How I Leveraged My 5-Year-Old to Prepare for AI
Over the span of my career, I have had opportunities to mentor future data-driven business leaders. The advice I used to give primarily revolved around the hottest analytical tools and certifications and how to tell stories through data. Five years ago, however, my advice evolved in a very dramatic way, based on a reasonably benign event.
My wife, our two daughters and I were on a multi-state road trip. Early on, we decided to make a pit stop. My wife gave the girls $5 each to buy goodies for the road — with no conditions. Unleashed from the shackles of healthy snacking, my older daughter set about making the most her newfound economic freedom. Analytically inclined, my oldest began optimizing for the right combination of quantity, quality and taste that would provide her with the maximum overall satisfaction. My younger daughter (five years old at the time), quickly picked up her favorite fruit candy, asked my wife for a suggestion and purchased that, as well. Eager to get back on the road, I asked my oldest to finalize her decision quickly. My request was met with a look of sheer horror and frustration as she frantically searched for the optimal basket of goods that $5 would buy. With hope that the optimal solutions was only minutes away, she begged for more time to no avail.
Back on the road, my younger daughter offered my wife a substantial portion of the candy she had recommended. Astonished, my wife says, “Sweetie, if you share that with me, you will have less for the trip.” To which my daughter replied, “That’s okay, Mom. I know you like these candies. Can I have another five dollars?” To which my wife uncharacteristically replied: “Of course!” Shocked at these turn of events, my older daughter protested “What? No fair, you can do that!?”
Data Is an Equal Opportunity Enabler
I often think about that incident; especially when I am trying to help clients achieve better results through analytics. This incident is a great allegorical example of why data-driven decisions, when done well, can improve specific results, but many times fail to change the overall game. A 2015 study by KPMG identified operational efficiencies as the primary beneficiary of data and analytics in the near horizon and a more recent study in HBR also confirms that most data and analytics success is still focused on low-hanging operational opportunities. In both reports, business leaders also recognize the transformational opportunities of data and analytics. However, they will also identify an acute need for new and unique skill sets to make those transformational changes a reality.
This brings me back to the car ride. Before you assume this is a lesson about how customer empathy beats algorithms, I can assure you it is not. Not only has my younger daughter’s strategy failed on several other occasions, but I have also seen plenty of well-researched market advice from customer-centric strategy firms fail, as well. Nor do I believe this anecdote implies optimization leads to strategic myopia. (This is also not about which kid I am betting on, as they both manage to amaze and worry me in equal doses.) Instead, the lesson for me is that while analytical rigor can be foundational to disruptive innovation, the optimal solutions algorithms provide only reflect the audacity of the optimizer’s vision.
The body of recent research on successful disruptors dispels the belief that they are solely the product of a brilliant idea conceived by a highly intuitive visionary. Instead, their very existence is often an optimization exercise involving many experiments. Not only do successful new entrants go through many failed iterations, but they also emerge through the crucible of other competing ventures with similar industry disrupting objectives. Once emerged and unleashed, there is still no guarantee that the new ventures are the absolutely optimal solution. One needs only to think of MySpace, AOL and Yahoo if there is doubt. As a result, the body of knowledge on innovation is now focusing around the concept of failing fast, failing early and failing often. A critical component of the "failing for success" strategy involves testing, measuring, and optimizing rapidly and regularly and but also involves having a broad view of the playing field and the bravery to challenge existing assumptions.
AI Whisperers Wanted
The career implications of these trends for data-driven talent are significant. As analytics takes a central role in strategic business functions, it does not necessarily mean that my fellow quant jocks will rule the future. This is because traditional optimization algorithms are just beginning to transition into artificial intelligence-based solutions with the ability to learn on their own and at some point human talent will no longer be needed to build models. If you are in analytics today, it will be important to keep up with the evolution of AI solutions, but even more critical is developing your analytical creativity and bravery.
Shiv Gupta is a principal at Quantum Sight LLC. He helps clients develop data, analytics and digital technology strategies to drive compelling relationships with customers. In this blog, he'll discuss ways in which marketing organizations can regain their strategic bearings and leverage their tech stack for both short-term and long-term gains. Reach him at email@example.com.