How to use AI in Confluence: a practical guide for 2026

What you'll need before you start

This guide covers the AI that's built into Confluence itself, plus the option of bolting on a third-party AI. Before any of it works, three things have to be true:

• A paid Cloud plan. Rovo ships with Confluence Standard, Premium, and Enterprise on Cloud. It is not on the Free plan, and the full feature set isn't available to self-managed Data Center deployments without a companion Cloud subscription. If you're still pricing this out, our Confluence pricing guide lays out the tiers.
• Admin access. An org admin has to activate the AI features in Atlassian Administration, per product. You also can't enable Rovo on a site registered with a personal email domain like gmail.com, it needs a verified business domain.
• Content actually in Confluence. Rovo answers best when the knowledge it's reasoning over lives in Confluence pages. The thinner your wiki, the thinner the answers, so it pays to get your Confluence AI knowledge base in shape first.

One naming note before we dig in, because it trips everyone up: "Atlassian Intelligence" is the umbrella brand, and "Rovo" is the product you actually click on. We spell out the overlap in is Atlassian Intelligence the same as Rovo; for this guide, just read them as the same thing.

Step 1: turn on AI in Confluence

There's no separate purchase. Atlassian confirmed that Rovo rolls out automatically to every eligible Standard, Premium, and Enterprise Cloud site, so for most teams the AI is already sitting there waiting for an admin to flip it on.

As an org admin, head to Atlassian Administration and toggle the AI-powered features on for Confluence. You can activate or deactivate them per product, which matters more than it sounds, plenty of teams want AI in Confluence but not in Jira, and that split is allowed. A few platform-level pieces (Rovo Search, Studio, Bookmarks) can't be turned off because Atlassian classes them as core platform, not optional AI.

Worth knowing up front: AI answers respect your existing permissions automatically. If a user can't see a Confluence space, Rovo can't surface its content to them either, and an admin can't expose restricted pages to everyone through the AI. That permission-aware retrieval is one of the real strengths of the Atlassian approach, and independent write-ups like Onyx's enterprise-search teardown list Rovo among the tools that actually honour per-document access controls rather than leaking across them.

Step 2: ask questions and find answers

This is the feature most people mean when they ask how to use AI in Confluence. Once Rovo is on, you can type a plain-English question and get a written answer with its sources cited, instead of hunting through a dozen pages.

There are two surfaces for it. Rovo Search is AI-powered search across your Atlassian apps plus 100+ third-party connectors, returning personalized, permission-aware results. Rovo Chat is the conversational version: ask a question, get a natural-language knowledge base answer grounded in content you already have access to, with follow-up prompts suggested underneath.

You can reach Rovo Chat inside Confluence, Jira, and Jira Service Management, through a Chrome browser extension, on Confluence mobile by voice or text, or by going straight to chat.rovo.com. The search filters let you narrow by app or content type when a broad query returns too much.

Under the hood, the engine doing the work is what Atlassian calls the Teamwork Graph, a semantic map of the people, projects, content, and goals across your environment. Here's roughly what happens when you ask:

A couple of things really shine here. The "who owns X" style question is answered with a knowledge card, surfacing the person, team, or project rather than making you read a page to find a name.

For deeper questions, Rovo Deep Research searches, reasons, and synthesizes across your Atlassian apps, connected SaaS, and optionally the web into a multi-page, fully cited report in minutes. It's the closest Confluence gets to "do the reading for me."

The honest caveat: Rovo Chat is great at "summarize the page I'm on" and noticeably weaker at open-ended reasoning. One admin in the Atlassian Intelligence and Confluence feedback thread put it cleanly:

"Atlassian Intelligence is decent for stuff inside Confluence, like summarizing a page you're already on. But as a Q&A agent for general research..."

r/atlassian, ~10 months ago

So treat search and chat as a fast way to find and summarize what's already written down, not as a general reasoning engine. If broad, natural-language Q&A over your whole knowledge base is the goal, it's worth comparing how dedicated AI search engines handle it.

Step 3: generate and edit content on a page

Inside the page editor, the AI doubles as a writing assistant. You can ask it to draft a section, change the tone, fix structure, or summarize a long Confluence page down to the decisions, all without leaving the editor. The release-notes use case is where teams report the cleanest win: one documentation writer described producing release notes for a new version "in 5 minutes" by copying the previous page and letting the AI rework it.

A smaller feature that quietly earns its keep is Definitions. When you hit an unfamiliar acronym or internal codename on a page or ticket, Rovo looks across your company knowledge and explains it inline, with sources.

Note the small "Content quality may vary" line in that popup. That's not decoration, it's Atlassian's own hedge. Their trust documentation warns that answers "may not accurately reflect the content they are based on" and advises against relying on Rovo where you "need current and accurate information about people, places, and facts." In plain terms: it can hallucinate, so verify anything load-bearing before you ship it. For the broader picture of what the writing and editing features can and can't do, our Confluence AI guide goes deeper.

Step 4: automate work with Rovo agents

Beyond answering and writing, Rovo includes agents, "always-on AI teammates" that handle recurring tasks. There are two flavours.

Out-of-the-box agents are pre-built by Atlassian: a Meeting Insights Reporter that summarizes meetings and flags action items, a Brainstorm Facilitator, an Employee Onboarding agent, an Issue Organizer, and more. You enable one and point it at the right content.

Custom agents are built in Rovo Studio, a no-code, prompt-based builder. You can give an agent a specific knowledge base, a set of skills, and the ability to take actions, creating or editing Jira tickets and Confluence pages, posting to Slack, scheduling calendar events. There's real organic enthusiasm for this; one team described wiring a Rovo agent into a Confluence automation that posts meeting-page updates straight into their Slack channel.

The friction shape is "easy to start, ceiling appears fast." Studio is truly no-code for the templated stuff, but the moment you want to extend an agent (a developer asked how to add a custom MCP server and found the UI didn't support it), you hit a wall. If agents are your main reason for being here, the guides on Rovo agents and agentic AI in Confluence are worth a read before you commit.

Step 5: watch the credits (this is the real cost)

Here's the part the "included with your plan" framing glosses over. Rovo is bundled, but the moment you do anything substantive, you spend Rovo credits, and each paid plan ships a thin monthly allowance.

Asking Rovo Chat to draft something, running Deep Research, or having an agent produce a status report each draw down the pool. (Plain Rovo Search itself doesn't consume credits, which is the one free-flowing exception.) On Standard, 25 credits per user per month is not a lot once a team gets active, which is exactly the gap between "included" and "useful" that shows up across community threads.

The frustration is loudest among heavier users. The pattern is unanimous enough that it's worth quoting one verbatim datapoint from Atlassian's own community forum, where an admin watched a single code review eat most of his monthly pool:

"Rovo had a look at the PR and made a few suggestions and in doing so appeared to use 965 of my 2000 credits... Either I'm doing something amazingly wrong or that's not value for money at all."

Chris Mingay, Atlassian Community, Jan 2026

That example is from Rovo Dev (the engineering coding agent, which runs on a separate, larger credit pool), but it illustrates the same dynamic Confluence teams hit at smaller scale: credit burn is unpredictable, and overage is billed per-credit with no built-in ceiling. Before you roll Rovo out widely, read up on Rovo AI credit usage and Atlassian Intelligence cost breakdown so the first invoice isn't a surprise.

Common mistakes and limitations to plan around

A few pitfalls catch teams out. Knowing them up front saves a rollout from stalling.

Expecting answers in Slack or Teams. This is the biggest one. Rovo lives inside Atlassian's surfaces; it doesn't come to where your team already chats. You can add Rovo connectors to search Slack and Drive from inside Confluence, but employees can't natively converse with it from Slack. A JSM admin piloting it hit exactly this wall: "Did you figure out how to connect the Rovo agent to Teams/Slack? ...can only get Rovo added as an app to Teams." If your team's day happens in chat, this gap is the whole ballgame, and it's why the Rovo Slack integration gets so much search traffic.

Assuming it reads every Confluence object. It doesn't, yet. One team found Rovo Chat couldn't read Confluence Databases at all: "It works very well with our Confluence content, except [databases]." Check coverage for your specific content types before you promise the org wall-to-wall answers.

Turning it on everywhere by default. AI surfaces that grab focus annoy power users fast. One long-time Confluence fan on The Register's forum asked Atlassian to let them switch it off, describing the experience as "encumbered with social media style sparkly bullshit." Enable AI deliberately, per product, rather than blanket-on.

Trusting answers blindly. Back to that hallucination caveat. A Jira admin described Rovo making things up on admin tasks while ChatGPT stayed accurate. Keep a human in the loop on anything that matters.

When to layer an external AI over Confluence

Native Rovo is the obvious default when your team lives in Atlassian and your knowledge mostly lives in Confluence. But two patterns push teams toward a dedicated AI layer instead: knowledge spread across many tools (Notion, Google Drive, Zendesk, a help center), and a team that works in Slack or Teams all day. Here's the simple way to decide:

This is where a tool like eesel fits. Rather than asking people to come to Confluence, you connect eesel to Confluence (and to Slack, Google Drive, Zendesk, and 100+ other sources) and let it answer questions right inside the chat tool your team already uses, with no per-seat credit meter ticking in the background. It's the same "ask a question, get a sourced answer" promise, pointed at heterogeneous knowledge and delivered where people work. If you want the mechanics, we wrote a step-by-step on how to link Confluence with an AI knowledge bot, and a roundup of AI tools for internal support teams if you're comparing options.

Try eesel over your Confluence wiki

If your knowledge lives in more than just Confluence, or your team asks its questions in Slack and Teams rather than inside Atlassian, eesel is built for exactly that gap. It connects to Confluence and 100+ other sources, learns from your existing pages and past tickets, and answers questions as an AI teammate inside the tools people already use, with usage-based pricing and no per-seat credit cap. You can wire it up and see real answers over your own content in minutes.