Because of the complexity and lack of AI
experience within the enterprise, it is often
difficult to determine whether candidates
actually have the skills needed.
inherently inexperienced, as significant AI efforts have only recently been
undertaken.
Third, because of the complexity and lack of AI experience within the enter-
prise, it is often difficult to determine whether candidates actually have the
skills needed. It seems there are lots of resumes these days with the term data
scientist on them. But does your hiring manager know enough about a given
discipline to tell whether a candidate actually has the required skill, or is faking it?
We’ve repeatedly seen examples of enterprises struggling to get the right can-
didates hired. More often than not, we see this as the biggest impediment to
moving AI initiatives forward with the desired velocity.
Conclusion
We’ve now seen how loaded the topic of AI is, and how it can take a bit of dis-
section to be able to compile a useful working definition. We’ve also broken
down many of the moving parts to provide a high level overview of what’s
needed. Lastly, we’ve looked at some of the major challenges large enterprises
face in getting their AI initiatives off the ground. As the topic continues to gar-
ner lots of press attention, are you ready to get going?
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