Atlassian’s Head of AI Product, Jamil Valliani, reveals substantial progress with Rovo, highlighting expanded access to the Teamwork Graph and a surge in chat usage that enhances how teams collaborate across diverse industries.
- Rovo’s Teamwork Graph now accessible to any AI tool with connectors
- Chat interactions on Rovo spike 250% in six months
- Rovo’s AI models advance toward dynamic planning and long-running task management
What happened
Atlassian has transitioned Rovo’s core data layer, the Teamwork Graph, from being an exclusive feature to a widely accessible resource for any AI tool that connects to Atlassian’s ecosystem. This move means the valuable context embedded in Jira and Confluence histories can now support diverse AI workflows across organizations.
Concurrently, customer usage of Rovo’s chat interface has surged by 250% within six months, driven primarily by increased confidence in the system’s ability to respond accurately to complex queries. This growth has prompted Atlassian to enhance Rovo Chat with capabilities such as self-assessment reasoning and the ability to manage lengthy background processes through cloud-based workflows.
Why it matters
The open accessibility of the Teamwork Graph signals a strategic shift for Atlassian, expanding Rovo’s utility beyond a standalone AI assistant into an underlying infrastructure that powers multiple AI applications and integrations. This democratization of organizational context enhances ROI on existing data and simplifies AI adoption.
Meanwhile, the rapid rise in chat usage underscores a broader trend of delegating routine and complex tasks to conversational AI, reshaping workplace productivity. Rovo’s evolving reasoning and task management features promise to reduce workflow friction and enable more autonomous AI participation in complex business processes.
What to watch next
Further development of Rovo’s chat capabilities will be critical to watch, particularly the planned launch of cloud-based process management that allows Rovo to run extended tasks in the background while maintaining conversational user engagement. This feature could redefine how enterprises integrate AI into daily operations.
Additionally, the ongoing refinement of Rovo Studio’s modular design—distinctly separating reusable 'skills' from tailored 'agents'—will impact how customers customize AI solutions for unique challenges. Continuous improvements to this framework could make AI deployment more modular and scalable across industries that extend well beyond Atlassian’s traditional software development base.