The scientific method for venture creation and growth
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“Exploring the unknown requires tolerating uncertainty.”
In 2011, after more than 20 years of research, Harry Klee, a professor in the Horticulture Sciences Department at the University of Florida, developed the “perfect” tomato. The Garden Gem tomato was not only prolific, disease resistant and easy to harvest, it was also so delicious that anyone who tasted it immediately wanted it — it was nothing like those cardboard-tasting tomatoes you get in the supermarket whose key attribute is they are easy to ship. Harry Klee thought it would be easy to get this new tomato into consumers’ hands. He quickly found out he was mistaken. Distributors and others in the supply chain didn’t want a more flavorful tomato. As one commercial grower shared at a conference he and Klee attended, a flavorless tomato had yet to cost him a sale. And the unfortunate reality was, this statement was true.
This is an example of the type of uncertainty that continually confronts innovators and entrepreneurs, whether they work in large or small companies. In this case, it’s not uncertainty about whether the technology will work (it does) or whether customers will want it (they do). Rather, it’s uncertainty about the unknown and unexpected interactions and influences of a complex system, a system with many different players each with varying motivations, mindsets and behaviors that must change in order for any new thing to succeed.
Uncertainty is the bane of corporate management. For large companies that strive for operational excellence, uncertainty gums up the works. Efforts such as Six Sigma and business process design are specifically engineered to minimize uncertainty. The inability to deal with uncertainty causes many of the failures featured in business school case studies.
But when playing the innovation game, uncertainty not only is inevitable; it is necessary. We humans naturally generate future uncertainty through our constant striving to create, and adopt, new things. Uncertainty is an inevitable outcome of the human desire for the new, the better and the unique. When you innovate, you create uncertainty for your competitors, and that is good. But your competitors, and your customers, also constantly create uncertainty you must deal with. The better you recognize and manage uncertainty, the more resilient you will be.
The ability to recognize, measure and manage uncertainty is critical to the act of innovation. It may in fact be the most critical thing, foundational to all we know about how innovation happens and to the processes, methods and tools we use to innovate. Looking at these tools — open innovation, voice of the customer, test and learn, design thinking, to name only a few — through the lens of uncertainty provides a new perspective about how to approach innovation.
But not all innovation benefits equally from the ability to measure and manage uncertainty. For example, with incremental or sustaining innovations in which, by definition, the uncertainties are few, relatively small and easily addressed, measuring and managing uncertainty is less important. But for strategic innovations in which, by definition, the company is pushing boundaries, an uncertainty framework, and new methods and tools derived from it, are essential. By using a well-defined uncertainty framework, assumptions and evidence about new opportunities can be assessed at every stage of development, from nascent idea to fully realized solution.
What was the key uncertainty Harry Klee faced when he tried to get his tomato to market? And how could he have known this uncertainty would be his major obstacle before he spent decades developing the new tomato? It’s questions like these that innovators need to ask and then have the tools to answer.
Based on our work over the years on hundreds of innovation projects with dozens of companies across many industries, we’ve developed an uncertainty framework that assesses four distinct dimensions of uncertainty — Organizational, Demand, Design and System (ODDS) — and an Opportunity Readiness Level (ORL) scale from 1 to 9 for each dimension.
This ORL uncertainty framework for measuring and managing uncertainty improves the flow of strategic opportunities through the innovation pipeline.
Innovators must manage uncertainty when bringing a strategic opportunity to fruition. The four dimensions of the ORL uncertainty framework give form and structure to the many and often ambiguous, even conflicting, issues that need to be addressed and helps speed the flow of strategic opportunities through the innovation pipeline.
With this framework, the strategic innovation process becomes one of systematically identifying and reducing opportunity uncertainties in these four dimensions. Any strategic opportunity starts out as a nascent idea with high uncertainty in all four dimensions. As innovators think about, research and work on the opportunity, uncertainty diminishes. The opportunity readiness level (ORL) helps measure the four dimensions of uncertainty to give the innovator and the organization clear direction on what needs to be resolved next.
Companies, especially those with a strong technical foundation, tend to pay the most attention to the Design dimension, since in many respects it is the most familiar and comfortable to work on. Reducing “technical risk” often gets priority in innovation projects simply because the project team consists mainly of scientists and engineers.
In recent years, the Demand dimension has been getting increasing attention. The emphasis on “design thinking,” voice of the customer and test and learn methods, for example, explicitly acknowledge, and require, understanding details of the customer experience and what drives demand. Too often, however, companies substitute “Market” for “Demand.” Market research is not Demand insight. Estimating future market size is not a substitute for understanding the deep motivations of people who may become customers as they adopt and adapt to innovations. In the uncertainty framework, market segmentation, size and growth rates, competitive analysis and other “macro” market factors are part of the System dimension.
System uncertainty includes not only the “traditional” macro-market factors listed above but also all the exogenous uncertainties inherent in a complex eco-system. This is the dimension that encompasses PESTEL uncertainties, many of which have well-developed methods and tools to address them.
More strategic innovations in large organizations are killed because of organizational uncertainties than by all the other three uncertainty dimensions combined.
These four dimensions of uncertainty determine how strategic innovations progress from nascent idea to fully realized offerings and business models, and the ORL uncertainty framework provides the basis for creating new methods and tools that can be incorported into an innovation system and improve its operation and governance.Even if Demand, Design and System uncertainties are top-of-mind and well-managed, the thing that trips up large organizations time and time again when attempting to undertake strategic innovation is Organization uncertainty. Can the organization handle an opportunity that doesn’t nicely fit within existing business boundaries? One key advantage of the ORL uncertainty framework is that it explicitly accounts for organizational uncertainty and provides a way to identify, measure and deal with all of its various aspects. These can range from how new strategic innovations are funded and where they will eventually fit into the organization once they become real businesses to how the innovation will affect current lines of business and what new competencies need to be developed. These are the types of issues that are the most difficult for a business to accommodate because they require changes in organizational behavior and mindset. More strategic innovations in large organizations are killed because of organizational uncertainties than by all the other three uncertainty dimensions combined.
Because uncertainty is central to the innovation process, innovators need to be able to measure and manage it. But before you can measure it, you have to define it. Uncertainty is often confused with risk and uncertainty management with risk reduction. You will often hear comments such as “we need to reduce the technical risk of this project” as the motivation for experimental research, proof-of-concepts, prototype development, etc.
But the two are explicitly different:
Risk – We don’t know what is going to happen next, but we have an idea what the probability distribution looks like.
Uncertainty – We don’t know what is going to happen next, and we have no idea what the probability distribution looks like.
In practice, risk and uncertainty look something like this:
Design uncertainty – very low. The technology has been proven and the design of the tomato (taste, cultivation, harvestability, transport, etc.) are all good.On the left, risk. If your business relies on the price of a lumen, this is a critical number. In 10 years, the price of a lumen could be X or it could twice X. One outcome would result in a profitable business, the other in a disaster. Betting on the price is risky, but the range and distribution of possibilities is constrained and can be easily modeled and hedged if necessary.
Uncertainty is more difficult to accommodate. Take the example of inventor Harry Klee described earlier and the uncertainties he faced.
While risk can be managed with statistical models, uncertainty cannot. Companies have many tools to manage risk but few to manage uncertainty. Yet it is the most uncertain opportunities that often turn into the greatest wins. These are those Horizon 2 and 3, breakthrough, disruptive, transformational (choose your term) opportunities companies constantly search for.In Harry’s case, uncertainty does not involve making customers like a more flavorful tomato. They naturally do — without any effort other than tasting it. Managing uncertainty instead involves changing the mindset of the distribution channel. Risk cannot even be estimated until this question is answered. And it is not a quantitative question subject to statistical analysis.
The path to any successful new, innovative offering is long and often littered with barriers; missteps are all too easy, and the prospect of failure is a constant. But what if innovators could see precisely where on the path they are and avoid wrong turns? This is the motivation behind the Opportunity Readiness Level (ORL).
If the term sounds vaguely familiar, that’s because the ORL has a few precedents. The Technology Readiness Level (TRL) framework was conceived by NASA in the early 1970s and formally defined in 1989 as a means of measuring the maturity of a technology-based solution with respect to its development and deployment. It has been used to great effect, not just by government organizations and defense contractors, but also by technology-focused companies.
More recently, the principles and motivations that led to creation of the TRL scale have been used to create the investment readiness level and the innovation readiness level scales. While not widely used, these last two are intended to do for innovation what the TRL did for technology development —provide a means of assessing how mature an innovation is or, in other words, how far along it is along its path to customer and market success. The Innovation Readiness Level, in particular, focuses on the business model aspects of an innovation. So why not use one of these existing metrics?
In short, we created the ORL because these measures, and some others that have been proposed from time-to-time, do not adequately capture all the complexities inherent in the innovation process.
Opportunity Readiness is all about the nonlinear, experimental, recursive and iterative path an opportunity takes from initial concept to fully realized offering and business model. It is a measure that lets you ascertain where a concept is on this path and helps you determine what to do next.
In the figure above, an ORL level is a state, not a stage. In state-based systems, the move from one state to another is based on the current situation and context. The path from state-to-state does not need to be linear and this aligns with the inherent nature of uncertainty.
Resolving an uncertainty requires the innovator to experiment or test based on a hypothesis. But the resolution of the uncertainty can go either way. If, through an experiment, you find that one of your assumptions is wrong, then, in a state-based world, you go back to a previous state, not on to the next one. For example, if one is uncertain about the demand for a new opportunity, then testing the hypothesis, “Demand will be strong within this segment or population,” can result in various outcomes.
It is precisely this nonlinear, state-based structure of the ORL that makes it such a powerful tool for innovation. Not only does it accommodate the four distinct dimensions of uncertainty encountered by every strategic innovation; it also accommodates the nature of uncertainty itself and forces the innovator to face the fact that, sometimes, you must go back in order to move forward.
The way to approach these uncertainties is to experiment, test, learn and adapt. The ORL helps you do that, just perhaps not in the way you expect.
The ORL measures uncertainty at nine levels along the four ODDS dimensions as shown in the following diagram.
Each level in each of the four ODDS dimensions is evaluated using a set of questions and criteria (not shown) the innovator or entrepreneur can use as a basis for forming hypotheses (i.e. asserting assumptions) and designing experiments to test. The running measure of the uncertainty (or it’s inverse, readiness) in each of the four dimensions can be used to see if one or more dimensions are lagging or leading the others. This can indicate that, for example, in the current situation, more attention needs to be paid to reducing demand uncertainties (i.e. customer needs) and less attention to reducing design uncertainty (i.e. technology).
Imagine a company that makes and sells home water filtration products comes up with an idea for a new opportunity that combines hardware and software to provide a “whole house” pure-water system. This opportunity includes a new business model that lets the purchaser “pay for use” rather than the conventional “buy and install” model. Water management — using sensors, cloud computing, predictive analytics and an intelligent water advisor (i.e. software) — is a key feature of this system. This company has never sold software or information before, but the idea surfaced during one of its discovery initiatives and is under consideration by the innovation group as one of several opportunities to spend some time exploring. The group looked at the opportunity’s various uncertainties and reached the following assessment:
Now the innovation group can clearly see where time and attention should be spent at this time — on demand and organizational uncertainties rather than design uncertainties. This is the opposite of what many companies actually do. Often, they spend large amounts of time and effort investigating the technologies, doing proof-of-concept projects, developing requirements, specifications and prototype designs while ignoring the uncertainties that could ultimately derail the project. The ORL uncertainty framework and assessment can prevent this and improve the overall innovation process.
You may have noticed that throughout this discussion of uncertainty, nothing has been said about how “good” an opportunity is. That’s because opportunity goodness is different from opportunity readiness and has its own set of measures (covered elsewhere). Both measures need to be in place and, although distinct, they do interact with one another.
For example, if demand uncertainties are reduced (the opportunity’s Demand readiness is high) that means demand hypotheses have been validated (customers really do want what you are creating). The size and strength of the demand and the potential value derived are on the goodness side of the equation. The readiness assessment is used to assure that the goodness assessments have been appropriately validated, whatever they may be.
The ORL uncertainty framework is based on the following premises:
Experience and research in innovation have shown these premises to be true. What has been lacking is a robust way to measure and manage these inherent uncertainties. The ORL uncertainty framework enables us to do so in a systematic way that results in better visibility into, and more reliable decision-making for, the complex process we call innovation.
 The term “Strategic Opportunity” refers to non-incremental opportunities. It encompasses the commonly used terms Horizon 2 and 3, adjacent, transformational, disruptive, breakthrough or any other term an organization uses to refer to opportunities that are farther from the core and therefore more challenging.
 Blank, Steve, It’s Time to Play Moneyball: The Investment Readiness Level, November 25, 2013. steveblank.com
 Evans, J. and Johnson, R., Tools for Managing Early-Stage Business Model Innovation, Research-Technology Management; vol. 56, issue 5; 2013.
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