The future won’t be ticketed
Freshworks VP Jason Aloia on why ITSM’s latest revolution hinges on agentic systems earning the trust to act autonomously
IT has spent the last few decades absorbing shock after shock—the cloud, mobile, the pandemic's sudden reinvention of how (and where) work gets done. AI is the latest shock, and the biggest one yet. Everyone wants work to happen faster, with less friction, or to mostly work itself out.
There’s so much change in the AI wake that Gartner, for example, un-retired its Magic Quadrant™ for IT Service Management Platforms entirely. (Freshworks is proud to be named a Leader.)
On the heels of that news, we asked Jason Aloia, VP of product management at Freshworks, about the latest shifts in ITSM—from storing information to executing on it, and now to acting on it without being told—and what the changes demand from IT organizations already stretched thin.
Gartner retired the Magic Quadrant for ITSM a few years ago because the category had settled. What’s the better question to ask now: What’s changed, or what hasn’t?
The better question is: What has the role of IT become?
The fundamentals of IT haven’t changed much. Employees still need help. Systems still fail. Devices, applications, and infrastructure still need to be managed. Security, governance, and compliance remain essential. And IT is still expected to do more with less.
What’s changed is what’s expected of IT. For decades, people adapted the way they worked to fit the technology. Today, that expectation has flipped. Technology is expected to adapt to how people and organizations choose to work—personally, dynamically, and intelligently.
The goal is shifting from managing work efficiently to preventing certain kinds of work from existing in the first place, or resolving it before anyone notices. That changes what organizations should evaluate in an IT platform. It’s no longer just about workflows, ticketing, or ITIL maturity. It’s about which platform is best positioned to become an intelligent operational platform that understands what’s happening, reasons over enterprise context, and acts on the organization's behalf.
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You’ve talked about ITSM moving through stages—from storing information to executing on it to now acting autonomously. What’s pushed IT from one stage to the next?
At every stage, IT removed a systemic bottleneck.
First, we digitized information because paper couldn’t scale. Then we digitized workflows, because humans couldn’t coordinate complex work fast enough. We’ve largely solved information and workflow. The bottleneck now is decision-making.
Modern IT organizations are drowning in data—millions of events, alerts, requests, asset changes, vulnerabilities, and conversations. There simply aren’t enough experienced people to evaluate every signal and respond in real time.
AI changes that equation. It lets organizations automate not just repetitive tasks, but the decisions that connect those tasks into an outcome people care about.
Read also: Freshworks named a Leader in the 2026 Gartner® Magic Quadrant™ for IT Service Management Platforms
What’s different about the current shift to autonomous action compared to earlier stages?
Previous waves of automation required people to explicitly define every step: If X happens, do Y. Autonomous systems are different. They can understand intent, evaluate context, choose from multiple possible actions, and adapt as conditions change.
That’s a truly fundamental shift.
We’re moving from software that follows instructions to software that collaborates with people and, over time, earns the right to operate independently within defined guardrails.
The destination isn’t an IT organization with fewer people. It’s a redefined mission. As operations become more autonomous, IT becomes the function that empowers the rest of the business to innovate how work gets done. That requires new skills: AI governance, systems design, orchestration, service architecture, and organizational change. IT is no longer just managing technology. It’s operationalizing the collective intelligence of the enterprise.
What does autonomous operation require underneath in terms of data, context, and governance?
This is probably the most misunderstood part of AI: The model isn’t the product.
An LLM knows a tremendous amount about the world and almost nothing about your organization. To operate autonomously, AI needs enterprise context—your assets, services, people, workflows, policies, history, priorities. It also needs governance. Organizations need to know why a decision was made, what context informed it, what actions were taken because of it. And AI needs to know exactly when a human should stay in control.
We’re moving from software that follows instructions to software that collaborates with people and, over time, earns the right to operate independently.
If IT is handing over more execution, how does that change the relationship between companies and the vendors they depend on?
Customers have always evaluated vendors on trust, but that matters more now than ever. When software starts taking actions instead of simply recommending them, reliability, transparency, and governance become imperatives.
Organizations will ask different questions. Why was this decision made? Where does “autonomy” start, and where does it end? Which AI model is best for this job? Am I ready for my next audit?
That’s a much deeper relationship than buying workflow software. Vendors become operational partners.
