Should Confluence optimize for information retrieval or knowledge retrieval in the era of Rovo AI?

[AGASTYA ANOOP SHARMA]

Traditional Confluence search focuses on retrieving relevant pages.

Rovo shifts the experience toward retrieving synthesized answers instead.

These are fundamentally different objectives.

Should enterprise knowledge management optimize for:

finding documents,

understanding concepts,

or generating contextual answers?

How should organizations measure success when users may never open the original documentation?

[Ajay _view26_]

Hi @AGASTYA ANOOP SHARMA 

Thats an interesting question :)

In practice, the answer is "both, but measured differently."

Optimising purely for synthesised answers can create blind spots - ie  people can stop navigating to source pages, which meant nobody notices  when those pages go stale. The AI keeps serving confident answers from increasingly outdated content.

For operational knowledge (runbooks, how-tos, onboarding docs): optimize for synthesized answers. People need the answer, not the page. But we tag these pages with an explicit review cadence and use Confluence analytics to flag pages that Rovo references frequently but haven't been updated in 90+ days.

For strategic/architectural knowledge (design decisions, post-mortems, compliance docs): optimize for document retrieval. Context matters here, and a synthesized summary loses the nuance of why a decision was made. Configure Rovo agents to link to source pages rather than summarize for these spaces.

For measuring success — page views is now a flawed metric. Tracking "answer confidence + source freshness" as a composite score is a good option. If Rovo answers a question but its source material is 6+ months old, flag it for review.

Let me know you thoughts as well :)