Inquiry / Feature Request: Leveraging Atlassian Intelligence (GenAI) to automatically refactor compl

[Leon]

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:

1. Massive Manual Toil: Refactoring these wrapped macros into native Cloud Editor blocks requires an unrealistic amount of manual effort from our end-users.

2. 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:

1. Does Atlassian currently offer any GenAI-powered scripts, tools, or official best practices to assist with structurally refactoring these complex legacy pages?

2. 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.

[Robert Eiser]

I am curious about the answers here, but honestly, Atlassian needs to support nested macros! We shouldn’t have to work around their depreciation of the legacy editor. 

[Barbara Szczesniak]

@Leon Welcome to the Atlassian community.

I would refer you to this article: https://community.atlassian.com/forums/Confluence-articles/The-Legacy-Editor-is-being-deprecated-in-Confluence-Cloud-Here-s/ba-p/3046832 

This article contains information and links to pages with additional information.

[Leon]

Hi Barbara,

Thank you for the quick response and for sharing the deprecation article.

However, I’ve already reviewed the standard migration documentation. My specific inquiry is focused on the "Legacy Content Macro" limitation regarding Atlassian Intelligence. Currently, the auto-conversion merely "wraps" complex content, which creates an AI data silo—Atlassian Rovo cannot effectively index or summarize content within these macros.

To clarify and move this discussion forward, could you or the product team provide insights on:

1. AI-Powered Refactoring: Is there any plan to use Atlassian Intelligence to "intelligently rebuild" these wrapped macros into native Cloud blocks (rather than just wrapping them)?
2. Rovo Compatibility: Since our organization is adopting Rovo, having critical SOPs trapped in Legacy Macros is a major blocker. Is there a roadmap to make these macros searchable/summarizable by Rovo?

As our leadership (CIO) is looking for a scalable AI-driven solution to reduce manual toil, any specific guidance on the GenAI roadmap for content conversion would be greatly appreciated.

[Barbara Szczesniak]

@Leon More information would need to come from the Atlassian Product Team. I am just a user, like you, except that I never used the legacy editor, so I have no experience with pages converted from the legacy editor to the fabric editor.

[Leon]

Hi Barbara,

Thank you for the clarification! I appreciate you sharing the general resources even without direct experience with the Legacy Editor.

Since this issue specifically concerns the integration between legacy content structures and the new Atlassian Intelligence (Rovo) indexing, it seems we need more technical insights from the Product Team.

I’m tagging this as a #FeatureRequest and #AtlassianIntelligence inquiry. To any Atlassian Product Managers (PMs) or Community Managers reading this:

1. Is there a plan to use Atlassian Intelligence to "uncover" and refactor content trapped in the Legacy Content Macro?
2. How can we ensure that our critical SOPs (currently in Legacy Macros) are fully indexed and searchable by Rovo without thousands of hours of manual refactoring?

As an organization heavily investing in the Atlassian Cloud ecosystem, we see a massive opportunity for GenAI to solve this "migration debt." I would love to hear any official perspective on the roadmap for AI-driven page refactoring.

[Melek Jebnoun_Vectors_]

Hello Leon, 

My name is Melek and I'm Vectors' CPO. We are the vendor behind Morph, an AI-Powered tool that let you format your pages using AI (via dedicated feature or Rovo) and Content Formatting Toolkit, a formatting macros App. 

I feel the struggle concerning this topic - Unfortunately -  especially as Formatting App provider. Our macros are based on nesting aspect and the encapsulation within a legacy content bloc has been a show stopper for several of our customers. Atlassian is advising to split pages and adopt new architecture reflexes but for many of them as you mentioned it's a titan work to do this manually. 

When we developed and shipped our Morph App we made it to help our customers format pages using our macros or Atlassian macros. We mastered how IA can understand page content, split it into macros (Our macros and Atlassian native macros) based on page context. 

You can see more about it here: https://www.youtube.com/watch?v=OBkWrDhKlww

After dealing with our "angry" customers the topic of splitting Legacy Content Bloc has been back to the table. Either via our Rovo Agent included in Morph or via native feature to reformat the page we want to explore this topic and we can go faster on it. We are just collecting concrete use cases to understand what type of content our customers want to split. 

I would be really happy to discuss all this via a call conference and check if we can help you with your use case. You can just raise a ticket on our support portal https://support.covectors.io/ or send me an email at melek.jebnoun@covectors.io and we can schedule all this or discuss it further via email. 

Many thanks