The impact of creative destruction
With the pervasiveness of digital technologies that is ongoing and new businesses that have been created, the resulting speed of creative destruction of companies i.e. the rate at which incumbents are being replaced by new companies in the Fortune 500 list has increased tremendously.
Several reasons hinder the incumbent companies from adapting to technological shifts – some of the most common sighted reasons are:
- Lack of knowledge about the new technology and associated uncertainties
- Processes being tightly integrated with legacy systems
- Underestimation of the tech impact due to managerial biases
- Inertia to pursue with old technology due to familiarity with old tech
Another reason why companies pursue the trajectory of incumbent technologies is the totally rational choice of managers to focus on core customers as the existing technology performs well on most of the requirements of core customers.
The new technology however initially only caters to the needs of a small subset of customers who are mostly technology savvy. In time, however, the trajectory of the new technology is so fast that it outcompetes the legacy technology in satisfying the needs of most users.
This has been proven time and again, the most recent example being in the electric car industry. Another example is Nokia’s focus on the legacy Symbian platform in the midst of Apple’s introduction of the iPhone.
Why are companies still failing to deliver on their innovations?
What is the implication to a company’s technology strategy in the midst of new technological advances such as Generative AI, AI and ML, new sustainable technologies, etc?
A go-to mantra being followed in many companies is to gain experience in new technologies by “trying out” some projects. However, most incumbent companies still fail to bring breakthrough and radical innovations using new technologies, and the speed of them getting outmaneuvered by start-ups has only increased.
The uncertainty with these new technologies is naturally high and some projects will therefore not meet the expected returns. The same is true of startups, as the majority of them do fail.
The investors, however, mitigate this risk by diversifying their portfolio into multiple independent projects and companies. Can a company bring in such a portfolio approach while developing their innovation projects?
By diversifying the innovation portfolio, companies enhance the likelihood of at least one project’s success.
How to form your portfolio as part of your AI/Data Strategy?
How can companies routinize the portfolio based selection and management of projects within their operating models and structures.
A good starting point is to cluster the innovation portfolio into independent or semi-independent technology platforms. For example, the following level of clusters could be considered for segmenting computer vision use cases into partially independent clusters.
However, to really take the portfolio approach to the next level, companies should further cluster the projects into dimensions like product-market development, technology maturity and merit etc.
Also, different criteria needs to be developed for selecting projects within the portfolio clusters. Applying the same approach and metrics to all parts of the portfolio usually biases the portfolio to investments in one cluster.
Should this introductory blog piques your interest & resonate with your organizational perspective, we invite you to engage with us further. Together, we can delve deeper into refining these strategies for optimal enterprise outcome.
Recommendations and Key Insights:
-
Embrace Technological Evolution:
Established companies must recognize and adapt to the accelerated pace of technological displacement. Instead of solely focusing on core customers with incumbent technologies, companies should invest in understanding and adopting emerging technologies. Being proactive can prevent being sidelined by newer entrants that leverage these advancements. - Adopt a Portfolio Strategy for Innovation: Just as investors diversify their portfolios to mitigate risk, companies should employ a similar approach for their innovation projects. By spreading resources and efforts across a range of projects (especially independent ones) and technologies, businesses can increase their chances of achieving success in at least one initiative while minimizing the impact of any single project's failure.
-
Segment and Tailor Project Selection Criteria:
When forming a comprehensive AI and data strategy portfolio, categorize innovation initiatives into distinct technology-centric platforms. Further refine by considering factors such as product-market trajectory and technological maturity. Adopt specific criteria for each segment to ensure balanced investments and to prevent unintentional biases towards a particular cluster.
