Background: Our organization is actively preparing for the Confluence Legacy Editor deprecation. We have a large volume of critical documentation (e.g., SOPs, policies) that were built using the Legacy Editor, many of which contain complex nested tables and legacy macros.
Current Pain Point: The current auto-conversion mechanism either breaks the page layout or wraps these complex elements in a Legacy Content Macro. This creates two major issues for enterprise users:
Massive Manual Toil: Refactoring these wrapped macros into native Cloud Editor blocks requires an unrealistic amount of manual effort from our end-users.
AI Data Silos: Content trapped inside the Legacy Content Macro cannot be effectively indexed, searched, or summarized by Atlassian Rovo. This severely limits the value and adoption of Rovo within our organization.
The Ask / Feature Inquiry: Our CIO recently highlighted this issue and suggested using GenAI to solve it. We are looking for an automated way to use AI to parse the underlying storage format of legacy pages and intelligently refactor them into clean, native Cloud Editor structures (rather than just wrapping them).
Questions for the Atlassian Team:
Does Atlassian currently offer any GenAI-powered scripts, tools, or official best practices to assist with structurally refactoring these complex legacy pages?
Is there any plan on the product roadmap to utilize Atlassian Intelligence (or Rovo) to automate the conversion of Legacy Content Macro blocks into native Cloud Editor elements?
Providing an AI-driven conversion tool would drastically reduce manual toil and significantly accelerate our readiness to fully adopt Rovo. We look forward to your insights or any workarounds you can provide.
