Oct 26, 2021. By S V Aditya
In the current age, e-mails are the primary form of business communication across any industry which uses digital technology - which is to say, practically all industries. All office-based employees are comfortable shooting off emails daily so that they can track their activities and have a written record of their interactions. So it's no surprise that emails remain one of the most common ways IT end-users and customers also interact with the IT Help Desk. They have a written record of their issue along with dates and descriptions of their problem. They can also describe their problem freely without worrying about what category it belongs to - especially if they are not technically skilled.
Problems with Email-based support
Conversely, what is easier for the end-users is not always the same for IT Service Desk teams. All of these emails go to the L1 support team which is supposed to respond to solve simple requests quickly as well as redirect the complex requests to the right teams. To do this, they have to sift through hundreds of emails at a staggering pace to meet the enterprise SLA requirements of effective response times. These L1 teams are also typically low-skilled compared to higher levels and are usually recruits to the company. Tight deadlines on response requirements along with the lower skill-level of L1 teams means that many of these emails are often miscategorized, leading to delays as these tickets bounce from team to team till it is assigned to the correct owner. As a consequence, the meantime to fulfill user requests or react to incidents and complaints raised by users grows longer. This is a recipe for frustrated users and results in poor engagement scores, negative feedback, and waste of time. Inefficiencies like this also cause the workload on L1 teams to artificially bloat up, increasing their frustration, causing burnout, and eventually - a high turnover rate. L1 support needs to change fundamentally to combat these omnipresent challenges.
AI-based Automated email to ticket management
This is where AI-based technologies can help out! Let's see how AI can drive a completely Automated Mail-to-Ticket system. In the first step, once an email arrives from an end-user, Natural Language Processing(NLP) cleans the text and extracts critical information that indicates user issues and their related information. This is passed to text classification algorithms that can predict if the email is an incident or a service request. Moreover, the next set of algorithms do a multi-classification to identify which category, sub-category, and product the issue belongs to. After this, a priority predictor then predicts the importance of the issue and the degree of severity for the user. At this point, AI has determined what's the type of user issue, what team it should go to, and how urgent is the request. But there's more. Each ticket needs summarized information so the operator can quickly glance and prioritize their tasks. It also needs a detailed description that can summarize the issue to the operator but also cut down on the fluff words used by most end-users. Here advanced text mining and NLP techniques truly shine. Intelligent algorithms can extract the most syntactically important terms to create a ticket title that is meaningful, coherent, and short. For long emails, NLP can simplify the email body and extract a short description that still contains all the relevant details required by operators. Now your email has become a true ticket - assigned to the right categories and users, filled up in its complete form, and with an associated priority rating. But AI doesn't stop here. Finally, a Natural Language Generation model generates a humanized response to the user that speaks to them in the context of their problem. This response is sent back to the user. All of this is done within a few minutes.
Advantages of Automated Mail to ticket Generation
So what are the true advantages you can see by adopting automated mail-to-ticket conversion? Firstly, there will be a faster time to respond. This happens to be one of the key SLA criteria for ITSM and can easily be achieved here. Secondly, these responses will not just be robotic acknowledgements with ticket numbers - they will be contextual and related to user issues. Thirdly, accurate classification means that the right owners are assigned to the incident or service request immediately - cutting down on all the inefficiencies from tickets bouncing around. Consequently, the fourth advantage is that the meantime to resolve, remediate, or fulfill incidents and service requests is cut down drastically. All of these advantages go to the ultimate goal of ITSM - keeping the IT user engagement process smooth, hassle-free, and the end-user satisfied with the experience. By automating the mundane, companies can achieve higher efficiency and better user engagement.
To learn more about how AIOps improves user engagement, please visit https://www.algomox.com/incident-remediation/