Many of us have seen a surge in ticket volumes, where support teams find themselves handling repetitive questions, chasing the same details over and over. However, there are ways to minimize these repetitive tickets so your team can focus on matters that genuinely require a personal touch. Before diving in, note that we have demos suitable for IT and non-IT use cases, showcasing exactly how AI-driven features can be applied across various scenarios.
In this blog, we’ll explore how AI integrations in Jira can guide customers to self-serve solutions, recommend quick answers, and even detect patterns that signal deeper issues. The end result? Fewer incoming tickets and a better experience for everyone.
1. The Rising Influence of AI in Customer Support
Why AI is Gaining Attention
Over time, software providers have discovered that many tickets are relatively simple or follow a predictable pattern. This is especially true for IT help desks dealing with password resets or marketing teams addressing repeated event queries. By letting AI handle these routine matters—often via chatbots or automated knowledge base searches—you free agents to tackle complex or high-impact cases.
Added Value for Teams
AI isn’t just for big enterprises. Small teams can benefit too. Whether you need automated routing or suggestions for known articles, AI can lighten the load. If your team is used to answering the same request multiple times a day, it might be time to see how AI features in Jira can step in.
2. Automation in Jira Service Management
Auto-Assign and Auto-Close
Many teams already use basic automation to assign incoming tickets to specific groups. This helps direct the ticket to the right place immediately. With AI features, you can go further by identifying if a request is truly a duplicate or if a user has already been provided with the same information. Check out Automation from Atlassian for examples of triggers, conditions, and actions that can be combined to handle routine work.
Escalation and Notifications
You can set up rules that watch for tickets which mention certain keywords or remain in a particular status for too long. AI can enhance these rules by analyzing text for urgency or complexity. When the system detects key phrases like “server not responding” or “repeated crash,” it can escalate the ticket to senior agents.
3. Smart Suggestions
Knowledge Base Recommendations
One approach that reduces ticket volumes is to guide requesters toward existing solutions. For example, Atlassian has been experimenting with AI-driven suggestions that appear as a user types a request. By analyzing the text, it can suggest relevant articles. If the requester sees an article that addresses the issue, they might solve it on their own, avoiding a ticket altogether. See Atlassian’s Announcement of Atlassian Intelligence for a glimpse of their larger plans in this space.
Previous Ticket Resolutions
Beyond knowledge base articles, AI can look at historical tickets. Suppose someone logs a request about an error message that occurred last week for another person. AI can point them to the steps used previously to fix the issue. This kind of reference can significantly reduce how many new tickets get created.
4. Detecting Patterns for Long-Term Improvements
Spotting Repetitive Issues
When AI scans through ticket data, it can group similar requests or highlight a recurring theme. This might be a technical glitch in a specific application or a user interface quirk that confuses new employees. By seeing these patterns early, your team can address the underlying cause and potentially eliminate a large chunk of tickets moving forward.
Proactive Measures
A major benefit of analyzing data in this way is the ability to be proactive. If the AI notices that multiple users are reporting slow performance after a system update, the support team can publish an advisory or fix the root cause, preventing a flood of additional tickets. For more on how AI is shaping proactive strategies, see Atlassian’s page on How AI is changing incident management.
5. Applying AI to Non-IT Departments
HR Self-Service
An HR department could see a spike in questions every time open enrollment rolls around for benefits. AI can suggest relevant policies or forms, cutting down the volume of repetitive queries like “Which health plan is available?” or “How do I add a dependent?”
Finance Inquiries
Simple requests such as “When is my reimbursement processed?” or “Where can I find the invoice template?” can be answered by referencing a knowledge base. Once you add a bit of AI logic, employees might find what they need without opening a support ticket.
Customer-Facing Portals
External clients might have simple questions about product features, shipping methods, or subscription details. An AI-driven widget can handle these queries instantly. When a question is genuinely complex, it’s routed to a human agent.
6. Balancing Human Support and AI
When AI Should Hand Off
AI can do a lot, but certain issues still require a personal touch. A well-designed AI system recognizes when it doesn’t have enough information or when a user is expressing dissatisfaction. In these cases, handing off to a human agent preserves trust and maintains the quality of service.
Training Agents on AI Tools
Agents need to understand how AI suggestions are generated and how to apply them. If an AI recommendation is off-target, the agent should provide feedback so the system can learn and improve. Likewise, if a user is stuck because the AI’s suggestions are missing the mark, an agent can step in and resolve the request more directly.
7. Reducing Ticket Volumes with a Multilayered Strategy
Layer 1: Knowledge Base and AI-Driven Suggestions
Encourage customers or employees to seek self-service answers first. This includes knowledge base articles, FAQs, and any AI-driven chat tools. If the question is answered, a ticket never gets created.
Layer 2: Automated Checks and Escalations
When a user does decide to create a ticket, automation helps categorize and assign it. If an issue is repeated from the user’s past tickets, the system might prompt them with existing solutions. If the wording is urgent, it escalates to the correct team.
Layer 3: Human Agent Intervention
Agents handle issues that are unique, complex, or sensitive. They also provide the final check if the user is unsatisfied with the AI’s suggestions.
Layer 4: Continuous Review
Look at the data generated by the AI, including how many tickets were resolved through suggestions. If certain articles aren’t cutting it or if certain topics lead to repeated tickets, update the content or fix the root cause.
8. Potential Challenges and How to Address Them
Despite the benefits, there are some points to consider:
- Quality of Knowledge Base
If your knowledge base is outdated or disorganized, AI suggestions might point users to irrelevant material. Keep articles current and easy to read. - Accuracy of AI
AI models rely on training data. If the data is old or incomplete, the AI might not make helpful suggestions. Regularly review analytics to see how often suggestions actually solve the problem. - User Adoption
Some users prefer speaking directly to a person. While AI can assist, it’s wise to ensure a human route remains available. Over time, if users see that AI suggestions are helpful, they’ll likely become more comfortable with the process.
9. Example Scenario: AI in Action for a Software Company
Suppose you run a software business with multiple product lines. Users often ask the same set of questions about installation, licensing, or feature access. Without AI, your support team sees a large queue of repetitive tickets. With AI:
- Self-Service Portal: As soon as a user starts typing a question, the system offers relevant articles.
- Smart Categorization: If the user continues to submit the ticket, the system recognizes keywords like “installation” and routes it to the “Installation Support” group.
- Auto-Responses: The system emails the user a summary of known fixes right away.
- Analysis of Patterns: After a few weeks, the AI sees that a major portion of tickets reference a complicated installation process on MacOS. The support team takes steps to improve the installer, publishes a clearer article, and invests in a short tutorial video.
- Ongoing Improvement: As fewer tickets roll in about MacOS installation, the system flags new trends or recurring questions that might emerge over time.
10. Looking to the Future of AI in Jira
AI in Jira is evolving, and Atlassian has indicated plans to deepen these capabilities. While specifics continue to develop, you can start with existing AI integrations and automation rules to handle the most common queries. By proactively refining your processes and knowledge base, you’ll be well-prepared for future AI enhancements in the Jira ecosystem. You can also learn more about what Jira can do by visiting the Jira Software page.
11. What’s Your Experience?
Have you tested AI-based features to reduce ticket volumes in Jira or other platforms? Maybe you’ve noticed certain categories of requests nearly vanish once you introduced self-service suggestions. Feel free to share your success stories. Others might appreciate the insights on how best to combine human support with automated assistance.
We are gold solution partners with Atlassian. Interested in exploring AI for your IT or non-IT teams? Contact us at 📧 sales@clovity.com or visit 🌐 atlassian.clovity.com.
