Teams | Collaboration | Customer Service | Project Management

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.

In Q4 FY26, Atlassian’s People Insights (PI) team stopped treating AI as a productivity add-on and committed to something harder: fully agentifying its operations—giving AI persistent access to the team’s context, data, definitions, and workflows so it can act as a genuine collaborator rather than a smarter search engine.

89% of executives say AI has increased speed. Only 6% can point to org-wide ROI. IDC’s Wayne Kurtzman and Atlassian’s Liz Fosslien unpack what’s missing – and where CIOs should start. Everyone on your team is faster. So why isn’t your organization moving further?

When a tool can do in seconds what used to take hours, it’s natural to wonder if there will be any work left for humans. But new research from Atlassian’s Teamwork Lab suggests the opposite: Rather than shrinking an employee’s remit, AI appears to be expanding both the breadth of what employees take on and the depth at which they operate.

Less than six months ago, the Atlassian Rovo Model Context Protocol (MCP) server went GA, giving Claude, Cursor, and every major AI agent direct access to Atlassian for our customers. Today, over one million users trust it every month to do real work through agents. But that number isn’t the story. The story is what’s happening inside those interactions: how AI agents are actually being used at enterprise scale, and who’s getting the most value.