The Ever-Evolving Balancing Act: Sustaining Innovation vs. Strategic Vision
In the realm of business strategy, the conversation often orbits around the balancing act between…Read more
If you decide that you are only going to do things that you know are going to work, you’re going to leave a lot of opportunity on the table. – Jeff Bezos
The two most dangerous words which will kill innovation are: “Prove it”. If you want your organization to last more than a single iteration, ban the words “prove it”. Try it. – Roger Martin
A recent study by McKinsey reinforced what many innovation researchers and practitioners have been saying for a long time:
How big is the difference in economic returns? McKinsey claims that it can be up to 2.4 times greater. They came to this conclusion by surveying many companies about their innovation approach and matched the answers with financial performance. The survey focused on eight ‘innovation essentials’ that they deemed important to innovation. While this type of survey-based research is valuable, it ends up with amorphous terms and definitions for essential elements, such as “Aspire”, that are often difficult to translate into practice. What do Aspire and the seven other essentials look like when implemented in a firm’s innovation system?
One way of testing the McKinsey claim is to build a model, a ‘digital twin’, of an innovation system within a large company. A system dynamics model of how innovation is done within large firms was created  and is being used to answer questions about what influences the effectiveness of a large company’s innovation system. The first test of the model was done to see if the McKinsey results hold up and, if they do, why. The results of this test are shown below.
Figure 1 – Model Simulation Results of Three Scenarios
The model is constructed with two paths an opportunity can follow from initial idea to market launch. The first is a path designed and optimized for sustaining opportunities, typically a process based on a Stage-gate® methodology. The strategic path is designed and optimized for strategic opportunities. It uses the Discover-Incubate-Accelerate  framework as its architecture. The model lets a user turn the strategic pathway on and off and see the difference in the outcomes the innovation system produces over a period of time (the default is 10 years).
In this test of the model, three runs were done using identical starting conditions and model parameter values except as follows.
Turning on the strategic innovation path has a significant positive effect on value creation, as does increasing the firm’s innovation maturity level. The difference is dramatic. The economic value created by adding a strategic innovation capability to an existing sustaining innovation capability is close to what the McKinsey analysis concluded.
The model has already provided some key insights:
The fact is that without a separate strategic path, strategic opportunities don’t stand much of a chance. They get crowded out by the more numerous sustaining opportunities that are incremental and closer to the core. But it is the strategic opportunities that drive accelerated growth, not the sustaining ones.
What this effort has shown is the following:
Of course, how the model is constructed and used is important. It cannot tell you everything about a specific innovation system but a well-built model can lead to many interesting insights.
The system dynamics model  created captures the innovation processes taking place inside a firm. It does not model anything happening externally. For example, it doesn’t care where ideas come from, only their relative input distribution. Likewise, it does not model the larger ecosystem, the competitors, or the markets, etc., that the company operates in. It only models the distribution of outcomes from blockbuster (as good as anything the company has ever done) to bad (doesn’t meet ROI goals). The modeler sets what value these represent. The results are relative, not absolute.
In addition, the model does not predict what happens to any specific opportunity. It operates on the aggregate collection of opportunities (a stock) as they flow through the system to calculate and use probabilistic distributions that determine aggregate outcomes. In these aspects, the model is analogous to a climate change model rather than one used to predict the weather.
An illustration of the model architecture is shown below.
Figure 2 – The System Dynamics Model Architecture
The model consists of ‘stocks’ and ‘flows’ with various feedforward and feedback loops. The main stocks are opportunities, knowledge, and resources (i.e., money, people and attention). Opportunities flow through the system and are affected by various factors based on the opportunity itself and how the organization behaves.
This diagram is necessarily a high-level and abstract depiction of reality (and of the model itself). In the model it is possible to change different variables to run ‘what-if’ scenarios and see the results and their causes at many different levels. The possible combinations are nearly infinite and experimenting with the model leads to interesting insights and additional questions. One advantage of having such a model is that it allows the modeler to open the ‘black box’ and peer inside to see what is causing the results.
The main control parameters in the system are:
A key feature of the model is that the two paths, sustaining and strategic, differ in the way they handle knowledge accumulation (i.e. learning), decision velocity, and decision error rates for different types of opportunities. These factors influence each other: better decisions are made when more knowledge is available.
The model provides interesting insights into many of the complex dynamics of a corporate innovation system, but it’s left to practitioners to map this onto the real world and design the specific systems that are implemented.
The following table lists insights gleaned from the model based on the results of just this one test.
Without a Strategic Path
With a Strategic Path
|Input bias  – How do new ideas enter the system and receive the up-front attention and interest necessary to even be considered? What ideas get summarily dismissed?
|People submitting new ideas to the system self-sensor. Opportunities are not put on the table because they are thought to be too ‘out-there’ or ‘not us’ and they know that they will be rejected immediately. Bias is towards ideas closer to the core and strategic opportunities don’t even make it into the system.
|People feel free to submit ideas that are farther ‘out there’ and don’t quite fit existing businesses. They know that they will at least be considered and that there is a mechanism for seriously evaluating strategic ideas.
|Competition for attention – How is the scarce resource of attention allocated? What gets attention and why? What are the attention biases and how do they affect what moves forward?
|Strategic opportunities, especially uncertain ones farther from the core, get killed or stalled because they are much less common and are drowned out by the volume of sustaining opportunities. Attention is a scarce resource and the type of attention demanded by uncertainty requires special effort and skills which may not be readily available, especially if these people are required to work on large volumes of sustaining ones.
|When strategic opportunities have their own strategic path to follow, they are competing for attention and resources with other ‘like’ opportunities. They don’t need to be specially identified (except in the initial triage step) and can be assessed against each other without having to contend with the vast volume of sustaining opportunities that they would otherwise need to compete with.
|Difficulty of work – How is work done on opportunities as they flow through the system. What happens when opportunities are ‘difficult’, i.e., answers don’t come easily.
|Strategic opportunities are more difficult to work on. Their case is harder to make and support can be hard to find. The sustaining path doesn’t readily support iterations, pivots, redesigns and longer delays and time until launch. Issues get handled as ‘exceptions’.
|The required iterations, experiments, pivots, etc. are built into the process and don’t require exception handling. Moreover, the work required is recognized up front and is managed through successively increasing investments in time, attention and resources.
|Decision mechanisms – How are decisions made (especially with incomplete and uncertain knowledge)? What is the decision velocity? What is the error rate (false positive and false negative)?
|Uncertainty and complexity are higher for strategic opportunities and good metrics aren’t in place. Information is incomplete and ambiguous resulting in subjectivity and cognitive biases slowing decision making down and increasing errors.
|Governance and decision-making systems designed for decisions under uncertainty and incomplete information are in place. Cognitive biases are minimized. Competition between opportunities creates options and changes the types of decisions being made.
|Acceptance and Adoption – What happens to opportunities as they are getting ready for launch and after launch? Is the organization doing the launch capable and committed?
|Strategic opportunities are more difficult to transition into acceleration and scaling. There may not be a good ‘landing place’ for the opportunity and it may find a business unprepared and uncommitted. Pushback from the businesses often torpedoes even the best strategic opportunities.
|There are multiple possible paths to a sustainable value creation entity. Some paths may not involve an existing business. If it is an existing business, the transitions – to incubation, to Acceleration, to Scale – are not done until both the opportunity and the organization are ready.
The case for building a distinct and separate strategic innovation capability in a large corporation is clear. Research supports this conclusion (see McKinsey’s research article), as does the insight gleaned from the test done using the system dynamics model of innovation described above.
The fact is that without a separate strategic path, strategic opportunities don’t stand much of a chance. They get crowded out by the more numerous sustaining opportunities that are incremental and closer to the core.
The model also lets us define exactly what is meant by terms like ‘Aspire’. In the context of the model, ‘Aspire’ translates into improving the quality of the input channels so that they feed the system with better ideas with more inherent potential. We can now discuss specific mechanisms to do this and the model can indicate what happens when the input channel mix changes for the better (or the worse).
Considering that we now know the problems companies face with strategic innovation and we have the tools and knowledge needed to solve these problems, there is no reason why companies today should not build a strong, state-of-the-art, strategic innovation capability. The test described above is just one small sample of what a model such as this can reveal. What could it reveal about your innovation system?
 The concept of strategic innovation, and how it is different from sustaining innovation, is central to this discussion. A full definition of terms, and how they compare to disruptive, radical, breakthrough, horizon 3, etc., is described in Sustaining and Strategic Innovation
 This is technically a model externality but is important nonetheless and is reflected in the different relative proportions of the number of sustaining and strategic opportunities coming in through the three channels.
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