Increasing efficiency with AI: How to automate repetitive tasks in Jira with LLMs

In many modern companies, ticket systems are essential for organizing and tracking work processes. Nevertheless, inquiries, problems or tasks often still reach the relevant departments by email.

Problem: Resource consumption due to repetitive classification work

As soon as the relevant emails are received by the responsible persons, they are usually processed and classified manually before they can be handled in ticket systems such as Jira from Atlassian. This process ties up valuable personnel resources that could be used more efficiently, for example for strategic work.

An understanding of the context is required to categorize the issues and fill the fields in the ticket system appropriately. Until now, there were no technical options for automating these workflows. Thanks to the further development of artificial intelligence (AI), these use cases can now be looked at in a new light.

Solution: Automation with Jira Automation in conjunction with LLMs

Large Language Models (LLMs) are a special type of AI that are trained to understand, process and generate human language. The ability to recognize patterns and relationships in a context that makes these models so exciting for the scenario described: With the integration of LLMs in Jira Automation, recurring tasks can not only be automated, but also made more intelligent.

One exciting use case is the processing of incoming security updates. Thanks to the power of LLMs, the context of these updates is automatically recognized and relevant custom fields in Jira are filled accordingly.

Imagine the IT security team regularly receives security-critical information about the systems used in the company. Until now, the workflow for processing these messages in Jira tickets has been as follows: The messages arrive in Jira via interfaces / the connection of an email inbox, the messages are then classified by a 1st-level team and the information is finally transferred manually to the corresponding fields within Jira.

By using an LLM in conjunction with Jira Automation, it is possible to automatically recognize which systems are affected and check whether they are available in the company. In addition, prioritizations can be made and important information such as the affected group can be derived from the context. Automated assignment to the relevant teams is also possible.

This solution significantly reduces manual effort and ensures that no relevant information is overlooked.

Data protection and security with locally hosted LLMs

A key concern for many companies in connection with the use of AI is data protection. Particularly in industries that work with sensitive data, such as healthcare, the financial sector or security-critical IT systems, there are often concerns about passing on sensitive information to external, cloud-based AI systems located in the US. Concerns about compliance with strict data protection guidelines such as the GDPR are justified, and many companies therefore decide against using AI in order to avoid potential data breaches.

This is where the use of locally hosted LLMs offers an ideal solution. By implementing such models on-premise or in data centers within the EU, companies can retain full control over their data and ensure that it is not transferred to external service providers or countries outside Europe. European cloud providers and local data centers make it possible to keep the entire data flow and processing within the EU, which allows the strict requirements of the GDPR to be met.

Another advantage of locally hosted LLMs is that they can be customized to a company's specific requirements. Sensitive data such as email content, customer information or security-critical messages can be processed securely without leaving the company's protected IT environment. In addition, regular security updates and internal audits allow security gaps to be proactively identified and closed.

For companies looking for a security-compliant and privacy-friendly AI solution, the combination of Jira Automation with locally hosted LLMs offers the perfect approach to reap the benefits of automation without compromising on data protection. We will be happy to help you select and use a local LLM that is suitable for your company.

Added value: Increased efficiency thanks to intelligent automation

By using LLMs and Jira Automation, companies can not only save time and resources, but also ensure the accuracy and consistency of processing. Important aspects are reliably recognized and the relevant fields in Jira are automatically completed. This allows the team to concentrate on the really important tasks, while repetitive work is covered intelligently and efficiently by the system.

The solution offers the following advantages in particular:

• Automated classification of incoming e-mails
• Reading out context information & checking using assets
• Employees can focus on other core tasks or problem solving instead of wasting a lot of time on the manual work of sifting through incoming mails
• Faster detection of security gaps and therefore less risk of failure

Outlook: The future of AI-supported automation

The integration of LLMs in Jira Automation offers enormous potential for holistic service provision as part of enterprise service management with reduced additional effort. In the future, the use of AI could be further optimized by operating LLMs locally to better protect sensitive data. 

In addition, integrations with other systems, such as the Frends automation platform, can also be implemented to enable even more seamless workflow management. The use of Atlassian Forge for deeper integration, for example to include attachments in incoming emails, also opens up exciting possibilities.

Conclusion: A step into the future of automation

The combination of AI and the automation options in applications such as Jira opens up completely new ways for companies to optimize processes and free up valuable resources.