Problem:
Customers who use Jira Service Management portals to raise support tickets have no native Atlassian AI-powered way to search, query, and analyze their own historical ticket data. The only option currently is to rely on third-party AI tools (e.g., Microsoft Copilot, ChatGPT) integrated with Jira Cloud to achieve this — which means Atlassian is effectively ceding this capability to competitors despite the data already residing in the platform.
Current state:
Rovo is limited to internal (licensed) users only
JSM Virtual Agent focuses on deflecting new requests via Knowledge Base articles, not on enabling customers to explore their own ticket history
Customers can view/filter past tickets on the portal, but there is no AI-assisted search or analytics capability
Desired outcome:
Provide a Rovo-powered (or equivalent) AI agent accessible through the JSM Customer Portal that allows customers to:
Search their own historical tickets using natural language
Identify patterns, recurring issues, and trends across their ticket history
Find past resolutions relevant to new issues they're experiencing
Generate basic analytics/summaries of their support interactions
Business value:
Keeps the AI experience within the Atlassian ecosystem rather than driving customers to third-party AI tools
Leverages data that already exists in JSM — no additional data pipelines needed
Improves customer self-service and satisfaction
Reduces repeated escalations by surfacing past solutions
Strengthens Atlassian's competitive position in the AI space
Use case example:
A managed services provider uses JSM (project: TCK) to handle customer support. Their customers want to analyze historical tickets to reference past solutions and identify recurring issues — but currently must integrate third-party AI tools to do so, despite all the data living natively in Atlassian.