When the IT team at Seagate decided to replace the ITSM platform that had run their global IT operations for more than a decade, they had three months to do it.
That was the deadline imposed by a hard contract expiration. Three months to move 30,000 employees across Seagate’s global storage and infrastructure operations onto an entirely new system. Most organizations, in that situation, do the obvious thing: lift the existing configurations, drop them into the new environment, and reconcile the mess later. It’s the safer path. It’s also the one that almost guarantees the AI capabilities the team was counting on will never fully work.
The team chose the harder path. They rebuilt from the ground up — restructured the service catalog, established consistent SLAs across regions, rewrote the category hierarchies so tickets could route themselves without an agent guessing where they belonged. They did so because they intentionally did not want to bring forward their legacy processes. A year in, the AI agent the team deployed on top of that foundation now deflects roughly a third of incoming tickets. First-contact resolution is now 27% above the industry standard.
That decision — to rebuild rather than replicate — is the real story of what separates the companies pulling ahead with AI from the ones that aren’t. And it has almost nothing to do with which model they’re running.
