Crossing the ‘production chasm’ is now enterprise AI’s defining test

Enterprise AI is moving fast, but not fast enough to outrun the governance blind spots that are quietly undermining the rush from controlled pilots to at-scale production.

With one in five organizations reporting a breach tied to shadow AI and only 37% having policies in place to manage or detect it, the governance gap has become as consequential as the spending gap. Bridging controlled experimentation and safe, at-scale production is now the defining infrastructure challenge of 2026, according to Rhys Oxenham (pictured), vice president and general manager of AI at SUSE S.A.

“Pilots are relatively straightforward to demonstrate value, but then how do you take that from that initial, ‘We’ve proven it here. How do I then do it at scale in production with critical data, with the need for that safety net and that governance?'” Oxenham told theCUBE. “That is the production gap — or the production chasm — that our customers are having to cross and where we are helping them solve it.”

Oxenham spoke with theCUBE’s Paul Nashawaty at SUSECON 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed SUSE’s private enterprise AI strategy, digital sovereignty, agentic infrastructure and the operational imperatives for running AI safely at scale. (* Disclosure below.)

Private enterprise AI and the sovereignty imperative

SUSE frames its answer to the production gap around what it calls private AI — a model built on open standards, hybrid deployment flexibility and full organizational ownership of intelligence. The approach is designed to let businesses deploy workloads wherever business requirements dictate, from on-premises data centers to public cloud to the far edge, without ceding control to any proprietary stack, Oxenham explained.

“Digital sovereignty is not a European compliance check mark anymore,” he said. “Every organization in the world needs to think about their independence, their autonomy [and] their resilience planning for what they do with their infrastructure.”

But when organizations face executive pressure to show AI results but lack the governance scaffolding to do so safely, individuals bypass sanctioned systems entirely, meaning organizations lose control of their data, Oxenham noted. The SUSE AI stack, built on SUSE Rancher Prime and SUSE Linux Enterprise Server, is positioned to provide the observability, security and automation needed to keep hardware running at full utilization while enforcing policy guardrails across agentic workflows.

“If these agents are taking responsibility or they’re actually performing the action on behalf of the user, how do I make sure that that agent is doing it in a way that is aligned with corporate policy?” Oxenham said. “Governance, security, observability — these things start to become incredibly important.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of SUSECON 2026:

(* Disclosure: TheCUBE is a paid media partner for SUSECON 2026. Neither SUSE, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE