Artificial intelligence strategies tackle 'reasonable goals' -- not moonshots

The Gartner Data & Analytics Summit I’ve been writing about recently was filled with prescriptive advice for data analytics leaders. Much of that advice, unsurprisingly, was focused on the red-hot topic of enterprise artificial intelligence and how AI technologies — from natural language generation to deep neural networks — are poised to radically disrupt enterprise analytics programs.

Just thinking about how to cover this new reality for our CIO audience was enough to make this reporter sweat. I can’t imagine the pressure enterprise leaders in charge of this stuff must be feeling. Fortunately for those who attended the event, a session by Gartner distinguished analyst Whit Andrews sought to allay the concerns of digital leaders still in the early stages of forming their artificial intelligence strategies.

Andrews, who sets the agenda for Gartner’s AI practice, began by giving a simple definition of AI that differs from Gartner’s. He calls it his “starting point” definition:

“AI projects grant organizations superpowers to classify and predict in ways workers can’t on their own.”

That’s the definition digital leaders should keep uppermost in mind when jumpstarting an artificial intelligence initiative in their own organizations. It will remind them that AI should not be used to reinvent the wheel but to invent.

Andrews also wanted the audience to know a “real truth” about artificial intelligence strategies that tends to get drowned out by the relentless buzz. Their organizations are probably not behind on AI, despite what events like this and the endless coverage in the press might suggest. He pointed to a Gartner CIO survey showing that only one in 25 CIOs are employing AI today in their organizations. Only five in 25 CIOs said they were in a short-term planning stage or actively experimenting with AI, according to the same survey. Only six in 25 are even in the medium- or long-term planning stage with AI.

Of course that doesn’t mean CIOs and digital leaders shouldn’t get rolling on developing and implementing their artificial intelligence strategies. Andrews said the first thing they should do is to go after “historical desires,” that is, use AI to address something they’re already trying to do. The most successful AI use cases come from organizations who first address reasonable and possible goals — not moonshots, he added.

In general, Andrews said organizations starting out with AI should aim for fairly “soft” outcomes, such as improvements to processes, customer satisfaction, products and financial benchmarking.

“True ROI is hard to calculate, which can create a barrier to AI experimentation,” he said.

Other advice for digital leaders for fleshing out their artificial intelligence strategies are as follows:

  • Plan for the transfer of [AI] knowledge from external service providers and vendors to enterprise IT and business workers.
  • Choose AI solutions that offer means of tracking and revealing AI decisions by using action audit trails and features that visualize or explain results.
  • Deploy AI to solve challenges in which you lack the resources or corporate worker base to succeed.
  • Document applications that can improve through training.
  • Fool around with some AI technologies and solutions for pure learning purposes, not ROI.
  • Develop a training, hiring and sourcing plan to build AI capability over the next three years.
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