It sounds easy enough to an IT pro: is it an applications ticket or a hardware ticket?
Simple enough. Why is this one question so important? For starters, it’s how you track the performance and success of the teams who provide internal support. In addition, collecting simple data points like “category” and “subcategory” can drive a better, faster experience for all the employees in your organization.
The problem is, the sales manager (or accountant, or creative designer) doesn’t know the difference between an application support issue and hardware breakdown, and they’re not familiar with IT’s processes that rely on such information.
That’s where artificial intelligence (AI) can help. SolarWinds® Service Desk uses AI in a few different ways– suggested data points for tickets was the first AI-powered functionality we introduced. Suggested categories and subcategories provide users some direction based on keywords within the subject or description of their ticket and the composite history of ticket data in the system. The goal is for requesters, regardless of their tech understanding, to enter complete and accurate ticket data thanks to those suggestions.
This data can drive automated ticket routing and priority. It can empower your teams to carve out unique SLAs for specific types of tickets (and trust the tickets are categorized correctly). It can make the difference between granular, accurate performance data and, well, this:
This might look familiar: the dreaded “other” category. When users (or agents, for that matter) don’t know how to identify the category, your reports will look something like this. It’s time to say goodbye to this useless chart. AI will see it to the door by suggesting the correct data points up front.
Let’s look at some use cases for AI in action.
Powering Ticket Automations
One of the most important sections in the configuration of a SolarWinds Service Desk environment is the automations engine. This where you’ll identify types of tickets you can route directly to a certain group, keywords indicating high priority, or breaches requiring specific escalation processes.
Those automated actions depend on data collection when a ticket is entered. The information the user enters will correspond directly with an action, so it needs to be correct for an automation rule to work.
This is where AI can help. As you can see from the next example, there are suggested categories and subcategories as soon as they click those required drop-downs. The suggestions are based on the information they’ve already entered combined with historical data from the service desk environment. When they choose “hardware” and “laptop” with the help of those AI-powered suggestions, the custom fields appear for “battery type” and “type of laptop.”
Why is this important?
You can create an automation rule to route these tickets directly to the appropriate support group. The AI-powered suggestion unlocks those custom fields to help you pinpoint the exact nature of the issue.
In the example below, you’ll see an automation rule to route Mac device issues directly to the “Mac Device Technical Support” group.
With the help of AI-powered suggestions, you’ll receive the crucial piece of information driving the automation rule. Now these tickets will skip the general queue and arrive instantly with the “Mac Device Technical Support” group. You’ve saved the requester time waiting for a resolution and your IT team time parsing through a general ticket queue.
Self-Service and Suggested Request Forms
Requesters may not realize the benefit of the suggested categories because they’re unfamiliar with how your teams use the data. But in this next example of AI in the service desk, the benefits will be plainly evident.
This is where your service catalog and knowledge base reach their maximum potential. For a long time, it was very difficult for IT to encourage users to leverage self-service articles or request forms driving workflows. Simply put, some users will always default to creating a ticket, no matter what resources might be available through the portal.
When IT replies to the ticket with a link to a request form, the user now needs to complete the form after already submitting a ticket. That’s a poor experience, and it’s time wasted on both ends.
To simplify the experience, you can meet them with suggested resources wherever they are in the portal. If they like to use the search bar, suggested service catalog forms and self-service articles will appear. If a requester is the “always submit a ticket” type, AI-powered suggestions will pop up with request forms or knowledge articles as they fill out the subject line of the ticket.
Not only are you anticipating the service they need, but you’re giving them every single opportunity to leverage the resources your team has made available.
So, for now, there are three major benefits AI has brought to the service desk:
1) Complete and accurate data collection to drive automation and reporting
2) Access to appropriate request forms, driving automated workflows
3) Opportunities to self-resolve
As this technology grows, so will the possibilities for proactive measures to save time and avoid disruption to employees who depend on the technology you support.