IT leaders need to stop ‘pushing AI for the sake of AI’

Enterprises are falling into a trap of using AI when it’s not necessarily needed, and it’s costing them dearly, according to Roger Lee, AVP of solutions consulting at Appian.

Speaking to ITPro, Lee said the hype surrounding AI - particularly agents - is pushing some IT leaders to apply the technology in an ‘AI for the sake of AI’ type approach.

“I think a lot of organizations and a lot of senior executives and staff within organizations have been told to ‘do AI’,” he said.

“So rather than just pushing AI for the sake of AI, look at what the decisions are we’re trying to improve, what data we are using, and where does that human oversight sit.”

Lee’s comments come after Accenture told staff to stop using AI for unnecessary tasks amid surging costs. As adoption of the technology accelerates, he told ITPro that enterprises need to have a serious conversation on how they integrate AI tools.

Agents vs business rules

Pursuing the ‘AI for the sake of AI’ approach means that many businesses aren’t just incurring huge costs, but adding needless complexity to processes that were already streamlined prior to the generative AI race.

“The key thing is really trying to identify the right place for AI versus just trying to use AI to solve everything,” he said.

“In many cases, if it’s straightforward automation, if there’s a business rule where you’ve got a definitive yes or no answer, then sometimes business rules and workflows that result in a definitive answer are the right way to go.”

Admittedly, Lee said there are instances where agents are necessary, especially with regard to research or complex problem solving tasks. Acknowledging that this is a case of different strokes is crucial, however.

“I take an example from insurance with an automation claim, where you wanted to do some research and do some work, then an AI agent might be the right solution for that,” he said.

“But it's really understanding the right place to use a role versus an AI agent, and the impact that that can give.”

This is particularly relevant in regulated industries, Lee said, where margins of error can have huge ramifications.

Agents are highly effective in terms of dynamic reasoning, he noted, but in cases where businesses need a “really definitive zero error response” then IT leaders need to know where and when to automate.

“Regulated industries is another area where humans in the loop are really important,” he said.

“It's what makes processes accountable. So to ensure that not everything is automated, where you have things that are exceptions or outliers, that you absolutely have humans in the loop to respond to those.”

Shoehorning AI into processes

Enterprises have found themselves shoehorning AI into areas and processes that aren’t applicable, research from Gartner shows. Moreover, they’re expecting near-immediate returns with the technology.

Ironically, a key factor behind this lies in the desire to deliver returns on investment with AI. Yet by doing so they’re less likely to unlock any benefits.

Speaking at the time, Melanie Freeze, director of research at Gartner, said that many AI initiatives are “overly ambitious or poorly scoped” and don’t align with “real operational needs”.

Ultimately, Lee told ITPro that as with any adoption process, AI should still be viewed as a “business pull rather than technology push” approach.

“I think that the whole value that you can get from AI within an organization is all around what the business needs, rather than what the technology can push to the organisation,” he said.

“First and foremost, what problems are we trying to solve, rather than where can we use AI, and that changes the perspective on what we're trying to do,” Lee added.

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