A practical guide to KBQ-led enterprise AI strategy

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When “big data” reached the apex of the technological hype cycle several years ago, one would have been hard-pressed to find an 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.

As 2018 draws to a close, enterprises are facing growing pressure to engage with a new cutting-edge technology: artificial intelligence (AI). Recent research shows that 10 percent of companies currently use AI, 10 percent plan to start using it within the next 12 months, and another 10 percent plan to start using it within 12 to 24 months. Among companies employing 5,000 or more people, however, these splits fall at 31 percent, 18 percent, and 16 percent, respectively.

In other words, for enterprise-scale organizations, AI is on the brink of arriving in full force. If we are in fact just two years away from nearly two-thirds of enterprises adopting AI in earnest, it’s incumbent upon every enterprise decision-maker to start crafting a strategic plan for AI implementation.

To that end, high-level stakeholders would be well-advised to keep their organizations’ key business questions in mind as they go about drafting their AI implementation roadmaps. Not unlike big data, AI will be most effective not when used for its own sake, but when used to pursue actionable answers to clearly articulated questions.

Putting actionability first

The success of enterprises’ preliminary AI efforts hinges on the kinds of questions they ask. “In which areas do we want to leverage AI to stimulate growth?” “Which of our processes do we want to make more efficient through the use of AI?” “Which key business questions do we hope AI will help us answer?” These — not, “How can we deploy AI faster and/or more extensively than our competitors?” — are the hallmarks of a sophisticated AI implementation .

And while, ordinarily, the viability of a new project depends on both its actionability and its potential business impact, when it comes to AI, it’s the former that’s of the utmost importance. Granted, the higher the business impact, the better, but incremental improvements driven by nascent AI initiatives tend to compound remarkably quickly, and thus should never be discounted.

That said, assessing an AI-powered tech solution’s actionability — even one that has been deliberately chosen in view of a set of clear business questions — requires a comprehensive review of the environment into which it’s being deployed.

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