Read on to learn how AI can overcome the most common hurdles that accompany manual ticket triage and the long ticket resolution times.
Resolving IT tickets is time-consuming and expensive, but it is essential to delivering IT services to employees and customers. Companies must provide IT as a service to improve operating efficiency, reduce costs, promote transparency, and ensure accountability for operations. And ticketing is evolving beyond basic IT support to encompass other customer service areas.
Organizations find that implementing advanced IT service management (ITSM) can be challenging. The most common hurdles for deploying ITSM are the inefficiencies that accompany manual ticket triage and the long ticket resolution times. When IT response is slow and inefficient, it can result in high customer churn and lost revenue.
More organizations are adopting artificial intelligence and machine learning (AI/ML) to automate ITSM ticketing and stay competitive.
The Problem with Manual Ticket Triaging
Before delving into new strategies for ITSM, let’s consider how the manual process works today.
Here’s a classic example—an end-user calls the IT department seeking to restore access to an account. The user fills out a request on an online form that triggers a workflow in a chatbot and generates a ticket. That ticket is then placed in a queue organized by importance and priority, waiting for its turn to be addressed.
The process looks simple and efficient on paper, and it may be adequate if your IT support team only receives a manageable number of tickets each week. As the number of tickets grows, manual processes break down. When you have to start processing thousands of tickets, the support team can’t read, tag, and route those tickets as quickly as they should. Tickets begin to backlog, leading to long delays.
The truth is that manual or even partially automated ticketing systems are slow, inefficient, and prone to errors. The longer the issue of manually triaging tickets remains, the bigger the problem becomes for various reasons:
- Longer wait times erode customer satisfaction. According to the American Customer Satisfaction Index (ACSI), service wait times are among the top factors that affect customer satisfaction.
- Ticket delays due to manual processing result in operational bottlenecks that translate into more overhead, higher labor costs, and reduced productivity.
- Manual ticket processes have a higher error rate which can drive employees and customers away from seeking IT support.
Adopting automated ITSM processes addresses these problems by taking manual delays and errors out of the ticket triage workflow.
Using AI/ML to Automate Ticket Triaging
Applying AI/ML to ITSM workflows saves time and money by making processes more efficient and accurate. The challenge isn’t only dealing with growing ticket volume. It’s also eliminating the errors that delay ticket processing.
Consider what happens when your IT team must deal with more tickets, and many of those tickets are incorrectly categorized due to inaccurate self-reporting. This makes manual ticket processing an even bigger headache:
- Miscategorized tickets are improperly handled down the line, which creates delays and affects all parties.
- Manually recategorizing tickets requires extra time and resources, i.e., adds overhead.
- Efficiency deteriorates, which makes it harder to address urgent requests and delays other requests.
Even if your ticketing system has partial automation and a well-designed UI for self-service, it still doesn’t eliminate all errors. You still must account for the human factor.
Using AI/ML to automate ITSM can compensate for shortfalls due to human error (and laziness). Implementing an AI-powered solution adds machine learning that learns how to respond to different situations over time. For example, machine learning can be trained to improve ticketing triage using a human-in-the-loop (HITL) approach. Tickets with a low accuracy tag can be reviewed manually. Once the tickets are tagged correctly, the machine learning model collects that data and uses it to retrain and improve ticket processing.
Automating ticketing triage with AI/ML can virtually eliminate manual processes altogether. Tickets can be tagged and routed automatically. Tickets can also be resolved using ML models that apply responses from a set of preprogrammed options or auto-generated responses created by natural language processing (NLP). An automated ITSM system can respond to a user ticket in seconds. One client has automated about 80% of ticket responses and 100% in some categories.
Automation Improves Customer Satisfaction
AI and machine learning can automate multiple types of operations. However, for AI to work, data must be easily accessible for machine learning training and analytics insights. You must also train your workforce. Once you start using AI/ML to automate workflows, you will realize additional benefits, such as more efficient operations, increased productivity, and reduced overhead costs.
When it comes to ticket triaging, using AI/ML to enhance or eliminate manual can dramatically reduce ticket resolution times and improve customer satisfaction, minimizing churn and reducing costs. AI is proving to be a powerful technology that is changing how we approach ITSM. Customers expect superior service, including quick ticket resolution. The only way to meet those expectations is with fast, at-scale, end-to-end delivery of IT services powered by AI/ML.
Co-Author: Rinat Gareev, ML Practice Lead
Rinat Gareev is a Solution Architect and ML Practice Lead at Provectus with focus on MLOps and NLP. Rinat’s expertise enables him to cover the whole ML process, from problem framing to model deployment and monitoring. At Provectus, he applies his vast experience to design, develop, and operationalize ML solutions for the customers.
