Teams | Collaboration | Customer Service | Project Management

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.

This strategy is grounded in findings from the Executive Insights Report: Strategies for Cloud and AI Transformation. Developed in partnership with AWS, this research shows how C-suite leaders are making a connected cloud foundation a top priority to unlock the full potential of AI.

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.

Most teams have already adopted AI in some form. The bottleneck now is orchestration: knowing who’s accountable, what agents are actually doing, and how they fit into your existing process. AI coding tools boost productivity by 10-15%, but the orchestration gap limits teams from realizing AI’s full value (Source: DX). Agents living outside of where the work happens means more context switching, manual bridging, and accountability blind spots.

While most enterprises are talking about how they approach AI transformation, increasingly knowledge management and arranging the required context are important factors in whether or not AI can work effectively. This shift is creating demand for a new role called the Knowledge Architect. We have Systems Architects to design the overall structure of software and hardware systems. Knowledge workers execute on work across different functions like Sales, Marketing and Engineering to achieve business goals.

Today, Gartner named Atlassian a Leader in the 2026 Magic Quadrant for DevSecOps Platforms, and placed us highest in execution. It’s our fourth consecutive year as a Leader across the Gartner DevOps and DevSecOps research – a category that has changed underneath all of us as security shifted from a downstream checkpoint to a first-class concern in every team’s delivery loop. This one matters to me for a specific reason: it validates a bet we made four years ago.

Building high-impact AI agents is a skill any team can master – no coding required. In this session, we break down the exact steps for creating effective AI agents that transform everyday workflows, from identifying the right automation opportunities to designing seamless user experiences. Join Shihab Hamid (Head of Product, Atlassian) and Sushant Koshy (Senior Product Manager, Atlassian) as they share industry best practices for Rovo Studio.

New research from Atlassian’s Teamwork Lab surfaces a tension at the heart of AI at work: adoption is nearly universal, but acceptance is lagging far behind.