A certain level of FOMO seems to be driving businesses to spend big on generative AI, but scaling the tech up can be tricky.
That’s one takeaway from a new survey released by IT consultancy Cognizant and Oxford Economics. It found that around 70% of companies report they are not moving fast enough to implement AI, with 82% worrying a delay in implementation could lead to a competitive disadvantage. Only about 26% of respondents reported having implemented cross-enterprise use cases.
The survey is a companion to a larger report that the tech firm released earlier this year that aimed to map out a timeline for the rise of generative AI and its effects on jobs. That report predicted that up to 13% of businesses will have adopted the tech in the next three to four years, with that number rising to 46% over the course of the next decade.
It comes as processors needed for data centers continue to fly off of the figurative shelves and companies keep investing in AI infrastructure, although some investors have begun to wring their hands about a potential bubble. Cognizant itself anted up $1 billion to be spent on the tech over the next three years, and other consultancies have put forth similar investment pledges.
Naveen Sharma, SVP and global practice head of AI and analytics at Cognizant, said companies often face a handful of key hurdles in scaling AI. They should look to ensure their data foundation is in order across the board and understand the tech stack needed, he said. Sharma also advised companies to re-imagine business processes beyond simply adding a chatbot to existing operations and to encourage cultural shifts around how employees think about AI as a tool.
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“The most prevalent use case is, ‘Oh, we’ll build a chatbot,’ and a chatbot is a companion use case. It doesn’t take the fundamental business process that you have and change it. It just sort of tries to accelerate that process or remove some of the friction from that process,” Sharma told Tech Brew. “Until we get to a place where the fundamental business process itself…is re-imagined, I think the impact is going to be a little bit of a slow drip.”
Sharma mentioned AI agents helping users book airline tickets or tools that can help speed up time-consuming office processes in the healthcare industry as a couple examples of how companies are transcending the initial chatbot experimentation phase.
As for whether a chill from a potential AI winter could put a damper on any of Cognizant’s timelines, Sharma said he expects tech adoption to continue to grow through any dip in the hype cycle, if at a more gradual rate.
“My sense is the trajectory is going to be upward, no matter what the degree by which it goes up,” he said. “Is it a hockey-stick curve? Is it one of those small, slow, steady increases? I think that’s going to be something to keep an eye on. My sense is that at the pace at which this technology is moving, we’re going to end up somewhere in between.”