Atlassian’s cloud platform is evolving with built-in artificial intelligence that empowers teams to be more efficient, creative, and informed. As an Atlassian Solution Partner, Atlas Bench has a front-row seat to these innovations. We’re here to give you an overview of how AI is infused into Atlassian products, from intelligent search and chat assistants to AI-driven features in Jira, Confluence, and Jira Service Management (JSM). This overview will show how your team can succeed with Atlassian’s AI-powered toolkit.
Atlassian has introduced Atlassian Intelligence, an AI layer across its ecosystem that functions like a smart teammate available 24/7. It’s not a single product, but rather a collection of AI features integrated into the tools your team already uses. Atlassian Intelligence learns from your organization’s content (Jira issues, Confluence pages, support tickets, etc.) to provide helpful suggestions and automations in context. From drafting content to answering questions, it augments human work rather than replacing it. In essence, it’s Atlassian’s way of bringing the power of generative AI directly into your daily workflows.
Context-aware assistance: Atlassian Intelligence understands the context of your projects and documentation. It can suggest relevant Jira tickets or Confluence articles when you’re typing, reducing time spent searching for information.
Natural language processing You can interact with Atlassian tools more naturally: ask a question in Confluence like “How do we implement feature X?” and get pointed to the right requirements page or design spec, courtesy of AI.
Automated content creation: Need to write a draft update or user story? Describe what you need, and Atlassian’s AI can generate a first pass that you can refine, saving you from starting from scratch.
Insight generation: The AI can surface patterns and insights (e.g. flagging similar support tickets, summarizing long comment threads) to help teams make decisions faster with better information.
One of the most exciting AI advancements in the Atlassian ecosystem is Rovo : Atlassian’s AI-powered assistant that works across all your Atlassian products (and even beyond). Rovo is like having a knowledgeable colleague who instantly knows where everything is and how to get things done. It consists of three core capabilities: Search, Chat, and Agents, which together supercharge how you find information and automate tasks.
Rovo Search: A powerful AI-driven search engine that combs through Jira issues, Confluence pages, pull requests, documentation, and even integrated third-party apps (like Google Drive or Slack) to return the most relevant answers. Unlike a normal keyword search, Rovo Search understands context and intent. For example, you can search a high-level question (“Is Project Phoenix on track?”) and it will fetch up-to-date status from Jira, related Confluence updates, and any relevant documents. Results come with context, so you don’t just get a link; you get a quick insight or summary that tells you why that result is relevant. Permissions are respected too: team members only see what they’re allowed to see, so sensitive data stays secure.
Rovo Chat: An interactive AI chat assistant that you can converse with to dig deeper into knowledge or get things done. Think of it as a chat window where you ask, “Give me a summary of last quarter’s sales campaign,” or “Who owns the payment microservice application?” and Rovo responds in plain language with answers drawn from your Atlassian data (and other connected sources). You can ask follow-up questions to refine the answer or get more detail, just like you would with a teammate. Rovo Chat can even help brainstorm content or troubleshoot problems by leveraging your organization’s knowledge base. It learns from your data, so the more you use it, the smarter it gets at providing the answers you need.
Rovo Agents: AI-powered agents that act as virtual teammates to automate tasks and workflows. Agents can be set up to handle specific jobs or processes. For instance, you might have an Agent watching your Jira Service Management queue to automatically categorize and assign incoming tickets based on their content, essentially a smart triage agent that prioritizes urgent issues (using sentiment or keywords) and routes requests to the right team. Another Agent could assist with DevOps by monitoring Jira issues linked to deployments and alerting the team via Slack if something needs attention. Atlassian provides pre-built agents for common use cases (like an IT Helpdesk virtual agent, or a stand-up meeting notes generator), and you can customize agents to suit your team’s needs. These agents operate within the rules and knowledge you give them, so they augment your team’s capacity while you maintain control.
Jira Software is the backbone for many development and project teams. With Atlassian’s AI enhancements, Jira is becoming much more than a place to track issues; it actively helps you manage and complete work. Atlas Bench has seen teams benefit from features that cut down manual effort in Jira, allowing them to focus on value-adding work.
Intelligent issue creation: Instead of manually filling out every field for a new Jira issue, you can now simply describe the task in natural language. Jira’s AI will interpret your description and draft a well-structured issue for you, suggesting a title, setting a priority, and even recommending relevant components or team assignments. For example, you might type “Add dark mode toggle to the mobile app, with user preference saved in settings” and Jira could draft a user story with acceptance criteria from that sentence.
Auto-summarized issue context: Long ticket conversations and lengthy descriptions can slow down understanding. Jira’s AI can provide a brief summary of an issue’s discussion or history at the top of the ticket. Team members coming in to help can quickly get up to speed without reading through pages of comments. It’s like a TL;DR for your Jira issues, generated in seconds.
Related issue recommendations: Jira intelligence can suggest related issues or duplicates as you’re typing an issue or comment. If you’re reporting a bug that’s similar to something logged last month, the AI may alert you: “This looks similar to ISSUE-1234.” This helps reduce duplicates and ensures you benefit from past work and knowledge.
Smart automation and triage: Beyond Rovo Agents, Jira itself employs machine learning to help with automations. For instance, the system might learn from your usage patterns and suggest an automation rule (“Issues with ‘urgent’ in the title get assigned to Tech Leads”) or automatically tag issues with labels based on their content. These AI-driven automations keep the backlog organized and make sure nothing falls through the cracks.
Confluence has long been the place where teams document plans, decisions, and knowledge. Now, with AI augmentation, Confluence transforms into an intelligent collaborator that helps you create and discover content effortlessly. At Atlas Bench, we’ve helped customers turn Confluence into a dynamic knowledge hub where the right information finds you (instead of you hunting for it).
Instant page summaries: Confluence can automatically generate a summary of any page, giving readers a quick overview of the content. If you have a lengthy requirements spec or meeting minutes, one click on “AI Summary” provides a concise synopsis of the key points and action items. This is a huge time-saver when catching up on documentation.
Content creation assistance: Staring at a blank Confluence page? Let AI jumpstart the process. Provide a prompt, for example, “Project Apollo Q3 Goals and Objectives”. And Confluence’s AI will draft a structured outline or even a first draft of the page. It can generate text based on similar documents and known context, which you can then edit and refine. This takes writer’s block out of the equation for project plans, retrospectives, or any documentation.
Question answering (Chat in Confluence): Much like Rovo Chat, Confluence now allows you to ask questions across your wiki content. Instead of manually searching or clicking through spaces, you might ask, “What decisions were made about the database migration?” The AI will search your Confluence spaces and return the specific answers or point to the exact paragraphs in meeting notes or design docs where that question is addressed. It’s like having a knowledgeable librarian for your internal wiki.
Content harmonization: As documentation grows, inconsistency can creep in (different templates, styles, outdated pages). AI can help by suggesting updates to align content with standards: for example, flagging pages that haven’t been updated in two years or identifying two Confluence pages that cover similar topics and might need merging. This keeps your knowledge base tidy and up-to-date with minimal manual effort.
Not all knowledge lives in text documents; a lot is shared in meetings and video calls. That’s where Loom (a popular video messaging and recording tool) comes in as an invaluable ally to Atlassian’s AI ecosystem. Atlas Bench often recommends capturing important discussions via Loom and integrating them with Confluence so that spoken insights don’t get lost. How does AI help here? By transcribing and summarizing videos, and making that content searchable through Atlassian tools like Rovo.
Automatic transcription: When you record a meeting or demo using Loom, you can easily embed the video in a Confluence page. The AI steps in by transcribing the conversation in the video. Instead of rewatching a 30-minute meeting, anyone can quickly scan the transcript or search it for keywords. This transcript becomes part of your knowledge base, indexed and ready to be queried.
AI-generated highlights: Beyond raw transcription, AI can pull out the key points or decisions from a Loom recording. For example, if a team lead records a Loom video update on a project, the AI might extract highlights such as “Key Milestone achieved: Beta release ready” or “Action item: UX team to revise the signup flow by May 15.” These highlights can be listed at the top of the Confluence page or in meeting minutes, giving viewers the high-level takeaways at a glance.
Centralized, searchable video knowledge: With Loom recordings integrated into Confluence (and possibly Jira for specific issues), all that rich information becomes searchable via Rovo Search or Confluence’s own search. This means if someone later asks, “When is the new feature launch deadline?” the answer might come from a spoken update in a Loom video that was transcribed. AI links the unstructured spoken content to the structured knowledge base, breaking down information silos.
Privacy and permissions: Importantly, all these AI-driven transcripts and summaries respect permissions. If a Loom video is shared only with certain groups, Rovo and Confluence will not expose its contents to anyone without access. Your team gets the benefit of broad knowledge discovery without compromising security.
For IT and service teams, Jira Service Management (JSM) is the platform to handle requests and incidents. Here, AI is making a particularly big impact by speeding up support and improving user satisfaction. Atlas Bench has helped organizations implement AI-driven JSM features that reduce the load on support agents while helping customers get what they need more quickly.
Virtual agents for instant help: JSM now can deploy AI-powered virtual agents (thanks to Atlassian Intelligence and Rovo Agents) on your help center or chat channels. These bots can greet users and handle common requests through conversation. For example, an employee could ask in the IT help portal, “I can’t log into VPN, what should I do?” The virtual agent will use knowledge base articles (Confluence pages, how-to guides) to provide a solution (“Try resetting your VPN token via this link…”) or troubleshoot via a dialogue. If the issue is resolved, no human needed; if not, the bot collects relevant details and escalates to a human agent with a complete summary of the conversation. This means faster answers for the user and less repetitive Q&A for the support team.
Smart ticket triaging: When customers or employees do need to submit a ticket, AI assists behind the scenes. The content of the request can be analyzed by machine learning to determine the urgency and category. JSM’s AI might detect sentiment (e.g., an email saying “I am extremely frustrated my account is still locked!” could be flagged as high priority due to negative sentiment) and automatically set the priority to high. It can also pick out key words to assign the ticket to the appropriate team or suggest relevant service categories. This intelligent classification ensures the right people see the ticket ASAP and reduces manual sorting.
Suggested knowledge base articles: The moment a user begins typing a request in the portal (“reset password issue…”), the system’s AI can suggest helpful articles in real time. Many times, the user might find their answer without even needing to submit the ticket. This deflection technique, powered by understanding the intent of the question, means faster resolutions and fewer tickets for the team. And if the user does create a ticket, those article suggestions are shown to the support agent as well, saving them time in hunting for solutions.
Automated resolution of routine tasks: With AI and automation working together, some routine service requests can be resolved end-to-end without human intervention. Imagine an onboarding request where a manager asks for access for a new hire. An AI agent could automatically fill the request by creating accounts, granting permissions based on company policies, and then confirm completion, all triggered by the initial service ticket. While not every task can be auto-resolved, each one that is saves valuable time. Atlassian’s AI features in JSM are increasingly enabling these kind of “zero-touch” service fulfillments for standard tasks (like password resets, software installations, etc.).
The integration of AI into Atlassian products isn’t about flashy tech for its own sake; it’s about making work life easier and more productive for you and your team. From finding the needle in the haystack of your company’s knowledge, to drafting that project plan, to ensuring every support ticket gets the attention it needs, Atlassian’s AI capabilities are there to assist at every step. Atlas Bench has deep expertise with these AI features; we’ve been hands-on, from configuring Rovo for advanced searches to training virtual agents for IT service desks. The result we’ve seen is teams that respond faster, plan better, and spend more time on the creative and strategic aspects of their work (letting the AI handle the drudgery and complexity in the background).