I was the Design Thinker for the Wells Fargo Artificial Intelligence Enterprise Solutions group. Our group helped Wells Fargo's 17 lines of business understand how machine learning and natural language processing could improve operations.
I used Design Thinking methodology to provide a solution-based approach to solving problems, resulting in better-defined projects with improved clarity on the data needed for ingestion. My process focused end-to-end, from conception to data ingestion and then model exploration, seasoning, and deployment to production.
Early in the engagement with a new group, I'd identify a vertical team consisting of executive sponsors, crucial stakeholders, and data owners. This created efficiency by putting the right people together earlier and derisked data ownership and availability problems later in the process.
I worked through multiple organizational challenges, including an executive perception that design thinking was too emotional and that data science should mirror a software development lifecycle both in time and scope. I addressed the first problem by tailoring my message to fit the environment. Empathy building became "know your customer," and so forth. The second problem was a misunderstanding of what data science is and that it is a process of the scientific method. A thoughtful demonstration of how hypothesis-driven experimentation led to greater understanding, and eventually, a solution improved this understanding dramatically.
