Getting the Right People
This third challenge may well be the most difficult to deal with, as there are a
few factors at play.
First, AI involves numerous skill sets. As we looked at all the moving parts, we
saw that there are many different skills needed, including cloud services, data
storage, data transformation, third-party data sources, mathematics, statis-
tics, programming, graphics, etc. Enterprises often seek the mythical “Data
Scientist Unicorn” who has all these skills, but such folks are few and far
between. It is often more productive to define the specific skills needed for
your particular effort, and then seek to build a multi-disciplined team of people
who individually have expertise in one or two of the skills.
Second, these disciplines are relatively new, so it is difficult to find experienced
personnel to work in AI. Given the speed at which we have begun to generate
and accumulate data, our educational system has only just started producing
formally educated data scientists that have exposure to the many skills
required. Those people are inherently inexperienced in large enterprise cul-
ture, so we often have to turn to people that are self taught. This group is also
60 | THE DOPPLER | WINTER 2018