Analyzing IBM’s operational strategy for hybrid AI – SiliconANGLE

As artificial intelligence expands to cover an increasing number of industries, an emerging topic is hybrid AI — which combines the hybrid cloud model with AI’s data processing capabilities.

What constitutes hybrid AI practically and how (if at all) is it poised to add concrete value to the existing AI transformation wave?

“The first fundamental thought out here is that there is a very significant symbiotic relationship between hybrid cloud and AI,” said Varun Bijlani (pictured, left), global managing partner of hybrid cloud services at IBM Corp. “Today, AI is supremely accelerating the execution of hybrid cloud. At the same time, you can’t really go from pilot to production to scale without a robust architecture. I think that’s the first reason why this conversation is so relevant.”

Bijlani and Hillery Hunter (right), chief technology officer of infrastructure and general manager of innovation at IBM spoke with theCUBE Research’s Dave Vellante at IBM Think, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the combination of hybrid cloud infrastructures and advanced AI tools, allowing businesses to optimize their operations and drive innovation. (* Disclosure below.)

IBM’s strategic position in hybrid AI

From being nonexistent a decade ago, hybrid cloud infrastructures have become the operating standard. A recent IBM study reported that 56% of respondents have hybrid cloud operations. This transition reflects a broader industry trend toward integrating diverse and heterogeneous cloud environments rather than relying on a single, unified platform.

“Our differentiation is going to come when you can take your data and use that with those right models,” Bijlani said. “I think that’s where we’ve decided to make our conversation much more open — and you heard today, Granite is now open-sourced.”

Hybrid cloud and AI  have a symbiotic relationship. AI accelerates the execution and utility of hybrid cloud infrastructures, while a robust cloud architecture is essential for scaling AI from pilot projects to full production. This mutual enhancement underscores the relevance of combining these technologies to maximize data value.

“The AI can be where your data is, where your workloads need to be and where your customers are,” Hunter said. “Our platform approach to AI means that managing, viewing and optimizing a bunch of different environments and the developer experience can all be consistent.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of IBM Think: 

(* Disclosure: IBM Corp. sponsored this segment of theCUBE. Neither IBM nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE