Author Archives: Greg Satell

About Greg Satell

Greg Satell is a popular speaker and consultant. His latest book, Cascades: How to Create a Movement That Drives Transformational Change, is available now. Follow his blog at Digital Tonto or on Twitter @Digital Tonto.

The Shareholder Value Myth

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The Shareholder Value Myth

GUEST POST from Greg Satell

The Business Roundtable, an influential group of almost 200 CEOs of America’s largest companies, a few years ago issued a statement that discarded the old notion that the sole purpose of a business is to provide value to shareholders. Instead, it advocated serving a diverse group of stakeholders including customers, employees, suppliers and communities.

The idea is not a new one. In fact, Jack Welch once called shareholder value the dumbest idea in the world. Nevertheless, The Wall Street Journal opinion page immediately pounced, suggesting that the move was just an attempt to “appease the socialists” and that it would undermine financial accountability.

It’s hard to see how acknowledging accountability to stakeholders other than investors would undermine accountability to investors. Shareholders, after all, have the power to fire CEOs. Even more importantly though, the notion that performance can be reduced down to a single metric is foolhardy and dangerous. Managing a business is simply tougher than that.

The Principal-Agent Problem

Every business seeks to make a profit. Ones that do not achieve that basic requirement do not stay in business for long. However, that doesn’t mean that the only reason a business exists is to make money. Clearly, in order to earn a profit over the long term, you need to provide value for others. Anybody who has ever run a business knows this.

Yet a large corporation is very different from an ordinary business in that there is what’s known as a principal-agent problem. The shareholders are a dispersed group that have relatively little information, while the managers of the business are a small group with an asymmetric informational advantage.

So you can see how the concept of shareholder value can be attractive. If you can reduce performance down to a single metric, such as stock performance, then the principal-agent problem is solved. Shareholders, as principal owners of the company, can hold managers, as their agents, accountable.

Yet this is a fantasy. There are many things that a manager can do, such as reducing investment or making a lot of sexy acquisitions, that can increase short-term financial performance, but hurt performance in the long run. So the concept of shareholder value has always been a murky one.

From Value Chains To Ecosystems

For decades, the dominant view of strategy was based on Michael Porter’s ideas about competitive advantage. In essence, he argued that the key to long-term success was to dominate the value chain by maximizing bargaining power among suppliers, customers, new market entrants and substitute goods.

Yet there was a fatal flaw in the notion that wasn’t always obvious. In an industrial economy, where technology is relatively static, value chains are stable. However, in a fast moving information economy, firms increasingly depend on ecosystems to compete. That drastically changes the game.

Ecosystems are nonlinear and complex. Power emanates from the center instead of at the top of a value chain. You move to the center by connecting out. So while an industry giant may possess significant bargaining power, exercising that bargaining power can be problematic, because it can weaken links to other nodes in the ecosystem.

So the increased emphasis on stakeholders is not merely some newfound socialistic altruism, but a realistic strategic shift. In a networked-driven world you need to continually widen and deepen links to other stakeholders within the ecosystem. That’s how you gain access to resources like talent, technology and information.
Building Power Through Gaining Trust

In a famous 1937 paper, Nobel Prize winning economist Ronald Coase argued that the function of a firm was to minimize transaction costs, especially information costs. For example, it makes sense to keep employees on staff, even if you might not need them today, so that you don’t need to search for people tomorrow when a job comes in.

Another way to minimize transaction costs is through building trustful relationships. If the stakeholders within ecosystems that you operate trust you, you gain greater access to information and decrease the amount of resources you need to spend on enforcing formal and informal norms. In fact, a study from Accenture Strategy recently found that building trust with stakeholders is increasingly becoming a competitive advantage.

In The Good Jobs Strategy MIT’s Zeynep Ton found that investing more in well-trained employees can actually lower costs and drive sales in the low-cost retail industry. While the sector is often thought of as highly transactional, her research indicates that a dedicated and skilled workforce results in less turnover, better customer service and greater efficiency.

For example, when the recession hit in 2008, Mercadona, Spain’s leading discount retailer, needed to cut costs. But rather than cutting wages or reducing staff, it asked its employees to contribute ideas. The result was that it managed to reduce prices by 10% and increased its market share from 15% in 2008 to 20% in 2012.

In other cases, competitors collaborate to improve their industrial ecosystems for customers. So it is should not be surprising that firms are increasingly investing in structures that are focused on ecosystems, such as Internet of Things Consortium, Partnership on AI and the Manufacturing Institutes. Again, power in an ecosystem resides at the center, not at the top, so to compete you have to connect.

Clearly, it could be argued that by investing in these partnerships, business are increasing shareholder value. However, to do so would be to essentially argue that investing in stakeholder ecosystems and pursuing shareholder value are equivalent, which reduces the debate to one of semantics rather than substance.

Manage For Mission, Not For Metrics

Perhaps one of the most interesting lines in the Business Roundtable statement was the assertion that “each of our individual companies serves its own corporate purpose,” because it acknowledges that the notion of purpose can’t be reduced to a single concept or metric.

Historically, the lines between industries were fairly clear-cut. Ford competed with GM and Chrysler. Later, foreign competition became more important, but the basic logic of the industry remained fairly stable: you produced cars and sold them to the public through a network of dealers.

Today, however, industry lines have blurred considerably. A company like Amazon competes with Walmart in retail, Microsoft, IBM and Google in cloud computing, and Netflix and Warner Media in entertainment. The company itself is much more than simply a bundle of operations competing in different value chains, but a platform for accessing a variety of ecosystems of talent, technology and information.

In much the same way, automobile manufacturers are making investments to transform themselves into mobility companies. To do so, they are building ecosystems made up of technology giants, startups and others. They are not seeking to “maximize bargaining power,” but rather to prepare for a future that hasn’t taken shape yet.

That’s why today, business leaders need to manage for mission, not for metrics. Building trustful relationships among a diverse set of stakeholders may not be as simple or as clear cut as “maximizing shareholder value,” but it’s increasing what profit-seeking businesses need to do to compete.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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What I Learned Solving a Business Crisis

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What I Learned Solving a Business Crisis

GUEST POST from Greg Satell

By 2006 we knew we had a serious problem. Our company’s onetime flagship product, called Afisha, was in a steady decline and it was becoming all too clear that something had to be done. What had once been a market leader that generated huge profits, which fueled the growth of our company had slowly, but surely, lost its market position.

It was clear that the business was in crisis, but nobody was exactly sure what to do about it. Operationally, nothing had really changed. We still believed in our product and our people. Nevertheless, the marketplace had evolved and our business model, which once had seemed bulletproof, was no longer viable.

We didn’t know it at the time, but Afisha’s brightest days were still ahead. We were able to reimagine the business model, strengthen the brand and return to profitability. What we learned is that solving a crisis is not a straightforward linear process, but a journey of discovery. You never know what you’ll find so you need to be willing to experiment.

Acknowledging The Problem

As I explained in Mapping Innovation, when Afisha came out in 2000, it was an immediate hit. At its core, it was simply a guide to restaurants, nightlife and other entertainment, somewhat similar to Timeout. Its restaurant, music and movie columnists quickly became tastemakers in Kyiv, while its sex advice column, achieved a cult-level status. Ad dollars soon came rolling in

In 2006, all of those elements that had made Afisha successful were still in place, but the business environment had changed significantly. The ad market, which had been worth less than $100 million dollars in 2000, was now quickly approaching a billion dollars. Strong multinational publishers like Hearst, Hachette and Rodale had begun investing heavily into Ukrainian versions of top international titles like Cosmopolitan, Elle and Men’s Health.

What we had to accept was that Afisha, although still popular with readers, was no longer a dominant brand. At the same time, the free distribution model which it had once depended on to quickly achieve wide readership was now seen as a liability among advertisers. That diminished our ability to command top ad rates while, at the same time, the booming media market sent our editorial costs through the roof.

None of this happened all at once, so it was easy to believe that Afisha was just going through a temporary downturn. It was only when we were able to acknowledge that our once-successful model had become fundamentally broken that we were able to start moving forward.

Assembling A Broad-Based Team

Once we had acknowledged the problem we assembled a meeting to come up with a strategy to move forward. This included the publisher and editor-in-chief of Afisha, several of the key staff, our company founder, me (as CEO) as well as several company leaders outside of Afisha who had specific knowledge and skills and who were widely respected.

The composition of the meeting was important. Clearly, the Afisha team had to be deeply involved in the process. Having the company founder and me there made it clear that the business had the full backing of the executive leadership. However, in many ways, it was those outside the core Afisha team who had critical impacts.

For the Afisha team and the executive leadership, the business model was so familiar it seemed almost like second-hand. Bringing in other leaders from around the company helped us look at the business in new ways. They asked questions that challenged us, made observations that we hadn’t seen and suggested things that wouldn’t have occurred to us.

Identifying Issues And Developing Options

As the working group met and got down to business, we began to identify problems. First, as noted above, the competitive landscape had shifted dramatically and, although Afisha remained a beloved brand, international titles had taken away significant market share. Second, the free distribution model was no longer financially viable.

As we discussed options, we were able to quickly build consensus on two actions. We would redesign the magazine and the website to beef up the editorial content and better compete with the international titles. We would also look for partners to license Afisha to other cities in Ukraine and create a more national brand.

We also came up with a third option that was considerably more speculative. For years, we had been giving paid subscribers Afisha cards to receive discounts at local merchants. We thought that we could add value to the card by creating an event calendar that was exclusive to Afisha card holders.

Our reasoning was that if we could increase subscribers through upgrading the Afisha card, we could reduce our reliance on free distribution and improve the economics of the business. It seemed like a longshot, but it was also low risk. All we had to do was sign up some partners for events and publish an event calendar in the magazine and on the website.

Finding The Unexpected

The editorial and licensing strategies, which seemed like no brainers, were, at best, mildly successful. Readers seemed to like the new design and expanded editorial content, but then again they liked the old Afisha too. We were able to set up licenses for five major Ukrainian cities, giving up close to national coverage, but the licensees struggled to earn a profit.

The Afisha card strategy, on the other hand, was an unexpected hit. We had hoped to be able to do one event a week, but were soon so deluged with partners that we had to limit events to one per day. From happy hours and shopping nights to club openings and movie festivals, it seemed like everybody wanted to work with us.

Before we knew it, we were able to upgrade events from a promotional activity to a seriously profitable business. We organized a nationwide Frisbee contest for a beer launch, a French movie festival for an upscale coffee brand and organized party trips with sponsors. To our amazement, the business just grew and grew.

What we learned from the experience is that you can’t plan your way out of a crisis. If we were able to plan effectively, we wouldn’t have been in the crisis in the first place. Our success wasn’t the product of our own brilliance, but our willingness to experiment. That’s how we came across the “happy accident” that led to the events business.

The truth is that it takes some bad luck to get into a crisis and it takes some good luck to get out of one. Sound management can help stem the bleeding, but if you are ever going to rebuild a successful business, you have to experiment and allow for the unexpected.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Reasons Change Management Frequently Fails

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Reasons Change Management Frequently Fails

GUEST POST from Greg Satell

In 1983, McKinsey consultant Julien Phillips published a paper in the journal Human Resource Management that described an “adoption penalty” for firms that didn’t adapt to changes in the marketplace quickly enough. His ideas became McKinsey’s first change management model that it sold to clients.

So it is notable, to say the least, that in 2015, more than 35 years later, McKinsey found that only 26% of organizational transformations succeed. It’s not hard to see why. While traditional change management models offer sensible frameworks for fairly obvious changes, truly transformational efforts almost always encounter fierce resistance.

That’s an important distinction that leads to a significant difference. As I found when researching my book, Cascades, successful transformations identify resistance from the start and effectively plan to overcome opposition. Clearly, today, when change is so often a matter of survival, traditional change management models are no longer enough.

Preparing For Resistance

The change management industry was developed to solve a particular and discrete problem. While there were clear and coherent models for other critical business functions, such as marketing and finance, there was a relative dearth of models to help drive change. Phillips’ model and those that came after sought to fill that gap.

Yet as the McKinsey data clearly shows, those models have not been widely successful and it’s not hard to see why. Much as any competitive strategy that doesn’t anticipate the response from competitors is doomed to failure, any transformation strategy that doesn’t take into account those who oppose change is unlikely to succeed.

In my research, however, I found that when resistance is anticipated and accounted for, transformational efforts can achieve astounding results. At Wyeth Pharmaceuticals, the team implemented lean manufacturing techniques across 17,000 employees and cut costs by 25%. At Experian, CIO Barry Libenson shifted its entire technological infrastructure to the cloud and improved profitability across the entire company.

What made the difference is that in both cases, those leading the transformation didn’t assume that the changes would be embraced. In fact, just the opposite. They expected resistance and built a plan to overcome it.

Mapping The Terrain

Traditional change management models start with steps that encourage communicating the need for change and building a sense of urgency. Yet that can often backfire. While communication efforts can and often do excite many about the prospect for transformation, they also alert the opposition to step up their efforts to undermine change.

So the first step is to map the terrain upon which the battle for change will be fought (and make no mistake, any significant transformation effort is always a battle). There are two tools, borrowed from nonviolent political movements, that can help you do this: The Spectrum of Allies and the Pillars of Support. Both have been battle tested for decades.

The Spectrum of Allies, helps you identify which people are active or passive supporters of the change you want to bring about, which are neutral and which actively or passively oppose it. Once you are able to identify these groups, you can start mobilizing the most enthusiastic supporters to start influencing the other groups to shift their opinions. You probably won’t ever convince the active opposition, but you can isolate and neutralize them.

The Pillars of Support identifies stakeholder groups that can help bring change about. Some of these may be internal stakeholders, such as business units or functional groups within an organization. However, some of the most important stakeholders are often external, such as customer groups, industry associations, regulators and so on.

At this point, you are still planning, rather than implementing change. Most of all, you are listening and remain respectful of others who don’t hold the same views you do. The information you gather in these early stages will be critical for overcoming resistance later on.

The Myth of A Quick Win

One of the key tenets of change management is the need to achieve some quick, short term wins to help build momentum. The truth is that these types of objectives are often not meaningful to many, if not most, key stakeholders. In fact, they can often signal to those skeptical of change that the initiative is not serious.

In my research, I found that every successful transformation I studied identified a keystone change which had a clear and tangible goal, involved multiple stakeholders and paved the way for greater change down the road. Because these require the involvement of multiple stakeholders, they are never quick or easy.

For example, in the Wyeth transformation noted above, the keystone change was to reengineer factory changeovers, a difficult and complex task. In Experian’s shift to cloud technology, the keystone change was to build internal API’s. During Lou Gerstner’s historic turnaround at IBM in the 90s, he sought to shift the company from a “proprietary stack of technologies” to its “customers’ stack of business processes.”

In each case, key constituencies in the Spectrum of Allies were mobilized to influence key institutional stakeholders in the Pillars of Support. That takes time, patience and no small amount of effort. In some cases, it took a few tries to identify a keystone change that could succeed.

Every Revolution Inspires Its Own Counter-Revolution

Many change management efforts start with a large kickoff, complete with a vigorous communication campaign designed to create a sense of urgency and rally the troops. What’s often overlooked is that these efforts often alert those who are opposed to change that they need to begin undermining change efforts before they gain momentum.

As the change efforts gain momentum, these undermining efforts may quiet somewhat, but they very rarely disappear, even after the goals of the transformation have already been achieved. For example, at Blockbuster Video, initial efforts to address the disruptive threat posed by Netflix were successful, but that strategy was quickly reversed when a new CEO came aboard.

That’s why it’s crucial that you set out from the beginning to survive victory and you do that by rooting your efforts not in specific goals or objectives, but in common values. As Irving Wladawsky-Berger, a key player in IBM’s historic turnaround, told me, “Because the transformation was about values first and technology second, we were able to continue to embrace those values as the technology and marketplace continued to evolve.”

Perhaps most of all, you need to remember that there’s a reason that the vast majority of transformational efforts fail: Change is hard and it can’t be easily managed. Yet history has shown that it can be achieved, even under the worst conditions and against the greatest odds, if you learn to anticipate and overcome those who would seek to undermine it.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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The End of the Digital Revolution

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Here’s What You Need to Know

The End of the Digital Revolution

GUEST POST from Greg Satell

The history of digital technology has largely been one of denial followed by disruption. First came the concept of the productivity paradox, which noted the limited economic impact of digital technology. When e-commerce appeared, many doubted that it could ever compete with physical retail. Similar doubts were voiced about digital media.

Today, it’s hard to find anyone who doesn’t believe in the power of digital technology. Whole industries have been disrupted. New applications driven by cloud computing, artificial intelligence and blockchain promise even greater advancement to come. Every business needs to race to adopt them in order to compete for the future.

Ironically, amid all this transformation the digital revolution itself is ending. Over the next decade, new computing architectures will move to the fore and advancements in areas like synthetic biology and materials science will reshape entire fields, such as healthcare, energy and manufacturing. Simply waiting to adapt won’t be enough. The time to prepare is now.

1. Drive Digital Transformation

As I explained in Mapping Innovation, innovation is never a single event, but a process of discovery, engineering and transformation. Clearly, with respect to digital technology, we are deep into the transformation phase. So the first part of any post-digital strategy is to accelerate digital transformation efforts in order to improve your competitive position.

One company that’s done this very well is Walmart. As an old-line incumbent in the physical retail industry, it appeared to be ripe for disruption as Amazon reshaped how customers purchased basic items. Why drive out to a Walmart store for a package of toothpaste when you can just click a few buttons on your phone?

Yet rather than ceding the market to Amazon, Walmart has invested heavily in digital technology and has achieved considerable success. It wasn’t any one particular tactic or strategy made the difference, but rather the acknowledgment that every single process needed to be reinvented for the digital age. For example, the company is using virtual reality to revolutionize how it does in-store training.

Perhaps most of all, leaders need to understand that digital transformation is human transformation. There is no shortage of capable vendors that can implement technology for you. What’s key, however, is to shift your culture, processes and business model to leverage digital capabilities.

2. Explore Post-Digital Technologies

While digital transformation is accelerating, advancement in the underlying technology is slowing down. Moore’s law, the consistent doubling of computer chip performance over the last 50 years, is nearing its theoretical limits. It has already slowed down considerably and will soon stop altogether. Yet there are non-digital technologies under development that will be far more powerful than anything we’ve ever seen before.

Consider Intel, which sees its future in what it calls heterogeneous computing combining traditional digital chips with non-digital architectures, such as quantum and neuromorphic. It announced a couple of years ago its Pohoiki Beach neuromorphic system that processes information up to 1,000 times faster and 10,000 more efficiently than traditional chips for certain tasks.

IBM has created a network to develop quantum computing technology, which includes research labs, startups and companies that seek to be early adopters of the technology. Like neuromorphic computing, quantum systems have the potential to be thousands, if not millions, of times more powerful than today’s technology.

The problem with these post-digital architectures is that no one really knows how they are going to work. They operate on a very different logic than traditional computers, will require new programming languages and algorithmic strategies. It’s important to start exploring these technologies now or you could find yourself years behind the curve.

3. Focus on Atoms, Not Bits

The digital revolution created a virtual world. My generation was the first to grow up with video games and our parents worried that we were becoming detached from reality. Then computers entered offices and Dan Bricklin created Visicalc, the first spreadsheet program. Eventually smartphones and social media appeared and we began spending almost as much time in the virtual world as we did in the physical one.

Essentially, what we created was a simulation economy. We could experiment with business models in our computers, find flaws and fix them before they became real. Computer-aided design (CAD) software allowed us to design products in bits before we got down to the hard work of shaping atoms. Because it’s much cheaper to fail in the virtual world than the physical one, this made our economy much more efficient.

Yet the next great transformation will be from bits to atoms. Digital technology is creating revolutions in things like genomics and materials science. Artificial intelligence and cloud computing are reshaping fields like manufacturing and agriculture. Quantum and neuromorphic computing will accelerate these trends.

Much like those new computing architectures, the shift from bits to atoms will create challenges. Applying the simulation economy to the world of atoms will require new skills and we will need people with those skills to move from offices in urban areas to factory floors and fields. They will also need to learn to collaborate effectively with people in those industries.

4. Transformation is Always a Journey, Never a Destination

The 20th century was punctuated by two waves of disruption. The first, driven by electricity and internal combustion, transformed almost every facet of daily life and kicked off a 50-year boom in productivity. The second, driven by the microbe, the atom and the bit, transformed fields such as agriculture, healthcare and management.

Each of these technologies followed the pattern of discovery, engineering and transformation. The discovery phase takes place mostly out of sight, with researchers working quietly in anonymous labs. The engineering phase is riddled with errors, as firms struggle to shape abstract concepts into real products. A nascent technology is easy to ignore, because its impact hasn’t been felt yet.

The truth is that disruption doesn’t begin with inventions, but when an ecosystem emerges to support them. That’s when the transformation phase begins and takes us by surprise, because transformation never plays out like we think it will. The future will always, to a certain extent, unpredictable for the simple reason that it hasn’t happened yet.

Today, we’re on the brink of a new era of innovation that will be driven by new computing architectures, genomics, materials science and artificial intelligence. That’s why we need to design our organizations for transformation by shifting from vertical hierarchies to horizontal networks.

Most of all, we need to shift our mindsets from seeing transformation as set of discreet objectives to a continuous journey of discovery. Digital technology has only been one phase of that journey. The most exciting things are still yet to come.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Is China Our New Sputnik Moment?

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Is China Our New Sputnik Moment?

GUEST POST from Greg Satell

When the Soviets launched Sputnik, the first space satellite, into orbit in 1957, it was a wake-up call for America. Over the next year, President Eisenhower would sign the National Defense Education Act to spur science education, increase funding for research and establish NASA and DARPA to spur innovation.

A few years ago, a report by the Council on Foreign Relations (CFR) argued that we are at a similar point today, but with China. While we have been steadily decreasing federal investment in R&D over the past few decades, our Asian rival has been ramping up and now threatens our leadership in key technologies such as AI, genomics and quantum information technology.

Clearly, we need to increase our commitment to science and innovation and that means increasing financial investment. However, what the report makes clear is that money alone won’t solve the problem. We are, in several important ways, actually undermining our ability to innovate, now and in the future. We need to renew our culture of innovation in America.

Educating And Attracting Talent

The foundation of an innovation economy is education, especially in STEM subjects. Historically, America has been the world’s best educated workforce, but more recently we’ve fallen to fifth among OECD countries for post-secondary education. That’s alarming and something we will certainly need to reverse if we are to compete effectively.

Our educational descent can be attributed to three major causes. First, the rest of the world has become more educated, so the competition has become stiffer. Second, is financing. Tuition has nearly tripled in the last decade and student debt has become so onerous that it now takes about 20 years to pay off four years for college. Third, we need to work harder to attract talented people to the United States.

The CFR report recommends developing a “21st century National Defense Education Act” to create scholarships in STEM areas and making it easier for foreign students to get Green Cards when they graduate from our universities. It also points out that we need to work harder to attract foreign talent, especially in high impact areas like AI, genomics and quantum computing.

Unfortunately, we seem to be going the other way. The number of international students to American universities is declining. Policies like the muslim ban and concerns about gun violence are deterring scientific talent coming here. The denial rate for those on H1-B visas has increased from 4% in 2016 to 18% in the first quarter of 2019.

Throughout our history, it has been our openness to new people and new ideas that has made America exceptional. It’s a legitimate question whether that’s still true.

Building Technology Ecosystems

In the 1980s, the US semiconductor industry was on the ropes. Due to increased competition from low-cost Japanese manufacturers, American market share in the DRAM market fell from 70% to 20%. The situation not only had a significant economic impact, there were also important national security implications.

The federal government responded with two initiatives, the Semiconductor Research Corporation and SEMATECH, both of which were nonprofit consortiums that involved government, academia and industry. By the 1990s. American semiconductor manufacturers were thriving again.

Today, we have similar challenges with rare earth elements, battery technology and many manufacturing areas. The Obama administration responded by building similar consortiums to those that were established for semiconductors: The Critical Materials Institute for rare earth elements, JCESR for advanced batteries and the 14 separate Manufacturing Institutes.

Yet here again, we seem to be backsliding. The current administration has sought to slash funding for the Manufacturing Extension Partnership that supports small and medium sized producers. An addendum to the CFR report also points out that the administration has pushed for a 30% cut in funding for the national labs, which support much of the advanced science critical to driving American technology forward.

Supporting International Trade and Alliances

Another historical strength of the US economy has been our open approach to trade. The CFR report points out that our role as a “central node in a global network of research and development,” gave us numerous advantages, such as access to foreign talent at R&D centers overseas, investment into US industry and cooperative responses to global challenges.

However, the report warns that “the Trump administration’s indiscriminate use of tariffs against China, as well as partners and allies, will harm U.S. innovative capabilities.” It also faults the Trump administration for pulling out of the Trans-Pacific Partnership trade agreement, which would have bolstered our relationship with Asian partners and increased our leverage over China.

The tariffs undermine American industry in two ways. First, because many of the tariffs are on intermediate goods which US firms use to make products for export, we’re undermining our own competitive position, especially in manufacturing. Second, because trade partners such as Canada and the EU have retaliated against our tariffs, our position is weakened further.

Clearly, we compete in an ecosystem driven world in which power does not come from the top, but emanates from the center. Traditionally, America has positioned itself at the center of ecosystems by constantly connecting out. Now that process seems to have reversed itself and we are extremely vulnerable to others, such as China, filling the void.

We Need to Stop Killing Innovation in America

The CFR report, whose task force included such luminaries as Admiral William McRaven, former Google CEO Eric Schmidt and economist Laura Tyson, should set alarm bells ringing. Although the report was focused on national security issues, it pertains to general competitiveness just as well and the picture it paints is fairly bleak.

After World War II, America stood almost alone in the world in terms of production capacity. Through smart policy, we were able to transform that initial advantage into long-term technological superiority. Today, however we have stiff competition in areas ranging from AI to synthetic biology to quantum systems.

At the same time, we seem to be doing everything we can to kill innovation in America. Instead of working to educate and attract the world’s best talent, we’re making it harder for Americans to attain higher education and for top foreign talent to come and work here. Instead of ramping up our science and technology programs, presidential budgets regular recommend cutting them. Instead of pulling our allies closer, we are pushing them away.

To be clear, America is still at the forefront of science and technology, vying for leadership in every conceivable area. However, as global competition heats up and we need to be redoubling our efforts, we seem to be doing just the opposite. The truth is that our prosperity is not a birthright to which we are entitled, but a legacy that must be lived up to.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Questions Are More Powerful Than We Think

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Questions Are More Powerful Than We Think

GUEST POST from Greg Satell

When I was 27, I moved to Warsaw, Poland to work in the nascent media industry that was developing there. I had experience working in media in New York, so I was excited to share what I’d learned and was confident that my knowledge and expertise would be well received.

It wasn’t. Whenever I began to explain how a media business was supposed to work, people would ask me, “why?” That forced me to think about it and, when I did, I began to realize that many of the principles I had taken for granted were merely conventions. Things didn’t need to work that way and could be done differently.

That’s when I first learned the power of a question. As Warren Berger explains in A More Beautiful Question, while answers tend to close a discussion, questions help us open new doors and can lead to genuine breakthroughs. Yet not all questions are equal. Asking good questions is a skill that takes practice and effort to learn to do well. Here’s where to start.

Why?

When we are young, we ask lots of “why?” questions. Why is the sky blue? Why can’t we fly like birds? Why do I have to go to bed at a certain time? It is through asking why that we learn basic things about the world. Yet as we get older, we tend to think we know things and stop questioning fundamental assumptions.

That’s where I was when I first arrived in Poland. I had gone through extensive training and knew things. I was proud of the knowledge that I had gained and didn’t question whether those things were necessarily true. My new Polish colleagues, on the other hand, were emerging from 50 years of communism and so were unencumbered with that illusion of knowledge.

In researching my book, Mapping Innovation, I spoke to dozens of world class innovators, and I was amazed how often breakthroughs started with a “Why?” question. For example, Jim Allison, a prominent immunologist who had lost family members to cancer, asked himself why our immune system doesn’t attack tumors.

“Why?” questions can be frustrating, because there are rarely easy answers to them, and they almost always lead to more questions. There’s even a technique called the 5 Whys that is designed to uncover root problems. Nevertheless, if you want to get beyond fundamental assumptions, you need to start with asking “why?”

What If?

While asking “why?” can help alert us to new opportunities, asking “What if” can lead us into new directions and open new doors. Einstein was famous for these types of thought experiments. Asking “What if I would ride on a bolt of lightning?” led to his theory of special relativity and asking, “What if I was riding on an elevator in space?” led to general relativity.

Often, we can use “What if?” questions to propose answers to our “Why?” questions. For example, after Jim Allison asked himself why our immune system doesn’t attack tumors, he followed it up by asking, “what if our immune system actually does attack tumors, but shuts off too soon?”

That took him in a completely new direction. He began to experiment with regulating the immune response and achieved amazing results. Eventually, he would win the Nobel Prize for his role in establishing the new field of cancer immunotherapy. It all started because he was able to imagine new possibilities with a “What if?” question.

Another way we can use “What If? questions is to remove or add constraints. For example, we can ask ourselves, “What if we didn’t have to worry about costs?” or “What if we could only charge our customers half of what we’re charging now?” Asking “What if? Questions can often alert us to possibilities what we weren’t aware of.

How?

Asking “Why?” and “What if? questions can open up new opportunities, eventually we need to answer the “How?” question. “How?” questions can be especially difficult because answering them often involves knowledge, resources and capabilities that we do not possess. That’s what makes “How?” questions fundamentally more collaborative.

For example, as a research executive at Eli Lilly, Alph Bingham became interested in why some chemistry problems never got solved. One observation he made was that when he was in graduate school, if there were 20 people in a class, they would often come up with 20 different approaches to a problem, but in industry scientists generally worked alone.

Long an admirer of Linux, he was fascinated with the way thousands of volunteers were able to create and advance complex software that could compete with the best proprietary products. So he began to think “What if we could do something like Linux, but with a bounty?” He thought that if he got more people working on the “How?” question, he might be able to solve more problems.

The fruit of his efforts, called Innocentive went live in June 2001 with 21 problems, many of which the company had been working on for years. Although the bounties were small in the context of the pharmaceutical industry — $20,000 to $25,000 — by the end of the year a third of them were solved. It was an astounding success.

It soon became clear that more challenges on the site would attract more solvers, so they started recruiting other companies to the platform. When results improved, they even began inviting competitors to post challenges as well. Today, Innocentive has over 100,000 solvers that work out hundreds of problems so tough that even the smartest companies can’t crack them.

Building A Culture Of Inquiry

When I first arrived in Poland, I was prepared to give all the answers, because that’s what I was trained for. The media business in New York had been around for a long time and everything was supposedly worked out. Follow the model, I was told, and you’ll be successful. That’s why the questions my new colleagues posed took me by surprise.

Yet once I started asking questions myself, I began to see opportunities everywhere. As I travelled and worked in different countries, I found that everywhere I went, people ran nearly identical businesses in completely different ways and most were convinced that their way was the “right” way. Most saw little utility in questioning how things were done.

That’s why most people can’t innovate. In fact, while researching Mapping Innovation, I found that the best innovators were not the ones who were the smartest or even the ones who worked the hardest, but those who continually looked for new problems to solve. They were always asking new questions, that’s how they found new things.

The truth is that to drive innovation, we need to build a culture of inquiry. We need to ask “why” things are done the way they are done, “what if” we took a different path and “how” things can be done differently. If you don’t explore, you won’t discover and if you don’t discover, you won’t invent. Once you stop inventing, you will be disrupted.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Humans, Not Technology, Drive Business Success

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Humans, Not Technology, Drive Business Success

GUEST POST from Greg Satell

Silicon Valley is often known as a cut-throat, technocratic place where the efficiency of algorithms often define success. Competition is ferocious and the pace of disruption and change can be dizzying. It’s not the type of environment where soft skills are valued particularly highly or even at all.

So, it’s somewhat ironic that Bill Campbell became a Silicon Valley legend by giving hugs and professing love to those he worked with. As coach to executives ranging from Steve Jobs to the entire Google executive team, Campbell preached and practiced a very personal style of business.

Yet while I was reading Trillion Dollar Coach in which former Google executives explain Campbell’s leadership principles, it became clear why he had such an impact. Even in Silicon Valley, technology will only take you so far. The success of a business ultimately depends on the success of the people in it. To compete over the long haul, that’s where you need to focus.

The Efficiency Paradox

In 1911, Frederick Winslow Taylor published The Principles of Scientific Management, based on his experience as a manager in a steel factory. It took aim at traditional management methods and suggested a more disciplined approach. Rather than have workers pursue tasks in their own manner, he sought to find “the one best way” and train accordingly.

Taylor wrote, “It is only through enforced standardization of methods, enforced adoption of the best implements and working conditions, and enforced cooperation that this faster work can be assured. And the duty of enforcing the adoption of standards and enforcing this cooperation rests with management alone.”

Before long, Taylor’s ideas became gospel, spawning offshoots such as scientific marketing, financial engineering and the Six Sigma movement. It was no longer enough to simply work hard, you had to measure, analyze and optimize everything. Over the years these ideas have become so central to business thinking that they are rarely questioned.

Yet management guru Henry Mintzberg has pointed out how a “by-the-numbers” depersonalized approach can often backfire. “Managing without soul has become an epidemic in society. Many managers these days seem to specialize in killing cultures, at the expense of human engagement.”

The evidence would seem to back him up. One study found that of 58 large companies that have announced Six Sigma programs, 91 percent trailed the S&P 500 in stock performance. That, in essence, is the efficiency paradox. When you manage only what you can measure, you end up ignoring key factors to success.

How Generosity Drives Innovation

While researching my book, Mapping Innovation, I interviewed dozens of top innovators. Some were world class scientists and engineers. Others were high level executives at large corporations. Still others were highly successful entrepreneurs. Overall, it was a pretty intimidating group.

So, I was surprised to find that, with few exceptions, they were some of the kindest and most generous people I have ever met. The behavior was so consistent that I felt that it couldn’t be an accident. So I began to research the matter further and found that when it comes to innovation, generosity really is a competitive advantage.

For example, one study of star engineers at Bell Labs found that the best performers were not the ones with the best academic credentials, but those with the best professional networks. A similar study of the design firm IDEO found that great innovators essentially act as brokers able to access a diverse array of useful sources.

A third study helps explain why knowledge brokering is so important. Analyzing 17.9 million papers, the researchers found that the most highly cited work tended to be largely rooted within a traditional field, but with just a smidgen of insight taken from some unconventional place. Breakthrough creativity occurs at the nexus of conventionality and novelty.

The truth is that the more you share with others, the more they’ll be willing to share with you and that makes it much more likely you’ll come across that random piece of information or insight that will allow you to crack a really tough problem.

People As Profit Centers

For many, the idea that innovation is a human centered activity is intuitively obvious. So it makes sense that the high-tech companies that Bill Campbell was involved in would work hard to create environments to attract the best and the brightest people. However, most businesses have much lower margins and have to keep a close eye on the bottom line.

Yet here too there is significant evidence that a human-focused approach to management can yield better results. In The Good Jobs Strategy MIT’s Zeynep Ton found that investing more in well-trained employees can actually lower costs and drive sales. A dedicated and skilled workforce results in less turnover, better customer service and greater efficiency.

Its competitors maintained the traditional mindset. They reduced cut wages and employee hours, which saved them some money, but customers found poorly maintained stores with few people to help them, which damaged their brand long-term. The cost savings Mercadona’s employees identified, on the other hand, in many cases improved service and productivity and these gains persisted long after the crisis was over.

Management Beyond Metrics

The truth is that it’s easy to talk about putting people first, but much harder to do it in practice. Research suggests that once a group goes much beyond 200 people social relationships break down, so once a business gets beyond that point, it becomes natural to depersonalize management and focus on metrics.

Yet the best managers understand that it’s the people that drive the numbers. As legendary IBM CEO Lou Gerstner once put it, “Culture isn’t just one aspect of the game… It is the game. What does the culture reward and punish – individual achievement or team play, risk taking or consensus building?”

In other words, culture is about values. The innovators I interviewed for my book valued solving problems, so were enthusiastic about sharing their knowledge and expertise with others, who happily reciprocated. Mercadona valued its people, so when it asked them to find ways to save money during the financial crisis, they did so enthusiastically.

That’s why today, three years after his death, Bill Campbell remains a revered figure in Silicon Valley, because he valued people so highly and helped them learn to value each other. Management is not an algorithm. It is, in the final analysis, an intensely human activity and to do it well, you need to put people first.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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Building a True Revolution

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Building a True Revolution

GUEST POST from Greg Satell

“Revolution” is a term that gets thrown around a lot. There was an Industrial Revolution powered by steam and then another one powered by oil and electricity. The Green Revolution transformed the way we fed ourselves. Many political revolutions have overthrown powerful regimes and the digital revolution changed the way we work with information.

My friend Srdja Popović, who helped lead the Bulldozer Revolution that overthrew Slobodan Milošević in Serbia, told me that the goal of a revolution should be to become mainstream, to be mundane and ordinary. If you are successful it should be difficult to explain what was won because the previous order seems so unbelievable.

The problem with most would-be revolutionaries is that they seek exactly the opposite. All too often, they seek attention, excitement and crowds of admiring fans. Yet all that noise is likely to create enemies just as fast as it makes friends. True revolutions aren’t won in the streets or on the airwaves, but through smart strategies that transform basic beliefs.

A Shift in Paradigms

The idea of a paradigm shift was first established by Thomas Kuhn in his book The Structure of Scientific Revolutions, which explained how scientific breakthroughs come to the fore. It starts with an established model, the kind we learn in school or during initial training for a career. Eventually, those models are shown to be untenable, and a period of instability ensues until a new paradigm can be created and adopted.

While Kuhn developed his theory to describe advancements in science, it has long been clear that it applies more broadly. For example, in my experiences in post-communist countries, the comfort of the broken, but relatively stable, system seemed to many to be preferable to the instability of change.

In the corporate world, models are not only mindsets, but are embedded in systems, processes and practices, which makes them especially pervasive. To bring change about, you need to disrupt basic operations and that comes with costs. Customers, partners and suppliers depend on the stability of how an organization does business.

So, the first step to driving change about is to create a new vision that can credibly replace the existing model without causing so much chaos that the perceived costs outweigh the benefits. As I explain in my book, Cascades, successful revolutionaries are more than just warriors, they are also educators that are able to mobilize others through the power of their vision.

Mobilizing Small Groups, Loosely Connected

We tend to think of revolutions as mass actions, such as protestors storming the streets or excited customers lining up outside an Apple store, yet they don’t start out that way. Revolutions begin with small groups, loosely connected, but united by a shared purpose.

For example, groups like the Cambridge Apostles and the Bloomsbury Group helped launch intellectual revolutions in early 20th century Cambridge. The Homebrew Computer Club helped bring about the digital revolution. Groups like Otpor, Kmara and Pora formed the grassroots of the Color Revolutions in the early 2000s.

What made these groups effective was their ability to connect and bring others in. For example the Homebrew Computer Club would hold convene informal gatherings at a bar after the more formal meetings of the club. In the Serbian revolution that overthrew Slobodan Milošević, Otpor used humor and street pranks to attract people to their cause.

Revolutions are driven by networks and power in networks emanates from the center. You move to the center by connecting out. That’s how you mobilize and gain influence. What you do with that power and influence, however, will determine if your revolution will succeed.

Influencing Institutional Change

Mobilization can be a powerful force but does not in itself create a revolution. To bring change about, you need to be able to influence institutions that have the power to drive change. For example, Martin Luther King Jr. didn’t write a single piece of legislation or decide a single court case but was able to influence the legislative and legal systems through his activism.

In his efforts to reform the Pentagon, Colonel John Boyd went outside the chain of command to brief congressional staffers and a small circle of journalists. As he gained support from Congress and the media, he was able to put pressure on the Generals and create a reform movement within the US military.

Now compare that to the Occupy Movement, which mobilized activists in 951 cities across 82 countries. However, they wanted to have nothing to do with institutions and actually refused opportunities to influence them. In fact, when Congressman John Lewis, himself a civil rights leader, showed up at a rally, they turned him away. Is it any wonder they never achieved any tangible change?

Make no mistake. If you truly want to bring change about, you have to mobilize somebody to influence something. Merely sending people out in the streets with signs won’t amount to much.

Preparing for the Counterrevolution

In his 2004 State of the Union Address, President Bush delivered a full-throated condemnation of same-sex marriage. Incensed, San Francisco Mayor Gavin Newsom decided to unilaterally begin performing weddings for gay and lesbian couples at City Hall, in what was termed the Winter of Love. 4,027 couples were married before their nuptials were annulled by the California Supreme Court a month later.

The backlash was fierce and led Proposition 8, an amendment to the California Constitution that prohibited gay marriage, on the ballot. It was passed with a narrow majority of 52% of the electorate and was so harsh that it not only galvanized LGBT activists, but also began to sway public opinion.

The tide began to change when LBGT activists, began to appeal to values they shared with the general public, such as the right to live in committed relationships and raise happy, healthy families. In a Newsweek op-ed, Ted Olson, a conservative Republican lawyer who had previous served as President Bush’s Solicitor General, argued that legalizing same-sex marriage wasn’t strictly a gay issue, but would be “a recognition of basic American principles.”

Today, same sex marriage has become, to paraphrase my friend Srdja, mundane. It has become a part of everyday life that is widely accepted as the normal course of things. That’s when you know a revolution is complete. Not when the fervor of zealots drive people out into the streets, but when those in the mainstream begin to accept it as the normal course of business.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

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Artificial Intelligence is Forcing Us to Answer Some Very Human Questions

GUEST POST from Greg Satell

Chris Dixon, who invested early in companies ranging from Warby Parker to Kickstarter, once wrote that the next big thing always starts out looking like a toy. That’s certainly true of artificial intelligence, which started out playing games like chess, go and playing humans on the game show Jeopardy!

Yet today, AI has become so pervasive we often don’t even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks.

As the technology becomes more powerful, it’s also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions. When machines start doing things traditionally considered to be uniquely human, we need to reevaluate what it means to be human and what is to be a machine.

What Is Original and Creative?

There is an old literary concept called the Infinite Monkey Theorem. The basic idea is that if you had an infinite amount of monkeys pecking away an infinite amount of keyboards, they would, in time, produce the complete works of Shakespeare or Tolstoy or any other literary masterpiece.

Today, our technology is powerful enough to simulate infinite monkeys and produce something that looks a whole lot like original work. Music scholar and composer David Cope has been able to create algorithms that produce original works of music which are so good that even experts can’t tell the difference. Companies like Narrative Science are able to produce coherent documents from raw data this way.

So there’s an interesting philosophical discussion to be had about what what qualifies as true creation and what’s merely curation. If an algorithm produces War and Peace randomly, does it retain the same meaning? Or is the intent of the author a crucial component of what creativity is about? Reasonable people can disagree.

However, as AI technology becomes more common and pervasive, some very practical issues are arising. For example, Amazon’s Audible unit has created a new captions feature for audio books. Publishers sued, saying it’s a violation of copyright, but Amazon claims that because the captions are created with artificial intelligence, it is essentially a new work.

When machines can create does that qualify as an original, creative intent? Under what circumstances can a work be considered new and original? We are going to have to decide.

Bias And Transparency

We generally accept that humans have biases. In fact, Wikipedia lists over 100 documented biases that affect our judgments. Marketers and salespeople try to exploit these biases to influence our decisions. At the same time, professional training is supposed to mitigate them. To make good decisions, we need to conquer our tendency for bias.

Yet however much we strive to minimize bias, we cannot eliminate it, which is why transparency is so crucial for any system to work. When a CEO is hired to run a corporation, for example, he or she can’t just make decisions willy nilly, but is held accountable to a board of directors who represent shareholders. Records are kept and audited to ensure transparency.

Machines also have biases which are just as pervasive and difficult to root out. Amazon had to scrap an AI system that analyzed resumes because it was biased against female candidates. Google’s algorithm designed to detect hate speech was found to be racially biased. If two of the most sophisticated firms on the planet are unable to eliminate bias, what hope is there for the rest of us?

So, we need to start asking the same questions of machine-based decisions as we do of human ones. What information was used to make a decision? On what basis was a judgment made? How much oversight should be required and by whom? We all worry about who and what are influencing our children, we need to ask the same questions about our algorithms.

The Problem of Moral Agency

For centuries, philosophers have debated the issue of what constitutes a moral agent, meaning to what extent someone is able to make and be held responsible for moral judgments. For example, we generally do not consider those who are insane to be moral agents. Minors under the age of eighteen are also not fully held responsible for their actions.

Yet sometimes the issue of moral agency isn’t so clear. Consider a moral dilemma known as the trolley problem. Imagine you see a trolley barreling down the tracks that is about to run over five people. The only way to save them is to pull a lever to switch the trolley to a different set of tracks, but if you do one person standing there will be killed. What should you do?

For the most part, the trolley problem has been a subject for freshman philosophy classes and avant-garde cocktail parties, without any real bearing on actual decisions. However, with the rise of technologies like self-driving cars, decisions such as whether to protect the life of a passenger or a pedestrian will need to be explicitly encoded into the systems we create.

On a more basic level, we need to ask who is responsible for a decision an algorithm makes, especially since AI systems are increasingly capable of making judgments humans can’t understand. Who is culpable for an algorithmically driven decision gone bad? By what standard should they be evaluated?

Working Towards Human-Machine Coevolution

Before the industrial revolution, most people earned their living through physical labor. Much like today, tradesman saw mechanization as a threat — and indeed it was. There’s not much work for blacksmiths or loom weavers these days. What wasn’t clear at the time was that industrialization would create a knowledge economy and demand for higher paid cognitive work.

Today, we’re going through a similar shift, but now machines are taking over cognitive tasks. Just as the industrial revolution devalued certain skills and increased the value of others, the age of thinking machines is catalyzing a shift from cognitive skills to social skills. The future will be driven by humans collaborating with other humans to design work for machines that creates value for other humans.

Technology is, as Marshal McLuhan pointed out long ago, an extension of man. We are constantly coevolving with our creations. Value never really disappears, it just shifts to another place. So, when we use technology to automate a particular task, humans must find a way to create value elsewhere, which creates an opportunity to create new technologies.

This is how humans and machines coevolve. The dilemma that confronts us now is that when machines replace tasks that were once thought of as innately human, we must redefine ourselves and that raises thorny questions about our relationship to the moral universe. When men become gods, the only thing that remains to conquer is ourselves.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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The Coming Innovation Slowdown

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The Coming Innovation Slowdown

GUEST POST from Greg Satell

Take a moment to think about what the world must have looked like to J.P. Morgan a century ago, in 1919. He was not only an immensely powerful financier with access to the great industrialists of the day, but also an early adopter of new technologies. One of the first electric generators was installed at his home.

The disruptive technologies of the day, electricity and internal combustion, were already almost 40 years old, but had little measurable economic impact. Life largely went on as it always had. That would quickly change over the next decade when those technologies would drive a 50-year boom in productivity unlike anything the world had ever seen before.

It is very likely that we are at a similar point now. Despite significant advances in technology, productivity growth has been depressed for most of the last 50 years. Over the next ten years, however, we’re likely to see that change as nascent technologies hit their stride and create completely new industries. Here’s what you’ll need to know to compete in the new era.

1. Value Will Shift from Bits to Atoms

Over the past few decades, innovation has become almost synonymous with digital technology. Every 18 months or so, semiconductor manufacturers would bring out a new generation of processors that were twice as powerful as what came before. These, in turn, would allow entrepreneurs to imagine completely new possibilities.

However, while the digital revolution has given us snazzy new gadgets, the impact has been muted. Sure, we have hundreds of TV channels and we’re able to talk to our machines and get coherent answers back, but even at this late stage, information and communication technologies make up only about 6% of GDP in advanced countries.

At first, that sounds improbable. How could so much change produce so little effect? But think about going to a typical household in 1960, before the digital revolution took hold. You would likely see a TV, a phone, household appliances and a car in the garage. Now think of a typical household in 1910, with no electricity or running water. Even simple chores like cooking and cleaning took hours of backbreaking labor.

The truth is that much of our economy is still based on what we eat, wear and live in, which is why it’s important that the nascent technologies of today, such as synthetic biology and materials science, are rooted in the physical world. Over the next generation, we can expect innovation to shift from bits back to atoms.

2. Innovation Will Slow Down

We’ve come to take it for granted that things always accelerate because that’s what has happened for the past 30 years or so. So we’ve learned to deliberate less, to rapidly prototype and iterate and to “move fast and break things” because, during the digital revolution, that’s what you needed to do to compete effectively.

Yet microchips are a very old technology that we’ve come to understand very, very well. When a new generation of chips came off the line, they were faster and better, but worked the same way as earlier versions. That won’t be true with new computing architectures such as quantum and neuromorphic computing. We’ll have to learn how to use them first.

In other cases, such as genomics and artificial intelligence, there are serious ethical issues to consider. Under what conditions is it okay to permanently alter the germ line of a species. Who is accountable for the decisions and algorithm makes? On what basis should those decisions be made? To what extent do they need to be explainable and auditable?

Innovation is a process of discovery, engineering and transformation. At the moment, we find ourselves at the end of one transformational phase and about to enter a new one. It will take a decade or so to understand these new technologies enough to begin to accelerate again. We need to do so carefully. As we have seen over the past few years, when you move fast and break things, you run the risk of breaking something important.

3. Ecosystems Will Drive Technology

Let’s return to J.P. Morgan in 1919 and ask ourselves why electricity and internal combustion had so little impact up to that point. Automobiles and electric lights had been around a long time, but adoption takes time. It takes a while to build roads, to string wires and to train technicians to service new inventions reliably.

As economist Paul David pointed out in his classic paper, The Dynamo and the Computer, it takes time for people to learn how to use new technologies. Habits and routines need to change to take full advantage of new technologies. For example, in factories, the biggest benefit electricity provided was through enabling changes in workflow.

The biggest impacts come from secondary and tertiary technologies, such as home appliances in the case of electricity. Automobiles did more than provide transportation, but enables a shift from corner stores to supermarkets and, eventually, shopping malls. Refrigerated railroad cars revolutionized food distribution. Supply chains were transformed. Radios, and later TV, reshaped entertainment.

Nobody, not even someone like J.P. Morgan could have predicted all that in 1919, because it’s ecosystems, not inventions, that drive transformation and ecosystems are non-linear. We can’t simply extrapolate out from the present and get a clear future of what the future is going to look like.

4. You Need to Start Now

The changes that will take place over the next decade or so are likely to be just as transformative—and possibly even more so—than those that happened in the 1920s and 30s. We are on the brink of a new era of innovation that will see the creation of entirely new industries and business models.

Yet the technologies that will drive the 21st century are still mostly in the discovery and engineering phases, so they’re easy to miss. Once the transformation begins in earnest, however, it will likely be too late to adapt. In areas like genomics, materials science, quantum computing and artificial intelligence, if you get a few years behind, you may never catch up.

So the time to start exploring these new technologies is now and there are ample opportunities to do so. The Manufacturing USA Institutes are driving advancement in areas as diverse as bio-fabrication, additive manufacturing and composite materials. IBM has created its Q Network to help companies get up to speed on quantum computing and the Internet of Things Consortium is doing the same thing in that space.

Make no mistake, 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 eventually, it’s just a matter of time. It’s always better to prepare than to adapt and the time to start doing that is now.

— Article courtesy of the Digital Tonto blog
— Image credit: Pexels

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