UPDATED 14:10 EDT / APRIL 15 2024

Scaling enterprise gen AI use cases: HCL's Siki Giunta and Alan Flower discuss at Google Cloud Next. AI

Gen AI unleashed: Transforming industries through tailored tech solutions

Having sped past the speculation and exploring phases, artificial intelligence is now nearing maturity. From the manufacturing to telecom and life sciences fields, companies are taking steps to implement gen AI in aller valuable operational area possible.

As the gen AI wave sweeps through, how is the tech poised to overhaul industries and drive efficiency, productivity and profitability?

“We have a great solution with the Cloud Native Labs; we just funnel all our requests to one spot and [they] help us understand the magic use cases,” said Siki Giunta (pictured, left), executive vice president and head of CloudSMART and industry cloud consulting at HCL Technologies Ltd. “We have a year of experience, but we have decided that the upper layer of AI, the industry-specific [layer], is the one that is going to impact the business the most. There are some horizontal use cases we are taking to market. The last thing is we partner with Google on Gemini.”

Giunta and Alan Flower (right), executive vice president and head of cloud native and AI labs at HCL, spoke with theCUBE Research analysts John Furrier and Rebecca Knight at Google Cloud Next 2024, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed AI and cloud-native transformation, providing a glimpse into the evolving needs and challenges enterprises face today. (* Disclosure below.)

From tepid exploration to mission-critical gen AI execution

Specificity is the name of the game. Directive gen AI solutions must be tailored to isolated enterprise operational areas for true efficacy. Partnering with Google LLC on projects like Gemini, HCL is positioning itself to explore those use cases and industrialize solutions tailored to diverse client needs.

“In the space of barely a year, we’ve seen our clients go from tepid exploration, maybe looking at some horizontal use cases, but then rapidly gaining confidence,” Flower said. “The remarkable thing is we’ve done well over 1,000 workshops with clients now around the globe. We think we’ve got a pretty good insight into those use cases. Fundamentally, it’s the business leadership.”

C-suite business executives have done their homework — they know the particular use cases where gen AI benefits them the most. Subsequently, they approach solutions partners, such as HCL, to ideate the solutions that drive those use cases, Flower explained.

One key challenge with AI implementation, especially in its current nascent stage, is hallucination. Sensitive industries, such as finance, can’t afford those mistakes, meaning those that rush into extensive AI adoption often retrench early, according to Giunta.

“The other big area, especially, for instance, in financial services, they need to have things like 99% governance and security, so there is a little bit of a hesitation on things that impact private information and money,” she said. “But anything that is process acceleration, like ‘I have to do a loan application,’ those things will go fast. My advice is just to understand what the business needs. This is not a magic wand, and the data is not [all] there.”

The rapid pace of client journeys raises questions about the industry’s readiness to scale AI solutions for production. Given the dynamic nature of AI infrastructure,  there’s an emphatic need for ongoing monitoring, model refinement and cost management to ensure sustained value delivery, Flower added.

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE Research’s coverage of Google Cloud Next 2024

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

Photo: SiliconANGLE

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