Enterprises racing to achieve true AI-native status are discovering that the technology gap is often the smaller obstacle. Instead, the harder challenge is reshaping the people, processes and organizational structures that AI must ultimately run on.

As AI-driven workflows transform how enterprise teams plan, build and deliver, the pressure on large organizations to modernize their underlying infrastructure has never been more acute. Cloud readiness and data governance are now emerging as the non-negotiable preconditions for any credible AI-native strategy, according to Sarah Cooper (pictured), general manager of AI-native at Amazon Web Services Inc.

“AI is reshaping what it is we do — how we do it, how we think about products, how we think about engaging with customers — end to end,” Cooper said. “If you aren’t ready with a very flexible cloud environment, with global connectivity, with security and data privacy and the depth of services that AWS helps bring to the table, you’re not able to move fast enough in an AI world.”

Cooper spoke with theCUBE’s Christophe Bertrand and Alison Kosik at the Atlassian Team event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how the AWS and Atlassian Corp. partnership is helping enterprises navigate the migration to AI-native operations across cloud infrastructure, security and organizational collaboration. (* Disclosure below.)

AI-native migration requires cloud depth and people-first strategy

The migration to become AI-native is echoing earlier platform shifts, but the stakes and speed are far greater. As Atlassian continues to embed agentic AI across Jira and its broader platform, the partnership with AWS is designed to give enterprises both the infrastructure depth and the collaboration layer needed to execute at scale. Industries once considered slow to adopt — utilities, automotive, healthcare and life sciences — are now among the fastest movers, Cooper noted.

“Everyone has to move faster today and the world is less predictable,” she said. “Whether that’s tariffs, whether it’s global warming, it means that we all need to be able to move fast. That shift and change in the companies that have traditionally [had] long planning cycles, that have been very capital heavy, all of a sudden they’ve got to be able to be flexible in their business, which then translates down to their architecture.”

On data security — one of the most urgent enterprise AI concerns — the partnership addresses a core anxiety directly. Amazon Bedrock, which powers AI features inside Atlassian products, does not train on customer data, giving large enterprises control over their most sensitive information, Cooper explained. That assurance, combined with AWS’s enterprise-grade governance structures and 13 global availability zones, is critical for organizations navigating data sovereignty requirements.

“Bedrock doesn’t train with customers’ data,” she said. “Your data is never part of the model. You have control over what data is used. Additionally, the cloud has been enterprise grade for many years — so a lot of those features, the guardrails, the governance structures, the visibility, all of that is available and percolates through the Atlassian products.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of the Atlassian Team event:

(* Disclosure: TheCUBE is a paid media partner for the Atlassian Team event. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE