Misinnovation: When Innovation
Goes Awry

Innovation has long been hailed as the driving force behind human progress, propelling societies forward and revolutionizing global industries with transformative advancements. However, beneath the celebrations for modern innovations like central air, smartphones, electric vehicles, and generative artificial intelligence (AI), an underbelly to progress exists, and it deserves attention.

In our recent publication, “Innovation Frameworks: What They Are and How to Build One,” we introduced the concept of misinnovation to discuss the unintended negative consequences of innovation. While innovation typically raises images of positive change, misinnovation sheds light on the darker side of progress. Join us as we explore what exactly misinnovation is and unpack famous examples.

What Is Innovation?

The terms “innovation” and “invention” are often conflated, which can lead to a few misconceptions. Invention pertains to a new insight or product, often of great value. Innovation, on the other hand, surpasses invention. Innovation occurs when something novel and valuable is created, embraced, and adopted.

The automobile, for instance, was just another outlandish invention until it was adopted into the mainstream American culture—what do you need a car for if you already have horses and a buggy? Where do you buy gasoline, anyway? Or parts to repair the vehicle?

For a while, automobiles seemed bizarre and were only attainable by the wealthiest people. That was until Henry Ford mass-produced his Model T using the first-ever moving assembly line. The use of the assembly line made the car quite affordable in the coming years. Once people began buying automobiles and saw their utility, our society became reliant on cars, and they became such a prolific element of our daily lives that our infrastructure is now entirely built around them. From the parking lots, highways, school drop-off lines, and drive-throughs we all frequent, it isn’t easy to imagine a world where we did not widely adopt the automobile.

The distinction between invention and innovation is that of adoption. Innovation involves not just the creation of something new but also its successful integration and acceptance. We would not consider the automobile the innovation it is today if people had not seen its utility and continued to travel primarily by horse and buggy or by train.

Knowing that adoption is critical for something to be considered an innovation, innovation as a capability then refers to an organization’s ability to generate new ideas, bring those ideas to fruition, and then see the creation through to adoption.

Why Is Innovation So Important?

Harnessing and developing innovation as a capability is what makes certain businesses stand out from one another. The value of building innovation as a capability in your organization is enormous; organizations need to develop an innovation framework to do so.

But even if you develop a process for innovation specific to your organization’s needs and build a deep culture of curiosity, creativity, and creation, it is critical to be mindful of what it is that you’re producing and how you’re producing it, lest you experience misinnovation and are unable to self-correct.

What Is Misinnovation?

Innovation frameworks provide more than just a process to pursue valuable new ideas; they also offer ways to promote adoption. Flexibility is vital in this process, as adopting an innovation can sometimes impede our ability to make future adjustments.

The implementation of new products or services, if not done carefully, can become a hindrance to innovation itself. Likewise, if adopting an innovation establishes rigid production standards that resist future advancements, the growth of entire industries can be impacted.

Misinnovation is a phenomenon that occurs when innovations lack adaptability, meaning something created cannot bend and shift to new demands or blinds us from future possibilities. We must be careful and avoid rushing innovations to market if the resulting creation cannot be easily modified or adjusted to meet future needs.

Outside of not leaving room for future adjustments after adoption, misinnovation can also manifest in other ways. For example, someone may create a robust and extensive solution for a misdiagnosed business problem or run with an idea that sounds great on paper but has not mapped to any business goals or customer needs.

Misinnovation can also occur due to sloppiness during any stage of your innovation process. Many believe the implementation and adoption phase of the innovation process is what’s to blame and is what causes misinnovation. Still, the truth is that the implementation and adoption phases are often just the time during which people begin to realize an innovation’s shortcomings. And if something has been adopted at large, changing that innovation can be nearly impossible.

The way to avoid this? Be mindful of each step of your innovation process and anticipate shortcomings. Don’t get blinded by the shiny new object; keep a critical eye on your progress. Innovation is a science, after all, not magic.

The 5 Main Sticking Points That Trigger Misinnovation

As discussed in our piece on innovation frameworks, the process you use for your framework will depend on several factors, such as your industry, the type of innovation you wish to pursue, and your people’s preferences.

Regardless, all innovation processes begin with an idea and end with its adoption. At GP Strategies, we use a process called DRECI for our innovative work. Within the DRECI process—and really any process—each step can be vulnerable to misinnovation, beginning with discovery and ending with implementation and adoption.

Here is the process we follow and the actions commonly associated with each step.

  • Discovery:
    • Investigate the boundaries of the idea.Assess whether there is a need for the idea.
    • Conduct viability and competitor research.
  • Refinement:
    • Explore whether the idea solves a relevant business problem.
    • Calculate cost estimations and ROI projections.
    • Begin to build stakeholder alignment.
    • Finalize essential elements of the idea.
  • Experimentation:
    • Design an approach to experiment, test, and learn.
    • Build and test the idea.
    • Discard unhelpful elements of the solution.
    • Highlight the idea’s best features.
  • Collaboration:
    • Reflect on lessons learned.
    • Share knowledge and findings with peers.
    • Communicate adoption needs to stakeholders.
  • Implementation and adoption:
    • Discuss relevant business practices with those impacted.Launch the idea.Pulse out messaging to
    • Continue to monitor and assess progress over time.

The DRECI process is a guideline for innovation; only some innovations will follow these steps exactly and linearly. The middle of this process—refinement, experimentation, and collaboration—often involves some jumping around. After experimentation and collaboration, for example, you may revisit the refinement stage to readjust based on feedback and findings.

Examples of Misinnovation

Misinnovation can occur in countless ways, so we can only cover some here. However, here are some notable examples of misinnovation according to when they would occur if you were to follow our innovation process.


Identifying misinnovation during the discovery phase is more elusive than it is for other DRECI process steps.

Misinnovation during discovery can take on many characteristics. You may cast your net too wide or too narrow and then fail to redirect appropriately. Sometimes misinnovation also occurs when we do not read trend signals correctly, which is always tricky. One way to orient you and your organization is to benchmark yourself not against your immediate competitors but instead against much larger market disruptors. An example of this not occurring is when streaming services took over home entertainment after decades of people borrowing tapes from retail stores.

For example, Blockbuster specifically failed to replicate the utility that Netflix began bringing to the very customers Blockbuster relied on. Early on, Netflix was a monthly subscription that mailed select DVDs of your choice to your home, eliminating the need to stop at a store to return a film. And, of course, Netflix evolved. As Netflix continued to innovate and change the landscape of home entertainment by shifting its model to offer the first streaming services and then producing original feature films and original television series, Blockbuster withered away.

Blockbuster not only failed because it did not adopt some of the new market practices that made its customers’ lives easier; the company also did not foresee the future direction of home entertainment at large.


The refinement stage’s most important (and most missed) element is mapping an innovation to a relevant business or customer need. It is easy to get blinded by the new shiny object and not work through the more strategic side of planning. Another misstep is creating something for the sake of it—the world does not need a “new” toothpick, after all. We will unlikely improve or enhance the current design’s utility.

Misinnovation can also occur during this stage if you miss potential applications or audiences of your product. An example is when General Electric Healthcare designer Doug Dietz was tasked with redesigning MRI scanners. The original MRI model was designed with a medical-industrial style and was quite intimidating. Dietz spent two years reimagining the machine into a much sleeker, modern design that allowed patients to see through to the other side, which helped them with anxiety during the scan.

When traveling to locations where the new machines were being installed, Dietz encountered a young family with a six-year-old girl who became frantic and hysterical at needing to go inside the new and improved scanner Dietz had just finished designing. Ultimately, the girl had to be sedated to get the scan, which Dietz learned was the case for 80% of children who needed an MRI scan at that time.

Dietz realized he had failed at his job. His design did not consider what patients (particularly young ones) needed. So, he conducted empathy interviews for the next six months and developed a slightly redesigned version to help transport children out of their actual environment and into a fun experience. His idea involved redesigning rooms to look like pirate ships sailing the sea or a rocket ship in space and other fun, exciting environments, some enjoyable for adults, too. These redesigned rooms brought the sedation rate of children down to 8%. Had Dietz and his team conducted the pivotal empathy research that led to redesigning entire MRI rooms earlier, they could have saved many resources.


The experimentation phase—in which we test and learn more about our creation—is wrought with potential pitfalls, from the non-malicious use of too small of a sample size that produces misleading results to fully cherry-picking or even rigging data. Our biases may shine through this phase, potentially rendering an innovation ineffective.

We have seen misinnovation occur during this phase in the development of facial recognition software. In 2019, the National Institute of Standards and Technology found that commercial facial recognition systems used to identify suspected criminals falsely identified Black and Asian people between ten and 100 times more than White people. Additionally, the software had more difficulty correctly identifying women than men and struggled ten times more to accurately identify seniors versus their middle-aged counterparts.

The reason for these discrepancies likely lies in their sample set. Facial recognition software is a form of machine learning, and machine learning requires large datasets to work well. The more data provided, the better. Middle-aged White men are correctly identified at a much higher rate than minorities, women, and seniors because the sample set used to train the algorithm did not adequately represent the end population or provide enough diverse faces to train the system to recognize older individuals or those with darker skin tones.

Using facial recognition software to fight crime, especially in big cities, saves so much time and resources that it is an innovative endeavor. But, if we do not provide innovative technologies like this with the proper information that produces reliable results for everyone, it is most certainly a misinnovation.


During the collaboration phase of the innovation process, too much collaboration and rampant groupthink can negatively affect your innovation. Two great examples are the proliferation of the myth of learning styles and the 70-20-10 concept. What do these two concepts have in common? They are both false, yet, they have been adopted widely, and the ideas have proliferated and affected instructional design in both the corporate world and the grade-school classroom for decades. The 70-20-10 concept describes how learning happens on the job. This idea speculates that 70% of learning occurs from day-to-day work experiences, 20% from developmental relationships, and 10% from formal training. The 70-20-10 model—while “a useful reminder that employees are learning all the time,” as Jefferson and Pollock pointed out in 2014—is a theoretical model based on circumstantial and anecdotal evidence, not scientific fact. Learning styles refer to the presumption that everyone has a dominant learning style, suggesting that people are either auditory, visual, reading/writing, or kinesthetic learners. This idea is so abundant that most people identify with a particular learning style. However, in reality, these learning “styles” are more preferences than deeply ingrained parts of our nature. Scientists have also debunked the idea of learning styles. In fact, in 2019, the American Psychological Association published an article describing why the essentialist belief in learning styles can negatively impact students’ learning experience, who could all benefit from various learning approaches. Learning styles and the 70-20-10 concept make intuitive sense, but that doesn’t mean they are accurate or have not been impacted by confirmation and frequency bias. When we consistently communicate ideas improperly, like these were, falsehoods seep into processes and how work gets done for entire industries.

Implementation and Adoption

Unless innovators are mindful of their innovation processes and the pitfalls that can occur during each stage, the implementation and adoption phase of innovation is when most misinnovation becomes visible. We can see the over-adoption of a product (is it possible our society is experiencing an over-adoption of technology?), extensive adoption diffusion (like the zipper—it took nearly a century to really catch on), and all the little things we may have missed along the way. The issue with the implementation and adoption phase is not, however, that it helps us to see what we’ve done more clearly and from a different perspective. Innovations, along with their peculiarities and specifications, can become set in stone through wide-scale adoption. A notorious example of this misinnovation occurred when Bill Gates (allegedly) set personal computing back with his assumption that the 640K cache would be enough memory for most people. Although Gates denies ever having said this, he and his team rushed out a subpar product without sufficient memory, arguably significantly setting the PC market back. That amount of memory was arbitrary, but once it was adopted, it proliferated in the market, and that number became locked in. Adoption can hinder the ability to be flexible if we are not careful.

Responsible Innovation Unleashes Potential

It is natural to want to innovate and create new things, and working in environments where innovation happens often is exciting. But we must be careful and manage our expectations about what is helpful and feasible and ensure our creations are mapped to actual needs, whatever they may be.

By taking a methodical approach to our innovation process, we can ensure we bypass misinnovation and are not simply chasing after a shiny new object for the sake of it. Avoiding possibilities of misinnovation can ensure responsible growth and development, sparing much regret down the line.

For more information about navigating the challenges of learning innovation or how to unleash your organization’s innovation potential, check out our blog and award-winning podcast on the GP Strategies website.

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