When it comes to investing in innovation, the first step is often to estimate the return on investment. However, one fundamental element is frequently overlooked: the very real cost of not innovating. When evaluating the development of a new product, service, or process, the first step is typically to compare the assumed costs of development with the expected revenues generated by the new innovation. The two scenarios that are usually compared are as follows:
- The scenario where the innovation is introduced.
- The scenario where the innovation is not introduced, and everything remains the same.
The Red Queen’s Hypothesis
The problem with this comparison is that things will never remain the same. Revenues generated by existing products are not constant for the future, but inevitably decrease (except in very special cases). Two primary reasons behind this trend are as follows:
- Obsolescence of products due to innovations introduced by others that put the existing product out of the market.
- “Commodification” of the product: competitors gradually introduce similar products, causing the product to become a commodity offered without qualitative differences in the market, which reduces margins to tend toward zero.
It is often forgotten when evaluating innovation that existing products and processes generate diminishing returns over time. This scenario is what is called “The Red Queen’s hypothesis ,” a well-known concept in evolutionary biology. The term comes from Lewis Carroll’s famous novel “Through the Looking Glass and What Alice Found There” (the sequel to “Alice in Wonderland”), in which the Red Queen says, “Now, in this place, as you can see, it takes all the speed you have if you want to stay in the same place; if you want to go somewhere else, you have to run at least twice as fast as that!”
Estimating the Cost of “Not Innovating”
While the concept can be easily understood, it is less obvious to estimate the cost of not innovating. Like any forecast for the future, there are inevitably elements of uncertainty in the estimate. However, this is not to say that an estimate is impossible. First, data on the average life of past product lines can be used as a reference. If we know that products become obsolete every three years, we have the data for an estimate of what we can expect in the future.
Of course, in general, it is not possible to get a precise “expiration date” since obsolescence in many cases is not a hard boundary and is often fuzzy, with products not becoming obsolete from one day to the next. Similarly, it is unlikely that all past products have had the same lifespan, but it is essential to understand if there are any trends and to estimate an average. The lifespan of products before obsolescence may have shrunk over time, requiring more frequent innovations to remain on the market. A second aspect is to pay close attention to emerging technologies that could make the product obsolete and to possible moves by competitors (including potential ones) to anticipate the entry of products that could make ours no longer competitive.
As anticipated, this approach only allows for an estimate with elements of uncertainty (which is inevitable, since we are looking to the future), but it is crucial that this estimate be made to more consciously address choices related to innovation projects.
