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Initial AI projects should home in on pain points

The hype around AI has reached decibel levels so high that CIOs may wonder why their organizations haven’t pulled off a bonafide AI project. Whit Andrews, analyst and AI agenda manager at Gartner, is of the mind that it’s way too early to be panicking over the role AI will play in enterprise IT strategies.

He tells his clients they should look at AI projects as experimental and thus be guided by the strategies and governance policies used for any experimental opportunity. But he also recommends that experimental AI projects be used to address historical challenges for the organization — specifically, pain points that haven’t been solved because there will never be enough employees to solve them.

The approach, Andrews contends, will move the organization in the right direction — to pin down where the organization can improve, figure out what skills to hire for, increase the use of data science, exploit what infrastructure capabilities are needed — and create the right environment for future AI projects.

He provided an example during a recent webinar presentation of an insurance organization that used image analytics to address an historical problem. The company has to determine if homes have architectural features that are likely to sustain damage during a major storm. Because the company doesn’t have the manpower to send an insurance representative out to every dwelling, it asks the property owner if those features are present.

“And when they get the response, they have to decide: Should we take the response at face value? Should we check the response with a human visit? Or should we decline the response?” Andrews said. “If the company refuses in the future to fulfill the claim because what the homeowner described was not factually correct, that’s an enormous challenge from everyone’s perspective.”

Rather than rely on property owners, the insurance company has started analyzing publicly available images of the dwellings to determine if the architectural features are present. “That means you’re not sending out somebody for every single check. And you’re not spot-checking either. You’re actually doing directed checking,” Andrews said.

If the analysis determines the architectural features are absent, then the company sends out an employee to double check, which Andrews described as “an effective use of your existing staff.”