Unlocking the potential of AI to break free from break-fix IT support
September 4 | 07 mins read
In today's digital landscape, where work hinges on technology, the IT service desk plays a pivotal role in ensuring the smooth functioning of IT operations and business. However, it has become evident that relying solely on traditional IT support models can no longer suffice in our current dynamic environment.
The break-fix model, while functional for isolated issues, simply cannot keep pace with the ever evolving demands of a real-time, digital workplace. Thankfully, the integration of artificial intelligence and machine learning technologies into service desk practices allows organizations to move from a reactive firefighting approach to a more proactive stance.
To illustrate this better, here's a table that points out the disparities between the traditional break-fix model and a proactive IT support model.
| Reactive IT support with a break-fix model | Proactive IT support with an AI enabled service desk | |
|---|---|---|
| Ticket creation | Manual and reactive ticket creation where issues are looked into only after the end user reports them. | Automatic ticket creation based on early anomaly detection. |
| Categorization, prioritization, and ticket assignment | Rule-based automations that are often rigid and reliant on predefined parameters. | Intelligent automations based on historical data to save time and effort during the initial stages of the ticketing process. |
| Analysis and diagnosis | Manual checks and tasks to pinpoint the root cause of issues that are time-consuming and error prone. | Quick analysis on trends to identify patterns and potential anomalies to pinpoint the root cause of issues faster and more accurately, allowing for faster diagnosis and resolution. |
| Knowledge management | Minimal access to information and a disorganized knowledge base. | A dynamic and up-to-date knowledge base that automatically extracts solutions from tickets and conversations |
Here's a simple use case that shows how AI can help you proactively handle issues in your employees' endpoints:
Say a significant portion of your workforce relies on a specific software application for its daily tasks. Over time, let's assume you haven't prioritized updating this software to the latest version. This means that your end users are now vulnerable to compatibility issues, security risks, and other performance problems. But thanks to AI and ML technologies, you can proactively address the issue even before it impacts your end users.
Here's how;
1. Early anomaly detection based on Digital Employee Experience (DEX) scores
The organization's application performance monitoring solutions will continuously monitor performance for this software application, tracking metrics like load times, number of crashes, error rates, and responsiveness within the application—to analyze the application's performance from the end-user's perspective. These metrics allow you to quantify your employees' experience and their overall sentiment towards this particular software.
Now, it's pretty clear that poor application performance = unhappy end users.
Moreover, the frustration doesn't stop there. There's the added burden of reporting this issue or waiting on hold for the service desk to sort things out.
Thankfully, with AI, you can fix these issues before your end users are even aware of them.
With the insights you gain from the metrics mentioned above, you find that the DEX score falls below your benchmark values. Based on this data, ML algorithms can be trained to trigger alerts automatically, creating tickets in the organization's service desk software. The alerts can be sent in real time, and the tickets can be populated with relevant data like the type of endpoint, application affected, and the nature of the anomaly to give the support team a better idea of what the issue entails.
This proactive approach to detecting potential issues early on contributes to a service desk that can prioritize and resolve incidents quickly, minimize downtime, and ultimately boost end user experience.
2. Intelligent categorization, prioritization, and assignment of tickets
Once the ticket is logged in to the service desk software, the focus is then on expediting and automating the ticketing process. ML technologies can facilitate this by analyzing various aspects of the ticket and correlating that information with historical data from previous tickets.
By analyzing specific keywords like "slow" or "unresponsive", the application causing the issue, and the error messages included, ML would first categorize the ticket with the tag "application performance issue - CRM v1.2.05". It would further assess the impact levels (isolated incident or widespread issue) and the urgency for resolution ( based on the criticality of the application) to assign the ticket's priority level as "high", thereby flagging it for immediate attention. Using its knowledge of past resolutions and team expertise on similar issues, ML can route the ticket to the most qualified team or individual. By intelligently categorizing, prioritizing, and assigning the ticket, ML eliminates the need for manual intervention, saving you lots of time and effort in the initial stages of the ticketing process.
3. Analysis and diagnosis of the issue at hand
Next, by identifying patterns and similarities between multiple tickets based on similar field attributes, ML algorithms can group together similar tickets, in this case, all the tickets mentioning slow performance with the same application. Through AI clustering, your ticket queue will now be organized, allowing for faster resolution of similar incidents instead of you having to sort through a trove of tickets individually.
Going further, by analyzing ticket descriptions, generative AI can generate concise summaries, highlighting key details about the incident. This means that your IT support team need not spend much time deciphering the issue.
Now, to drill down into what is causing the issue in the first place, AI can be leveraged to initiate a root cause analysis, with the RCA zeroing in on a broader software issue. This proactive approach eliminates the need for IT to wait for further tickets to trickle in and also prevent future incidents. Based on past ticketing data and reports, ML algorithms can identify a correlation between performance issues and specific versions of the software, prompting the support team to update the software to the latest version on the affected endpoints.
4.Optimized knowledge management for quick incident resolution
Now that the support team has identified the underlying issue to be outdated software, it can then focus on developing a plan to swiftly deploy the latest version and address the performance issues. By analyzing internal knowledge articles based on previous upgrade rollouts or searching across external repositories, GenAI can suggest relevant troubleshooting methods, step-by-step upgrade guides, or even highlight a specific patch known to resolve similar performance issues. This helps further expedite the ticket resolution process as the support team need not scour through an endless pool of knowledge articles to land on the one that is relevant to the issue that they are fixing.
By implementing these strategies, the support team can not only proactively address the issue of application slowness but also take preventive steps to address potential occurrences in the future.
Bottom line
We have only scratched the surface of the transformative potential of AI in IT service management. As the capabilities of AI continue to develop, its role in ITSM will only become more crucial, shaping the future of how we manage our digital experiences. The integration of AI into the service desk represents exciting possibilities for the future of IT service management. From automated support processes to proactive problem identification and resolution, AI technologies offer countless opportunities to help you take your service delivery processes to a whole new level.
