AI-powered automation is already producing real gains for service teams: higher resolution rates, faster response times. Yet a lot of IT leaders say it doesn’t feel transformative. Why?

Venkat Venkataraman: I don’t dispute the numbers. Across our customer base, AI agents and Copilot are driving 65%-plus employee query deflection, roughly a 40% lift in agent productivity, and a 77% reduction in average resolution time. Those are real, measurable gains. But most of that improvement is still happening inside the same old loop: problem, ticket, resolution.

AI is making an existing operating model run faster and more efficiently than ever. But because the underlying model is essentially unchanged, that makes it feel less transformational. Even if the results showcase impressive levels of efficiency.

“Shifting left means moving the point of intervention earlier than the ticket itself. Early enough that, ideally, the ticket is never the first point of contact at all.”

Venkat Venkataraman
Vice President, Product – AI Platform & Strategy, Freshworks

You’ve talked about how AI can help service operations “shift left.” What does that look like and how is that changing the old model?

Shifting left means moving the point of intervention earlier than the ticket itself. Early enough that, ideally, the ticket is never the first point of contact at all. Instead of assisting with a problem after it’s reported, AI watches for the signal that precedes it: an anomaly, a change with downstream risk, a pattern that has historically preceded an outage. It predicts what’s about to go wrong and triggers remediation automatically, with the ticket pushed to the background. It becomes a record of what happened, not something IT has to manage.

If today many IT teams are deploying AI as an automation layer on top of an unchanged loop, then shifting left moves us to a new operating model that’s proactive by design, built on signals, predictions, and interventions.

Instead of reacting to tickets, we get to prevent them from even happening.

If IT shifts to prevent issues before they happen, how will that change how leaders quantify IT’s value to the business?

This is where the conversation has to move past the metrics IT has always reported. Average handling time and deflection rate measure how efficiently you run the old loop, but they say nothing about the incidents that never occurred.

As prevention becomes the point, the value shifts toward what didn’t happen: outages avoided, risky changes caught before rollout, capacity freed up because fewer people are stuck triaging routine breakage. Time-to-insight for IT leaders is also moving from hours to minutes, which changes the kind of conversation you can walk into with the business, from “here’s what we closed” to “here’s what we kept from ever becoming a problem.”

It’s a shift that focuses IT’s value on outcomes, not activity. And as AI identifies recurring issues, closes knowledge gaps, improves workflows, or surfaces risky patterns earlier, all of these become part of the business case. IT is not just improving efficiency. It’s improving resilience, continuity, and capacity to keep the business moving forward.

What steps can IT leaders take to start shifting left today? What do you tell customers who are just getting started with AI agents?

I tell customers to start simple, to first automate routine, well-documented, high-volume work. Things like password resets, leave requests, or policy questions, areas where knowledge and workflows already exist, the risk is manageable, and teams can demonstrate value quickly.

Once you have that starting point, build and test your approach in a limited channel, so you can learn, adjust, and gain confidence before you deploy to production.

And, through all these steps, always monitor and learn from the patterns you see, whether it’s resolution rates, escalations, or any other signal, so you can continually tune and improve outcomes.