Atlassian Corporation (TEAM) Q3 2026 Earnings Call Transcript
Access earnings results, analyst expectations, report, slides, earnings call, and transcript.
Overview
The earnings call summary and Q&A session highlight strong financial performance, AI-driven growth, enterprise adoption, and market leadership. Positive factors include record high revenue, robust cross-sell growth, and strategic investments in AI and data governance. The announcement of data collection changes and competitive displacements further support a positive outlook. Although data center revenue recognition may be lumpy, overall sentiment is positive, with strong enterprise adoption and growth in the Service Collection. The absence of management avoiding direct answers also supports a positive sentiment.
Key Financial Performance
Total Revenue $1.8 billion, grew 32% year-over-year. Reasons for growth include excellent execution by the team and clear momentum across key strategic priorities such as enterprise, AI, and the system of work.
Cloud Revenue $1.1 billion, grew 29% year-over-year. Reasons for growth include strong cloud outperformance and expansion driven by AI initiatives like Rovo, which is growing ARR at roughly 2x the rate of non-Rovo users.
Remaining Performance Obligations (RPO) $4 billion, grew 37% year-over-year. Reasons for growth include increased commitments from large enterprises and competitive displacements from major ITSM providers.
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Operating Highlights
AI Rovo: Millions of monthly active users added, with Rovo credit usage growing more than 20% month-over-month. Customers using Rovo are growing their ARR at roughly 2x the rate of non-users.
Competitive Displacements: Largest ever quarter for competitive displacements from a major ITSM provider, as customers move away from legacy systems to Atlassian's modern AI-native platform.
Revenue Growth: Total revenue grew 32% year-over-year to $1.8 billion. Cloud revenue surpassed $1.1 billion, growing 29% year-over-year. RPO grew 37% year-over-year to $4 billion.
AI and Context: AI is seen as a transformative opportunity for Atlassian. The company emphasizes the importance of context in AI-native systems, leveraging the Teamwork Graph to connect knowledge, work, people, and code.
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Risk or Challenges
Forward-looking statements: The company acknowledges that forward-looking statements involve known and unknown risks, uncertainties, and assumptions. If these risks or uncertainties materialize, or if assumptions prove incorrect, results could differ materially from expectations.
Competitive pressures: Atlassian is experiencing competitive pressures as it displaces major ITSM providers and rivals. This indicates a challenge in maintaining its competitive edge and market share.
Economic and industry changes: The company recognizes significant changes in the industry, which could pose challenges to its operations and strategic objectives.
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Guidance & Outlook
Enterprise: Some of the world's largest enterprises, including Siemens Energy, Rheinmetall, and Wayfair, have deepened and broadened their commitments to Atlassian.
AI: Continued addition of millions of monthly active users to Rovo, with AI Rovo credit usage growing more than 20% month-over-month. Customers using Rovo are growing their ARR at roughly 2x the rate of customers who are not using Rovo.
System of Work: More enterprises are embracing Atlassian's platform-wide vision using the Atlassian system of work to see the full picture of their organization. The Teamwork Graph connects knowledge, work, people, and code, providing a rich enterprise context graph.
Competitive Displacements: Largest ever quarter for competitive displacements from a major ITSM provider, as customers move away from legacy systems to Atlassian's modern AI-native platform.
AI and Context: AI is seen as a significant positive development for Atlassian. The focus is on context as an anchor to avoid chaos in AI-native environments.
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Shareholder Return Plan
The selected topic was not discussed during the call.
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Key Q&A
Q:How does the Teamwork Graph and Atlassian platform lower the cost of AI investments?
A:The Teamwork Graph and Atlassian platform provide better, faster, and cheaper AI answers by using less tokens, which reduces AI costs. Customers benefit from higher quality AI answers due to the platform's context graph, which includes knowledge about the organization, projects, goals, and people. This results in reduced token usage and lower costs for AI investments.
Q:What drove the data center outperformance in Q3, and what does it mean for FY '27?
A:The data center outperformance in Q3 was driven by greater-than-expected upfront term license revenue, with approximately $50 million more recognized than expected. This was due to pull-forward purchasing and expansion activity, a pricing change in March, and robust retention rates. For FY '27, there is expected lumpiness in revenue recognition, and Atlassian plans to enhance disclosures to help investors understand the timing effects and underlying business strength.
Q:How is Rovo positioned against third-party agents?
A:Rovo is integrated into Atlassian's platform and surfaces across various tools like Jira, Confluence, and JSM. It benefits from the Teamwork Graph, which provides the best context for AI-driven workflows. While third-party agents are supported, Rovo's native integration offers advantages like better access to Atlassian's platform and context, leading to better quality answers and faster results. Customers are increasingly adopting Rovo due to its superior AI capabilities.
Q:What is driving seat expansion in Jira and cloud revenue acceleration?
A:Seat expansion in Jira and cloud revenue acceleration are driven by strong cross-sell performance, particularly in the Teamwork Collection, which includes Jira, Confluence, Loom, and Rovo. Customers are upgrading to the Teamwork Collection for increased AI credits and agent capabilities. Atlassian's platform remains core to enabling workflows and collaboration, with AI-driven features enhancing value and driving seat expansion.
Q:What is driving competitive displacements in the Service Collection?
A:Competitive displacements in the Service Collection are driven by Atlassian's strong user experience, comprehensive data in the Teamwork Graph, and AI capabilities. The Service Collection's ability to connect IT, HR, and other business teams provides a unified platform for service management. Atlassian's focus on quality software and AI-driven automation has led to its largest quarter for competitive displacements, particularly in ITSM and employee service management.
Q:What drove the decision to announce data collection changes, and what are the expected benefits?
A:The decision to announce data collection changes was driven by Atlassian's commitment to transparency and trust. The changes clarify how customer data is used and the benefits provided. These changes enable more powerful AI features, such as benchmarking engineering organizations and building the Teamwork Graph. Customers have responded positively due to Atlassian's openness and focus on delivering value.
Q:What is driving momentum in ITSM and the Service Collection?
A:Momentum in ITSM and the Service Collection is driven by strong adoption across regions, non-IT use cases, and AI features. Atlassian's ability to connect teams and provide a unified platform for service management is a key strength. AI-driven features like AI ops and Rovo enhance efficiency and value. The Service Collection's milestone of $1 billion in ARR highlights its success.
Q:What is driving cross-sell growth in cloud through service and Teamwork Collections?
A:Cross-sell growth in cloud is driven by the Teamwork Collection's value proposition, which includes Jira, Confluence, Loom, and Rovo. Customers benefit from increased AI credits and agent capabilities, leading to higher Rovo credit usage and ARR growth. The Service Collection's ability to connect teams and provide AI-driven automation also contributes to cross-sell growth.
Q:What is Atlassian's approach to pricing strategy, and how does it align with customer needs?
A:Atlassian's pricing strategy is customer-led, focusing on meeting customer needs. The seat-based pricing model remains the primary approach, with collections like the Teamwork Collection offering broader value. Atlassian also has consumption-based pricing meters for various features. The strategy emphasizes delivering outcomes and value to customers, as seen in strong RPO growth and customer commitment to the platform.
Q:How is Atlassian leveraging AI internally to drive efficiencies?
A:Atlassian is leveraging AI to enhance operational efficiency, particularly in engineering and platform operations. Investments in AI have led to cost reductions in running the platform while maintaining reliability. AI-driven automation and features are also improving internal processes, enabling Atlassian to deliver durable, profitable growth.
Q:Review of Unclear Management Responses
A:No questions were identified where management avoided giving a direct answer or lacked clarity in their responses.
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Earnings Word Cloud
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Atlassian Corporation (TEAM) Q3 2026 Earnings Call Transcript
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The earnings call summary and Q&A session highlight strong financial performance, AI-driven growth, enterprise adoption, and market leadership. Positive factors include record high revenue, robust cross-sell growth, and strategic investments in AI and data governance. The announcement of data collection changes and competitive displacements further support a positive outlook. Although data center revenue recognition may be lumpy, overall sentiment is positive, with strong enterprise adoption and growth in the Service Collection. The absence of management avoiding direct answers also supports a positive sentiment.
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Frequently Asked Questions
Where does this earnings call transcript come from?
All transcripts are sourced directly from the official live webcast or the company’s official investor relations website. We use the exact words spoken during the call with no paraphrasing of the core discussion.
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They are generated by a specialized financial-language model trained exclusively on 15+ years of earnings transcripts. The model extracts financial figures, guidance, and tone with 97%+ accuracy and is regularly validated against human analysts. The full raw transcript always remains available for verification.
