Mar 18, 2021. By Aleena Mathew
In recent years, IT organizations are completely transiting to the digital era. This transition calls for more IT resources, and this resource utilization adds more heterogeneous IT infrastructure elements into the IT organizations. This heterogeneity introduces more complexity and brought in more pressure on the IT team. The IT team had to be more responsible for handling every IT request or IT ticket, whether it be an incident ticket or a service request ticket, which occurs in the system. The number of IT tickets issued just started to multi-fold and made it difficult for the IT operators. The IT team had to spend hours sorting out tickets based on priority, user creation, and so on. The most important or higher priority task for any IT operator is to resolve any IT tickets before the SLA gets breached. The main reason is the lack of a single window to access and analyze every IT ticket logged. So what is the solution here?
AI-based service analytics is the perfect solution here. AI-based analytics helps in providing a precise analysis of every IT ticket logged into the system. This mechanism gives the correct information of each ticket, such as the ticket type, which operator issued the ticket, the SLA of the ticket, the priority, etc... Also, a single-window view on every IT request or ticket view was possible with AI analytics. Also, with the AI-based service analytics, IT tickets were being resolved even before the SLA breach that is faster resolutions were able to be achieved. Let's look into some of the use cases and benefits of AI-based service analytics and IT automation.
AI-based IT Automation and Analytics Usecases:
AI for IT service management is a great promise. AIOps helps to automate almost every IT task and enables IT to AI-based IT automation. AI helps in the pro-active management of IT operations and enables in providing a deeper level of analytics. Let's look into some use cases on AI-based IT service analytics and IT automation.
1. IT Ticket Prioritization and Routing:
Every IT tickets issued came in with a particular priority and, tickets needed to be resolved based on their priority. Also, an operator should be assigned to the corresponding tickets, whether it be an incident ticket or a service request ticket. This prioritization and operator assigning was possible when it came with a limited number of tickets. But the scenario changed when tickets started to pile up. No proper mechanism to prioritize tickets and, no operator was assigned, which lead to a situation where most tickets were untouched in the ticket queue. AI helped in resolving this. Ticket prioritization and routing were possible with AI. Whether it be a high or low priority ticket, it will get routed to an IT operator automatically based on the ticket priority. In this way, tickets generated will be assigned automatically and resolved before the user gets it escalated.
2. Auto Remediation and Auto-fulfillment:
Auto remediation and auto-fulfillment are the two applications of AI. One of the concerns faced by IT operators was that they failed to resolve tickets generated on time. Even with the proper routing and prioritization, this was a great challenge. That's where AI stepped in. AI-based models were capable of automating workflows necessary for auto-remediating incident tickets or auto-fulfillment of service request tickets.
3. Change Risk Management and Analytics:
Change management is always associated with the concept of risk management. Managing and monitoring the changes that occur within an IT organization is an absolute must. AI can play a crucial role here. Any change that occurs within the system is always associated with risk. Manually getting the risk analysis done is a challenging and time-consuming task. AI-based models are capable of identifying the risk score automatically for an associated change. In this way, the IT operator can get to know pre-hand the risk factor or how much a particular change will affect the system.
4. SLA Analytics:
Meeting the service level agreement(SLA) set for every IT ticket is a fundamental factor. Every IT ticket issued will have an SLA set which states maximum response and resolution time. The AI-based models will help in ensuring that the SLA set is met before getting breached. AI provides SLA analytics which helps in clearly providing proper analysis of the entire SLA history.
5. Helpdesk Agent Performance Analytics:
Agent performance is the measure of analyzing the performance of help desk agents by tracking how they perform on key metrics. AI helps in scrutinizing the performance of help-desk agents and analyzes each agent's load and achievement. This analysis list out the number of tickets resolved, the number of tickets breached, tickets reopened, etc... Using the agent performance analysis, the IT team can easily map customer satisfaction.
Benefits of AI-based IT Automation and Analytics:
1. Faster Decision Making:
The benefit of analytics is the capability of providing faster decisions. Analytics helps in giving deep insights into every IT data collecting from every IT service. These insights thus help in enabling the IT team to make the right and faster decisions. The chance of manual errors or any process working going wrong is avoided here.
2. Faster time to Resolve:
With AI-based ticket prioritization and routing, IT tickets generated are efficiently handled and resolved on time before they get breached. The auto-remediation and auto-fulfillment of every ticket helped automatically initiating work-flows that effectively resolved the tickets based on their priority and type. The system can automatically identify the ticket type and the associated issues. In this way, the tickets will get automatically resolved.
3. Minimize SLA breach:
With the implementation of AI, the minimization of SLA breach was possible. AI-based models can route every IT ticket and automatically resolving them, ensuring that the resolution SLA does not get breached. With AI-based models, IT tickets were resolved before getting escalated. This IT analytics and IT automation eventually helped in improving end-user satisfaction.
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