How to Build an AI Native Team with Mike Cannon-Brookes

In this conversation, Atlassian co-founder and CEO Mike Cannon-Brookes speaks from two perspectives: as a leader internally adopting AI tools across a 13,000-person organization, and as a platform builder helping 300,000+ enterprise customers do the same. He opens by describing the real friction in enterprise AI adoption — not just deploying models, but navigating security requirements, compliance controls, and the cultural work of getting employees to actually use, share, and learn from AI tools. He uses the example of Atlassian's acquisition of Dia (the browser company), where initially only 4 of 13,000 employees could use it due to security concerns, illustrating how enterprise controls must be built before broad rollout is possible.

Cannon-Brookes frames enterprise AI value as 'intelligence multiplied by context,' arguing that while model intelligence is accelerating rapidly and visibly, context is the differentiating layer that Atlassian is uniquely positioned to provide. He describes Atlassian's Teamwork Graph — built over seven to eight years — as the best enterprise context graph available, now operating at 150 billion objects and connections for their largest customers. Major new capabilities announced at Team26 include a full semantic index of customer codebases, expanded people and org chart data (including calculated skill profiles), and physical asset tracking — all feeding into a richer context layer accessible via a new Teamwork Graph CLI and expanded MCP server integration.

On the product strategy side, Cannon-Brookes articulates a deliberate dual approach: helping customers work faster within existing workflows (e.g., AI writing assistance in Confluence, agent-assignable Jira tickets) while simultaneously introducing new paradigms (e.g., 'Create with Rovo' for turning documents into slide presentations, Rovo Studio for no-code agent and app creation). He notes Atlassian has passed five million agents created through Rovo Studio, and emphasizes that security and compliance controls are baked into all of these tools through the underlying Atlassian platform.

When discussing what separates AI leaders from laggards among enterprise customers, Cannon-Brookes observes that leading organizations are pushing for 20-30% improvements rather than incremental gains, asking platform-level questions about how AI tools connect across their stack, and focusing on quality of output and engineering throughput rather than raw token consumption. He highlights their DX acquisition as enabling engineering organizations to understand AI tool adoption and productivity correlation at scale.

He closes by predicting that the most exciting AI development of 2026 will be AI moving beyond chat interfaces into natural, embedded product experiences — making powerful AI capabilities accessible to everyday users without requiring prompt engineering expertise.