Teams | Collaboration | Customer Service | Project Management

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.

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.

AI makes your organization’s knowledge easier to find and use. That’s the whole point. But it also means sensitive data moves faster, surfaces in more places, and becomes harder to track. The pressure to act is real, but the playbook isn’t new. You still need to know where sensitive data lives, govern how it’s being used, and prevent it from unauthorized exposure. AI is now giving you a reason to revisit your data security posture and make sure it’s strong enough to keep up.

Adopting AI technology without an effective strategy is costing the Fortune 500 an estimated $161 billion a year.* Enterprises are making big investments in this space but are struggling to realise the returns. We know the technology is designed to make businesses more efficient, but we’re still seeing the opposite because most businesses are treating AI as purely a technology transformation. That’s where they get stuck.

The best ideas often come from false starts, conflicting opinions, and rapid iteration. Teamwork is messy, and AI might make it even messier. But the Chief Design Officers of Atlassian and Figma are designing for that reality to turn friction into a creative edge.

AI is a helpful tool but, without guardrails, its writing can fall flat. We joke about AI tells like em-dash overuse and “it’s not X, it’s Y,” but issues with over-reliance on AI writing go deeper. On Atlassian’s Brand team, these areas are top of mind. We want to move fast with AI but still put out content that is useful, fun to read, and trustworthy.