Why AI projects fail, according to EPA CIO

US Environmental Protection Agency CIO Carter Farmer has a blunt message for AI hype-chasers: Shiny-object syndrome too often drives teams to leap into AI without defining a clear use case or vetting their data, leaving them to wonder why it doesn't work.

Speaking during a webinar hosted by FedInsider, Farmer said that AI isn't a cure-all business operations wonder drug as many people assume. It must be brought into a business with a specific use case in mind, the EPA CIO said. 

"People hear AI and they think it can solve any problem," Farmer argued. "They see what it's done at other organizations and jump in without asking the right questions."

Many times the problem you're trying to solve doesn't need AI

He explained, "Many times the problem you're trying to solve doesn't need AI," adding that jumping to keep up with the latest buzzwords can ultimately slow growth down rather than accelerate it. 

"If you don't ask those questions up front you can go down a road pretty far before you realize you need to back up, reverse and figure out where you should have gone down first," Farmer said. 

That's a reasonable take when you consider investment returns on AI have been found to be pretty poor of late, with just one in four AI bets are paying off according to a recent survey of 2,000 CEOs.

Farmer made his comment in response to Ed Bodensiek, customer experience market leader at government tech services firm Maximus.

Bodensiek said he's heard other government tech leaders observe that there are too many bots at government agencies. His company has developed an entire "mission readiness assessment" process it uses to determine the appropriate approach to solving a problem - which might not end up involving AI at all. 

So what's going wrong with so many AI implementations? For Farmer, it comes down to two things: Processes and data. 

Applying AI to business processes doesn't mean simply mapping what one does now onto an AI, Farmer explained. 

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"What people should be doing is redefining and reimagining that process as it would apply to automation," the CIO said. "[The] lift and shift mentality has to be stopped in its tracks."

As an example, Farmer described a presumably hypothetical situation where a form that had been part of a business process for years was still being filled out, even though the same function was now accomplished automatically, and digitally. Without examining the business process behind that form, it might get included in an AI workflow when it wouldn't need to be. 

A process review can force teams to ask really critical questions - like whether AI-ifying a particular thing is really worth the effort. "Value add might come at 2x, 3x or 4x the cost of doing it manually," Farmer explained. 

In short, if AI is only as good as the data that goes into it, an AI project is only as good as the data that went into planning it. 

Farmer has shown his work on that end. The EPA keeps an inventory of AI applications at the agency, part of his philosophy of "finding the actual best use cases where AI can be best applied," as he explained during the webinar. ®