quantum computing.

The truth is that you never really have to fail because, if you make your efforts sustainable, you can always learn from mistakes and try again. Failure rarely stems from a lack of effort, but is guaranteed by a myopic vision.

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GUEST POST by Greg Satell

We live in a transformational age. Powerful technologies like the cloud and artificial intelligence are quickly shifting what it means to compete. Social movements like #MeTo are exposing decades of misdeeds and rewriting norms. The stresses of modern life are creating new expectations about the relationship between work and home.

Every senior manager and entrepreneur I talk to understands the need to transform their enterprise, yet most are unsure of how to go about it. They ordinarily don’t teach transformation in business school and most management books minimize the challenge by reducing it to silly platitudes like “adapt or die.”

The truth is that change is hard because the status quo always has inertia on its side. Before we can drive a true transformation, we need to unlearn much of what we thought we knew. Change will not happen just because we want it to, nor can it be willed into existence. To make change happen, we first need to overcome the myths that tend to undermine it.

Myth #1: You Have To Start With A Bang

Traditionally, managers launching a new initiative have aimed to start big. They work to gain approval for a sizable budget as a sign of institutional commitment. They recruit high-profile executives, arrange a big “kick-off” meeting and look to move fast, gain scale and generate some quick wins. All of this is designed to create a sense of urgency and inevitability.

That works well for a conventional initiative, but for something that’s truly transformational, it’s a sure path to failure. Starting with a big bang will often provoke fear and resistance among those who don’t see the need for change. As I explain in my book, Cascades, real change always starts with small groups, loosely connected, united by a shared purpose.

That’s why it’s best to start off with a keystone change that represents a concrete and tangible goal, involves multiple stakeholders and paves the way for future change. That’s how you build credibility and momentum. While the impact of that early keystone change might be limited, a small, but successful, initiative can show what’s possible.

For example, when the global data giant Experian sought to transform itself into a cloud-based enterprise, it started with internal API’s that had limited effect on its business. Yet those early achievements spurred on a full digital transformation. In much the same way, when Wyeth Pharmaceuticals began its shift to lean manufacturing, it started with a single process at a single plant. That helped give birth to a 25% reduction of costs across the board.

Myth #2: You Need A Charismatic Leader And A Catchy Slogan

When people think about truly transformational change, a charismatic leader usually comes to mind. In the political sphere, we think of people like Mahatma Gandhi, Martin Luther King Jr. and Nelson Mandela. On the corporate side, legendary CEOs like Lou Gerstner at IBM and Steve Jobs at Apple pulled off dramatic turnarounds and propelled their companies back to prosperity.

Yet many successful transformations don’t have a charismatic leader. Political movements like Pora in Ukraine and Otpor and Serbia didn’t have clear leadership out front. The notably dry Paul O’Neill pulled of a turnaround at Alcoa that was every bit as impressive as the ones at IBM and Apple. And let’s face it, it wasn’t Bill Gates’s Hollywood smile that made Microsoft the most powerful company of its time.

The truth, as General Stanley McChrystal makes clear in his new book, Leaders: Myth and Reality, is that leadership is not so much about great speeches or snappy slogans or even how gracefully someone takes the stage, but how effectively a leader manages a complex ecosystem of relationships and builds a connection with followers.

And even when we look at charismatic leaders a little more closely, we see that it is what they did off stage that made the difference. Gandhi forged alliances between Hindus and Muslims, upper castes and untouchables as well as other facets of Indian society. Mandela did something similar in South Africa. Martin Luther King Jr. was not a solitary figure, but just one of the Big Six of civil rights.

That’s why McChrystal, whom former Defense Secretary Bob Gates called, “perhaps the finest warrior and leader of men in combat I had ever met,” advises that leaders need to be “empathetic crafters of culture.” A leader’s role is not merely to plan and direct action, but to inspire and empower belief.

Myth #3: You Need To Piece Together A Coalition

While managing stakeholders is critical, all too often it devolves into a game theory exercise in which a strategically minded leader horse trades among competing interests until he or she achieves a 51% consensus. That may be enough to push a particular program through, but any success is bound to be short-lived.

The truth is that you can’t transform fundamental behaviors without transforming fundamental beliefs and to do that you need to forge shared values and a shared consciousness. It’s very hard to get people to do what you want if they don’t already want what you want. On the other hand, if everybody shares basic values and overall objectives, it’s much easier to get everybody moving in the same direction.

For example, the LGBT movement foundered for decades by trying to get society to accept their differences. However, when it changed tack and started focusing on common values, such as the right to live in committed, loving relationships and to raise happy, stable families, public opinion changed in record time. The differences just didn’t seem that important any more.

In a similar vein, when Paul O’Neill took over Alcoa in 1987, the company was struggling. So analysts were puzzled that when asked about his strategy he said that “I intend to make Alcoa the safest company in America.” Yet what O’Neill understood was that safety goes part and parcel with operational excellence. By focusing on safety, it was much easier to get the rank and file on board and, when results improved, other stakeholders got on board too.

Myth #4: You Will End With The Vision You Started With

When Nelson Mandela first joined the struggle to end Apartheid, he was a staunch African nationalist. “I was angry at the white man, not at racism,” he would later write. “While I was not prepared to hurl the white man into the sea, I would have been perfectly happy if he climbed aboard his steamships and left the continent of his own volition.”

Yet Mandela would change those views over time and today is remembered and revered as a global citizen. In fact, it was the constraints imposed by the broad-based coalition he forged that helped him to develop empathy, even for his oppressors, and led him to govern wisely once he was in power.

In much the same way, Lou Gerstner could not have predicted that his tenure as CEO at IBM would be remembered for its embrace of the Internet and open software. Yet it was his commitment to his customers that led him there and brought his company back from the brink of bankruptcy to a new era of  of prosperity.

And that is probably the most important thing we need to understand change. In order to make a true impact on the world, we first need to change ourselves. Every successful journey begins not with answers, but with questions. You have to learn how to walk the earth and learn things along the way. You know you’ve failed only when you end up where you started.

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GUEST POST by Greg Satell

Imagine yourself as the CEO of a Dow component company in 1919. You are fully aware of the technological forces that would shape much of the 20th century, electricity and internal combustion. You may have even be an early adopter of these technologies. Still, everything seems like business as usual.

What you don’t see, however, is that these inventions are merely the start. Secondary technologies, such as home appliances, radio, highways and shopping malls, would reshape the economy in ways that no one could have predicted. Your company has a roughly 50% chance of remaining on the Dow a decade later.

We are at a similar point today. New inventions, such as quantum computing, neuromorphic chips, synthetic biology and advancements in materials science already exist. It is not those inventions, however, but the ecosystems they spawn that will shape the decades to come. We’re all going to have to learn how to compete in a new era of innovation.

A 50-Year Boom In Productivity

By 1919, electricity was already a 40-year old technology. In 1882, just three years after he had almost literally shocked the world with his revolutionary electric light bulb, Thomas Edison opened his Pearl Street Station, the first commercial electrical distribution plant in the United States. By 1884 it was already servicing over 500 homes.

Yet although electricity and electric lighting were already widespread in 1919, they didn’t have a measurable effect on productivity and a paper by the economist Paul David helps explain why. It took time for manufacturers to adapt their factories to electricity and learn to design workflow to leverage the flexibility that the new technology offered. It was the improved workflow, more than the technology itself, that drove productivity forward.

Automobiles saw a similar evolution. It took time for infrastructure, such as roads and gas stations, to be built. Improved logistics reshaped supply chains and factories moved from cities in the north — close to customers — to small towns in the south, where labor and land were cheaper. That improved the economics of manufacturing further.

Yet all of that was just prelude to the massive changes that would come. Electricity spawned secondary innovations, such as household appliances and radios. Improved logistics reshaped the retail industry, shifting it from corner stores to supermarkets and shopping malls. As Robert Gordon explains in The Rise and Fall of American Growth, these changes resulted in a 50-year boom in productivity between 1920 and 1970.

The Digital Revolution

In 1984, Steve Jobs and Apple launched the Macintosh, which heralded a new era of computing. Based on technology developed for the Xerox Alto in the early 1970s, with a bitmapped screen, a graphical user interface and a mouse, it made computing far more accessible to regular consumers.

Before long, personal computers were everywhere. Kids would use them to write term papers and play video games. Lotus 1-2-3 spreadsheet software became a staple for small businesses and entrepreneurs. Desktop publishing helped democratize the flow of information. The computer age had begun in earnest.

Yet much like electricity and internal combustion earlier in the century, the effect on productivity was negligible, causing the Nobel Prize winning economist Robert Solow to quip, “You can see the computer age everywhere but in the productivity statistics.” In fact, it wouldn’t be till the late 90s that we saw a measurable impact from computers.

Once again, it wasn’t any particular invention that made the difference, but an ecosystem that built up over years. The Internet paved the way for open-source software. Hordes of application developers created industry specific tools to automate almost every imaginable business process. Computers converged with phones to create the mobile era.

The 30 Years Rule

Look back at the two major eras of technology in the 20th century and a consistent theme begins to emerge. An initial discovery of a new phenomenon, such as electricity and internal combustion, is eventually used to create a new invention, like the light bulb or the automobile. This creates some excitement, and builds the fortunes of a few entrepreneurs, but has little impact on society as a whole.

Yet slowly, an ecosystem begins to emerge. Roads and gas stations are built. Household appliances and personal computers are invented. Secondary inventions, such as shopping malls, home appliances, the Internet and application software help create new business models. Those business models create new value and drive productivity.

The truth is that innovation is never a single event, but a process of discovery, engineering and transformation. As a general rule of thumb, it takes about 30 years for all of this to take place, because thousands, if not millions of people need to change their behavior, coordinate their activity and start new businesses.

That’s why the future will always surprise us. It is not any one great event that tips the scales, but some hardly noticeable connection that completes the network. Network scientists call this type of thing an instantaneous phase transition and there’s really no way to predict exactly when it will happen, but if you learn to look for telltale signs, you can see one coming.

A New Era Of Innovation

Today, we appear to be in a very similar situation to what those executives faced in 1919. We have decoded the human genome. Artificial intelligence has become a reality that everyone, for the most part, accepts. New computing architectures, such as quantum computers and neuromorphic chips, are in late stages of development by a variety of companies.

Yet once again, the impact has been negligible and it’s not hard to see why. While these inventions, in some cases at least, are relatively mature, they have yet to create the ecosystems that can drive a true transformation. Today, however, we can clearly see those ecosystems being created.

In fact, in artificial intelligence we can already see a fairly well developed ecosystem emerging already. In synthetic biology and genomics we can begin to see one as well, although it is still nascent. IBM has created a Q Network of major companies, research labs and startups to support quantum computing.

Here’s what’s important to know: We can’t predict exactly when the system will tip, but it’s a good bet it will happen in the next decade. It is also likely that the impact will be equal to or greater than the 50 year boom that began in the 1920s. Finally, it won’t be driven by any particular invention, but by ecosystems. You need to start figuring out how you will connect.

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GUEST POST by Greg Satell

In early 2004, Viacom announced it would spin off Blockbuster Video, leaving CEO John Antioco master of his own fate. He moved quickly to meet the threat posed by Netflix head on, launching Blockbuster Online in 2004 and, after successfully testing the concept in a few markets, ending late fees in early 2005.

Still, not satisfied with playing catch-up, Antioco searched for model that would return his company to dominance. He found it in 2006 with the Total Access program. Within a few weeks of announcing the promotion, Blockbuster was winning the majority of new subscribers, outstripping Netflix for the first time.

It was a textbook case of sound strategy and execution meeting a disruptive threat, but it would not end well. In 2010 Blockbuster would declare bankruptcy and become a cautionary tale. We tend to think that driving change is merely a matter of coming up with a clever plan and executing well. Yet that isn’t enough. You also need learn how to survive victory.

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GUEST POST by Greg Satell

One of the questions I get asked quite often, both at conferences and when coaching executives, is what type of personality is best suited for innovation so that they can optimize their hiring. Are technical people better than non-technical people? Introverts better than extroverts? Is it better to hire foxes or hedgehogs?

The first thing I tell them is that there has been no definitive research that has found that any specific personality type contributes to innovation. In fact, in my research I have found that there is not even a particular kind of company. If you look at IBM, Google and Amazon, for example, you’ll find that they innovate very differently.

The second thing I point out is that every business needs something different. For example, Steve Jobs once noted that since Apple had always built integrated products, it never learned how to partner as effectively as Microsoft and he wished it would have. So the best approach to hiring for innovation is to seek out those who can best add to the culture you already have.

Foxes vs. Hedgehogs

In Good to Great, author Jim Collins invokes Isaiah Berlin’s famous essay about foxes and hedgehogs to make a point about management. “The fox,” Berlin wrote, “knows many things, but the hedgehog knows one big thing.” Collins then devotes an entire chapter to explaining why hedgehogs perform better than foxes.

Yet as Phil Rosenzweig points out in The Halo Effect, this is a highly questionable conclusion. Even if it were true that the most successful companies focus on one core skill or one core business, that doesn’t mean that focusing on “one big thing” will make you more successful. What it probably means is that by betting on just one thing you increase your chances of both success and failure.

Think about what would have happened it Apple had said, “we’re going to focus just on computers” or if Amazon had focused on just books. There is also evidence, most notably from Philip Tetlock, that foxes outperform hedgehogs on certain tasks, like making judgments about future events.

So the best strategy would probably be to hire a fox if you’re a hedgehog and to hire a hedgehog if you’re a fox. In other words, If you like to drill down and focus on just one thing, make sure you have people around that can help you integrate with other skills and perspectives. If you like to dabble around, make sure you have people who can drill down.

Introverts vs Extroverts

We tend to see leaders as brash and outgoing, but my colleague at Inc, Jessica Stillman points out that introverts can also make great leaders. They tend to be better listeners, are often more focused and are better prepared than social butterflies are. Those are great qualities to look for when adding someone to add to your team.

Still, you wouldn’t want to have an entire company made up of introverts and, in Social Physics, MIT’s Sandy Pentland explains why. Perhaps more than anything else, innovation needs combination. So it’s important to have people who can help you connect to other teams, both internally and externally, bring in new ideas and help take you in new directions.

Consider Amazon, a company that is not only incredibly successful but also highly technically sophisticated. You might expect that it hires a lot of introverted engineers and I’m sure that’s true. Yet the skill it is most focused on is writing, because it understands that to create a successful product, you need to get a lot of diverse people to work together effectively.

So much like with foxes and hedgehogs, if you’re an introvert you should make sure that you have extroverts that can help you connect and if you are an extrovert, make sure you have people who can focus and listen.

Technical vs. Non-Technical People

By all accounts, Steve Jobs was never more than a mediocre engineer, but was clearly a legendary marketer. Nevertheless, he felt strongly that technical people should be in charge. As he once told his biographer, Walter Isaacson, in an interview:

“I have my own theory about why the decline happens at companies like IBM or Microsoft. The company does a great job, innovates and becomes a monopoly or close to it in some field, and then the quality of the product becomes less important. The product starts valuing the great salesmen, because they’re the ones who can move the needle on revenues, not the product engineers and designers. So the salespeople end up running the company.”

Yet the story is not nearly as clear cut as Jobs makes it out to be. When IBM hit hard times it was Lou Gerstner, who spent his formative professional years as a management consultant, that turned it around. Steve Ballmer clearly made missteps as CEO of Microsoft, particularly in mobile, but also made the early investments in cloud technology led to Microsoft’s comeback.

So much like with foxes vs. hedgehogs and introverts vs. extroverts, the choice between technical and non-technical people is a false one. Far more important is how you build a culture in which people of varied skills and perspective can work closely together with a shared sense of purpose.

Today, as we enter a new era of innovation, organizations will need a far more diverse set of skills than ever before and building a collaborative culture will be key to success.

Collaboration Is The New Competitive Advantage

Over the past few decades, the digital revolution has shaped much of our thinking about how we advance a business. Digital technology required a relatively narrow set of skills, so hiring people adept at those skills was a high priority. Yet now, the digital era is ending and we need to rethink old assumptions.

Over the next decade, new computing architectures like quantum and neuromorphic computing will rise to the fore. Other fields, such as genomics and materials science are entering transformative phases. Rather than living in a virtual world, we’ll be using bits to drive atoms in the physical world.

That will change how we need to innovate. As Angel Diaz of IBM told me a few years back, “…we need more than just clever code. We need computer scientists working with cancer scientists, with climate scientists and with experts in many other fields to tackle grand challenges and make large impacts on the world.”

That’s why today collaboration is becoming a real competitive advantage and we need to focus far less on specific skills and “types” and far more on getting people with diverse skills, backgrounds and perspectives to work together effectively.

Image credit: Pixabay

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GUEST POST by Greg Satell

When IBM first debuted its Watson system on the game show Jeopardy! in 2011, it was like something out of science fiction. Here was a computer that could not only understand spoken questions, but answer them faster and more accurately than the best human players. Nobody had ever seen anything even remotely like it before.

Today, less than a decade later, artificial intelligence has been transformed from the incredible to something approaching the mundane. Not only do we have capabilities that are similar to the original Watson system on our phones, we can access top-notch resources from a number of companies, often for free.

Yet probably the biggest difference from those early days of Watson is how AI has evolved from a system to an ecosystem. Much like the evolution from mainframes to personal computers, companies now can access different components to build a system designed specifically for their business. That’s how a technology becomes transformational.

Acquiring Data Resources

What makes artificial intelligence different than earlier technologies is that the system learns as data is fed into it. So data is the fuel that makes the whole thing work. That’s why Josh Sutton, CEO of Agorai, a platform that helps companies build AI applications for their business, calls data, “most valuable business asset that does not reside on a balance sheet.”

“The first thing I suggest a company asks is ‘what value can I create with AI?’” Sutton told me. “From there, you can figure out what data you need to drive those applications, what data assets you already have, what you can generate and what you need to buy. I would also look at what data I have that may be valuable to others, but not of strategic value to me, that I can use to generate a revenue stream.“

Additionally, he points out that because AI algorithms can adapt the way the application works to the data you feed it, these technologies are much more accessible to small and medium sized businesses who can’t afford expensive consultants to customize an entire system for them.

David Schatsky, a Managing Director at Deloitte who focuses on emerging technology trends, notes that advances in synthetic data techniques are making it possible to train a system even in industries where actual data is hard to obtain. That means that very high performance can be obtained for a reasonable cost even in highly regulated fields.

“In healthcare, for example, where much of the data is protected, it’s very difficult to access the data we need to create a good model using machine learning. However, new techniques like synthetic data generation allow us sidestep that problem so we can build systems that automate processes and improve performance” he told me.

Advancing Both Software And Hardware

In the Master Algorithm, the renowned machine learning researcher Pedro Domingos explains that there are five basic machine learning approaches, including neural networks, vector machines, genetic algorithms and inductive learning algorithms. IBM’s Watson, in fact, combined a number of these approaches to beat human contestants on Jeopardy!

Since 2011, in part because of the excitement that Watson generated, these approaches have advanced dramatically. For example, Google’s Brain project was able to significantly increase the number of layers in a neural network system, which made its algorithms much more accurate.

Another important area of advancement is in hardware. The chipmaker Nvidia’s expertise in graphics processors has helped it dominate the market for AI chips. Google and Microsoft are creating their own chips specifically designed to run their algorithms. IBM developed neuromorphic chips which mimic the design of the human brain which, because they use very little power, have the potential to bring AI capabilities to edge computing.

Most business never see these technologies directly, but their advancement is what drives the capabilities of AI applications. It is one level up the stack that business value begins to be created.

Capability Components

While algorithms and hardware are important, what makes applications useful is their ability to mimic human intelligence. Things like machine vision, language analysis, document summarization and process automation are the basic building blocks of any intelligent system.

Often, these can be accessed for free through open source platforms, such as Shogun, or through the cloud computing platforms of the major tech firms. For example, Amazon, Google and IBM all make API’s available to developers. Often these are used as a loss leaders for their cloud platforms, so are free to anybody who wants to use them.

Working with these capabilities directly takes significant expertise. However, even amateurs with little or no technical skills can access them through no-code platforms. So a marketing manager who wants to build a simple chatbot can access API’s for natural language processing and text-to-speech capability without writing a single line of code.

Where business value really begins to be created is when these capabilities are bundled into solutions and then trained on data for a particular task. Increasingly, for tasks that are fairly common, there are pre-trained models that can plug into existing systems, with relatively little need for expensive integration.

“What we’re seeing with our clients is that there a lot of well defined problems where there is no need to reinvent the wheel,” Schatsky of Deloitte told me. “So pre-trained models can be enormously helpful in automating basic processes. That frees up resources that can be focused on more specialized systems and can help create a true competitive advantage.”

Creating Business Solutions

What’s most important for business leaders to know is that AI is no longer some kind of “gee whiz” technology, but increasingly key to competing effectively in today’s marketplace. As the technology continues to evolve from complex integrated systems to a modular ecosystem, even small and medium enterprises will find that they need to adopt these capabilities or fall behind.

“What most business leaders aren’t always aware of, but are quickly waking up to, is that there are an untold number of industry and process specific AI applications that can accelerate their business,” Sutton says. He points to firms like Primer.ai, which summarizes massive data sets into analyst reports for financial companies and Daisy Intelligence, which optimizes supply chains for retail companies, as just two examples.

“At Agorai, we’re focused on helping to connect those businesses to the companies offering solutions that can help them,” he told me. “What I tell business leaders is that AI is useful for tasks you understand well enough that you could do them if you had enough people and enough time, but not so useful if you couldn’t do it with more people and more time. It’s a force multiplier, not a magic box.”

Schatsky of Deloitte sees many of the same trends. “There is a relentless pace of progress, the tools are getting better, the models are getting better and the systems are getting better and we’re seeing companies achieve real results. It’s still early days, but the future is really exciting,” he says.

Image credit: Pixabay

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GUEST POST by Greg Satell

I was recently invited by Accenture Strategy, along with other thought leaders such as Bruce Weinstein and Andrew Winston, to discuss its research on trust and competitive agility. In a study of 7000 companies the firm found that trust among a diverse ecosystem of stakeholders is increasingly becoming a competitive advantage.

One of the most interesting aspects of the discussion was how crucial trust is for driving transformation and change. We tend to think of trust as static, but Accenture’s research, as well as that of the participants, made it clear that trust is especially important when you need to drive an organization to do something different.

All too often, transformation is seen as a simple matter of strategy and tactics, but it’s far more than that. Nobody can really drive change alone. You need buy-in from a variety of stakeholders, such as customers, employees, suppliers, analysts and investors to make it work. So before you set out to transform your organization, you first need to build trust.

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