How to Make AI Work for You, and Why It Won't Replace Software Engineering
At Gartner's annual expo, analysts offer a deeper dive into how businesses should approach AI, from when to avoid gen AI and how to scale for a future dominated by the technology.
Not surprisingly, AI was a major theme at Gartner's annual Symposium/IT Expo in Orlando last week, with the keynote explaining why companies should focus on value and move to AI at their own pace. But I was more interested in some of the smaller sessions where they focused on more concrete examples, from when not to use generative AI to how to scale and govern the technology to the future of AI. Here are some of the things I found most interesting.
"AI does not revolve around gen AI, although it might feel like it right now," Gartner Fellow Rita Sallam said in a presentation entitled "When Not to Use Generative AI." She noted that while boards may now be asking technology leaders to use generative AI, in reality many organizations have used AI of different kinds for many years, in things such as supply chain optimization, sales forecasting, and fraud detection.
Sallam shared data from a recent survey that showed that gen AI is already the most popular technique that organizations are using in adopting AI solutions, followed by machine learning with things like regression techniques.
She stressed that generative AI is very useful for the right use cases, but not for everything. She said it was very good at content generation, knowledge discovery, and conversational user interfaces; but has weaknesses with reliability, hallucinations, and a lack of reasoning. Generative AI is probabilistic, not deterministic, she noted, and said it was at the "peak of inflated expectations" in Gartner's hype cycle.
She warned that organizations that solely focus on gen AI increase the risk of failure in their AI projects and may miss out on many opportunities.
Gen AI is not a good fit for planning and optimization, prediction and forecasting, decision intelligence, and autonomous systems, Sallam said. In each of these categories, she listed examples, explained why gen AI fails in those areas, and suggested alternative techniques.
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