Enterprise data platforms must turn AI pilots into measurable business returns while overcoming the data access, sovereignty and cost barriers that stand between experimentation and production.
The conversation is shifting from what AI can do to how companies make it pay off. One data platform provider has responded by folding its post-sales teams under a single leader, giving customers one voice from onboarding through renewal, according to Patricia Bogoevici (pictured), chief customer officer of Cloudera Inc. The mandate now is to align those teams with customers’ business objectives and help them make better use of the technology they have already paid for.
“When I look at things in terms of customers, for me, technology makes sense only when customers are using it,” Bogoevici said. “I am measured on how customers realize value with Cloudera.”
Bogoevici spoke with theCUBE’s Rebecca Knight at Cloudera EVOLVE26, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed moving AI into production, improving customer adoption and measuring business value. (* Disclosure below.)
Why enterprise data platforms shape AI’s path to production
The barriers customers face fall into three categories, starting with data access, according to Bogoevici. A study of more than 1,300 information technology specialists found that roughly 80% named data access as the biggest obstacle. Governance and sovereignty come next, because data sits scattered across silos with differing rules on where it can live.
“Not [all] data can be moved to cloud. Not [all] data needs to be in every place,” she said. “It’s about protection, it’s about sovereignty, it’s about on-prem and it’s about air-gap environment.”
The final hurdle is scale. A pilot running on a subset of data rarely shows what a full deployment will cost or return, which leaves total cost of ownership and return on investment unclear, she explained. Cloudera’s answer is Anywhere Cloud, which it plans to release in November, alongside a partnership with Mistral AI SAS that brings open-weight models into the platform.
“It’s one thing to run the pilot in a subset of data,” Bogoevici said. “It’s a completely different thing to run it [at] full scale and understand the results you get from the AI models and understand the value and how you calculate it.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Cloudera EVOLVE26:
(* Disclosure: TheCUBE is a paid media partner for the EVOLVE26 2026 event. Neither Cloudera, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE