For a long time, organizations have measured the productivity of their IT Service Desk based on how fast a pending ticket is resolved. Faster ticket resolution means that support agents are able to handle ticket requests productively and as per customer expectations. In practice, tickets keep pouring in – even for the same service requests – and the IT Service Desk is inevitably overwhelmed by the number of tickets pending in the queue pipeline.
In order to optimize the resource utilization of the IT Service Desk, ITSM organizations need to look beyond metrics such as resolution time that provide a single-dimensional view of the ticket volume problem. Aiming for faster resolution time translates into workforce productivity, but does it also reduce the burden on the IT Service Desk over the long term?
Without optimizing ticketing workflows, providing proactive root cause resolution and eliminating waste processes involved in ticket resolution, the speed of ticket resolution will have minimal impact on reducing the ticket queue. This comes down to a simple fact: for a technology-enabled organization, IT service request volumes can grow faster than the resources capacity of a limited IT Service Desk support workforce. In order to keep up with growing ticket volumes, the IT Service Desk should not only aim to resolve tickets at a fast pace, but also identify and resolve the workflow bottlenecks and waste processes that restrict ticket resolution performance.
Let’s first review some of the most common bottlenecks in the IT Service Desk ticket resolution pipeline that contribute to a never ending ticket queue:
While front-line agents try to extract the most conclusive relevant information pertaining to a service request with a first interaction with end-users, the communication is not always sufficient to guide faster resolution processes. End-users are not always able to describe issues and service requests accurately; front-line agents have limited knowledge and expertise to provide an immediate solution where applicable or guide to the right resolution teams.
Tickets hop between resolution teams until the requested issue is accurately identified and a known solution exists. The ticket waits in the queue while resolution teams overcome the barriers of siloed knowledge repository, slow collaboration between disparate teams; or failing to target the right problem due to limited insights. End users are often provided with a fixed set of ill-described troubleshooting categories that are unrelated to the underlying problem; correct category assignment then requires manual intervention.
When an IT incident occurs, engineers are forced to reduce the damage impact to end-users and the business through a temporary and immediate fix. This is especially necessary when the incident is not foreseen or predicted, and any long-term fix requires a long duration and more resources than available beforehand. This impacts the ticket queue in two ways: a peak of tickets from impacted users until a temporary fix is applied; and repetitive ticket requests from the same frustrated users when the same problem reoccurs due to a lacking long-term solution. Lack of insights into system and application performance, combined with the manual approach to handling tickets serves as a performance bottleneck for the IT Service Desk.
The traditional IT Service Desk tends to follow a well-defined workflow for routing tickets. The workflow is designed based on business priority and impact of a service request, and available resources. In practice, service requests do not occur with a well-defined or static pattern. Factors such as business growth, scale of users, external market and geopolitical circumstances, and unexpected IT incidents not only affect the volume of new tickets but also the business value associated with resolving an individual request. The challenge for technology-driven business organizations is to optimize ticket routing and workflows based on these evolving factors. The lack of insight into business and technology operations, user sentiments and external factors creates a bottleneck for handling tickets optimally from a business perspective.
In order to reduce the ticket volumes, especially when the tickets are waiting in the queue over mundane issues and service requests, the IT Service Desk needs to create an information-rich and up-to-date self-service resolution center. Many ITSM organizations fail to establish such a mechanism when the knowledge base of the IT Service Desk is tied to individuals and teams operating in silos. The lack of a centralized knowledge repository makes it impossible for front-line agents or end-users to search through all possible solutions for known issues, which can be easily implemented to resolve a majority of requests waiting in the ticket queue.
These common issues slow down the ticket resolution process or efforts to reduce the ticket volume waiting in the queue, which is why a majority of the tickets spend the most duration of their lifecycle in the queue simply waiting to be handled by a support agent or a resolution team. These bottlenecks share some common characteristics: traditional manual framework for guiding ticket requests, lack of insights for decision-making and ticket resolution and a reactive approach to handling tickets. These are also some of the most compelling use cases of hyperautomation intelligence technologies that use advanced AI algorithms to identify the bottlenecks and align ticketing workflows optimally with the business goals such as revenue impact and end-user satisfaction.
Watch the video below to learn more how Swish can significantly improve all your ITSM’s KPIs by using the Swish Agent Load and Bottleneck Detection Module.