Today’s hype cycle for generative artificial intelligence is booming. However, within that clamor is a growing sentiment, particularly among chief information officers, to tamper excitement with realism. CIOs are increasingly skeptical of the gen AI hype, questioning its value and adopting a more tactical approach to its integration.

“Gen AI is going to cost three to five times that of traditional systems do the same thing,” said David Linthicum, enterprise technology analyst at theCUBE Research. “If they’re not funded, it’s going to be very difficult for them to make the move. That’s the concern right now. You’re seeing CIOs pushing back on a lot of the hype that the vendors are starting to produce. I don’t know how many billions of dollars are spent on generative AI marketing, but it’s got to be a huge amount.”

Linthicum discussed this and more during a recent AI Insights and Innovation podcast episode from theCUBE, SiliconANGLE Media’s livestreaming studio. He explored a tactical approach to the gen AI hype, proposing lean AI and smaller, planned deployments, allowing enterprises to realize the benefits of AI without being overwhelmed by its complexity or cost.

Applying pragmatism to the gen AI hype

Generative AI systems can cost three to five times more than traditional technologies. The pressure from boards and stakeholders to adopt AI solutions is immense, but without proper funding, CIOs face serious challenges. These financial constraints make it difficult for companies to explore the technology’s potential fully, and as a result, many are questioning whether it’s worth the investment, according to Linthicum.

“They’re not seeing a fit between the perception of value that gen AI brings and what they’re actually able to do,” he said. “Ultimately, CIOs are facing challenges, and I think the main one is trying to identify effective use cases for gen AI.”

The challenge lies in aligning AI capabilities with specific business needs. Organizations may experiment with AI, but without a clear roadmap or defined use cases, they risk investing in solutions that don’t meet real business requirements. Generative AI may offer agility, cost savings, and innovation, but demonstrating a clear return on investment remains difficult, particularly in the early stages of adoption.

“CIOs in terms of looking at how it’s going to fit within their stack are not having as much luck in finding those killer use cases,” Linthicum said. “They want [the first few projects] to be a knock out of the park and have value metrics where they can demonstrate the ability to bring value back to the business based on the money being spent.”

Rather than building massive, complex large language models, many organizations choose smaller language models that focus on niche applications such as supply chain management or inventory control. This is the “lean AI” concept, and it entails purpose-built models able to deliver value without the high costs and complexity associated with larger systems, according to Linthicum.

“We have Agentic AI and certainly using things like small language models where we’re leveraging generative AI and AI in general for more tactical implementation,” he said. “It’s dealing with supply chain integration, dealing with inventory control. We’re not building LLMs, and I don’t think the businesses out there are going to get the value from building huge LLMs that they think they’re going to get.”

Here’s the complete episode:

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