Freshworks CEO: why agile enterprises are winning the AI race — and what they did differently

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.

A growing share of enterprise AI investment is being consumed before any value reaches the business. MIT found that 95% of generative AI pilots fail to scale into production. Boston Consulting Group’s September 2025 research found that 60% of companies generate no material value from AI — a figure that worsened from the year prior, despite better tools and more experience. Freshworks’ upcoming Cost of Complexity research puts a finer point on why: one quarter of AI budgets get eaten by integration work, data cleanup, and the labor of forcing systems that were never designed to talk to each other into some kind of coherent conversation.

The pattern is consistent across industries. Programs stall, reset, or quietly get cut. Not because the models don’t work. Because the operating environment........

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