What’s so different about disruption? Isn’t innovation just another word for doing good business? The answer is: it depends on the level of innovation involved. Doing good business requires incremental innovation. Transformational and breakthrough innovation, however, is quite different.

Innovation fatigue

Mention the word innovation in a corporate context, and you’re likely to see some eye rolling. The word “innovation” has turned into something of a buzzword whose application is so broad and nebulous that it often feels useless.

“Of course we’re innovating,” they say, “we do it every day. So let’s stop talking about it and get back to business.” One of the greatest management thinkers of the 20th century obviously agreed:

“Business only has two functions: marketing and innovation.”

—Peter Drucker

And for good measure (and from the person who developed the theory of “creative destruction”):

“Carrying out innovations is the only function which is fundamental in history.”

— Joseph Schumpeter

After all, aren’t companies constantly striving to innovate in order to grow market share and enter new markets? If you see a big opportunity, you weigh the risks and expected outcome against costs and alternatives. If it looks good, you go for it, and if you’re good, you execute well and probably win.

Try to talk about disruption, and you’re also likely to be dismissed. “We’ve weathered storms before,” they might say. “Don’t ask us about disruption, go ask the other guys that went out of business.”

The reality is that many—perhaps most—enterprises face an existential threat from disruption. Dismissing disruption as more of the same is based on a failure to discern between the different levels of innovation. Disruption involves a fundamentally different type of innovation, and has significant implications for organizational design and leadership—both for the disruptors and the enterprises trying to adapt.

Cutting through the hype

There are meaningfully different types of innovation, each of which has important implications for organizational design and leadership. In order to distinguish between the types, we need to have a framework.

Let’s start with our broader definition of innovation:

“Innovation is the process of creating value by applying novel solutions to meaningful problems.”

You can read more here about why we use this definition, and why we believe so many people get it wrong.

I explain here how we explored different approaches to differentiate types of innovation and settled on one that uses two vectors of differentiation:

System is the value creation model (e.g., business model)

Domain is the combination of resources and context, including underlying technology, geography, and other components to the system of value creation.

The vectors range along a spectrum from familiar (to the team), to unfamiliar (to the team), to novel (to the world):

Known to me  —  New to me  —  New to the world

In other words, innovations in both systems and domains that are known to me are the simplest type. Innovations involving both systems and domains that are new to the world are the most complex and difficult.

Level 1—Optimization: Familiar system and domain. This is a known system of value creation using known resources and technology. These sorts of innovation tend to have clear proxies, and offer predictable timing, budget, and resource requirements. It’s easy to measure and manage risk, and it’s reasonable to expect a steady upward progress. It’s arguable this isn’t even really innovation, although we think it’s fair to use the term here as long as we’re careful to differentiate from other types of innovation.

Level 2—Change: Unfamiliar system and / or domain. There’s nothing truly novel (to the world) here, but something is unfamiliar to the team. Perhaps it’s the implementation of new (to you) technology or a new (to you) revenue model. This sort of innovation still offers a relatively predictable path, but it’s riskier and harder to predict than level 1. These innovations tend to take longer than level 1 innovations.

Level 3—Transformation: Somewhat novel system and / or domain. Level 3 innovation often involves new technologies and business models, albeit not radically so. Usually there are somewhat close proxies or partially understood or known technology. It starts to become hard to predict path, timing, budget, and required resources. Risks are very hard to assess. Often the complexity is in the combination. This sort of innovation tends to take a longer time, and to represent a significant challenge for established organizations.

Level 4—Breakthrough: Truly novel system and / or domain. Breakthrough innovation is the process of bringing to market a truly novel business model or domain. Either or both of the system or domain is new to the world. These sorts of innovations tend to take a very long time and are fraught with risk and uncertainty. They also tend to create significant value when they’re successful. Established organizations almost always seem to fail when pursuing this sort of innovation.

When we’re discussing disruption, we’re talking about both level 3 and level 4 innovation unless we state otherwise explicitly.

A world of VUCA

Disruption involves creating new and different systems in new domains. It’s easy to believe a careful assessment and planning methodology can eliminate most of the uncertainty, define the risks, and identify the most efficient path to success.

Millions of entrepreneurs and I are here to tell you that couldn’t be further from the truth. I have tried to find the best way to explain just how complex the process is, and how impossible it is to define in traditional management terms.

The best way I have found to explain it is rooted in military theory; after all, they probably understand better than most the difficulties of operating in complex, risky, and unpredictable contexts. General Maxwell R. Thurman, US Army, first described V.U.C.A, which stands for Volatility, Uncertainty, Complexity, and Ambiguity. I think it’s a useful way to begin a discussion about disruption.

The very nature of disruption involves so much volatility, uncertainty, complexity, and ambiguity that it absolutely defies reliable prediction. That’s not to say that you can’t reliably create value by disrupting. But due to the nature of disruption, success requires methodologies and organizational designs that are quite different from traditional management orthodoxy.

Much greater uncertainty and ambiguity

Disruption is so complex and involves so many moving parts that huge swathes of what’s before you are uncertain from the start. I liken it to a blueprint for a successful business. All of it has to work for the business to work. The problem is, most parts of it are plain wrong. And you don’t know which ones. Because you’re doing something new, you lack useful proxies.

So, instead of a detailed engineering blueprint, you have what amounts to a wacky Rube Goldberg puzzle. Except that’s giving it too much credit, because the boxing glove is stuck, the bowling ball is too heavy anyway, and the levers are going the wrong way.

All of that means that you can’t assign a probability of success for your overall project or even for many of the components involved. It’s not a question of risk; you simply don’t know. And you don’t even know what you don’t know—the unknown unknowns.

In the context of your business endeavor, that means that most of the key inputs are unknown:

• Timeframe
• Resources required
• Skills required
• Market demand
• Metrics for success
• Size of the opportunity

I often describe it as a 10,000 piece puzzle. But there’s no picture on the box. And extra pieces. And some pieces are missing. And you’re being timed as you put it together knowing that if you’re not fast enough you’ll be zapped.

Changing hearts, minds, and behaviors

An important corollary of the uncertainty vector is the fact that virtually every disruptor has to change hearts, minds, and human behaviors. If I’ve learned one thing over the past 25 years as an entrepreneur and investor, it’s that people are the most unpredictable factors in virtually every disruption.

I can’t say how many times I have seen startups flounder on the rocks of human behavior. No matter how solid their logic and the strength of their conviction that the choice for customers was obvious, there’s a huge chance that the expected behavior won’t materialize in important ways.

This fact has tremendous implications for best practices in pursuing disruption, and is a key determinant in some of the challenges we’re discussing in this section. It’s also one of the areas where entrepreneurs tend to fail despite their most careful planning.

Much higher risks

Even where you’re able to transform uncertainty into risk (yes, turning something into a risk is often a good thing in the context of disruption), the risk you uncover is often substantial. And because you’re working in the context of an interconnected system, the risks usually amplify each other.

One way to look at risk is to average out the likelihood of success across a high volume of attempts. The numbers are stark:

• 80% of intrapreneurship projects fail according (Beth Altringer at Harvard)
• 92% of startups failed within three years (Genome Startup Project)

The odds of failure for an average innovation project or startup are very high. It’s worth noting that intrapreneurship projects tend to be much less disruptive than the average startup; they’re typically more in the level 1 or 2 innovation zone. As a result, the 80% number grossly understates the risk of a disruption project in enterprise. I’d estimate the disruption failure rate in that context at much closer to 99%—and we probably need to add a bunch of nines to that.

Higher volatility

The risks inherent in disruption aren’t limited to the overall success of the venture; they’re embedded in every step along the way. The odds of failure for any given effort or initiative in the context of disruption are very high. As a result, the path to success tends to involve violent ups and downs.

Over the years I’ve had many team members ask me why I don’t get upset when things go badly. “Do you see me get excited very often?” I ask. “The reason I don’t is because I’ve learned that as soon as something goes well, something else will go to hell. And vice versa.” Violent and unexpected ups and downs are par for the course in disruption.

That volatility extends to the range of outcomes you can expect to see in any given set of disruption attempts. Whereas most traditional enterprise projects succeed or fail in a relatively narrow band, startups have a much broader range of reasonably likely outcomes.

One thing that’s interesting is that disruption attempts often have extremely high theoretical potential upsides, and more modest downsides. In other words, you might make billions, but you’re unlikely to lose billions trying.

That would be great except for the fact that the expected outcomes are distributed on a power law curve where only a very, very select few really hit the big time. Here’s a simpler way of describing it: The average angel investor makes 5x money, while the median loses money.

Complexity and coordination challenges

Disruption involves modifying and / or creating systems. Systems by nature are interconnected. Changing one part almost always has implications for other parts of the system. This introduces complexity and coordination challenges that are fundamentally different from traditional business execution.

Because disruption usually occurs in the context of new domains (technologies), the burden of knowledge almost always comes into play. One or more of the elements of your system are likely to test a knowledge frontier. This has significant implications for the capabilities of the team you bring to bear. It also introduces requirements for domain expertise that are at odds with the adaptability needs inherent in dealing with the uncertainty implicit in a system redesign.

Complexity and uncertainty inherently require more information processing.

“The greater the task uncertainty, the greater the amount of information that must be processed among decision makers during task execution in order to achieve a given level of performance.”

— Jay Galbraith

Bringing these threads together, disruption involves:

• a much higher level of complexity and uncertainty
• which require both substantially more information processing
• and tighter coordination
• among a team of experts comfortable at the knowledge frontier
• who are also highly adaptable and comfortable with volatility

That’s a tall order.

Faster cycles

In traditional execution where the broad strokes of the system and domain are well defined, you have a limited set of variables you need to test and validate in order to begin to fully understand and optimize your value creation engine.

But it’s quite different in the context of disruption. Because you’re doing something new and complex, you have to break your efforts into many small experiments to explore the contours of the system and better understand what works and what doesn’t. You have to constantly reassess whether parts of your blueprint are working, and whether you’re moving in the right direction. And because you’re constantly tweaking different parts of the system and domain, you often have to revisit past experiments to understand how they change in the context of the evolving system.

Essentially you’re focused on learning instead of execution. And it happens in very fast, often overlapping cycles. That’s at odds with the traditionally much longer planning and execution cycles adopted by enterprises optimized for executing a known business model.

A much longer arc

Due to its complexity and interconnected nature, and despite the typically shorter cycles, disruption almost always takes longer overall than traditional execution. It’s only after many small experiments that you can arrive at what appears to be a working model with product-market fit. And from there you typically have to build out new operational and growth structures appropriate to your new model, which adds further time.

"Disruption almost always takes longer overall than traditional execution."

The evidence is clear when you consider the asset value creation cycle for major venture backed companies, for example when you consider the difference between DPI and TVPI (realized versus unrealized gains). You have to look back eight years before half of the value is realized. Unrealized value continues to represent a meaningful percentage of asset value as far back as twelve years. It typically takes years to begin to realize value in the context of disruption.

Risks and uncertainties also tend to endure much longer into a disruption project. Take, for example, the notion of scale dynamics. There are many types of businesses where you can’t evaluate or understand key parts until you have achieved meaningful scale. Facebook is a great example of this. Their revenue model remained unclear for an extended period of time, and only really became feasible as they achieved significant scale. So, while they were engaging in very rapid development cycles, the arc of their overall learning curve was quite long.

A very different beast

Disruptive innovation is very different from traditional execution. Creating a new system in the context of a new domain introduces uncertainties, risks, and operational imperatives that tend to be very much at odds with best practices in established organizations.

Next: Read about How Great Companies Get Disrupted