A Practical Guide to KBQ-Led Enterprise AI Strategy

by Kevin Troyanos

As we approach the peak of the artificial intelligence hype cycle, enterprises need to take a strategic approach to implementing their cutting-edge tech solutions.

When “big data” reached the apex of the technological hype cycle several years ago, one would have been hard-pressed to find an enterprise that wasn’t scrambling to accumulate massive unstructured datasets — whether they needed them or not. The haphazardness of this bandwagoning was arguably a large part of why, in 2015, around 60 percent of big data projects were failing to advance beyond the piloting and experimentation phase.

While big data analytics has subsequently become a fixture of the enterprise landscape, its rocky path to prominence is symptomatic of the ill-conceived approach large companies often take to the adoption of emerging technology. Instead of carefully considering how the tech solution du jour will help solve a distinct business problem, overeager enterprises tend to focus on adopting the solution — in whatever form — as quickly as possible.

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