AI is only as useful as the context behind it. Without a unified, connected foundation, enterprise AI delivers fragmented insights, generic recommendations, and disappointing returns. The real question is not what your AI knows in the abstract. It is whether your AI understands how work actually gets done in your organization, how sellers qualify deals, how support engineers debug issues, how risk teams make decisions, and who is allowed to see what along the way.

In this session, Atlassian will introduce the Teamwork Graph as the context layer that makes AI genuinely useful at enterprise scale. You will hear directly from two customers operating in very different industries, with different tech stacks, workflows, and team structures, who will share how Rovo adapted to the DNA of their organizations from day one. Together, their stories illustrate a shared truth: regardless of how differently teams operate, connecting AI to real organizational context is what separates meaningful AI value from generic output. The session will walk through how to enrich and configure the Teamwork Graph, what makes it architecturally distinct, and how to extend that context across your toolchain.

Key Takeaways

• Discover how enriching the Teamwork Graph dramatically improves the accuracy, relevance, and everyday usefulness of Rovo across your organization's unique workflows and tools.
• See how two customers from different industries, with different tech stacks and operating models, each used the Teamwork Graph to get meaningful value from Rovo starting on day one.
• Leave with a practical framework for configuring context-aware AI that respects permissions, maintains enterprise governance, and scales without sacrificing performance or data privacy.