‘Popular’ AI use cases aren’t those delivering results. Gartner says focus on the basics for success and easy wins

IT leaders need to get back to basics and prioritize AI integration in areas where it can deliver real impact, according to Gartner, and that requires a more considered approach to adoption.

Tina Nunno, distinguished vice president and Gartner fellow, told ITPro that many IT leaders are still falling into a trap of following hype when it comes to AI, and it’s hampering success.

“We’re human beings, and we follow hype,” she said. “We're curious about what we last saw published, or in a keynote, or we're curious about what the vendors are talking about.”

“But we still have to have, I think, a reasonable balance and need to think about what’s the business case? Some of the basics still really matter here.”

Nunno’s comments come after a Gartner survey found the most popular AI use cases are rarely the ones that deliver real business impact and value.

The survey of C-suite executives warned that adoption projects are being influenced by “heavily hyped” applications of the technology over those that actually generate value.

“We calculated what the most common use cases were, and then we compared them to the ones where they were getting the most reliable rates of return, and they didn't match,” Nunno told ITPro. “There wasn't even a lot of overlap.”

Choosing the ‘popular’ AI use cases doesn’t always work

Gartner’s survey found that the three most popular AI use cases included cybersecurity threat detection and response, identified by 54% of respondents, alongside IT service desk automation (54%), and automated code generation and refactoring (44%).

Yet only the latter of these use cases ranked among the use cases that delivered positive operational or financial returns.

Intelligent IT asset and cost optimization (40%), synthetic data generation (28%) and automated code generation and refactoring (23%) all ranked as the most successful use cases.

While hype is a key factor behind this disparity, Nunno told ITPro that it shows some ‘high performers’ are taking a more surgical approach to integrating the technology.

These businesses are focusing on overhauling or streamlining specific processes in a highly strategic manner, rather than taking a slap-dash approach and seeing what works.

“When we look at what’s most common, sometimes they’re more foundational AI implementations,” she explained. They’re not specific to the business unit. They’re not specific to the specific function that they’re attempting to perform.”

Nunno added that approaching adoption with a degree of generality rarely delivers tangible returns. It’s why having a clear-cut goal from the get-go is critical.

“There was a bit more specific nature in those that actually were showing returns,” she said. “They appeared to be more targeted, and as a result, because they were more targeted, they seem much more likely to deliver”.

Gartner’s findings align closely with recent research from Dell Technologies, which also highlighted the importance of clear goals when it comes to AI adoption.

More than half (52%) of AI ‘front runners’ – those Dell identified as having the most success with the technology - had “clearly defined specific AI use cases”.

Be prepared

A lack of clear objectives and strategies is having a direct impact on scaling of AI across the enterprise, Gartner found. Indeed, just 22% of respondents said they’ve successfully scaled the technology across multiple business units.

Nunno told ITPro that this reinforces the importance of laying solid foundations before even embarking on projects. Building maturity is critical.

“For some organizations, we've seen there's a very strong correlation between the maturity of the organization and their ability to take advantage of AI,” she said.

“They haven't yet grown the AI skill sets that they need to bring the AI to scale, and that can be anything from the current state of their data, for example.”

Nunno admitted that this is often easier said than done. Basic foundational tasks like getting data in good shape and order are “a lot of work”.

“There’s still a very real investment that many of our clients are telling us they’re putting in to build those foundations to actually scale AI in the way that they would like,” she said.

“There’s a lot of heavy lifting that sometimes has to happen.”

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