Today, the delivery of IT services to employees and customers is rapidly evolving beyond basic IT support. Companies want to — and actually have to — deliver IT as a service to improve efficiency, reduce costs, and ensure accountability and transparency of their operations. Advanced IT service management (ITSM) can be highly challenging. But changing the paradigm for IT service delivery from rudimental IT support to artificial intelligence (AI)-powered end-to-end ITSM is now essential for staying competitive. This article considers two common hurdles for ITSM: inefficiency of manual ticket triage and high customer churn due to long ticket resolution times. We show you how to begin changing the IT delivery paradigm in your organization by addressing these challenges with AI and automation.This article looks at two common hurdles for #ITSM: inefficiency of manual ticket triage and high customer churn due to long ticket resolution times. #servicedesk Click To Tweet
The Why of Automating Ticket Triage
Imagine a classic ITSM scenario.
One of your end-users wants to restore access to their account and submit a corresponding request to your support department by filling out a ticket on a support page or triggering a workflow in a chatbot. One way or another, the ticket ends up in a queue and eventually gets addressed according to importance and priority (through ticket triage).Triaging of tickets is slow, inefficient, and prone to error. Here's how AI can help. #AI #ITSM Click To Tweet
This process looks good on paper, especially if your IT support team only handles a few hundred tickets a week. But what if they have thousands of tickets? Your IT support team will not be able to promptly read, tag, and route all tickets (even if advanced routing and workflows are there in the first place). Resolving the tickets in the most efficient order becomes a never-ending challenge.
The fact is, manual — or even partially automated — ticket triage is slow, inefficient, and prone to error. It’s a burden for your organization, and here’s why:
- Longer wait times erode customer satisfaction and loyalty. According to the American Customer Satisfaction Index (ACSI), wait times are among the top factors impacting customer satisfaction.
- Operational bottlenecks, which are unavoidable with manual operations, lead to growing overhead, increased labor costs, and poor productivity.
- High ticket triage processing error rates force employees and customers to never come back. The human factor is a real problem to consider.
No wonder organizations looking to maximize their ITSM potential head toward AI and automation to streamline their manual ticket triage and resolution operations.
Automated Ticket Triage with AI and Machine Learning
Revisiting our typical ITSM scenario, the sheer number of tickets is not the only issue that IT support teams may face.
Imagine that your team has to deal with thousands of tickets, and a considerable portion of these tickets are not categorized correctly in the system due to inaccurate self-reporting. You end up with three consequences:
- Miscategorized tickets are mishandled further down the line, which affects all parties involved
- Tickets are re-categorized manually by your team, which takes extra time and resources
- Your team becomes less efficient: you are unable to react to urgent issues quickly and to triage less urgent issues
Remember that your organization can have advanced ticket routing systems and workflows in place, allowing for partial automation. The ticketing system can have a convenient UI, helping end-users to fill out all fields quickly and easily. But it still does not protect you from ticket triage errors and mistakes — from the human factor.This article shares examples of how automated ticket triaging with AI and Machine Learning can help streamline your processes and reduce customer churn. #AI #ServiceDesk #ITSM Click To Tweet
In a situation like this, having an intelligent AI-powered solution accounting for human error (and laziness) can make a huge difference. The advantage of any AI/machine learning solution is that it learns over time. Tickets with a low accuracy tag can be sent to a human team for review. Once tickets are correctly tagged, feedback on the machine learning model performance is collected and used for model retraining, fine-tuning, and enhancement. This cyclical approach to improving machine learning is called human-in-the-loop (HITL).
The automation aspect of AI/machine learning is also crucial. Not only can tickets be tagged and routed automatically. They can also be resolved by machine learning models using responses selected from a prepared pool or auto-generated by natural language processing (NLP) and sent to an end-user in a matter of seconds. For instance, one of our clients was able to automate ~80% of ticket responses (and up to 100% for specific categories).
AI to Reduce Churn, Improve Customer Satisfaction
Adopting AI and machine learning goes beyond the automation of certain operations. For AI to work, data must be easily accessible — both for machine learning training and as insights. You must also streamline multiple workflows and train your workforce. Along with these changes come additional benefits, such as more efficient operations, increased productivity, and reductions in overhead costs.
Regarding ticket triage, enhancements from the adoption of AI help dramatically reduce ticket resolution times and improve customer satisfaction, minimizing churn and leading to significant cost savings.
AI is a powerful technology that should be used extensively to augment ITSM. We would argue that customer expectations for fast, at-scale, end-to-end delivery of IT services can only be met if AI is applied.
What are your thoughts? If you have any questions, please post them in the comments section below or visit the Provectus website.
Marat Adayev is a Machine Learning Engineer at Provectus with 4+ years of experience, specializing in designing and building end-to-end, large-scale machine learning solutions for healthcare, marketing, education, and entertainment industries. Marat is involved in all stages of the machine learning development life cycle, helping to transform the machine learning business problem into a deployed and running in-production machine learning model.