Artificial intelligence has reached a critical inflection point, where enterprises are racing to convert AI pilots into production-ready systems that deliver measurable outcomes. The challenge is no longer about experimentation but about ensuring reliability, trust and scalability in how these systems are deployed.
At the same time, enthusiasm is running high at the executive level, with boards demanding concrete strategies and faster results. Yet beneath the excitement lies a harder reality: Many organizations are grappling with failed AI pilots, stalled initiatives and uncertainty about how to move from early-stage testing into resilient, agent-driven capabilities. This tension is shaping the next phase of enterprise AI, where trust and execution are proving just as important as innovation itself, according to Beth Williams (pictured), global portfolio lead for AI, apps and data at Dell Technologies Inc.
“I think there’s a number of reasons for that. Some of it is maybe the choice of the pilots to begin with. We learned the hard way within Dell. We’ve got to choose those initial use cases really well,” Williams said. “One of the things that we try and do with our customers is learn from our mistakes and make sure that you choose the right use cases to start with.”
Williams spoke with theCUBE’s Dave Vellante at theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed the challenges of moving AI from pilots to production and the importance of data readiness and governance for successful enterprise adoption.
Getting AI pilots right
Dell has sought to emphasize helping companies avoid common pitfalls by guiding them to select the right use cases from the start, according to Williams. This involves assessing business feasibility, technical feasibility and the data involved in the pilots.
“We have a kind of three-pronged approach,” she added. “We look at the high value, we also look at how technical it is, but we look at the data readiness as well. If we get all those things combined, if we can do a sort of proof of value with customers really early, make sure the pilots are tested on real data, what we can then show is a real good path to production and also a really good path to ROI.”
The initial selection of use cases is critical, but it’s equally important to determine where those use cases will ultimately reside. For some, that may mean running in the cloud, while for others it could be on-prem, where Dell AI Factory can provide strong support, There are other questions to consider, too. If the use cases are deployed in the cloud, data privacy becomes a key consideration, Williams noted.
“In the industry, what we’re seeing is a lot of AI pilots stalling, but then also not necessarily knowing where those AI pilots should run after they’ve gone through that pilot phase on-prem, cloud,” she said. “Where is that placement for both the AI and the data? We’re giving value in terms of implementing real use cases with real data whilst we’re building that data platform.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI Factories – Data Centers of the Future event:
