AI is rapidly becoming part of the service management conversation. But for service desk teams, the important question isn't simply “How can we use AI?”
It's “where can AI make a meaningful difference?”
Introducing AI for the sake of it is unlikely to transform your service desk. The real value comes when you use it to tackle the everyday challenges that slow teams down. But before you get to those use cases, there's an important first step.
AI can help your service desk work faster and more efficiently, but it still needs good foundations to work from. That means looking at the way your service desk operates today before deciding where AI fits.
Good data matters.
If ticket information is incomplete, categories are inconsistent or useful resolution details aren't being captured, AI has less reliable information to work with.
Before introducing AI, take a look at the quality of the data already within your service management system. Are analysts consistently recording resolutions? Are categories still relevant? Is historic information useful, or is there a lot of outdated content?
You don't need everything to be perfect. But improving the quality and consistency of your data gives AI a much stronger starting point.
It's also worth identifying where teams are spending unnecessary time.
Which tasks happen repeatedly? Where are analysts carrying out manual administration? What slows tickets down? Where do customers experience delays?
These are often the places where AI can have the greatest impact.
Rather than trying to apply AI everywhere, start with the processes that create the most friction.
Your organisation probably already holds a huge amount of useful knowledge.
The problem is that much of it can be hidden inside previous incidents, resolution notes and individual analysts' experience.
Building better knowledge management practices gives your team information that can be reused - and gives AI more useful material to work with.
As with any technology handling business and customer information, organisations need to understand how AI is being used.
Consider what information AI can access, where human oversight is required and what your organisation's policies are around data, security and responsible use.
AI should support your people and processes, not create another layer of uncertainty.
Finally, think about the outcome you're trying to achieve.
Do you want to reduce response times? Cut the amount of manual work analysts are doing? Increase self-service? Improve consistency? Better understand customer satisfaction?
Knowing where you are today gives you something meaningful to measure AI against.
Once you've got those foundations in place, the question becomes much more interesting: Where can AI actually make a difference to your service desk?
Here are five places to start.
Every ticket arriving at your service desk needs to be understood, categorised, prioritised and sent to the right place.
When that happens manually, your team is spending time on administration before anyone has even started resolving the issue.
AI can help analyse incoming tickets and automatically identify the appropriate category, priority and route.
The goal isn't simply to automate a process. It's to remove a bottleneck.
For your service desk, that can mean:
The real benefit is that analysts can spend less time sorting work and more time solving problems.
How often does your service desk solve a problem that someone has already solved before?
The answer might already exist in a previous ticket, an analyst's notes or a knowledge article. Finding it is another matter.
Searching manually through months or years of service desk data takes time. And if analysts can't find the answer quickly, they may end up solving the same issue all over again.
AI can help surface relevant information from your existing service desk data, giving analysts a much faster route to useful answers and previous resolutions.
That can lead to:
It's a good example of where the value of AI isn't necessarily generating something new. Sometimes, it's simply making the information you already have much easier to use.
Knowledge management has always made sense in theory.
Solve a problem once, document the answer and make it easier for everyone to resolve next time.
In reality, service desk analysts are busy. Once an incident is resolved, writing and formatting a separate knowledge article can easily fall to the bottom of the list.
The result?
Valuable knowledge stays buried inside closed tickets, and another analyst ends up investigating the same problem again in the future.
This is an area where AI can remove a significant amount of effort.
Instead of asking analysts to start from a blank page, AI can help turn successful incident resolutions into structured knowledge articles.
The outcome can be:
And the benefit compounds over time. Better knowledge doesn't just help analysts today; it makes future incidents easier to solve and creates more opportunities for users to find answers through self-service.
Customer satisfaction scores are useful.
But a score rarely tells you the whole story.
The detail is often sitting inside the comments customers leave behind: why they were frustrated, what went well, what keeps happening and where the service experience could be improved.
The challenge is volume.
If you're receiving hundreds or thousands of survey responses, someone has to spend a lot of time reading them before you can spot meaningful patterns.
AI can help analyse those comments at scale, making it easier to identify sentiment, recurring themes and early indications that something may be going wrong.
That means:
Instead of looking at customer satisfaction purely as a score, service teams can start understanding the reasons behind it.
Most service desk analysts can already access tools such as ChatGPT or Claude.
So why bring AI into the service management platform itself?
Because access to AI isn't quite the same as having AI built into your workflow.
Imagine an analyst wants help improving a customer update.
They open another tab. Copy the ticket information. Paste it into an AI tool. Ask for a rewrite. Copy the response. Return to the service desk. Paste it back in. Check it. Then finally send it.
Do that once and it's not a huge problem.
Do it repeatedly across an entire service desk and you've created another time-consuming process.
Bringing AI assistance directly into the service desk means analysts can get help improving, clarifying or summarising communications without constantly switching systems.
That can mean:
The technology might be similar to tools people can access elsewhere. The difference is putting it exactly where the work is happening.
The most useful way to think about AI in the service desk isn't as another tool or another box to tick on a technology roadmap.
It's about outcomes.
Can you remove repetitive administration?
Can analysts find answers faster?
Can you stop useful knowledge disappearing into closed tickets?
Can you understand what your customers are telling you?
Can you make communications clearer without adding more steps to someone's working day?
Those are the areas where AI starts to become genuinely useful.
And importantly, AI doesn't need to replace the people behind your service desk. It should help them spend less time on repetitive work and more time delivering a better service.
That's the principle behind Solvyr® - using AI to help service teams work faster, make better decisions and deliver better support, while keeping the human element at the heart of service management.
Ready to explore where AI could make a difference in your service desk?
Discover AI at Sunrise and meet Solvyr®.