When I began an exciting transition from academics into the business world several years ago, the first topic I became fascinated with was strategy. In hand I had a treasure-trove of skills in machine learning, big data, data science, programming, cancer research, biomedical research and genomics. Very soon though--during this journey of towards continuous innovation—the importance of strategy in leveraging data insights soon became clear. It is a combination of R&D and strategy, which drives break-through design and innovations.
Two approaches were helpful to gaining a better understanding of strategy. First was experience, i.e. ‘diving in’ with teams under the careful guidance of experienced mentors who saw my potential. The second was the favorite of any Ph.D. or academic – reading and thinking. The very same theme came up when I started my own entrepreneurial journey.
I knew I wanted to start a company, but what company, and where to start? Naturally I gravitated towards my comfort zone, which was data science, machine learning, artificial intelligence, and most of all bots. Now there is something many people do not realize about a start-up, especially if one is new to entrepreneurial endeavors (either within a larger organizations or outside an organization). There is no one right answer. As much as one wants to believe one knows, we are not oracles. We have no crystal ball. All the D&B Hovers, Owler, and Wall Street Journal market analysis will not predict the future. It will not answer whether people want the innovation we have to offer. It’s an experiment. We can’t be sure of the outcome until we test it. How to test whether there is a market for what we have to offer? Strategy. I was amused after my advisor, Jon suggested the same to me, and told me to read “Lean Start-up”. Eric Ries preaches that whether one is an entrepreneur within a larger corporation, or an entrepreneur at a start-up, strategic experimentation is necessary for any new endeavor. The strategy I decided to take was one of A/B testing for company type. I would create one company, which was within my comfort zone, i.e. a rapid prototyping and design-thinking consultancy that focused on machine learning and AI projects, and a ‘B’, a products ‘company’ within the company that focused on making products.
This strategy yielded some interesting results over the 6 months. First to my delight one of my first clients for the consultancy was my former employer, Accenture. Soon other clients came on for exciting projects. The ‘company within a company’, a products division, yielded my writing two provisional patents, and creating a patent-pending voice recognition system which is trained using one’s own voice. We have not launched the product yet, a wide variety of people have expressed interest.
So there was a market for both, and both were using skill sets I could naturally leverage. Yikes! That was unexpected. However an analysis of our profit-and-loss statements, although always showing profit, also showed me a harsh fact. Doing both was not going to be as profitable in the long term as choosing one over the other. Scaling both companies at the same time would require resources beyond our means. So here was another decision landing on my lap. Did I want to continue with the low overhead agency, customer-focused, model or an up-front investment in a products model? Or was there a third option? Maybe even a fourth? Yes, there are many. One thing I am sure of however is that doing work that is interesting and insightful, and capitalizes on one’s strengths, is typically the strategy that breeds success over time.