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Great firms don’t topple overnight. Industries don’t radically change in an instant. But far too often, it seems like they do. And when that happens, the consequences—for the executives in those firms, for the analysts who recommend them, for the investors who bet on them, and millions of others with a stake in the outcome—are devastating.

Now, the world’s leading expert on disruptive innovations and his team of researchers introduce a groundbreaking model that will enable “outsiders” with no proprietary information to predict how innovations will impact firms and industries—and to make the right decisions while there is still time to make them.

Imagine how different the outcome would have been if Western Union had recognized the telephone’s disruptive potential, rather than dismissing it as a “toy.” Imagine how different the fates of millions of investors would have been if analysts had been able to read the signs that trouble was afoot before Digital Equipment Corporation collapsed. Imagine how it would feel to be in the shoes of the investors who wisely put their money into eBay instead of the hundreds of dotcoms that burst along with the technology bubble.

Clayton M. Christensen, Scott D. Anthony, and Erik A. Roth argue that every industry-shifting disruption is preceded by strong “signals” that suggest that dramatic changes are underway. What’s more, by knowing what signals to look for, it is possible to determine where disruption is heading, which firms will find themselves in competition as a result of the disruption, which competitor is most likely to win, and which choices can change the game.

The Power of Innovation Theory

Seeing What’s Next is based on the proven innovation theories outlined in Christensen’s landmark books The Innovator’s Dilemma, which explained why successful companies are often unseated by usurpers armed with disruptive innovations, and The Innovator’s Solution, which outlined a predictable process would-be innovators can use to successfully create new growth businesses. In this book, Christensen, Anthony, and Roth argue that even those without proprietary information can use these theories to develop powerful insights into how the future will unfold in a given industry and to make wiser choices based on those insights.

Theory in Action

Using in-depth case studies of five industries— education, aviation, semiconductor, health care, and telecommunications—Seeing What’s Next outlines a three-part predictive model that will help readers separate true signals of change from meaningless “noise,” evaluate the likely winners and losers in competitive battles, and predict whether the choices firms make will increase or decrease their chances of success. The book also provides actionable diagnostics and tools and illustrates how these tools enable decision-makers to answer critical questions including:

• Could corporate universities prove to be a threat to leading business schools? What other developments might impact leading universities? What can they do to respond? Could disruption help fix our ailing public school system?

• Does the future of airplane manufacturing belong to companies such as Airbus and Boeing or companies such as Embraer and Bombardier? Why do the major airlines find it so difficult to sustain acceptable profits? Can discount airlines, regional jets or even point-to-point taxis reshape aviation?

• Is Moore’s Law losing its relevance for firms competing in the semiconductor industry? Would that be good news or bad news for Intel? Which startup firms are worth watching and what signs would indicate they are setting themselves up as disruptors?

• Do the theories of innovation apply to health care or is this industry somehow different? How can disruptions turn the “crisis” in health care into good news for consumers? Which consumers should disrupting firms target and with what kinds of products?

• Does “convergence” mean the end of telecommunications companies such as AT&T and Verizon? What impact will Voice over Internet Protocol have? Will 802.11 networks continue to grow and improve? Could seemingly simple technologies such as instant messaging prove transformative? What role could Microsoft have in telecommunications?

• What role does the government or its regulatory bodies play in enhancing or inhibiting innovation in an industry?

• What can a country’s government do to create an environment that favors disruptive innovation? How should companies think about expanding overseas? Are there ways for them to think about innovation overseas in a disruptive manner?

From phones to planes to Ph.D. programs, the tools in Seeing What’s Next provide invaluable insights into the future of many industries. More importantly, they prove a way of thinking that will help anyone with a stake in a firm’s success—investors hoping to make smarter choices to buy or sell shares, analysts hoping to make better recommendations for clients, or executives needing to separate threats from opportunities—develop an intuition for how to use the theories of innovation to predict industry change.

Return to Beginning

A Conversation with Clayton Christensen, Scott Anthony, and Erik Roth on How to See What’s Next

The Innovator’s Dilemma and The Innovator’s Solution were hugely influential in terms of helping managers to understand how innovation really works—and how to both survive and launch disruptions. How does this book take these ideas a step further?

While the previous two books were aimed at managers inside firms who wanted to defend against or attack with a disruption, Seeing What’s Next is written for those who watch industries from the outside, and who must make important decisions based on what they see. It will help executives, analysts, investors, and others who have a stake in a specific industry to evaluate the impact of innovations, the outcomes of competitive battles, and the moves made by individual firms—and to make smarter business decisions, forecasts, and stock recommendations based on those evaluations. The goal here is to dramatically increase the odds of getting things right in an arena where wrong decisions can be devastating.

Many people who make their living providing advice about the future express deep skepticism about theory-based prognostication. But you say businesspeople already use theory to make decisions—they just use the wrong theories.

That’s right. For example, many bank analysts use the implicit theory that “the past is a good predictor of the future.” Management consultants often give advice based on the theory that “companies find success when they mimic actions taken by ‘best practice’ companies.” Oftentimes these assumptions are correct and lead to great insights. But they have limits. The past is only a good predictor of the future when conditions in the future resemble conditions in the past. What works for a firm in one context might not work for another firm in a different context. Furthermore, relying on these implicit theories means people must throw their hands up when unassailably conclusive quantitative data doesn’t exist. And the truth is, data only becomes conclusive when it is too late to take action based on its conclusions.

How can theory be useful in a world where people seem to trust “hard numbers” more than anything else? And what makes your innovation theories so much more reliable than the implicit theories in use today?

Good theory provides a robust way to understand important developments, even when data is limited. And theory is even more helpful when there is so much data that it is tough to discern what information really matters. Theory helps us to identify signals of change amongst a deluge of meaningless “noise.” It provides insight into the likely winners, losers, and also-rans. It illustrates the strategic choices firms make that influence these competitive outcomes and provides a roadmap for understanding the implications at the firm and industry level. The reason our theories are so useful in a predictive sense is because they are grounded by a circumstance-based, cause-and-effect view of innovation. Our research has shown that innovation is not at all a random process, but a predictable one—if you understand the context managers are operating in as they make the critical decisions that determine the fate of an innovation. The theories we outline in this book will help readers to understand the forces that shape this context and influence managerial choices—and then to make smarter predictions about what will happen as a result of those choices.

You evaluate several industries in-depth using your predictive model—ranging from telecommunications to health care to aviation. Why did you choose these industries?

We chose a breadth of examples to show how a predictive approach can bring order to innovations in many different industry settings. The industries we chose—aviation, health care, education, telecommunications, and semiconductors—are all important industries where innovations are driving substantial change. Of course, there are many other industries that could have provided rich case studies, such as financial services, retailing, and software. But we tried to limit the number of focus industries to maintain a balance between breadth and depth.

Which customer groups should you look at to identify potentially industry-changing innovations?

We are often taught to look at “lead customers”—those at the high or performance-demanding end of the market—to assess how a market will change. Sustaining innovations are often deployed there first, and then trickle down into the volume tiers of the market. But for disruptive innovations, the lead customers are in new markets or in the low end of existing markets. Therefore, predicting industry change requires watching for “overshot” customers in the low end of the market or “nonconsumers” in new markets, and new contexts. Watching for these customer groups helps us to spot situations where change is possible, where we can expect the future to be materially different from the past. In these situations, we can expect firms to emerge with products, services, or business models that look very different from what we have seen in the past. The “entrance” of these firms might be invisible even to the most astute industry watcher because they can incubate seemingly far away from existing markets or seem too inconsequential to matter. But if you know where to look and what to look for, you can spot industry-changing firms before they emerge.

Conventional wisdom suggests that government or other non-market involvement is always bad for innovation. You disagree.

This view is too simplistic and ignores a long list of innovations that would not exist without the government’s support, from the Internet to modern advances in health care. Our research shows that there is actually an observable and predictable relationship between the natural progress of innovation and the actions governments take to oversee markets. We’ve developed a framework called the motivation/ability framework that helps readers determine whether a specific government or regulatory intervention will impede or increase innovation in a certain industry. Essentially, when barriers to innovation are market imbalances, which dampen motivation, or legal barriers, which limit ability—government actions that remove these barriers can spur innovation. But when the barriers to innovation are technical barriers or fundamentally poor economics, such interventions are unlikely to be effective.

You make a lot of intriguing predictions in the book—from the viability of VoIP in the telecommunications industry to whether Southwest’s run is over in the aviation industry, and from whether Moore’s Law still holds relevance for success in semiconductors to why the “crisis” in health care actually portends good news for consumers. There may be a lot of readers calling their stockbrokers after reading this book.

We hope that they do so only after doing their own careful analysis! The “predictions” we make in the book and whether they come to pass are far less important than how well readers become able to utilize the predictive model themselves. This is why we underscore the critical importance of understanding strategic choices. In any one of the industries we evaluated—and any other industry for that matter—the fate of a firm or industry can change depending on choices like the business models entrants choose, the skills entrants develop, and how incumbents choose to fight. If readers can use our approach to develop their own intuition about the future of innovation, they can make the right decisions and consequently play a bigger role in shaping their own destinies.

The Core Theories of Innovation

Several important theories—originally outlined in Christensen’s landmark books The Innovator’s Dilemma and The Innovator’s Solution—can help readers to see more clearly how innovation will impact the firms and industries they care about:

The Disruptive Innovation Theory refers to circumstances in which new entrants to an industry can use relatively simple, convenient, low-cost innovations to create growth and triumph over powerful incumbents. The theory holds that existing companies have a high probability of beating entrant attackers when the contest is about sustaining innovations (incremental or radical improvements to already existing products such as airplanes that fly further, computers that process faster, and televisions with clearer images). But established companies almost always lose to attackers armed with disruptive innovations (products or processes that either create new markets or reshape existing ones). Disruptive innovations can be of two types:

1. Low-end disruptions, which deliver a low-priced alternative to customers who are overshot by existing offerings (ie: Dell’s direct-to-customer business model); or

2. New-market disruptions, which create new growth by making it easier for people to do something that historically required deep expertise or great wealth (ie: eBay online marketplace, Kodak’s original personal camera).

The Resources, Processes, and Values Theory explains why existing companies have such difficulty grappling with disruptive innovations. The theory holds that resources (what a firm has), processes (how a firm does its work), and values (the criteria by which organizations allocate their resources) collectively define an organization’s strengths as well as its weaknesses and blind spots. Incumbent firms master sustaining innovations because their values prioritize them and their processes and resources are designed to tackle precisely those types of innovations. Why? Because sustaining innovations involve improvements in the core business and target their most profitable customers. Conversely, incumbents fail in the face of disruptive innovations because these types of innovations initially target the least profitable customers (through low-end disruption), or nonconsumers (through new market disruption). As a result, the incumbent’s values tend to not prioritize disruptive innovations. In addition, the business models and value networks used to deliver disruptive innovations are usually so different from the incumbent’s that even if it tries to co-opt the disruption, its processes and resources won’t allow it to do so effectively.

The Value Chain Evolution Theory assesses whether a company has made the right organizational design choices to compete successfully. The golden rule underlying this theory is that companies ought to control any activity or combination of activities within the value chain that drive performance along dimensions that matter most to customers. In other words, they should integrate to improve performance along dimensions that are “not good enough” for what customers need and outsource what is “more than good enough” (those features and improvements that customers don’t need and won’t pay more to use). Solving the hard problems allows firms to capture value. Forward-thinking firms move to solve tomorrow’s hard problems, because solving tomorrow’s hard problems creates tomorrow’s profits. They unwittingly follow the advice of hockey legend Wayne Gretzky who, when asked what made him so great, replied that he always tried to skate to where the puck was going to be, not to where it was.

3-Part Model for Predicting Change

Seeing What’s Next suggests following a three-part process (illustrated below) to use the theories of innovation in order to predict industry change.

First, look for signals of change, signs of companies emerging to meet the needs of three different customer groups: undershot customers for whom existing solutions aren’t good enough; overshot customers for whom existing solutions are too good; and nonconsuming customers, individuals who lack the skills, wealth or ability to benefit from existing solutions.

Process to Analyze Industry Change

Signs of undershot customers include consumers eagerly snatching up new products, steady or increasing prices and the struggles of companies offering de-featured products. Undershot customers look for sustaining innovations that close the gap between what is available and the job they are looking to get done.

Overshot customers consider existing solutions to be too good. Indications that a customer is overshot include reluctance to purchase new versions of products, declining prices and the emergence of companies offering de-featured products. Overshot customers welcome low-end disruptive innovations that offer good enough technological performance at low prices.

Signs of nonconsumption include customers that have to turn to someone with greater skills or training for service, a market limited to those with great wealth, and the need to go to centralized, inconvenient locations to consume. Nonconsumers welcome new-market disruptive innovations that make it simpler and more convenient for them to solve problems themselves.

Although most analysis of industry change focuses on the most undershot customers (often termed “lead” customers), watching for the innovations that have the most potential to drive industry change actually requires paying careful attention to the least-demanding, most overshot customers and nonconsumers seemingly in the fringe of the market.

The second part of the process requires analyzing competitive battles to see which firms are likely to emerge triumphant. There are two components to this analysis. The first is taking the tale of the tape, to identify each combatant’s strengths, weaknesses and blindspots. Taking the tale of the tape involves evaluating a company’s resources (what it has), processes (the way it does its business), and values (decision rules that determine how resources get allocated). Most of the analysis should focus on processes, which determine what a company can and cannot do, and values, which determine what a company will and will not want to do. Next, look for the company that has the sword and shield of asymmetries on its side. In other words, look for the company that is doing what its opponent has neither the skills nor the motivation to do.

The third component of the process looks at important strategic choices that can help to determine ultimate winners and losers. When evaluating entrants, start by looking to see if the company is following a preparation regimen that facilitates it finding the disruptive path. To do this, check the management team’s schools of experience, verify that the company is encouraging emergent forces, and confirm that the company’s investors will allow it to follow a disruptive path.

Next, check to see how entrants are choosing value network participants such as suppliers, distributors, and ancillary partners. Entrants residing in freestanding value networks that do not interact with incumbents have the greatest chance of driving industry change; entrants locating themselves within an established value network create the possibility of incumbent co-option.

Finally, look to see if incumbents have earned their disruptive black belts by developing the capability to capitalize on disruptive trends. Incumbents that have nurtured this capability could respond to a disruptive threat by setting up a separate organization or use an established process to parry the disruptive attacker.