A chatbot with a friendlier avatar. Shorter response time. The word "agentic" stamped prominently. That’s what you’ll see when researching agentic service desks. I get why. It's an easy story to tell and an easy budget line to approve.
But it sells its value short.
It might wear the same name, but an agentic service desk is a different operating model from what you already have. If you treat it like a tooling upgrade, you'll get a faster version of your current problems. If you treat it like an organizational redesign, you get something that changes what your team does all day, and what your employees experience when something breaks.
Good numbers can hide a bad experience
For decades, the service desk has run on service-level agreement (SLA) math: first response time, resolution time, and ticket volume closed. Every one of those numbers can improve while the actual employee experience gets worse, because none of them measure whether the problem was solved in a way that felt fast, human, and complete.
This is why I've spent the last five years pushing experience-level agreements (XLAs) into how we design and govern service delivery. XLAs build on SLAs and ask a harder question: did this interaction work for the person on the other end? When you add agentic AI to an SLA-only operating model, you make the wrong metric move faster.
Put AI in an XLA lens, and you build the service desk around a different question. The old question was, "How do we resolve more tickets?" The better one is, "What outcome is this employee trying to achieve, and what's the fastest path that still feels human?"
What is an XLA?
An XLA measures whether a service interaction worked for the person on the other end. The person, not the operational target, is the priority. XLAs build on traditional SLAs by adding that human outcome to the scorecard alongside response time and resolution time.
What gets redesigned?
When we built Unisys Agentic Service Desk, this was the tension on the table: what happens to our people once AI can take care of routine tasks? The workstream had to adjust roles, escalation paths, and staffing models once agents could resolve requests rather than deflecting them.
The shift is bigger than saved headcount hours. If your Tier 1 team was doing triage and simple fixes, you've eliminated the job as it was defined. What's left is exception handling, judgment calls, and the harder 20% that never fits a script. Your people must be re-skilled toward that work deliberately.
We've seen this play out concretely in production. In our work with E.SUN to design and implement an intelligent contact center, the real win came from redesigning how requests get routed, so agentic resolution handles the predictable volume, and human specialists are freed to own the complex, relationship-sensitive cases that need them. We took a similar approach with Flowserve for their tech support. The technology mattered, but reworking who does what, and when a human enters the loop, is what made the experience numbers move.
The one question leadership should ask
Before any organization brings agentic AI into its service desk, I'd ask leadership one question first: are you redesigning a job, or automating a task?
If the answer is framed around tickets, response times, or headcount reduction alone, you're building a faster version of a model that’s measuring the wrong thing. If the answer starts with the employee experience you want, then works backward to where agentic AI delivers it and where a human steps in, you're redesigning the organization.
That's the difference between an upgrade and a transformation. Only one of them is worth the disruption it causes.
Interested in redesigning your service experience? See how digital workplace solutions with Unisys can make it happen.
Frequently asked questions
What's the difference between an SLA and an XLA?
An SLA measures operational metrics like response time, resolution time, and ticket volume. An XLA (experience-level agreement) measures whether the interaction actually worked for the person involved, not just whether it hit a number.
Does agentic AI eliminate the Tier 1 service desk role?
It changes the role rather than removing the need for people. Once agents handle routine tickets, what's left for humans is exception handling, judgment calls, and the harder cases that don't fit a script.
How do you decide which requests agentic AI should handle versus a human?
Start with the outcome the employee needs, not the ticket type. Route predictable, well-defined requests to agentic resolution, and keep complex, relationship-sensitive, or judgment-heavy cases with human specialists.
