Teams | Collaboration | Customer Service | Project Management

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.

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.

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.

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.

We believe this is a once-in-a-career moment for the HR craft. If we get this right, HR practitioners will spend less time on administrative coordination and more time on the work that drew most of us to this field: landing the right person in the right role, helping a manager lead a team through a crisis, coaching someone who is struggling into confidence and performance, resolving a conflict before trust breaks, designing an org structure that unlocks something a leader thought was impossible.

The best way to get value out of AI is to scale at the team level, but while 85% of workers use AI, only 29% have embedded it into team workflows. The Atlassian Learning Team is in that 29%, and the lessons they’ve learned are a model for teams of all types. They shared three ways teams can start becoming AI native today. The Atlassian Learning Team helps Atlassian app users build skills and confidence with free on-demand courses, dynamic live team training, and app adoption guides for teams.

Built on Anthropic’s Claude Managed Agents infrastructure, Claude Agent for Jira lets you assign work items directly to Claude. Your agent’s work now sits in Jira alongside everything else: tracked in your project, visible in workflows, and connected to the goals it’s helping ship. The agent automatically processes the work item context, implements the required changes in a secure sandbox, and opens a draft pull request for your review.