At this year’s Atlassian Team ’26, one of the biggest themes across the keynote presentations was clear: the future of work will be powered by AI — but not just any AI. According to Atlassian co-founder Mike Cannon-Brookes, the organizations that succeed won’t simply have access to powerful AI models. They’ll have something far more valuable: context.

The keynote painted a bold vision of what Atlassian believes the modern workplace is becoming: an AI-native organization built around connected knowledge, intelligent automation, and collaboration across every system and team.

AI Models Are Becoming Commodities — Context Is the Real Advantage

One of the standout messages from the keynote was this simple idea:

“Intelligence is the engine. Context is the fuel.”

The point was that access to advanced AI models is becoming increasingly common. What differentiates organizations moving forward is whether their AI systems truly understand the company itself — its projects, decisions, customers, workflows, assets, documentation, and history.

That’s where Atlassian’s vision centers around the Teamwork Graph.

The Teamwork Graph: Atlassian’s Connected Intelligence Layer

Atlassian introduced the Teamwork Graph as the foundation that connects information across the enterprise. Rather than treating Jira tickets, Confluence pages, Loom videos, code repositories, meetings, and assets as isolated pieces of data, the Teamwork Graph connects them into a single intelligence layer.

The graph pulls together information from tools including:

• Jira
• Confluence
• Loom
• Bitbucket and code repositories
• Microsoft Teams
• Slack
• Google Drive
• Salesforce
• HubSpot
• Workday
• Figma
• And many other systems

The goal is to create a living organizational memory where every project, decision, document, asset, and conversation contributes to making the company smarter over time.

Rovo Is Becoming the AI Interface for Work

One of the most impressive demos showcased how Atlassian’s AI platform, Rovo, uses the Teamwork Graph to provide meaningful business context instantly.

The demonstrations showed Rovo:

• Summarizing decades-long customer relationships
• Pulling information from multiple systems simultaneously
• Understanding meeting history, documents, and customer records
• Generating recommendations and action plans
• Creating summaries, charts, and next steps automatically

Rather than functioning like a simple chatbot, Rovo is being positioned as a productivity layer that supports teams across engineering, marketing, HR, finance, service management, and leadership.

AI Agents Are Becoming Teammates

Another major theme throughout the keynote was the rise of AI agents embedded directly into workflows.

Atlassian demonstrated agents that could:

• Be assigned Jira work items
• Help with product planning
• Break down technical tasks
• Generate follow-up actions
• Assist with incident response
• Manage creative assets and presentations

The broader message was that humans will increasingly focus on strategy, priorities, and decision-making, while AI agents help execute the operational work.

For organizations already heavily invested in Jira Service Management, ITSM, procurement, or asset management workflows, this direction feels especially significant.

Jira Is Evolving Into the AI Control Plane for Work

One of the clearest strategic messages from Team ’26 was Atlassian’s positioning of Jira as more than a project management tool.

The keynote showed AI helping teams:

• Understand feature requests
• Pull business and technical context automatically
• Ask clarifying questions
• Build execution plans
• Create tasks and assign work
• Update documentation and architecture diagrams
• Coordinate between humans and AI agents

In many ways, Jira is evolving into a centralized operating system for planning and execution across organizations.

Assets and Real-World Operations Matter More Than Ever

A particularly interesting example featured the Atlassian Williams Formula 1 partnership and how Jira Assets can model physical components of a race car.

This reinforced an important idea: enterprise context is not just digital. Physical assets, inventory, procurement data, facilities, and operational systems are becoming part of the connected intelligence layer too.

For organizations focused on:

• IT asset management
• Enterprise service management
• Procurement
• Facilities
• Manufacturing
• Logistics

…the implications are huge.

At Mumo Systems, this especially resonated with us given our work helping organizations connect procurement, asset lifecycle management, and workflow automation within the Atlassian ecosystem.

Skills, Knowledge, and Organizational Intelligence

Another fascinating area discussed was how Atlassian can infer organizational expertise based on actual work activity.

By analyzing things like:

• Jira assignments
• Confluence contributions
• Code commits
• Project history
• Focus areas

…the platform can help identify who has the right experience or expertise to solve a problem or unblock a project.

It’s a compelling example of how connected work data can improve collaboration and resource planning across large organizations.

Security and Governance Remain Critical

While the keynote focused heavily on innovation, Atlassian also emphasized enterprise governance and security throughout the AI experience.

Key areas included:

• Data protection and permissions
• AI agent governance
• Visibility and auditability
• Administrative controls
• Enterprise security readiness

The consistent message was that AI should operate within existing permission structures and organizational policies — not outside them.

Final Thoughts

Team ’26 made one thing very clear: Atlassian is thinking far beyond individual tools. The company is building toward a future where AI, connected data, workflows, assets, and organizational knowledge all work together as a unified system.

The Teamwork Graph appears to be the foundation for that vision — connecting people, work, systems, and AI into a smarter operational layer for the enterprise.

For teams already using Jira, Confluence, Jira Service Management, or Assets, the next few years could fundamentally change how work gets planned, executed, and automated.

We’re excited to continue exploring what these innovations mean for our customers and how organizations can begin preparing for this AI-native future today.

---

FAQs

What is an AI-native organization?

An AI-native organization is a company that embeds AI directly into everyday work processes, decision-making, collaboration, and operations. Rather than using AI as a separate tool, AI becomes integrated into how teams plan, execute, communicate, and solve problems.

What is Atlassian’s Teamwork Graph?

The Teamwork Graph is Atlassian’s connected intelligence layer that links information across tools, teams, projects, and systems. It connects work items, documentation, code, meetings, assets, goals, and customer data so AI can understand organizational context more effectively.

Why is “context” so important for AI?

According to Mike Cannon-Brookes, AI models themselves are becoming widely available. The real advantage comes from giving AI access to meaningful organizational context — including project history, customer relationships, documentation, workflows, assets, and team knowledge.

What is Rovo?

Rovo is Atlassian’s AI-powered experience layer that helps teams search, summarize, recommend actions, and generate insights across connected systems. It uses the Teamwork Graph to pull information from tools like Jira, Confluence, Slack, Microsoft Teams, Salesforce, and more.

How does Jira fit into Atlassian’s AI strategy?

At Team ’26, Atlassian positioned Jira as a central coordination layer where humans, automation, and AI agents work together. AI-powered Jira workflows can help teams create plans, organize tasks, gather context, and accelerate execution.