Over 50% of all tickets passing through our service desk are now resolved without human intervention. This was something we discovered recently and found somewhat surprising. We were by no means trying to hit a percentage target, or even to automate ticket resolution as a goal in itself. After introducing AI into the service more than a year ago and continuing to build on it since, this is just where trying to deliver better service led us.
The number itself is interesting for our workplace technology team to hold up but more so for what it signals. If you can, somewhat accidentally, remove half of the tickets from a service desk, then the shape of that service is likely changing quite significantly from what it has been for so long.
The Traditional Service Desk Model
In just the very recent past, IT support still operated in a way that would be instantly recognisable from five or ten years prior. Requests are logged, tickets queue, someone interprets them, and either acts or passes them on.
Every step relies on someone reading, understanding, and deciding what to do next. From a client’s perspective, most of these steps are experienced simply as waiting.
Traditional automation did improve parts of this at the edges – tickets could be routed more effectively and simple requests handled automatically. But the key step of understanding what is being asked and deciding what action to take, that was all human logic.
“AI” in this context
Considering the ubiquity and broad sprawl of the term AI it’s worth clarifying exactly what we’re talking about when we say AI in this context.
Generally we mean using the capability of LLMs to look at signals like keywords, sentiment, prior patterns, and alerts from connected systems. This can be applied to operational work of service management: interpreting incoming requests, identifying intent and urgency, applying context, structuring the ticket, and in some cases triggering the right process to resolve the issue. It can then either prepare the work for a technician or, where the pattern is known and the guardrails are clear, act on it directly.
Where older automation followed rules and worked well when the input was predictable and the pathway was fixed. AI can do more of the interpretive work that sits before the rule is applied. The broader market is very visibly moving in this direction. The major systems that underpin IT service management are embedding AI into their products and, in some cases, making acquisitions to accelerate that shift. It is increasingly becoming part of how these platforms are expected to operate.
ConnectWise, for example, acquired agentic AI company zofiQ to automate high‑volume service desk operations. ServiceNow is rolling out its “Autonomous Workforce”, including an L1 IT service desk AI specialist that can resolve cases end‑to‑end alongside humans. Atlassian is taking a similar approach from a different angle, promoting its Rovo agents to intercept and resolve common requests before they even become tickets.
Good service will now be proactive and predictive
The natural first reaction is to see all of this as an efficiency gain. That is certainly true, but as said earlier, a shift of this scale points to something more fundamental. The service as a whole should move from being almost entirely reactive to proactive and predictive.
Good outsourced IT is still customer service, but the exact service part changes. It becomes less about having enough people available to fix the breaks, and more about using skilled people to oversee the environment, handle the more complex issues outside defined patterns and significantly focus on proactive improvement. Advising, architecting, strengthening resilience, and helping clients make better decisions about their technology environment.
For clients, this should feel like faster responses, fewer delays, and less unnecessary back-and-forth as a standard. More importantly, it should be a better environment to work in — a near elimination of recurring issues, problems picked up before they have an impact, and systems that behave more predictably and reliably day to day.
Cost sits in the background of this. If service delivery becomes more efficient, clients should expect that to show up somewhere — whether in lower cost to serve, more valueadd work, or a better overall experience. If the only visible outcome is the same reactive service delivered with fewer people behind it, then the model hasn’t really evolved and any efficiency gain is simply being absorbed by the provider.
A lesson more broadly for all service providers that just using AI, no matter how deeply, won’t be enough. Clients need to feel their service change as a result.
Why this matters more in superannuation
For superannuation funds and other regulated entities, an IT service desk has always needed to deliver well beyond convenience or efficiency. IT support sits alongside security, operational resilience, access control, auditability, and service provider management. A provider working in this environment needs to understand that context deeply.
Frameworks such as APRA’s CPS 234 and CPS 230 make that explicit. CPS 234 sets expectations around information security – ensuring systems, data, and access are protected appropriately. CPS 230 focuses on operational resilience and service provider management – requiring organisations to manage risk, maintain continuity, and demonstrate control over the services they rely on.
Service desk activity connects directly to these obligations in practical ways: how access is granted and removed, how incidents are detected and escalated, how controls are evidenced, how systems are monitored, and how exceptions are handled.
This is where the shift becomes especially relevant. Reducing routine ticket volume does not make the compliance work it’s tied to optional. If anything, it increases the importance of regulatory awareness and ensuring that controls are built into the process.
A client offboarding process, for example, is not valuable because it closes a ticket quickly. It is valuable because access is removed correctly, consistently, and in a way that can be clearly evidenced later.
That is also where a specialist provider should matter. In this environment, good service is not just speed. It is knowing which processes must be tightly controlled, which actions require a clear audit trail, and where the consequences of getting something wrong are higher — and designing the service so those requirements are met as a standard, including how AI is applied within it.
Where outsourced IT support is heading
This is all good news, especially if you find yourself submitting those simple, slightly embarrassing “technical support needed” tickets a bit more frequently than you’d like to.
In the short term, it means faster responses and fewer delays. Over time, it should mean fewer issues making it to a ticket in the first place, and more attention on improving the systems behind them.
Looking further ahead, the direction feels fairly clear. AI will continue to take on more of the high volume, lower complexity work it is already handling today and gradually move into more complex break fix scenarios as the technology evolves and where the correct guardrails can be put in place.
As with everything AI is touching at the moment it gets a bit hard to predict beyond that.
