Data modernization has evolved from being yet another technical upgrade to the critical prerequisite for enterprises attempting to unlock the power of agentic artificial intelligence. 

But while the industry has focused heavily on generative AI models over the last two years, the underlying infrastructure required to support autonomous decision-making is forcing a reevaluation of enterprise data strategies, according to Jeff Veis (right), chief marketing officer of Impetus Technologies Inc.

“We’ve spent the past 20 years automating tasks,” Veis said. “But now it’s a different mindset. It’s about automating decisions.”

Veis and Anand Raman (left), chief revenue officer of Impetus Technologies, spoke with Dave Vellante at AWS re:Invent, for an exclusive interview on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed solving complex data modernization challenges and the economics of cloud engineering.

Inside the data modernization race

The challenge for the modern chief information officer is stitching together disparate eras of technology. Enterprises often manage 40-year-old mainframes alongside decade-old cloud implementations, all while trying to integrate cutting edge generative AI capability, according to Raman. Solving this problem requires organizations to move beyond “rearview mirror” observability — simply reporting what happened. Instead, they must adopt predictive actions that can identify and resolve anomalies before they impact the business.

“How do you stitch all these systems together?” Raman said. “Modernization has [often] been, ‘Hey, eliminate the past and get into the new one.’”

As cloud costs rise, maintaining economic control is paramount. Impetus Technologies is addressing this demand by providing an AI-powered view across data platforms and hyperscalers, Raman noted. This allows companies to predict costs and optimize workloads proactively rather than reacting to a monthly bill.

“What we are doing now, from a CIO experience level perspective, is saying, ‘How do we bring economics to this — to this entire ecosystem?’” Raman said. “What we’ve built out is a system called Prism, which really provides the economics of the entire cloud ecosystem.”

Prism is a platform that forecasts cloud spend and surfaces optimizations — a capability that matters more than ever with the rise of agentic AI. Unlike traditional analytics, where a bad query might simply cost a few extra dollars, AI agents are empowered to take actions and resolve business issues autonomously, Veis explained.

“They’re saying agents are like giving a credit card to a teenager and going away for the weekend and hoping a good outcome comes,” he said. “You have these agents that are willing to scale, willing to consume and willing to act, and you need to be able to have some way to both unify, optimize and predict what’s going to occur.”

To mitigate risks, governance remains a top priority. As decision-making moves from humans to software, ensuring data security and preventing outliers from influencing autonomous agents is critical. The transition to agentic AI comes with the need to ensure the data foundation is solid enough to support agents safely and economically, according to Raman.

“I think what the CIO is ultimately worried about [is], ‘Is my data secure? Is my data not going out?” Raman said. “Or, ‘I’m not believing an outlier and that’s influencing the decision of an [agent].’”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of AWS re:Invent: 

Photo: SiliconANGLE