Putting AI to Practical Use with Atlassian

Implement Atlassian AI effectively and use it productively: for better self-service, faster access to knowledge, more efficient ticket handling, and secure AI workflows in your Atlassian Cloud. We’ll support you every step of the way, from selecting use cases to setting up your production environment.

Fewer Tickets Thanks to Improved Self-Service

Atlassian AI answers recurring service questions directly from the Confluence knowledge base. Customers get help faster, while fewer standard inquiries end up in the ticketing system.

Faster triage and more efficient processing

If a ticket is created, AI helps with summarizing, classifying, routing, and drafting responses. This ensures that inquiries reach the right team more quickly and can be handled more efficiently.

From Requirements to Actionable Tasks in Jira

AI quickly turns briefings, Confluence content, and existing tasks into structured work in Jira. This supports backlogs, stories, subtasks, and preparation for implementation.

Knowledge Management in Confluence

AI helps teams capture, summarize, and refine content more quickly. This makes onboarding, project work, and cross-functional collaboration easier.

Harnessing Distributed Knowledge with Rovo

Rovo integrates data from Atlassian and systems such as SharePoint, Google Drive, Slack, and Salesforce. This makes search, chat, and agent-based workflows more helpful.

Connect External AI Workflows to Atlassian

Existing AI environments, agents, and development workflows can be integrated with Atlassian. This allows the platform to become part of a larger AI, data, and process architecture.

Case Study: Atlassian AI in Service Management

With Jira Service Management, Confluence, and Atlassian AI, recurring questions can be answered directly through self-service. Customers find the help they need faster, while fewer routine inquiries end up in support.

If a ticket is created, AI assists with summarizing, triaging, routing, and drafting responses. At the same time, insights from real service cases are fed back into Confluence. This builds a knowledge base that continuously reduces the workload on support.

What Atlassian AI Encompasses Today

Atlassian AI helps teams in Jira, Confluence, and Jira Service Management find information faster, handle inquiries more efficiently, and create content more productively. Rovo adds search, chat, and agents to support company-wide knowledge and work processes.

What Companies Need from Atlassian AI

The foundation is a suitable Atlassian Cloud environment with AI features enabled and clear administrative controls. Other key elements include a structured knowledge base, appropriate permissions, relevant data sources, and clear governance for content, access, and usage.

If the requirements have not yet been fully met

Companies don't have to start out perfectly. It makes sense to first select a relevant use case, refine the necessary content and data sources, and then put Atlassian AI to work in a targeted manner. Especially in hybrid knowledge landscapes, a focused approach is more important than a complete migration.

Integrate external data sources and existing AI environments

Organizational knowledge is often stored in Google Drive, SharePoint, Slack, Salesforce, or internal systems. These sources should be integrated wherever they can significantly improve search, chat, self-service, or workflows. Existing AI environments and agent platforms can be effectively integrated with Atlassian.

Governance, Data Protection, and Modeling Strategy

Atlassian AI requires clear rules for data access, permissions, sharing, and connected sources. An appropriate model and governance strategy ensures that AI can be used productively while also meeting data protection, security, and compliance requirements.

1
Prioritize Use Cases
We start where Atlassian AI adds the most value to your business—for example, in service management, knowledge management, or structured work in Jira.
2
Verify knowledge, data, and permissions
We analyze your knowledge base, data sources, Confluence structures, and permissions. This ensures that AI can access relevant, up-to-date, and approved information.
3
Laying the Groundwork for Atlassian AI
We address Atlassian Cloud setup, governance, data protection, access controls, and the target vision. This creates a secure foundation for the productive use of AI.
4
Make Pilot Operational
We're implementing the first Atlassian AI use case into our daily workflows and testing whether it noticeably reduces the workload for teams and customers.
5
Expand the Use of AI in a Targeted Manner
Once we’ve gotten off to a good start, we’ll expand Atlassian AI step by step to include additional scenarios—in a controlled, measurable way that fits your processes.

AI for Real-World Workflows

We don't view Atlassian AI as just a list of features, but rather as a tool to support specific tasks, teams, and processes within your company.

Connecting Platforms, Knowledge, and Processes

The most powerful use cases emerge when Jira, Confluence, Jira Service Management, Rovo, and external data sources work together effectively.

Start with robust pilot cases

We prioritize Atlassian AI use cases that deliver quick results and can be easily integrated into everyday workflows.

Think About Governance from the Very Beginning

Permissions, data protection, data sources, and modeling strategy are clarified early on. This creates a solid foundation for the productive use of AI.

Integrating External AI Environments

If Atlassian is to become part of a larger AI architecture, we will integrate the platform, data sources, and AI workflows in a way that is both functionally and technically sound.

What exactly can Atlassian AI do today?

These include self-service responses in customer service, summaries, draft responses, knowledge search, content creation, search, chat, and agents.

Do you need Atlassian Cloud for this?

The Atlassian Cloud serves as the central foundation for the relevant AI features. Depending on the scenario, additional product and access requirements may apply.

What is the difference between Atlassian AI and Rovo?

Atlassian AI encompasses the AI features in each individual app. Rovo is the cross-product layer for Search, Chat, and Agents.

Can Atlassian use AI to help create stories, backlogs, or follow-up tasks?

Yes, as a support tool. AI helps prepare content, bring context together, and quickly turn requirements into structured work.

How are external data sources integrated?

About connectors and integration mechanisms that make relevant knowledge sources available for search, chat, and other AI scenarios.

Does Atlassian offer BYOM?

The focus is not simply on a “bring-your-own-model” approach, but rather on a sustainable interplay between the platform, data sources, governance, and model strategy.

What risks should be assessed before implementing Atlassian AI?

Before using Atlassian AI, you should review data quality, permissions, sensitive content, third-party tools, and responsibilities. This helps reduce access to inappropriate information or information that has been shared too broadly.

Is Atlassian AI Replacing Existing Processes?

No. Atlassian AI does not replace processes without verification, but rather provides targeted support—for example, with recurring tasks, access to knowledge, and clearly defined work steps.

Which AI use cases are best for getting started?

Good initial use cases are clearly defined, low-risk, and recurring—such as searching for information, creating summaries in Confluence, or compiling Jira data.