5 AI and Automation Use Cases in ITSM
Published: 2022-10-10 10:34:35

The widespread adoption of IT Automation (AI) within numerous firms in 2022 is already indicative of a broad enthusiasm for the technology. We discovered in a recent [global survey by IDG](https://www.brighttalk.com/webcast/18018/534971) that more than one-quarter (27%) of the respondents had fully installed AI-enabled ITSM/ITOM systems, and another 34% have undertaken first deployments in particular use cases and departments. Another 32% are investigating AI-based solutions or learning more about them concurrently. Therefore, considering the calculations, 93% of companies have already implemented or are investigating IT Automation (AI) solutions for their workplaces!

It's important to note that this technology does more than simply assist IT service management (ITSM) teams with the "heavy lifting" operations that consume IT resources. Additionally, it facilitates "heavy thinking" by allowing AI capabilities to process larger data sets than humans more quickly and to offer stronger insights (and actions as a result).

IT Automation is fantastic, but rushed adoption without full-proof planning and need analysis can lead to inefficient results. The same is true when applying AI; it produces the best outcomes when used in conjunction with an optimal status quo. Implementing AI capabilities "simply because you can" will never work. It won't be the ideal response to every opportunity or problem in ITSM. Additionally, as the AI-related resources are probably constrained, it's critical to concentrate all AI efforts where they will have the biggest impact or simply to pay attention to "what matters most."

To plan the ITSM implementation better, businesses need to be aware of the below listed top five AI use cases:

**Self Resolution or Task Allocation**

Without assistance from technicians, end users will be able to look for answers and handle difficulties. Through [IT Automation](https://automationedge.com/it-automation/), help desks can be trained to automatically scan incoming tickets and offer end users solutions based on the system's prior knowledge through machine learning. 

Also, through IT automation Chat boxes modelled like Google Assistant, companies can assist end users in resolving issues or obtaining information without ever submitting a ticket to the help desk. Help desks could also use historical data and expertise to send tickets or tasks to the right support teams or technicians, automating the ticket assignment process without the need for rules or workflows. The help desk team's efficiency would increase, and resolution times would be decreased with the use of machine learning.

**Effective Resource Management**

About 70–80% of resources are used for service desk and operational tasks like implementing service requests, addressing incident tickets, and delivering modifications. In order to free up technicians' time to innovate and help the firm achieve its objectives, organizations may employ IT Automation AI to intelligently automate these tasks.

By applying machine learning, for instance, service desks may be taught to automatically authorize help requests based on the employee's role, responsibilities, department, and other factors. So, let's say an employee requests access to the software. In order to save time and resources, the service desk can quickly approve the request and start a workflow without requesting management approval.

**Optimizing email notifications to users**

Standard protocols are used in the user notification process, which typically adheres to the three-strike rule. It may, however, be fully automated from beginning to end because it uses organized data and established rules.
A manual, repetitive back-office process that involves hundreds of emails per day and takes up to 5 minutes per case to handle is a burden on organizations. With RPA at play, all of this gets automated, bringing down the time by about 90%.

**The Reigning Popularity of Online Agents**

The usage of "virtual agents," which give users quicker access to self-service features or an appropriate IT automation group that can handle their concerns as promptly as possible, is one of the most prevalent and quickly expanding applications of AI in ITSM.

Depending on the nature of the problem, certain tickets may be accurately resolved and closed by utilizing technology without the involvement of a human. For instance, when end users employ virtual agents, they can quickly receive automated responses with the best chances of solving their problems without ever opening a ticket. With the help of virtual agents, situations can be addressed quickly, consistently, and effectively without the need for manual involvement, saving time, money, and effort.

**Problem Forecasting** 

Help desks will be able to analyze event patterns and foresee issues with the help of machine learning. Additionally, skilled help desks could automatically generate alerts or problem tickets for impending concerns so that the help-desk specialists can start an investigation as soon as possible. 

If an application server's performance begins to decline, help desks would be able to predict any application failures based on the server's historical performance statistics, alert any potentially impacted end users, create a problem ticket, and link any pertinent incident tickets to the problem ticket.

**Cycle of an Asset**

Numerous mishaps are caused by outdated IT assets whose performance has declined. Based on elements like their performance levels and issues connected to them, machine learning may assist in automatically identifying which assets would repeatedly fail. 

When those assets are found, the help desk can make use of machine learning to alert technicians and make buying replacements easier. The simplest scenario would be for the support desk to automatically generate requests for new printer toner after a predetermined number of printed pages. Machine learning potential is exponential in ITSM. The aforementioned scenarios are some of the most straightforward examples of how machine learning may simplify life for both the help desk team and end customers.

## Conclusion

IT Automated service desk staff must meticulously record all requests, issues, and modifications before implementing AI in order to maintain an accurate IT service desk database and build a thorough knowledge base.

There is no denying that the use of AI in ITSM may considerably enhance service delivery, but if organizations want to profit fully from the use of AI, they must create a plan to fully utilize AI in line with their bigger business objectives.

To know more reach out to AutomationEdge for a [FREE TRIAL](https://automationedge.com/it-robotic-process-automation-demo/)!