Theoretical solid-state physics and quantum mechanics (the transistor and superconductive materials), a weird programming language called LISP (enabler of much early AI), number theory (cryptography, the basis of all e-commerce),novel network protocols (the Internet),databases built on the mathematical theory of high-dimensional relations (SQL and 4th generation languages), finite automata and formal grammars (compilers, and natural language processing), quantum computing (wait and see). And why is IBM experimenting with black holes in Zurich?
Can you imagine the director of a research center garnering support for initiating the foundational research that led to one of these breakthroughs by performing a cost/benefit analysis? And creating timelines and milestones? Highly unlikely. Fortunately for society, there are research centers– academic, government, and industrial– whose purpose is taking big risks on highly speculative work, thus allowing for the assessment of potential in the abstract, without the necessity of predicting a specific outcome or target application. This is a unique model, one that is able to spawn unpredictable innovations.
Let’s first acknowledge that such a unique innovation model and its objectives create a radically different dynamic than exists in more traditional commercial sectors, such as retail and consumer packaged goods manufacturing. Nevertheless, there are lessons to be learned. It is notoriously difficult, yet vital, for the director of a science or technology entity, or funding agency, to say yes or no to promoting a particular avenue of research. This has been true through the annals of science. But there is an intriguing new dynamic that updates the age-old equation of what research to promote, a dynamic that has been created by the ascendency of fields like data science and machine learning. This has had the effect of moving potential cutting-edge R&D from the exclusive province of academia, national laboratories, and technology companies to customer-centric commerce sectors. Such sectors have invaluable data and huge opportunities, yet their enterprises are unaccustomed to performing speculative, longer-term foundational research. For CIOs and CSOs who operate in these worlds, knowing what to advance is an elusive, vital, and nascent art. The lessons from the more developed innovation models are transferable, but only with precise adaptation.
"In any sector potentially benefiting from foundational research, the key is research freedom, but with brilliantly chosen focus"
There are several considerations that make it attractive for retailers and CPGs to control, encourage, and support foundational, yet focused, research, and these differ from the motivations for advancing the more speculative types of R&D conducted at technology-oriented entities:
• It’s our data, and our customers.
• We have deep domain knowledge that exists nowhere else, and, understandably, we wish to keep it that way.
• We are not looking to create a commercial product, but rather a capability to help our business, and our customers, and not our competitors. We not only don’t look to commercialize any new genius that we create, we want to keep it cloaked for as long as possible.
• It is unlikely that a technology entity, aiming at the general marketplace, is going to create a solution that is a good fit for the needs and strategies of a company in the customer commerce sector; advancing the research in-house often is the only path.
Hence, the factors that in a research center might go into the equation of “let’s pursue exotic ideas and see where it leads” may not apply directly to industries outside of the technology sector. The outcomes of the R&D need to be linked to a targeted commerce, to understanding and serving the customer, there by contributing to their own financial health. The brilliance of a scientific/technological visionary is to have the taste and instincts for what could lead to something of value for the sponsor (e.g., their company).The wisdom of the sponsor is to know what scientific visionaries to trust, and to provide them adequate freedom, resources, and runway to pursue.
Freedom yes, but with guardrails. Yet, the guardrails must flex, because often we simply cannot know the exact application, the exact embodiment, the exact ROI, nor the exact timeline. In this sense, there is much shared with the more speculative research, in both unbounded opportunity, and difficulty of assessment. In either setting, it would be fettering if every innovation’s value had to be predictable or measurable, in order for it to advance.
It is absolutely correct that we can’t go willy-nilly down every path that excites the innovator in us. But, if we accept that applying standard value measures as a prerequisite to pursuing foundational research is too limiting, what, then, do we offer as alternatives for the assessment process? Here are some criteria:
• Have we performed due diligence to determine if a solution already exists and in such a form that it can be applied with very little customization?
• Do we have a firm grip on the foundational science that is required? Does our team have research credibility in this space so that we have reasonable potential for success?
• Will the technology allowing exploitation of the scientific solution be accessible to our company? This is an important factor, but it can’t be applied too myopically, since history shows that technology to implement futuristic ideas eventually does emerge.
• If the initiative is wildly successful, how and where could it potentially have an impact? This can be far reaching, but there must be a plausible outlet for our company.
• If it’s a five-year project, what happens along the way? Are there potentials for providing some incremental value or learnings as the research progresses? Though an affirmative answer is a strong argument for the go-forward side of the equation, incremental value should not be made a necessary condition to advancement.
• Can a kill decision be reached relatively quickly, before investing huge resources beyond the scientific researchers themselves, (e.g., hardware, other teams).
• How much would it hurt us if our arch-competitor (or unknown startup) perfected and deployed this technology after we’ve decided not to pursue. Even if we’ve decided not to pursue fully, can we at least protect our business model against a disruptor?
Not every innovation leading to value has a value that is predictable in advance. In any sector potentially benefiting from foundational research, the key is research freedom, but with brilliantly chosen focus.