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AI Ticket Resolution Solutions

Ticketing systems are essential for managing and resolving support requests. However, with the increasing volume and complexity of issues, it can be challenging for teams to keep up. AI can help teams automate and categorize tickets, making it easier to assign and resolve issues more efficiently.



AI can be used to categorize tickets based on the patient’s symptoms, history, and other relevant factors in a number of ways. One approach is to train a machine learning model on a dataset of labeled tickets. The model would learn to identify patterns in the data that are associated with different categories of tickets. For example, a model trained on a dataset of tickets related to respiratory symptoms might learn to identify keywords such as “cough,” “fever,” and “shortness of breath” as being associated with the “pneumonia” category.

Once the model is trained, it can be used to categorize new tickets as they are received. The model would simply be given the ticket text as input, and it would output a predicted category. For example, if a new ticket contains the words “cough,” “fever,” and “shortness of breath,” the model might predict the “pneumonia” category.

Another approach to using AI to categorize tickets is to use natural language processing (NLP) techniques. NLP techniques can be used to extract key information from the ticket text, such as the patient’s symptoms, history, and any other relevant factors. This information can then be used to categorize the ticket manually or to train a machine learning model to do so automatically.

Categorizing tickets using AI can help to ensure that tickets are routed to the right specialist for resolution. For example, if a ticket is categorized as “pneumonia,” it can be routed to a pulmonologist. If a ticket is categorized as “migraine,” it can be routed to a neurologist. This can help to reduce the time it takes to resolve tickets and can improve the overall patient experience.


AI can be used to experiment with ticket data for the past 12 months and run a categorization model to categorize simple tickets. As tickets come in the model should be able to categorize as easy, medium, or  complex as part of the ML process. Once the model is deployed, it can be used to categorize simple tickets as easy, medium, or complex as part of the ML process. This can be done by feeding the model the features from the new ticket and predicting the corresponding label.

The categorized tickets can then be used to run analytics against the PMs based on difficulty on ticket not quantity of tickets to have a better view of ticket resolutions times and where categorized tickets are coming from to identify patterns and make improvements.

Technician Services

Technician Services:

AI can be used to optimize the routes of technicians who are responding to on-site service calls. This can be done by taking into account factors such as the location of the technician, the location of the customer, the traffic conditions, and the estimated time to complete the service call. AI can also be used to reroute technicians in real time if there are unexpected delays or changes.

AI can be integrated with mobile apps to provide technicians with real-time information about the tickets that they are assigned to. This can include information such as the customer’s contact information, the problem that they are experiencing, and the priority of the ticket. Coupling this with GenAI can streamline the resolution of IT tickets by generating automated responses to common customer questions, generating scripts for technicians to follow, and generating reports on ticket trends.

To take it one step further IoT technologies, such as computer vision, can be paired with AI to save time on ticket resolution and increase overall productivity during service calls. For example, computer vision can be used to identify and diagnose problems with equipment without the need for a technician to physically inspect the equipment. This can help to resolve tickets more quickly and efficiently.

Benefits of using AI for IT ticketing:

Increased efficiency

Increased efficiency and productivity

AI can automate many of the time-consuming tasks involved in IT ticketing, such as ticket categorization and routing. This frees up IT teams to focus on more complex issues.

Improved customer satisfaction

AI can help to reduce the time it takes to resolve IT tickets, which can lead to improved customer satisfaction.

Reduced costs

AI can help to reduce IT support costs by automating tasks and improving the efficiency of the ticketing process.

How to get started with AI for ticketing:

If you are interested in using AI to improve your ticketing process, there are a few steps you can take:
  1. Identify your needs: What are your biggest pain points with your current ticketing processes? What tasks would you like to automate?
  2. Ensure your data is AI ready
  3. Utilize JUNO.labs JUNO INNOVATE to map out your use case and get how-to-guides and a pre-trained model
  4. Implement your model with JUNO Modelmesh
How to get started with AI for ticketing

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