Trusted AI data becomes the missing link as enterprises push models into production
Trusted AI data is emerging as the deciding factor between organizations that successfully scale AI and those still stuck cycling through pilots. As companies move beyond experimentation, many are discovering that the biggest obstacle isn’t building models — it’s knowing whether the data feeding those models can be trusted.
That gap between ambition and readiness is widening as enterprises juggle data scattered across software-as-a-service applications, the cloud, on-premises systems and even employee laptops. Establishing a governed foundation for that data has become a prerequisite for moving AI into production, according to Robin Braun (pictured, left), vice president of AI business development and hybrid cloud at Hewlett Packard Enterprise Co.
“When you think about how do you trust to scale out into production, you have to trust what you’re doing, and at the foundation of all AI is data,” Braun said. “Organizations have SaaS applications, they have data in the cloud, they have data on-prem, they have data probably on somebody’s laptop that they’re not sure about. Do they know how a model got trained? Do they know that it has the right parameters, right governance, right guardrails?”
