Rick McConnell
Analyst · Gray Powell with BTIG
Thanks, Noelle, and good morning, everyone. Thank you for joining us today. On our last earnings call in May, we expressed confidence that the growth drivers we put in place would drive a year of ARR acceleration in fiscal 2027. The strength we saw across the business in Q1 reinforces our conviction and ability to deliver this outcome. Here are a few of the noteworthy highlights from the quarter. Total ARR grew 17%. Net new ARR was $85 million, growing 66% and 41% organically. We achieved record new logo growth of more than 160%. Both total and subscription revenue exceeded the high end of our guidance. We delivered a non-GAAP operating margin of 29%, reflecting the disciplined investment approach you've come to expect from us. Q1 strength reflected healthy enterprise demand for end-to-end observability, stronger execution and growing complexity across customer environments. We are seeing AI contribute in 3 ways, which I will expand upon shortly. Increasing consumption across our platform, creating demand for new AI observability capabilities and directly monetizing agent usage. This Q1 performance reflects both the significant market opportunity and our strong execution to begin the fiscal year. This morning, I'd like to discuss the observability market, why we believe Dynatrace is built for an AI-first world and how we expect to drive incremental AI monetization. The observability market has entered a new era. Software that once took months to build now ships in days. AI agents are taking autonomous action across infrastructure and enterprise customers are now deploying AI rapidly, not because every risk has been resolved, but because standing still means falling behind. In this environment, unified observability matters more than ever. Systems are more interconnected, more autonomous and more difficult to manage manually than ever before. The enterprises winning in this environment are the ones that can keep complex, fast-moving systems working reliably and quickly understand when they are not. Additionally, AI workloads do not simply add volume. They behave differently. They can operate perfectly and still produce incorrect results. That's a problem observability has never had to solve before and addressing it represents a significant emerging opportunity. We estimate the AI observability total addressable market will exceed $10 billion by 2030, growing at more than 50% annually. We see AI observability as the next logical evolution of the broader observability market, and that evolution is already underway. What this means in practice is that observability in the age of AI has to answer far more questions than ever before. While the majority of enterprises are still in early phases of their AI journey, the requirements are evolving quickly. Let me walk through 3 of the questions that matter most today in an AI-first world. The first, is it working? Are applications, infrastructure and systems working as intended? This question is about business resilience and is the same question we ask of traditional workloads. Second is new. Is it accurate? More specifically, is the AI model delivering output that can be trusted and relied upon with confidence. Answering this means evaluating AI systems for accuracy and intended behavior, determining whether an AI system behaves as intended before it shifts is emerging as one of the most important aspects of observability. The third, are my agentic systems delivering the outcomes they were built for. Enterprises are deploying agents to build software at a pace that wasn't possible before. The advantage goes to those who can accelerate the full life cycle and trust the results. Code that's built well, ships safely and runs reliably. The last question is where our newest offering, Bluebox comes in. Built for AI-first teams, Bluebox helps development teams and their coding agents bring software into production in a way that customers can trust. It closes the loop between building and running. It gives coding agents live context from running systems before a change is released. Once that change is live, its agentic SRE capability finds root cause and returns an evidence-backed fix with the developer in control across the entire AI delivery life cycle. This is the moment for which Dynatrace was built. With AI agents increasingly acting alongside humans across development and operations, both need a common source of trusted context. Dynatrace provides that through Grail and Smartscape, giving agents and teams a unified understanding of system relationships and behavior. Dynatrace Intelligence turns that understanding into action, combining deterministic and agentic AI to deliver the precise causal insight that lets both people and agents act with confidence. These core differentiators give customers one operating foundation across both human and autonomous workflows. Our platform has a distinct advantage with this depth of insight. As agents become a larger part of enterprise operations, that distinction becomes even more important. Additionally, we are purposely building for an open interoperable ecosystem. Our newly acquired Bindplane supports the open standard for open telemetry data collection. DevCycle, acquired earlier this year supports the open standard for feature flags. These acquisitions aren't coincidental. They reflect a deliberate commitment to open standards and interoperability. Customers are not locked into proprietary pipelines. Our platform is built to work alongside the tools enterprises already use, including partners such as ServiceNow and to operate natively in MCP environments as the AI ecosystem evolves. We believe openness is a competitive advantage. It is one of the reasons enterprises trust Dynatrace as the intelligence foundation for AI-powered businesses, both powered by AI and built for AI. Our unified architecture becomes more valuable as AI increases complexity, and that growing value is reflected in higher consumption, broader platform adoption and the following 3 new monetization opportunities. First, AI workloads are similar to core observability workloads in that they leverage the same types of data such as logs, traces and metrics. But AI workloads generate dramatically more telemetry than the systems that came before them. This is one of the reasons why log management remains our fastest-growing product category, with consumption nearly doubling since surpassing the $100 million milestone just 2 quarters ago. BindPlane facilitates easier data ingestion, and it is already performing ahead of plan. Second, as I mentioned earlier, AI observability is an incremental monetization driver. It increases consumption of the platform as it validates whether the AI workloads are producing accurate results, behaving as intended and operating safely and efficiently. This is the newest capability of the platform and adoption is expanding quickly. Third, beyond AI workloads and the data they generate, we monetize our own AI and agents. Every time a customer uses Dynatrace Intelligence to get answers through AI function calls or MCP integrations or when one of our agents like the SRE or Assist agent takes autonomous action to resolve an issue, it drives DPS usage. As agents increasingly become consumers of observability, this represents a growing opportunity that didn't exist 2 years ago. Today, more than 1,000 customers use Dynatrace to observe AI and LLM workloads in production, up from roughly 850 last quarter. More than 800 are running operations autonomously with Dynatrace's Agentic capabilities, up from roughly 500 last quarter. Additionally, consumption growth for customers in these AI cohorts is 1.5x higher than that of non-AI cohort customers. Our platform integrates natively with Claude Code, ServiceNow, GitHub Copilot, Atlassian and the major hyperscalers, AWS, Azure and GCP, enabling autonomous action across development and operations at scale. Here are several examples of how customers are leveraging Dynatrace to advance their AI strategies and observability initiatives. In Q1, we signed a 7-figure ACV expansion deal, more than doubling ACV with a top global financial institution. This customer is using Dynatrace to validate model consumption, control costs and maintain full data lineage from prompt to response, helping it deploy AI with greater confidence while reducing compliance and audit risk. We secured a 6-figure ACV expansion, also nearly doubling ACV with a leading recreational vehicle retailer. This customer used Dynatrace as their operational system of record while building a custom CRM application through AI-assisted development, generating approximately 7 figures of savings and expanding usage of our platform. A leading digital insurance provider used Dynatrace AI observability to reduce onboarding time from days to minutes and identified an outdated model version that was driving unnecessary token consumption and costs. Finally, we secured an 8-figure ACV new logo win with one of Latin America's largest financial institutions. In a highly competitive sales process, the customer selected Dynatrace to consolidate a fragmented multi-vendor observability stack across a complex environment, supporting mission-critical citizen-facing services. Our differentiation continues to be recognized by independent analysts. Gartner named Dynatrace a leader in the Gartner Magic Quadrant for Observability Platforms for the 16th consecutive year. Gartner described Smartscape and Dynatrace Intelligence as the gold standard for real-time high-fidelity dependency mapping to automate root cause with Dynatrace and third-party agents. We believe this recognition validates both the strength of our architecture and our ability to help customers confidently scale AI and agentic workloads. Finally, as many of you have seen, Jim plans to retire from Dynatrace by the end of the fiscal year. We will conduct a thorough search for a successor over the coming months, and I'm confident we will have a smooth transition. Jim has been an exceptional partner, playing a critical role in scaling the business, strengthening our financial profile and positioning Dynatrace for its next phase of growth. I am deeply grateful for his leadership and many contributions, and we will miss his valuable insights and guidance when he retires. To wrap up, Q1 was a tremendous start to FY '27 and a powerful reflection of the momentum we are seeing across the business. Organizations are increasingly looking to consolidate on platforms that can help them manage growing complexity, unlock greater productivity and realize the full potential of AI. As enterprises accelerate their AI initiatives, we believe Dynatrace is uniquely positioned to help them innovate faster, operate more efficiently and maximize the return on their technology investments. In an AI-first world in which observability and autonomous operations become more critical day by day, we are more enthusiastic than ever about the opportunity. Jim, over to you.