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Freshworks expands AI service tools with new productivity metrics and governance features for mid-sized enterprises
Freshworks now tracks whether AI responses lead employees to higher-value work, not just response speed. The company also released no-code tools for building AI workflows and added governance controls to block unauthorized AI use.
How Freshworks Is Redefining Productivity Metrics for AI-Driven IT Operations
Freshworks has introduced a new way to measure whether AI actually helps IT teams work faster. The company's experience level agreement (XLA) analytics layer tracks whether AI responses make employees more productive, not just how quickly the AI responds.
Dennis Woodside, chief executive officer of Freshworks, said the shift matters because traditional metrics no longer apply. "When every response can be instantaneous because it's AI-driven, what you really want to measure is, 'Did the response make the employee more productive?'" he said.
The company measures productivity by observing what employees do after receiving an AI response. If they move to higher-value work, the system registers that as a productivity gain.
Seagate Technology migrated to Freshservice from a competitor in three months after adopting this approach. The company freed up IT staff to handle more complex projects instead of answering routine tickets.
One-Size-Fits-All AI Strategies Are Failing
Organizations rushing to deploy AI without a clear strategy risk wasting money on tools that don't deliver results. Julie Mohr, principal analyst at Forrester Research, said the problem is widespread: "The danger is that you just start implementing AI across the organization without intent."
Freshworks released two tools to address this gap. Freddy AI Agent Studio is a no-code environment for building custom AI applications or deploying prebuilt workflows. MCP Gateway lets teams use third-party models like Claude or ChatGPT inside Freshservice.
Kady Srinivasan, chief marketing officer of Freshworks, said IT teams need to work at two speeds simultaneously. Some core processes require careful reassessment. Others can be updated quickly through trial and iteration.
"You've got to be able operate at two different speeds," she said. "At the same time, there needs to be a fast agile speedboat type of a speed where you're learning quickly, you're trying things."
Governance and Interoperability Are Non-Negotiable
Shadow AI - unauthorized AI tools running across departments - is creating security and compliance headaches for enterprises. Freshworks built governance controls into every layer of its AI stack to prevent this.
Murali Swaminathan, chief technology officer of Freshworks, said the platform ensures AI tools work together while remaining under organizational control. "Everything needs to connect together. We need to interoperate with other AI systems," he said.
The platform includes data sovereignty policies and anonymization features to keep personal information within designated regions. Customers can trace AI decisions and audit what the system did or didn't do.
Swaminathan emphasized that trust requires multiple layers of control. "Trust is there in many layers, and that's what we believe in building the product or providing the tools for you to set it up the right way," he said.
For operations leaders evaluating AI tools, understanding these three principles - measuring productivity accurately, building strategy before implementation, and maintaining governance - can mean the difference between a successful rollout and wasted investment.
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