Teams | Collaboration | Customer Service | Project Management

AI use is accelerating across the modern enterprise. Teams are moving faster. The barrier to building will continue to drop. But the cost of building the wrong thing is about to skyrocket, because teams can now ship more of it, faster. Atlassian’s State of Teams 2026 report found that 89% of executives say AI has increased the speed of work. But only 6% feel confident they can point to specific organisation-wide AI ROI.

I sat down with engineers who use AI coding agents for the vast majority of their implementation work. Stack Overflow’s 2025 Developer Survey found 84% of developers using AI agents for software development are using them for actual implementation work, so I wanted to understand what that looks like up close. What I heard reshaped how I think about where developer tools need to go next.

As organizations work to bring humans, agents, and automation together, teamwork is getting even more complex. If your AI strategy feels like a collection of one-off experiments layered onto disconnected tools and siloed knowledge, join Atlassian leaders to see how Teamwork Collection brings together Jira, Confluence, Loom, and Rovo into a connected foundation for human-AI collaboration at scale. Key takeaways: Watch the full Founder Keynote here.

Think about your to-do list for a given work week. From responding to a colleague’s quick message to building a team strategy, how do you prioritize how much time and energy to allocate to each task? Behavioral science tells us most of us don’t optimize; we satisfice — we find the first “good enough” option, and we move on. The term, a mash‑up of “satisfy” and “suffice,” was coined by Herbert A.

Starting today, Jira teams can assign work directly to Cursor, where a cloud agent will pick it up and begin working. You can steer agents directly from Jira, your IDE, or Cursor on the web. When Cursor needs input or is ready for review, it will notify you in Jira. When it opens a pull request, it will be automatically linked back to Jira.

There’s a paradox happening with AI: Usage and productivity are up, but bottom-line results aren’t always as obvious. It’s a familiar pattern you might be seeing in your organization: Leadership invests in AI, and employees say they’re getting more done: more code, more campaigns, more analysis.

The learnings are based on our recent webinar, “Inside Reddit’s IT playbook for AI and scale”. Check out this session and other conversations with customers on-demand. When Reddit grew from fewer than 400 people before 2020 to roughly 4,000 globally distributed employees today, it wasn’t just their headcount that changed. The mandate for IT changed with it.

At Atlassian, on-call engineers live at the intersection of urgency and uncertainty. Floods of noisy alerts sap focus, energy, and productivity — especially when responders must decide what matters, what can wait, and what’s just noise. A single underlying issue can trigger dozens of near‑identical alerts in hours.

Since we first launched custom agents in May of 2024, we’ve seen teams use Rovo to build agents in Confluence that help them accomplish everything from turning customer feedback into PRDs to maintaining consistency across large sets of data and processes. And agents are getting even more popular, with over 5M invocations of agents every month. They take the foundation of knowledge that lives in Confluence, and act on it, saving customers over 200K hours in February alone.

Teams of all stripes have run billions of cross‑functional, multi‑tool workflows on Atlassian. After decades spent helping them plan, build, ship, and do, those same workflows are now lighting up with millions of agentic automations, up 7x in the last six months alone. All signals point to the rise of the AI‑native organization, where humans operate at critical junctures, deciding what matters and why, and agents do more of the execution.