Teradata Corporation

Good afternoon. My name is Piercy and I will be your conference operator today. At this time, I would like to welcome everyone to the Teradata 2026 third quarter earnings call. All lines have been placed on mute to prevent any background noise. After the speaker's remarks, there will be a question and answer session. If you would like to ask a question during this time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, press star followed by the number one again. Thank you. I would like to hand the conference over to your host today, Chad Bennett, senior vice president of investor relations and corporate development. You may begin your conference.

Good afternoon and welcome to Teradata's third quarter 2026 earnings call. Stephen McMillan, Teradata's President and Chief Executive Officer, will lead our call today, followed by John Ederer, Teradata's Chief Financial Officer, who will discuss our financial results and outlook. Our discussion today includes forecasts and other information that are considered forward-looking statements. While these statements reflect our current outlook, they are subject to a number of risks and uncertainties that could cause actual results to differ materially. These risk factors are described in today's earnings release and in our SEC filings. Please note that Teradata intends to file the Form 10-Q for the quarter ended September 30th, 2026 within the next few days. These forward-looking statements are made as of today and we undertake no duty or obligation to update them. On today's call, we will be discussing certain non-GAAP financial measures which exclude such items as stock-based compensation expense and other special items described in our earnings release. We will also discuss other non-GAAP items such as free cash flow, adjusted free cash flow, and constant currency comparisons. Unless stated otherwise, all numbers and results discussed on today's call are on a non-GAAP basis. A reconciliation of non-GAAP to GAAP measures is included in our earnings release, which is accessible on the investor relations page of our website at investor.terradata.com. A replay of this conference call will be available later today on our website. And now I will turn the call over to Steve.

Thanks, Chad, and thanks to everyone who's joining us today. Teradata delivered a solid third quarter performing as we expected across our operating metrics. We delivered another strong quarter of adjusted free cash flow growth up year over year and year to date. And we saw growth in recurring revenue up 4% year to date, maintained strong operating leverage and exceeded our non-gap earnings per share range. We're well positioned as we enter the fourth quarter. As in every year, the fourth quarter is our largest selling quarter and we've already closed the largest expansion deal in our Q4 plan. We remain confident in our total ARR guidance of two to 4%. Today, I'll focus on the broad set of products and capabilities we are bringing forth as a result of the investments we made in our innovation pipeline. This was done with one goal in mind, helping customers leverage all of their enterprise data and get their agentic AI initiatives into production. This is an imperative in order for AI to deliver durable value. Organizations around the world face the challenge of accelerating AI and moving from experimentation into production. We believe that for AI to work autonomously, it needs to be trusted, ethical and effective, and it needs enterprise context to be accurate. Organizations also have varied deployment requirements that are often shaped by regulatory and sovereign considerations. We're seeing increased interest in our hybrid capabilities from customers running Teradata on-prem, in the cloud, or in a combination of both. As enterprises move from asking questions of the data to delegating work to AI agents, the platform underneath has to do more than it ever has before. Agents use data far more intensively and very differently than people do. We built the autonomous knowledge platform to meet these challenges. And it is that backdrop for the announcements I will cover next, many of which are already available and being trialed by customers. In September, we announced a major evolution of Terra, our agentic co-worker for enterprise data work. It is designed so that everyone from business analysts to database administrators can describe what they need in plain language and see the work carried through without deep Teradata expertise. I personally use Terra in my daily work so can attest that even business leaders can ask natural language questions and get the answers they need. We built Terra to close two gaps that limit what enterprise AI delivers today. The first is business understanding. A model can be highly capable and still not know what a company's own terms, relationships, and policies mean. The second is execution, which has to be reliable, governed, and economical as agent activity grows. Terra is generally available today, and we're extending it to take on both gaps through Terra Context Engine, Tara Harness, and Agent Skills, all expected to arrive before the end of the year. Terra Context Engine addresses the first gap by giving agents governed business meaning to work from. Our industry knowledge models are designed to add expertise, validated with some of the world's most complex enterprises, so agents start with meaning rather than starting from scratch. The second component is Terra Harness, which addresses the execution gap. It is designed to coordinate the right skills, tools, data and models at each step of the workflow with guardrails and human approvals built in to stop high risk actions before they run. The third Terra element is agent skills, which take what Teradata has learned over decades and package it into functions that any agent can call and that people can request in plain language. Supporting all three components is our AI services team, which helps customers choose and implement the use cases where governed context will create measurable value. We're very pleased with how Terra performed on SWE Bench Pro, a widely used software engineering benchmark. Using the same model for internal testing, Terra used 73% fewer tokens than one of the leading AI coding agents, while achieving higher task completion rates and completing work 42% faster. On Data Engineering Bench, a public benchmark Snowflake Labs and Bespoke Labs developed for data pipeline engineering, Terry delivered 53% lower cost per reliably solved task than Snowflake Cortex code. This result is based on our internal testing using the benchmark compared to Snowflake's own published results. Terra also earned the highest accuracy score on the benchmark in the same comparison. For customers, this level of efficiency reduces cost as a constraint on scaling AI, which gives them greater confidence to move more of their workloads into production on Teradata. We're already seeing strong interest in Terra, with customers asking for proposals to utilize Terra across their entire environment for coding and data engineering. We also release Terra internally to all employees so they can utilize its power, bringing a specific language model that they want to use in their own roles. Our internal usage helps our teams gain AI fluency and efficiencies and provides near real-time feedback to our product organization on future enhancements to consider. Additionally, all of our engineering is now augmented by agentic development and we've launched our first product built mostly using agentic development. We continue to expand how we use AI to accelerate how we deliver product capabilities while strengthening the quality and security of what we ship. Two other recent announcements reflect our belief that AI should run for data already sets. In October, we announced the general availability of Elastic Python Compute and AI Studio. It gives data science teams a managed Python environment for scaling AI workloads in parallel directly inside the Teradata platform. This offering is available on AWS and Azure today, as well as on-prem with Teradata Factory, so customers get the same capability in whichever environment holds their data. In September, we announced an integration between our Teradata Autonomous Knowledge Platform and Microsoft One Lake. When running AI on Teradata, customers are able to use AI directly on data stored in One Lake without duplicating or migrating it. For some customers, AI has to run entirely inside their own boundary. An interest in sovereign AI continues to grow. We think about sovereignty in three parts, control over where data lives and who can reach it, control over who operates the environment, and freedom to choose the models and technology an organization depends on. At our most recent customer event in London, we expanded our sovereign offerings with two complementary deployment models. The first, It's Teradata Factory with AI Studio, a unified environment that brings data, vectors, models and agents into on-prem infrastructure. This is designed to give customers direct physical control over their AI execution. The second is Teradata Solver in Cloud on Google Distributed Cloud's air-gapped environment for customers who want a cloud operating model inside an isolated boundary. There's no connection to the public internet, is operated by the customer, and is designed for governments, defense organizations, and regulated industries. Control over where AI runs goes hand in hand with protecting the data itself. To this end, we also recently announced new privacy, security and resiliency capabilities. They reflect how enterprise AI requires sensitive data to move between users, applications, models and agents. This increases risk and we believe protection has to work as a single system to safeguard the intelligence behind AI without diminishing what makes it valuable. With these new capabilities, customers can protect the identities behind their data while keeping the relationships that AI and analytics depend on. Post quantum security and readiness, strength and protection against emerging quantum era threats and immutable backup protection keeps mission critical operations available and recoverable. These are available now on Teradata Cloud across AWS, Azure, and Google Cloud. And masking and classification are also available on Teradata Factory and on-prem. With the increased investment in our product pipeline, we've announced many new offerings that are already available, plus more are arriving this quarter and into early 2027. As agents take on more work, customers will need context, governance, and performance together in whichever environment the data sets. Technology alone doesn't get AI into production. So we pair our platform with AI services. Our teams work alongside customers starting from their data, their governance requirements and their business priorities. We take use cases from prototype to production against clear business outcomes. We're seeing this across industries from banking and aviation to telecommunications and pharmaceuticals. In each case, our teams are helping customers get AI into production and see value they can measure. This year we brought our new technology to the market at events around the world. These events encompass product announcements, customer use cases, live workshops and demos, all showing how Teradata can help move customers' AI initiatives into production. Attendance beat our goals and our teams are now following up with customers. In addition, the workshops were oversubscribed and we're bringing the workshops online to extend the reach. Now I'll walk through some customer examples that reflect the breadth of our offerings. A multinational electronics company in Japan is a new logo for us. It's elected Teradata for our hybrid capabilities as the firm required both high-performance analytics across trillions of records and the stability of on-prem operations. Running on our on-prem platform and Teradata on AWS, this next-gen analytical environment will provide quality insights on its fast-growing manufacturing operations. Our team won by demonstrating superior query performance and a robust data foundation. An EMEA public sector new account has selected Teradata to support a multimodal AI and analytics program for operational readiness and asset protection. This win reflects growing demand for trusted governed AI platforms, platforms that can run mission critical workloads on sovereign infrastructure and highly secure on-prem environments. Federal Transportation Agency in Latin America selected Teradata on AWS, AI Studio and Elastic Compute to integrate fragmented traffic conditions, accident and weather data. Teradata is also supporting AI driven predictive infrastructure decision making. This win reinforces our role as a trusted data and AI foundation at scale. and finally a utility company in the Americas chose our AI services and AI studio to help turn this trusted enterprise data into business value through AI. Building on its existing Teradata investment in governed intelligence and business context, it can reuse trusted data, metadata, business rules and analytical assets rather than creating a separate foundation for AI. This is to accelerate customer time to value with natural language experiences while supporting scalable, governed AI adoption across the organization. As we started the year, we said we would continue to drive top-line growth, improve profitability, and maintain healthy free cash flow. And we'd invest to advance our products in helping customers derive value from AI. And we've done just that. We are receiving positive feedback from customers on their AI initiatives with Teradata. Our more aggressive technology pipeline coupled with the enthusiastic response we've seen from customers underpins our confidence in our annual outlook. Now, I'll turn the call over to John.