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Financial Data and Key Metrics Changes - Atlassian reported over $5.2 billion in revenue and over $1.4 billion in free cash flow for FY 2025, achieving a balanced rule of 40 plus performance [6][10] - Free cash flow for the quarter was $360 million, down 13% year over year, primarily due to strong collections in the prior year [24][25] - The company achieved a cloud net revenue retention rate of 120% [7] Business Line Data and Key Metrics Changes - The Teamwork platform now serves over 300,000 customers, with significant growth in AI usage, reaching 2.3 million AI users, a 50% increase from the previous quarter [6][7] - Core applications such as Jira, Confluence, and Jira Service Management are growing in line or faster than total company revenue [10] - The Teamwork collection has exceeded expectations since its launch, contributing to strong revenue growth [10] Market Data and Key Metrics Changes - Atlassian's enterprise sales teams executed a record number of deals greater than $1 million in annual contract value (ACV), more than doubling year over year [6][10] - Data center to cloud migrations increased by 60% year over year, indicating strong customer commitment to the cloud platform [42][69] Company Strategy and Development Direction - The company is focusing on serving the enterprise, delivering AI innovations, and connecting teams through the Atlassian system of work [7][10] - A partnership with Google Cloud aims to enhance the AI-powered Teamwork platform and accelerate cloud transformation [8][10] - Atlassian is committed to a long-term growth strategy, targeting a compounded annual growth rate of 20% from FY 2024 to FY 2027 [33][34] Management's Comments on Operating Environment and Future Outlook - Management expressed confidence in the company's ability to achieve long-term growth targets despite macroeconomic uncertainties [33][34] - The company sees AI as a significant tailwind for business growth, with expectations of increased software creation and collaboration [20][21] - Management highlighted the importance of customer-centric processes and partnerships to drive enterprise growth [71][72] Other Important Information - Anu, the President of Atlassian, will transition away from her role in December after nearly twelve years [10][11] - The company is investing heavily in R&D and sales to support its strategic priorities [101][102] Q&A Session Summary Question: Concerns about tool generation tools and developer roles - Management stated that they are not seeing any negative impact on growth rates or adoption from the integration of code-generating AI tools, and user growth remains healthy [15][16] Question: Free cash flow trajectory for 2026 - Free cash flow for FY 2025 was flat at $1.4 billion, with expectations that cash flow will correlate with non-GAAP operating income trends moving forward [24][25][26] Question: Potential inflection points for revenue growth - Management highlighted multiple growth levers, including paid seat expansion, cross-sell, upsell, and new customer growth, with AI opening up additional opportunities [33][34] Question: Guidance for data center segment growth - Management explained that Q1 guidance reflects a smaller expiration base and headwinds from prior programmatic changes, leading to a cautious outlook [78][79] Question: Driving wall-to-wall deployments in enterprises - Management emphasized ongoing consolidation efforts and the growth of business user segments, indicating strong momentum in expanding usage across non-technical roles [81][84]

GPT-5 Capabilities & Enterprise Impact - GPT-5 represents a significant advancement, particularly for enterprises dealing with large volumes of unstructured data [2][3] - GPT-5 demonstrates improved accuracy compared to GPT-4, making it a leading model for enterprise applications [4] - Box is integrating GPT-5 into its AI studio, offering it to enterprise customers [4] Use Cases & Productivity - AI agents, powered by models like GPT-5, can automate tasks such as analyzing contracts and legal briefings, increasing productivity for professionals like lawyers [8][9] - The value proposition for customers includes increased productivity and potential for increased revenue or efficiency [7][9] - Enterprises can leverage AI to extract information, summarize documents, and perform calculations on data [6] Data & Model Evaluation - Box evaluates AI models using typical enterprise data like contracts, research data, invoices, resumes, and agreements [6] - The evaluation focuses on the accuracy of the model's responses to questions like extracting data or summarizing information [6]

MCP and AI Agent Development - MCP is presented as a way of interfacing between AI and external resources, enabling functionalities like database access and complex computations [3] - The industry is currently focused on building internal demos and connecting them to APIs, but needs to move towards robust authentication and authorization [9][10] - The industry needs to adapt existing tooling for MCP due to its dynamic client registration, which can flood developer dashboards [12] Enterprise Readiness and Security - Scaling MCP servers requires addressing free credit abuse, bot blocking, and robust access controls [12] - Selling MCP solutions to enterprises necessitates SSO, lifecycle management, provisioning, fine-grained access controls, audit logs, and data loss prevention [12] - Regulations like GDPR impose specific logging requirements for AI workloads, which are not widely supported [12] Challenges and Future Development - Passing scope and access control between different AI workloads remains a significant challenge [13] - The MCP spec is actively developing, with features like elicitation (AI asking humans for input) still unstable [13] - Cloud vendors are solving cloud hosting, but authorization and access control are the hardest parts of enterprise deployment [13]

Company Overview - Box is a B2B company operating as an unstructured data platform, serving large enterprises including Fortune 500 companies [1][2] - Box has over 115,000 companies as customers, tens of millions of users, and manages over 1 exabyte of data [2] - Box is often the first AI deployed within large enterprises due to existing trust relationships [3] Data Extraction Evolution - Box initially used a straightforward architecture for data extraction involving pre-processing, OCR, and large language models [8] - The initial AI deployment processed 10 million pages, but encountered challenges with complex documents, OCR accuracy, language variations, and the need for confidence scores [9][10][11] - The company experienced a "trough of disillusionment" as the initial AI solution proved insufficient for diverse customer needs [12] Agentic Approach Implementation - Box re-architected its data extraction process using a multi-agent approach, separating problems into sub-agents [12] - The agentic system intelligently groups related fields, dynamically determines data extraction methods, and incorporates a quality feedback loop for continuous improvement [13] - This approach allows for easier updates and specialization, enabling the company to quickly adapt to new document types and customer requirements [13] Engineering and Customer Impact - Building agentic systems helps engineers think about AI and agentic workflows, leading to better understanding of customer needs [13] - This approach facilitates the development of tools that integrate with customer-built agents, enhancing the overall ecosystem [13] - The company advises building agentic systems early when developing intelligent features [14]