Can we trust AI in Confluence?

[#1]

Most organisations today face a familiar problem: the more knowledge they create, the harder it becomes to find, manage, and keep it up to date. Pages multiply, documentation gets out of date, and onboarding a new colleague means sending them down a rabbit hole of old Confluence pages.

Artificial intelligence offers a way to turn that challenge into a system that learns, connects, and reduces repetitive work. But adopting it in practice — especially in Confluence — raises questions about trust, accuracy, and control. Let’s look at how AI is being used right now in knowledge management, what’s possible with tools like Atlassian Intelligence, Rovo, and 3rd-party apps, and what organisations should know before diving in.

AI in knowledge management for Confluence helps teams summarise content, automatically tag and structure information, translate multilingual pages, and support onboarding.
While some users remain cautious about data privacy and output quality, Atlassian’s current AI design keeps customer data isolated, encrypted, and governed by existing permissions.
The key is to use AI as an assistant, not as the decision-maker, focusing on tasks that reduce repetitive work rather than making judgments.

From Reading to Understanding

Few people have time to read entire long Confluence pages. Rovo now lets you request automated summaries for pages, blogs, and comment threads, making it easier to grasp what matters without scanning every paragraph.

Teams use this not just to shorten reading time, but to make their knowledge more usable. A detailed project review or strategy document can be condensed into a few key insights. A new joiner can catch up on a project’s background in minutes instead of hours.

Automating Structure, Preserving Consistency

Tagging and organising information is the unglamorous side of knowledge management, but without it, search quality drops fast.
New AI-based apps such as AI Tags+ for Confluence use OpenAI's ChatGPT GPT-5 models to suggest labels and categories automatically, helping maintain consistency across teams and spaces.

It’s not about replacing your information architecture; it’s about giving it structure. Instead of relying on each author to remember naming conventions or categories, AI can propose tags that match existing standards. That consistency pays off when someone searches later, the right information appears because the taxonomy stayed intact.

Breaking the Language Barrier

For global organisations, multilingual content is a daily reality. Translations for Confluence allows teams to maintain one page with several language versions — an essential feature for HR policies, product documentation, or compliance materials.

Adding Atlassian Intelligence to that workflow lets users create translations or adjust tone and phrasing with less manual effort and fewer tool switches. Content becomes accessible across teams and countries without increasing maintenance.

The Question of Trust

In a recent Communardo poll here in the Community, most respondents said they hesitate to use Atlassian’s AI features because they don’t fully trust the quality of results or the safety of their data. These are fair concerns and worth taking seriously.

Atlassian’s documentation provides information about this topic. Data used for Atlassian Intelligence is processed within the same trust framework that governs Confluence Cloud itself. Customer data isn’t used to train models, and permissions remain intact. Users only see what they already have access to. Encryption is standard, and the features are covered under Atlassian’s SOC 2 and ISO 27001 certifications.

Even so, the human element remains critical. AI will misinterpret tone, oversimplify summaries, or tag content oddly from time to time. The practical answer isn’t to reject it, but to define clear governance: where AI is used, who reviews its output, and how feedback loops are built.

Rethinking Knowledge Work

The more interesting story is not that AI saves time, but what teams do with that time.
When summarisation, tagging, and translation are handled automatically, the focus shifts from storing information to interpreting it. From managing pages to managing insight.

At Communardo, we already see this change in client conversations. Teams want to stop treating Confluence as a static archive and start treating it as a shared intelligence hub. One that learns from their work, not just records it. The shift is cultural as much as technical: AI is the spark, but structure, governance, and human judgement keep it meaningful.

Where to Start

If your organisation is exploring AI in knowledge management, start small. Pick one area where it genuinely reduces friction, for example, summarising meeting notes or automating translations for your internal policies.
Pilot it, measure the output, and gather feedback. Build from there.

Trust grows with familiarity, and so does value.
AI in Confluence doesn’t need to reinvent your knowledge base. It just needs to make it more readable, findable, and genuinely useful. One page at a time.