Understanding Confluence AI: Enhancing Knowledge Management with Rovo

Confluence AI refers to the native artificial intelligence capabilities built directly into Confluence Cloud, designed to help teams create, understand, find, and manage knowledge more effectively inside their existing documentation workflows.

Rather than introducing a separate tool or experience, AI in Confluence is embedded into the places where teams already work with knowledge: pages, comments, spaces, and collaborative content. It supports everyday tasks such as drafting documentation, summarizing updates, finding information, and keeping knowledge organized as it grows.

Today, Atlassian groups its AI experiences under Rovo, the company’s unified AI layer across Confluence, Jira, and other Atlassian Cloud products. Rovo provides the underlying intelligence that powers AI features in Confluence, including generative assistance, contextual understanding, and cross-product discovery.

In earlier documentation, this foundation was referred to as Atlassian Intelligence, a legacy term that may still appear in search results but is now being phased out in favor of Rovo.

It is important to understand that Confluence AI is not a single feature. It is a set of AI-powered capabilities woven into everyday Confluence workflows, helping teams write more clearly, stay aligned on changes, and surface relevant knowledge faster, without changing how Confluence functions as a source of truth.

By embedding artificial intelligence directly into Confluence Cloud, Atlassian reinforces Confluence’s role as the central knowledge hub for teams. AI enhances clarity, speed, and discoverability, while control, ownership, and governance remain firmly with people and the processes they already trust.

What is Confluence AI

Confluence AI is the set of native AI features inside Confluence Cloud that help teams work with knowledge more effectively as it is created, updated, and reused.

Rather than changing how Confluence is used, these features reduce friction in everyday documentation by making content easier to write, understand, and find.

In practice, Confluence AI applies machine learning and large language models to pages, comments, and page history. This allows AI to support common knowledge tasks such as drafting and refining content, summarizing long pages or discussions, answering questions using natural language, and helping teams keep information structured and usable over time.

What makes Confluence AI practical is where it appears. AI assistance shows up directly inside familiar Confluence workflows. Users encounter it while creating or editing a page, reviewing recent changes, catching up on discussions, or searching across spaces. There is no separate interface to learn and no parallel tool to manage. AI is present at the moment knowledge work happens.

It is also important to distinguish Confluence AI from traditional Confluence features like macros, templates, or static page layouts. Macros and templates provide predefined structure, but they do not adapt to meaning or intent. Confluence AI works differently. It responds to context, interpreting the content on the page and the user’s goal, then generating summaries, suggestions, or structured output dynamically.

In short, Confluence AI acts as an assistive layer inside Confluence. It helps teams move faster and stay aligned by improving clarity and discoverability, while leaving accuracy, approvals, and ownership firmly in human hands.

How Rovo Powers Confluence AI

This section explains the foundational layer behind Confluence AI, not the differences between products or features. Confluence AI is delivered through Rovo, Atlassian’s unified AI platform that brings intelligence into everyday work while applying consistent principles around assistance, security, and control.

Rovo provides the underlying capabilities that allow AI features to operate inside Confluence Cloud. It is responsible for generative assistance, contextual understanding, and the trust framework that governs how AI interacts with content. Confluence AI builds on this foundation, using Rovo to enhance knowledge work directly where teams create and consume documentation.

Generative AI for Knowledge Work

At the core of Rovo-powered Confluence AI is generative AI designed to support knowledge work, not to replace it. These capabilities help teams work with existing information more efficiently by accelerating expression and synthesis.

In Confluence, this includes the ability to:

• Draft new pages from short prompts or rough notes

• Refine existing content to improve clarity, tone, and structure

• Summarize long pages, blog posts, or discussion threads

• Surface key points and changes without rereading everything

The intent is not to define truth or create authoritative knowledge. Rovo-powered generative AI helps users articulate, organize, and condense what already exists. It reduces time spent rewriting or reformatting, so teams can focus on the substance of their documentation rather than the mechanics of maintaining it.

Permissions-Aware AI and Data Boundaries

Rovo-powered Confluence AI is permissions-aware by design. AI features only work with content that a user is already authorized to access under Confluence’s existing permission model.

Pages, spaces, and comments that are restricted remain restricted for AI as well. There is no separate AI access layer. The same rules that govern who can see content also govern what the AI can use for summaries, suggestions, or answers.

For enterprises and regulated environments, this has a clear practical implication. Teams can adopt AI assistance without changing their governance model. Data boundaries remain intact, and AI does not bypass existing controls or expose sensitive information to unintended audiences.

Trust, Models, and Admin Controls

Rovo is built around Atlassian’s trust and safety principles for AI. To deliver its capabilities, Atlassian uses a combination of Atlassian-hosted and carefully selected third-party hosted models.

According to Atlassian’s published guidance, customer inputs and outputs are not used to train AI models, and data is not retained by model providers beyond what is necessary to deliver the feature. This approach is designed to support responsible AI adoption at scale.

Control over AI features is managed centrally. Rovo-powered AI capabilities in Confluence are configured by administrators through Atlassian Administration, where availability and usage can be aligned with organizational policies and compliance requirements.

Together, these elements explain how Rovo powers Confluence AI: a shared AI foundation that enables useful assistance inside Confluence while preserving trust, permissions, and administrative control.

Core Confluence AI Features Available Today

Confluence AI is made up of several practical capabilities that appear directly in everyday documentation workflows. Rather than introducing a new interface or mode of working, these features support how teams already write, read, search, and organize knowledge inside Confluence Cloud.

Below are the core Confluence AI features available today, explained in terms of how they are used in real scenarios.

AI Writing and Editing Assistance

AI writing and editing assistance helps teams move faster when creating or improving content, especially when starting from incomplete or unpolished drafts.

With this capability, users can draft new Confluence pages from short prompts, outlines, or rough notes. Existing content can be rewritten to improve clarity, adjust tone, or better match the intended audience, whether that is a technical team, leadership, or a broader organization.

The focus is on readability and consistency. AI assistance helps smooth language, clarify structure, and reduce ambiguity across pages, while leaving ownership and accuracy firmly with the author. It supports documentation work without imposing rigid templates or changing how teams express their knowledge.

Page, Blog, and Comment Summaries

Summarization is one of the most immediately useful Confluence AI features, particularly in content-heavy spaces.

Users can generate one-click summaries of pages and blog posts to quickly understand the main ideas without reading every section in detail. For pages with active collaboration, AI can also summarize long comment threads, highlighting key discussion points and outcomes.

Another common use case is catching up on changes. Confluence AI can surface what has changed since a user’s last visit, making it easier to re-enter work on evolving documentation without manually scanning revision history or comments.

Natural Language Search and Answers

Confluence AI improves content discovery by enabling natural language search. Instead of relying on exact keywords or knowing where information is stored, users can ask questions in everyday language.

This allows teams to find relevant pages across spaces more easily, even when they are unfamiliar with the structure of a Confluence site. By making discovery more intuitive, natural language search helps reduce time spent hunting for information and lowers the risk of recreating content that already exists elsewhere.

The effectiveness of this feature improves as documentation becomes clearer and more consistently structured, reinforcing good knowledge management practices over time.

Action Items and Knowledge Structuring

Beyond writing and search, Confluence AI also supports turning unstructured information into more actionable knowledge.

One common scenario is converting meeting notes into clear action items, making follow-ups easier to track, and reducing the chance that tasks are lost in long pages. AI can also help improve page structure and flow, clarifying sections and making content easier to navigate.

These capabilities support scalable documentation standards by helping teams keep information organized as their Confluence spaces grow. Instead of replacing existing practices, AI assists with maintaining clarity and structure across large and evolving knowledge bases.

Taken together, these features show how Confluence AI enhances knowledge work in practical ways. Writing becomes more fluid, reading becomes more efficient, search becomes more intuitive, and documentation stays easier to maintain, all within the familiar Confluence experience.

Generative AI Inside Confluence: What It Helps With and What It Doesn’t

Generative AI inside Confluence is most valuable when it is used deliberately and with clear expectations. This section is not about listing features again, but about understanding how to use AI responsibly as part of a mature knowledge management practice.

Where Generative AI Helps Most

Generative AI in Confluence is particularly effective at improving how existing knowledge is expressed and reused.

It helps teams improve clarity by rewriting dense or informal content into language that is easier to understand. It supports consistency by aligning tone and structure across pages written by different authors or teams. It also makes documentation more reusable by organizing information in a way that is easier to scan, reference, and maintain over time.

These strengths make generative AI well-suited for refining process documentation, internal guidelines, onboarding material, and project pages that evolve continuously. The AI accelerates expression and synthesis, allowing teams to focus more on the substance of their knowledge rather than the mechanics of writing.

What Generative AI Cannot Do

Generative AI does not create knowledge that does not already exist. It cannot infer decisions, constraints, or intent that were never documented, and it cannot reliably fill gaps where information lives only in meetings, chats, or external tools.

If a Confluence page is outdated, incomplete, or unclear, AI-generated output will reflect those weaknesses. In these situations, results may feel generic or partially accurate, not because the AI is malfunctioning, but because the underlying content lacks the necessary context.

For this reason, generative AI should not be treated as a source of truth or an authority on organizational knowledge. It works with what is written, not with what is implied or assumed.

Why Review, Ownership, and Governance Still Matter

Human review remains essential in any AI-assisted documentation workflow. Teams are still responsible for validating accuracy, ensuring content aligns with internal standards, and managing approvals and ownership.

Clear governance practices, such as defined page owners, documentation guidelines, and review cycles, significantly improve the quality of AI-assisted outputs. When teams know what “good documentation” looks like, AI becomes a powerful accelerator rather than a risk.

Strong Documentation Habits Produce the Best Results

The effectiveness of generative AI in Confluence is directly tied to the quality of the knowledge base it works with. Well-structured pages, clear language, and up-to-date information lead to more accurate summaries, better rewrites, and more useful answers.

In practice, the best results come from combining AI assistance with strong documentation habits. When AI is used to enhance clarity and consistency, and humans remain accountable for accuracy and intent, Confluence becomes a more reliable, scalable knowledge hub for the entire organization.

AI Agents Using Confluence Content via Rovo

AI agents are an important part of Atlassian’s AI strategy, but they need to be described precisely to avoid confusion or overstatement. In the Atlassian ecosystem, AI agents are not Confluence-native bots. They operate through Rovo and use Confluence as a structured source of knowledge.

In simple terms, Confluence provides the context, and Rovo provides the agents.

Where AI Agents Actually Live

AI agents are part of Rovo, Atlassian’s cross-tool AI layer. They are designed to work across Confluence, Jira, and other connected tools rather than being embedded directly inside a single product.

This approach allows agents to reason over a broader knowledge landscape. Confluence plays a critical role by acting as a trusted knowledge base, but the agents themselves are configured, managed, and executed at the Rovo level. This keeps AI behavior consistent, permissions-aware, and scalable across the Atlassian platform.

How AI Agents Use Confluence Knowledge

When connected through Rovo, AI agents can work with Confluence pages, comments, and spaces to support common knowledge tasks.

In practice, agents can assist by preparing summaries of long pages or recent changes, helping users discover relevant pages or related sections, and organizing information ahead of reviews, handoffs, or discussions. Their role is to reduce the time spent searching, reading, and reformatting information, especially in large or fast-growing knowledge bases.

Agents do not change how content is stored or governed in Confluence. They work on top of existing structures and always respect the same permissions and access rules that apply to users.

Assistive by Design, With Humans in Control

A core design principle of Rovo Agents is that they assist rather than act independently. They help gather, synthesize, and present information, but decisions, approvals, and changes remain with people.

This model ensures that AI agents support productivity without undermining governance or accountability. Confluence remains the system of record for knowledge, and Rovo Agents act as virtual teammates that help users work with that knowledge more efficiently, while human control remains essential at every step.

Confluence AI vs Rovo: Understanding the Difference

This section is about scope, not architecture. Both Confluence AI and Rovo are part of Atlassian’s AI strategy, but they operate at different levels and solve different problems.

Understanding this distinction helps set realistic expectations and makes it easier to see how the two work together rather than overlap.

Confluence AI: Feature-Level AI Inside Confluence

Confluence AI refers to the native AI capabilities embedded directly into Confluence Cloud. Its scope is intentionally focused on improving how users interact with knowledge inside Confluence itself.

Confluence AI helps users draft and refine page content, summarize pages and comment threads, catch up on changes since a previous visit, and find information using natural language search. All of this happens within Confluence, using its existing page structure, spaces, and permission model.

In short, Confluence AI makes everyday knowledge work inside Confluence faster, clearer, and easier to manage, without extending beyond the product.

Rovo: Cross-Tool AI Across the Atlassian Ecosystem

Rovo operates at a broader level. It is Atlassian’s cross-tool AI layer that works across Confluence, Jira, and other connected Atlassian tools.

Rather than enhancing individual features, Rovo focuses on connecting information across systems. It enables enterprise search that spans tools, conversational exploration of knowledge, and AI agents that can assist using context from more than one product.

This makes Rovo especially valuable when documentation, work items, and discussions are spread across multiple places.

Complementary, Not Competing

Confluence AI and Rovo are designed to work together, not compete.

Confluence AI improves feature-level interactions inside Confluence. Rovo adds cross-tool awareness, broader context, and agent-based assistance on top of that foundation. Used together, they create a layered experience where knowledge is both easier to work with locally and easier to connect across teams and tools.

What Rovo Adds on Top of Confluence AI

While Confluence AI improves how users create and manage knowledge inside Confluence, Rovo adds an additional layer that connects information across tools and helps teams work with that knowledge more broadly. The value of Rovo is not in replacing Confluence AI, but in extending it when context spans beyond a single space or page.

Rovo focuses on discovery, synthesis, and assistance across systems, using careful, assistive behaviors rather than fully automated actions.

Unified AI Search Across Tools

Rovo introduces enterprise search that works across Confluence, Jira, and connected Atlassian tools. Instead of searching each product separately, users can ask questions in natural language and receive results drawn from multiple systems at once.

Search results are presented as knowledge cards that summarize relevant information and clearly reference their sources, such as specific Confluence pages or Jira issues. This source transparency helps users understand where information comes from and decide what to trust, rather than relying on opaque or context-free answers.

For teams that document decisions in Confluence and track execution in Jira, this unified search reduces fragmentation and makes it easier to connect knowledge with ongoing work.

Rovo Chat and AI-Assisted Workflows

Rovo also adds a conversational interface that allows users to explore knowledge across tools through chat. Users can ask follow-up questions, refine their understanding, and navigate related context without restarting their search or switching products.

Rather than executing actions on its own, Rovo is designed to assist and accelerate workflows. For example, it can help draft Confluence pages or prepare structured issue content for Jira based on existing discussions, documents, or search results. This reduces manual copying and preserves context, while keeping users in control of what is ultimately created or submitted.

The emphasis is on acceleration, not automation. Rovo supports creation workflows but does not bypass review, approval, or governance steps.

Rovo Agents and Scalable Assistance

Rovo Agents extend assistance beyond one-off interactions. These agents can be configured using no-code settings to support recurring knowledge workflows across tools.

Instead of relying on static macros or rigid automation rules, Rovo Agents use context to assist with tasks such as preparing summaries, surfacing relevant updates, or guiding users through structured processes. They help reduce repetitive manual effort while adapting to how information evolves over time.

Importantly, these agents remain assistive. They do not act autonomously or replace existing controls. Their role is to support consistency and efficiency while leaving decisions, ownership, and outcomes firmly with people.

Together, these capabilities explain what Rovo adds on top of Confluence AI: broader visibility across tools, richer context for understanding knowledge, and scalable assistance that helps teams work more effectively without hype or overpromising automation.

The Real Limitations of Native Confluence AI

Confluence AI brings meaningful improvements to how teams create and work with knowledge, but it is not designed to solve every documentation challenge on its own. Being clear about its limitations helps teams set realistic expectations and use AI features more effectively.

Limited Visibility into External Documents

Native Confluence AI primarily works with content stored directly inside Confluence, such as page text, comments, and page history. While pages can reference attachments or external links, AI does not deeply interpret the full contents of external documents by default.

When important details live inside PDFs, spreadsheets, or presentations stored elsewhere, Confluence AI may only see the surrounding page context. This limits how complete summaries, answers, or suggestions can be when key information exists outside the page itself.

Context Gaps and Knowledge Silos

Because Confluence AI focuses on Atlassian-native content, knowledge silos can still emerge. Even well-organized spaces often point to external files or tools that AI cannot fully reason over.

These context gaps are not a failure of Confluence AI. They reflect the reality of modern work, where knowledge is spread across multiple systems. However, without access to complete context, AI assistance can feel shallow or overly generic in more complex documentation scenarios.

Output Quality Depends on Content Quality

Confluence AI does not invent knowledge or verify facts. The quality of its output depends heavily on the quality, structure, and freshness of the content it works with.

Outdated pages, unclear structure, or inconsistent terminology can lead to less accurate summaries or weaker suggestions. Human review remains essential, especially for content that affects compliance, decision-making, or external communication.

Training, Adoption, and Governance Still Matter

Although Confluence AI requires minimal technical setup, effective use still depends on adoption and governance. Teams need to understand when AI assistance is helpful, when it should be questioned, and how to refine outputs efficiently.

Strong documentation practices, clear ownership, and ongoing governance remain critical. AI can reduce effort, but it does not replace thoughtful knowledge management or accountability.

In short, Confluence AI works best as an assistive layer. Its value increases when paired with good content hygiene, clear structure, and complementary tools that help close context gaps rather than replace human judgment.

Why Files and Documents Are the Missing Context

To understand where Confluence AI delivers the most value and where it struggles, it helps to look at how knowledge actually exists inside most organizations. The core issue is not an AI capability gap. It is a context gap.

Critical Knowledge Often Lives in Files

In real-world teams, the most detailed and authoritative information rarely lives entirely on Confluence pages. Specifications, design documents, financial models, contracts, reports, and presentations are usually created and maintained as files.

Confluence pages often act as entry points. They summarize decisions, explain intent, or provide links to supporting documents. For humans, this separation between pages and files is manageable. For AI systems, it creates an incomplete picture of the knowledge behind the work.

Native AI Has Limited Document-Level Understanding

Confluence AI is highly effective when it can work directly with page text, comments, and page history. However, it does not deeply analyze the full contents of attached or externally stored documents by default.

When essential details live inside a spreadsheet, PDF, or presentation, AI assistance relies mostly on what is written around the file rather than what is inside it. This limits how well AI can synthesize information, especially in documentation that depends heavily on supporting files.

Why Missing Context Affects AI Outcomes

AI quality is directly tied to the quality and completeness of its input. When pages and files are disconnected, summaries may lack nuance, answers may be partial, and suggestions may miss important constraints or details.

This does not mean Confluence AI is ineffective. It means its understanding is bounded by what it can see. The more critical knowledge lives outside page content, the more likely AI outputs are to feel shallow or require manual correction.

Recognizing files and documents as part of the knowledge context, not just attachments, is key to improving AI outcomes. When pages and documents are treated as a unified knowledge surface, AI can produce clearer summaries, more accurate answers, and more useful insights.

This context gap naturally leads to the role of integrations. By bringing document content closer to Confluence and making it accessible in a controlled way, teams can unlock more reliable value from AI without changing how their knowledge is created or governed.

Extending Confluence AI with Marketplace Apps

The context gap between pages and files explains why AI assistance in Confluence can sometimes feel incomplete. This is where integrations matter. Marketplace apps do not replace Confluence AI or Rovo. They extend the available context so AI can work with a more complete picture of how teams actually manage knowledge.

Bringing Cloud Files into Confluence with ikuTeam Files

Many teams rely on cloud storage platforms such as SharePoint, OneDrive, Google Drive, Box, or Dropbox to manage their most important documents. In standard Confluence setups, these files are often referenced through links or added as static attachments, which limits visibility and creates context switching.

ikuTeam Files connects these cloud storage systems directly to Confluence pages. Users can browse, embed, preview, and attach files without leaving the Confluence editor. Files remain in their original storage location and are never copied into Confluence.

This approach preserves existing permissions. Access to a document in Confluence reflects access in the source system, which is essential for enterprise, regulated, and security-conscious environments.

By embedding cloud files directly into pages, documents become first-class Confluence context rather than external references. Pages no longer just point elsewhere. They provide direct visibility into the materials that support decisions, processes, and shared knowledge, reducing friction for users and improving the foundation for AI-assisted workflows.

AI Summaries for Documents with ikuTeam Files Rovo Assistant

Even when files are visible inside Confluence, reviewing long documents can still slow teams down. This is where document-level AI summaries add meaningful value.

ikuTeam Files Rovo Assistant generates AI summaries for attachments and connected cloud files, including PDFs, Word documents, spreadsheets, and presentations. It works with both native Confluence attachments and files linked through platforms like SharePoint.

These summaries help users quickly understand what a document contains without opening or scanning the entire file. More importantly, they enrich the context available to Rovo by making document content easier to interpret alongside page-level information.

Richer document context improves the usefulness of Rovo-powered search, chat, and agent assistance. Instead of relying only on page text, AI can work with clearer signals from the underlying documents that often carry the most critical details.

Together, these Marketplace extensions show how Confluence AI can be expanded in a practical and credible way. By bringing cloud files into Confluence and adding AI-powered understanding at the document level, teams reduce knowledge gaps and unlock more consistent value from AI across their documentation workflows.

What the Future of Confluence AI Looks Like

The direction of Confluence AI is not about turning documentation into an automated system. Atlassian’s trajectory points toward AI that becomes more capable at supporting knowledge work while remaining predictable, controlled, and accountable.

One clear signal is the continued maturation of Rovo Agents. These agents are expected to become better at supporting recurring knowledge tasks, such as preparing summaries ahead of reviews, helping teams track how documentation evolves over time, or surfacing relevant pages when work or decisions are in progress. Their role is to assist with preparation and synthesis, not to act independently or make decisions.

Another important trend is deeper cross-tool reasoning. As Confluence AI and Rovo evolve together, AI will become more effective at connecting information across Confluence, Jira, and other connected tools. Knowledge rarely lives in a single place. Better cross-tool reasoning allows AI to understand how documentation, work items, and discussions relate to each other, reducing fragmentation and helping teams maintain shared context.

Improved document awareness is also central to the future of Confluence AI. As integrations and Marketplace apps make it easier to work with attachments and cloud files directly inside Confluence, AI will rely less on partial signals and more on complete, trustworthy context. This leads to higher-quality summaries, clearer answers, and more reliable assistance in environments where critical information lives inside documents rather than page text alone.

Across all of these developments, one principle remains unchanged. AI assists with understanding, preparation, and synthesis, while humans remain accountable for decisions, ownership, and outcomes.

The future of Confluence AI is not about replacing how teams manage knowledge, but about making that work clearer, faster, and easier to sustain as information grows.

Confluence AI FAQs

Does Confluence have AI?

Yes. Confluence includes built-in artificial intelligence features known as Confluence AI. These capabilities are native to Confluence Cloud and help users write, summarize, search, and organize knowledge more efficiently. Confluence AI works directly inside pages, comments, and spaces, supporting everyday documentation tasks without operating as a separate chatbot or external tool.

What does Atlassian use for AI?

Atlassian groups its AI capabilities under Rovo, its unified AI layer across Confluence, Jira, and other Atlassian Cloud products. Rovo combines large language models with contextual understanding from Atlassian tools and enforces existing permission controls, ensuring AI assistance is relevant, secure, and aligned with what each user is allowed to access. In older documentation, this AI foundation was referred to as Atlassian Intelligence, a legacy term that may still appear in external resources.

How is Rovo different from Confluence AI?

Confluence AI refers to the native, feature-level AI capabilities inside Confluence itself, such as drafting and editing pages, summarizing content, and improving search with natural language. Rovo, by contrast, operates at a broader scope. It connects Confluence with Jira and other tools to provide cross-tool search, conversational exploration, and AI agents that assist using context from multiple systems. In short, Confluence AI improves how work happens inside Confluence, while Rovo helps teams understand and use knowledge across the wider Atlassian ecosystem.