Teams | Collaboration | Customer Service | Project Management

Our Design Ops team supports more than 600 designers across Atlassian. And like most operations teams, we were seeing the same pattern over and over again: important questions landing in Slack, manual triage happening in the background, and too much useful knowledge trapped in docs that people either couldn’t find or didn’t have time to read. We built DOT, the Design Org Teammate, to change that.

Bring document knowledge you already have access to from Google Drive, SharePoint, and OneDrive into Rovo Search, Chat, and Agents so teams can move from questions to useful context faster. The project plan is in Google Drive. The financial model is in SharePoint. The work is tracked in Jira, and the decisions are documented in Confluence. Your team has the information it needs.

Connect any coding agent to your system of record to automate engineering loops AI coding agents have made individual developers faster. But faster individuals working in their local environments do not automatically create faster engineering organizations. The bottlenecks that slow software delivery are usually systemic: work stalls in human queues, triage waits for idle cycles, and routine handoffs depend on manual intervention.

AI is changing expectations for service. Employees want help that feels seamless. Customers want answers without long waits or repeated explanations. Operations teams want to get ahead of incidents and prevent disruptions rather than just respond. Those expectations rest on a critical dependency: context. AI is only useful when it can see the full picture – from people and knowledge to services, assets, and code – and all connections in between.

Each quarter, we ask some of our R&D teams to take a week away from the day-to-day to experiment and innovate on AI. We call this AI Builders Week, and it helps fuel our AI transformation. In past quarters, we’ve focused on building for individual workflows — how do we each get faster and more productive by inserting AI into different parts of our daily to-dos?

Great ideas can start with a random spark at 2 a.m., grow through a planning session with ChatGPT, or take shape in a brainstorm with Claude. AI helps you plan, but getting that plan into Trello has always been a manual, momentum-killing process. Today, that changes. Trello now uses MCP (a shared standard that lets AI tools talk to apps like Trello) to connect with assistants like Claude, ChatGPT, Gemini, and Cursor. That means you can manage your boards, lists, cards, and checklists just by asking.

According to Atlassian’s 2026 State of Teams report, a growing gap has emerged: while AI makes individual tasks faster, many teams are struggling to keep that work aligned. In this panel discussion, Atlassian executives share an inside look at how they are operationalizing AI within their own departments to turn early experimentation into durable, connected ways of working.

Every week, it seems like the rules change for building products. We’re all figuring this out in real time, and one of the best ways we’ve found to do that is to sit down with other leaders who are in the thick of it. That’s why Atlassian recently started a series called AI Talks at our San Francisco offices with senior leaders building AI products. We break down what’s working, what’s broken, and what feels uncomfortable.

For a long time, Jira was where you went to track work. You updated a status, closed a ticket. Something important has shifted now that agents can be assigned to work items. Jira is still a record of what happened, but now it’s something else too: a place where work can move between humans and agents. That shift got me thinking. What if I leaned into it fully and ran an experiment? What if each stage of the SDLC had its own specialized agent and the board itself handled the routing between them?

Whether I speak to customers or Atlassian’s own engineering teams, the message is consistent: the unprecedented adoption of powerful coding agents has transformed software development, but the hard parts of delivering software are…gasp…still pretty hard. Teams still have to decide what to build, and why it should exist. They need to understand the system they’re changing and which constraints matter.