How automation and GenAI can improve the service desk

Challenges and concrete cases where AITSM can be a value-add for businesses and IT teams

How AI can be a valuable support to IT teams managing and delivering business services for users who are increasingly working in hybrid mode.

With companies evolving to allow at least some employees to work partially or fully off-site remotely - according to Ivanti's 2023 global research you can see that 53% of employees and 78% of IT teams- the services needed to meet their needs are increasing in volume and complexity..

Think of new technology options to provide access to HR services, productivity monitoring tools, remote support, and the need to access networks and work software even from personal devices with new security-related variables to consider...all adding stress factor to already overburdened IT teams with suboptimal working conditions and experiences.

All this "digital" availability in terms of how end users can request the service desk-in addition to traditional channels (email, phone) there are now web portals, chats, apps, digital assistants, social-in fact, it is leading IT teams to have more work: 39 percent of IT professionals surveyed by Ivanti report having too many logins to handle, 47 percent too many digital notifications, and 42 percent having to juggle different tools and platforms. Potentially the ticket can come from anywhere.

Paradoxically Everywhere Work has improved the work lives of many people, but not IT teams. For many of them, burnout is just around the corner: according to a global study by Gallup, these figures appear to be particularly at risk.

Given the increasing complexity of the IT ecosystem - and the tools needed to manage - it-tech workers are feeling the pressure the most, with 68 percent saying they feel burned out from their jobs. What's more, the persistent shortage of IT skills in the marketplaceleads to stress on those already allocated, with the difficulty of retaining talent.

What is the biggest challenge for IT teams?

With the volume of requests to the helpdesk increasing due to the growth of technology workstations and troubleshooting related to them, one of the biggest challenges for IT teams in technical support seems to be "repetitive tasks." That is, that portion of mundane and routine work they must complete on a daily basis to respond to these requests.

Keep in mind that each ticket must be logged with a brief description, prioritized and prioritizes it, and assigned to the appropriate group to act on, respond to, repair, restore anything, or provide new equipment.

The streamlining of these procedures is still frighteningly manual and the more manual the process, the greater the chance for errors or omissions: think about incorrect categorization and how much it erodes the ability to monitor, measure, report on what is really happening in a broader perspectiveThen imagine how difficult it becomes to prioritize.

And even once the ticket is closed, it is difficult to learn from it: notes and comments are added during the resolution process that remain an end in themselves because this knowledge is not always structured.

Advances in AI, machine learning, and automation could come to the aid of overburdened teams, alleviating stress and also improving the end-user experience, just by reducing time. On the one hand, ticket volume could be reduced through automation, on the other hand escalations could be reduced by equipping frontline analysts with more knowledge.

Let's see how.

Scenarios in which AI could be an advantage

Here are some scenarios (but there are many more) in which technical support and service management could benefit from AI:

• automatic ticket opening from monitoring the health of individual devices in the network: AI could pick up on the signals and initiate maintenance processes before any anomalies are logged as problems, notifications, complaints, etc.
  
  
• routing of tickets through automated classification : no more time would be wasted on misclassifications and tedious and frustrating handoffs (both for the IT team and the end user). Paradoxically, AI is very effective at categorization, more so than humans who tend to act on opinion (e.g., is it really a printer problem?). With perfectly labeled data, it would become easier to prioritize as well.
  
  
• outsource low-level requests to virtual support agents (VSAs) : these are chatbots that can perform preprogrammed actions, and tasks such as resetting passwords or managing software updates, which are particularly repetitive, could be delegated to them.
  
  
• summary available to analysts: the AI could summarize all the actions and interactions from the various channels and steps within the service desk and condense them into a summary in seconds. In a team, for example, there are those who write unnecessary notes, those who write hundreds of lines, and those who put a placeholder for the sole purpose of closure: the AI's ability to summarize interactions would also increase the accuracy of the work because you would have a better view of what is happening.
  
  

  Having everything in one line would lay the foundation for better metrics, reporting, and more accurate planning. If we then anticipate a time savings of 3 minutes per ticket due to summarization, out of 18 tickets per day it becomes 54 minutes which depending on the size of the team can be 20/40 hours (so one FTE).

• automatic knowledge updatingticket resolution brings with it knowledge that is dispersed if not properly organized. AI could use existing helpdesk data to create a knowledge base available to the front line. Better escalation management would move existing IT employees up the value chain and empower less experienced employees to take on higher order tasks.
  
  

  With knowledge management powered by Artificial Intelligenceorganizations may find that they can rely on IT talent with less experience or expertise to handle complex or nuanced questions, and IT professionals themselves could easily query it in natural language to get clearly formulated answers in return and resolve tickets more quickly. In addition, a good knowledge base sets the stage for self-service: users could directly ask what they need to get the solution.

Concrete technology applications: WEGG and Ivanti

At WEGG we are experienced consultants in Service Management processes, which we support in their definition and implementation within technology thanks to our knowledge and experience of ITIL best-practices.

Every organization, every team has its own needs that need to be analyzed to identify the most effective flow: automation and AI are the last evolutionary step in brainstorming with stakeholders to design ad hoc technology.

We often talk about establishing accountable AI policies and governance : this collaboration with teams involved in service delivery in understanding the technology, the best way it could be applied to their roles, and the benefits it would bring, leading them to be more relevant (not less!) in those roles because of the time freed up for higher-value activities, also overcomes any internal resistance.  

Let's look at some concrete examples of AI applications within our partner Ivanti's Neurons technology:

• AI classification and Sentiment Analysis capabilities manage to measure users' lived experience across multiple areas (devices, security, service management, applications) to return a metric (the Digital Employee Experience Score) that gives insight into how to improve the services provided