When Shell first launched its AI Center of Excellence in 2013, it wasn’t even known as AI, but as predictive analytics, and it was tiny.
“It was just me,” says Dan Jeavons, who now leads what’s known as Shell’s Data Science Center of Excellence, which includes 180 full-time data scientists and engineers.
“They expanded it somewhat,” he says.
Originally, the purpose of the CoE was to support business units working on analytics-related projects.
“We had a whole bunch of business-line projects spinning up all over the place,” he says. “And we had some leaders who recognized that we needed to coordinate what we were doing. The original intent was to facilitate what was going on, but to allow the businesses to do the work themselves.”
But AI requires deep technical skills, and a business unit’s generalist data scientist may not have sufficient expertise in niche topics such as deep learning or machine vision or natural language processing to make the best use of it. In addition, allowing business units to do their own thing resulted in people taking different approaches to the same problems.