Teradata Corp (TDC) 2025 Q3 法說會逐字稿

Good afternoon. My name is Charlie, and I will be your conference operator today. At this time, I'd like to welcome everyone to the Teradata third quarter 2025 earnings call. (Operator Instructions) I would now 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.

午安.我叫查理，今天我將擔任你們的會議接線生。在此，我謹代表 Teradata 歡迎各位參加 2025 年第三季財報電話會議。（操作員指示）現在，我謹將會議交給今天的主持人，投資者關係和企業發展高級副總裁查德·貝內特。您可以開始會議了。

Good afternoon and welcome to Teradata's 2025 third-quarter earnings call. Steve McMillan, Teradata's President and Chief Executive Officer, will lead our call today; followed by John Eder, Teradata's Chief Financial Officer, who will discuss our financial results and outlook.

下午好，歡迎參加 Teradata 2025 年第三季財報電話會議。Teradata 總裁兼執行長 Steve McMillan 將主持今天的電話會議；隨後 Teradata 財務長 John Eder 將討論我們的財務表現和展望。

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, including our most recent Form 10-K and in the Form 10-Q for the quarter ended September 30, 2025, that is expected to be filed with the SEC within the next few days. These forward-looking statements are made as of today, and we undertake no duty or obligation to update them.

我們今天的討論包含預測和其他信息，這些信息都屬於前瞻性陳述。雖然這些陳述反映了我們目前的展望，但它們受到許多風險和不確定因素的影響，可能導致實際結果與預期有重大差異。這些風險因素在今天的收益報告中以及我們向美國證券交易委員會提交的文件中均有描述，包括我們最新的 10-K 表格和截至 2025 年 9 月 30 日的季度 10-Q 表格，預計將在未來幾天內向美國證券交易委員會提交。這些前瞻性陳述僅代表截至今日的觀點，我們不承擔更新這些陳述的義務。

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, constant currency comparisons, and 2025 revenue and ARR growth outlook in constant currency. 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.teradata.com. A replay of this conference call will be available later today on our website.

在今天的電話會議上，我們將討論一些非GAAP財務指標，這些指標不包括股票選擇權費用和我們在獲利報告中描述的其他特殊項目等項目。我們也將討論其他非GAAP項目，例如自由現金流、固定匯率比較以及2025年以固定匯率計算的收入和ARR成長展望。除非另有說明，否則今天電話會議上討論的所有數字和結果均以非GAAP為基礎。我們的獲利報告中包含了非GAAP指標與GAAP指標的調節表，您可以在我們網站投資者關係頁面（investor.teradata.com）查閱該報告。本次電話會議的錄音回放將於今日稍後在我們網站上提供。

And now I will turn the call over to Steve.

現在我將把電話交給史蒂夫。

Thanks, Chad, and thanks, everyone, for joining us today. Q3 marked another quarter of solid execution as we beat our revenue and recurring revenue guidance ranges. We delivered non-GAAP earnings per share of $0.72, soundly ahead of our outlook, and we delivered free cash flow ahead of expectations.

謝謝查德，也謝謝各位今天收看我們的節目。第三季業績表現穩健，營收和經常性收入均超乎預期。我們實現了非GAAP每股盈餘0.72美元，遠超預期，自由現金流也超乎預期。

We posted our second consecutive quarter of total ARR growth ahead of our initial target of the fourth quarter. With a return to total ARR growth ahead of schedule, we have strong conviction in our durable growth path and expect this growth to continue in 2026.

我們連續第二季實現了年度經常性收入 (ARR) 總成長，提前完成了我們最初設定的第四季度目標。由於總 ARR 提前恢復成長，我們對永續成長之路充滿信心，並預計這種成長勢頭將在 2026 年繼續。

We also expect the return to positive ARR growth, combined with the cost savings and productivity measures we've taken will result in meaningful free cash flow growth. Whether in cloud or on-prem, we are helping organizations build the data foundation and are delivering the enterprise context required for AI solutions, and we see the shift in our business from classic EDW towards the autonomous AI and Knowledge platform.

我們也預計，隨著 ARR 恢復正成長，加上我們採取的成本節約和生產力提升措施，將帶來可觀的自由現金流成長。無論是在雲端還是在本地，我們都在幫助組織建立資料基礎，並提供人工智慧解決方案所需的企業環境，我們看到我們的業務正在從傳統的企業資料倉儲 (EDW) 轉向自主人工智慧和知識平台。

We see enterprises reevaluating how to cost effectively deploy agentic AI. As we have noted for the past several quarters, we are seeing a resurgence of hybrid environments, which reflects a growing understanding of how enterprises can best leverage both on-prem and cloud capabilities.

我們看到企業正在重新評估如何以經濟高效的方式部署智慧體人工智慧。正如我們在過去幾季中所觀察到的，混合環境正在復興，這反映出人們對企業如何才能最好地利用本地和雲端功能有了越來越深入的了解。

It isn't just about choosing between environments anymore. It's about effectively operating across both to meet diverse business needs. Our platform is designed to give customers the opportunity to run a genetic AI at scale wherever that data resides in their business and public cloud, on-prem, or private cloud.

這不再只是在不同環境之間做出選擇的問題了。關鍵在於有效地在兩者之間進行運營，以滿足多樣化的業務需求。我們的平台旨在讓客戶有機會大規模運行基因人工智慧，無論資料位於其企業和公有雲、本地或私有雲。

Interest in AI and in particular, Agentic AI, continues to grow in virtually all industries. However, most companies are still in the early stages of deploying this technology and Teradata set squarely at the center of this revolution. We believe we provide the enterprise context that AI agents need to deliver trusted, reliable results at scale. Without this knowledge, even the most advanced models can be just a plain wrong.

幾乎所有產業對人工智慧，尤其是智能體人工智慧的興趣都在持續成長。然而，大多數公司仍處於部署這項技術的早期階段，而 Teradata 則處於這場變革的中心。我們相信，我們能夠提供人工智慧代理大規模交付可信賴、可靠的結果所需的企業環境。如果沒有這方面的知識，即使是最先進的模型也可能完全錯誤。

This shift also creates a very specific opportunity because agentic AI, with its 24/7, always on query potential can increase workloads on data platforms by up to 25 times and use 50 times to 100 times the compute resources and what was required by previous modern analytic workloads.

這種轉變也創造了一個非常特殊的機會，因為具有全天候、始終在線查詢能力的智慧人工智慧可以將資料平台上的工作負載增加多達 25 倍，並使用 50 到 100 倍的運算資源，而這些資源是以前現代分析工作負載所需的。

Teradata is uniquely built to handle these mixed workloads and high volumes of tactical queries as enterprises deploy potentially thousands of agents and evaluate millions of relationships across thousands of tables to make a single decision milliseconds matter.

Teradata 的獨特之處在於能夠處理這些混合工作負載和大量戰術查詢，因為企業可能會部署數千個代理，並評估數千個表中的數百萬個關係，以做出單一決策——毫秒之差至關重要。

We not only manage the critical enterprise data that powers these AI systems but we also can deliver the performance required at the level of performance and scale that AI needs. Teradata was built for these types of enormous workloads based on our massively parallel architecture patented workload management and query optimization that is designed to provide a high-performance environment with predictable costs that can deliver the most complex AI workloads.

我們不僅管理著驅動這些人工智慧系統的關鍵企業數據，還能以人工智慧所需的效能等級和規模提供所需的效能。Teradata 專為處理這類龐大的工作負載而構建，它基於我們大規模並行架構、專利工作負載管理和查詢最佳化技術，旨在提供高效能環境和可預測的成本，從而能夠交付最複雜的 AI 工作負載。

Our patented QueryGrid data analytics fabric provides seamless high-performing data access, processing, and movement across multiple data sources. Our industry data models are built on decades of working with the Global 1000. And through these, we bring deep context to language models, another area where we can bring unique benefits to our customers.

我們專利的 QueryGrid 資料分析架構可實現跨多個資料來源的無縫、高效能資料存取、處理和移動。我們的產業數據模型建立在與全球1000強企業數十年的合作經驗之上。透過這些方法，我們為語言模型引入了更深層的上下文，這是我們可以為客戶帶來獨特優勢的另一個領域。

We believe Teradata is the best autonomous AI and knowledge platform for agenetic workloads and that our platform provides the best price performance whether on-prem or in the cloud.

我們相信 Teradata 是人工智慧工作負載的最佳自主人工智慧和知識平台，無論是在本地部署還是在雲端部署，我們的平台都能提供最佳的性價比。

In the quarter, we were named a leader in the Forrester Wave data management for analytics platforms and the report noted that Teradata is a good choice for organizations seeking to support hybrid cloud DMA deployments, especially where reliability, scalability, and high availability are essential.

本季度，我們在 Forrester Wave 分析平台資料管理報告中被評為領導者，該報告指出，Teradata 是尋求支援混合雲 DMA 部署的組織的良好選擇，尤其是在可靠性、可擴展性和高可用性至關重要的情況下。

We're building the capabilities for the future, to enable AI, speed, and scale. Earlier this year, we announced enterprise vector store, a capability that enables organizations to include unstructured data and their integrated Norwich Foundation.

我們正在建立面向未來的能力，以實現人工智慧、速度和規模化。今年早些時候，我們發布了企業向量儲存功能，該功能使組織能夠包含非結構化資料及其整合的諾維奇基金會。

We also enhanced ClearScape Analytics with unified model ops capabilities designed specifically for agenetic AI. These provide seamless native support for open source models as well as CSP model APIs. We launched our MCP server to deliver faster context autonomously. And we've recently taken several more significant steps to further our position.

我們也增強了 ClearScape Analytics，使其具備了專為基因人工智慧設計的統一模型操作功能。這些功能為開源模型以及 CSP 模型 API 提供無縫的原生支援。我們推出了MCP伺服器，以更快地自主提供上下文資訊。最近，我們又採取了幾項更重要的措施來鞏固我們的地位。

In September, we announced Teradata agent Builder, a suite of capabilities designed to accelerate the development and deployment of autonomous contextually intelligent AI agents. Now in private preview, it leverages open source frameworks our MCP server and deep semantic access to enterprise data across cloud and on-prem environments provided by our Knowledge platform.

9 月，我們發布了 Teradata agent Builder，這是一套旨在加速開發和部署自主情境智慧 AI 代理的功能。目前處於私有預覽階段，它利用了我們的 MCP 伺服器的開源框架，並透過我們的知識平台提供對跨雲端和本地環境的企業資料的深度語義存取。

Customers can develop their own agents or use ready to deploy Teradata agents to accelerate implementation and deliver rapid impact. Launched at our Possible event last month autonomous customer intelligence as a software and services offering that embeds Teradata agents across the customer experience or CX journey.

客戶可以開發自己的代理，也可以使用現成的 Teradata 代理來加速實作並快速產生影響。上個月，我們在 Possible 活動上推出了自主客戶智能，這是一種軟體和服務產品，它將 Teradata 代理嵌入到客戶體驗或 CX 旅程中。

These agents can uniquely leverage four decades of Teradata innovation and contextual knowledge from solving mission-critical industry-specific data challenges. Our integrated approach make sure our agents are extensions of the enterprise data platform and broader knowledge ecosystem rather than generic tools that failed to deliver meaningful impact.

這些代理商可以獨特地利用 Teradata 四十年來在解決關鍵產業特定數據挑戰方面所累積的創新和背景知識。我們的一體化方法確保我們的代理商是企業資料平台和更廣泛的知識生態系統的延伸，而不是未能產生有意義影響的通用工具。

To help customers transform AI pilots into production-ready agentic solutions that deliver significant business value, we also launched new AI services. These new services are intended to make a genic AI, a reality at enterprise scale by combining embedded experts, proven methodology, and Teradata's best-in-class autonomous AI and Knowledge platform.

為了幫助客戶將 AI 試點計畫轉化為能夠帶來顯著商業價值的生產就緒型智慧解決方案，我們也推出了新的 AI 服務。這些新服務旨在透過結合嵌入式專家、成熟的方法論以及 Teradata 一流的自主人工智慧和知識平台，使基因人工智慧在企業規模上成為現實。

Using a sprint-based, use case-driven approach, Teradata AI services offer flexible tiered offerings that meet organizations at any stage of their AI journey from initial pilots to enterprise-wide agentic deployments.

Teradata AI 服務採用基於衝刺、用例驅動的方法，提供靈活的分層產品，可滿足組織在 AI 之旅的任何階段的需求，從最初的試點到企業範圍內的代理部署。

Unlike competitors who offer either consulting or technology, we believe Teradata uniquely delivers both enabling real-time context-aware agent decisioning that leverages our suite of AI tools, trusted data, and decades of industry innovation. Working with our partners in an integrated approach accelerates deployment of autonomous intelligence, CX or otherwise, to drive measurable business outcomes.

與那些只提供諮詢或技術的競爭對手不同，我們相信 Teradata 能夠獨特地同時提供這兩者，從而實現即時情境感知代理決策，並利用我們的人工智慧工具套件、可信任數據和數十年的行業創新。與合作夥伴採用綜合方法進行合作，可以加速部署自主智慧（包括客戶體驗或其他方面），從而推動可衡量的業務成果。

We have forward-deployed resources with deep expertise and talent. These AI/ML engineers and data scientists are working with customers across the globe positioning Teradata as a leading AI/ML player and helping customers move from proof of concepts to production. This team is on track to complete more than 150 AI engagements with customers this year. We're also seeing a significant turn in our pipeline towards AI fuel projects.

我們已在前線部署了擁有深厚專業知識和人才的資源。這些人工智慧/機器學習工程師和資料科學家正與全球各地的客戶合作，將 Teradata 定位為領先的人工智慧/機器學習廠商，並幫助客戶從概念驗證過渡到生產。該團隊預計今年將完成超過 150 個與客戶的 AI 專案。我們也看到，我們的研發管線正顯著轉向人工智慧燃料專案。

Let's look at a few examples of wins from the quarter. These demonstrate the breadth of our offers in hybrid environments, cloud, and on-prem. A multinational automotive manufacturer is expanding its Teradata cloud platform on AWS to support increasing AI/ML workloads as a combat cyber security.

讓我們來看幾個本季獲勝的例子。這些都體現了我們在混合環境、雲端環境和本地部署方面的廣泛優勢。一家跨國汽車製造商正在AWS上擴展其Teradata雲端平台，以支援不斷增長的AI/ML工作負載，從而應對網路安全挑戰。

Executing approximately 10 million SQL statements per day, the customer is moving beyond rule-based approaches and adopting AI/ML technologies to enhance its analytical capabilities. One of the largest US healthcare providers deepened its strategic alliance with us as it further scaled its Teradata cloud deployment running on Microsoft Azure.

該客戶每天執行約 1,000 萬個 SQL 語句，正在超越基於規則的方法，並採用 AI/ML 技術來增強其分析能力。美國最大的醫療保健供應商之一進一步擴大了其在 Microsoft Azure 上運行的 Teradata 雲端部署規模，從而加深了與我們的策略聯盟。

This expansion building on momentum from earlier this year underscores the providers continued confidence in our high-performance cloud platform to support mission-critical data and analytics workloads. With this expansion, the organization is further positioned to drive operational excellence and harness complex health care data at scale across its entire system.

此次擴張延續了今年稍早的發展勢頭，凸顯了供應商對我們高效能雲端平台的持續信心，該平台能夠支援關鍵任務數據和分析工作負載。透過此次擴張，該組織將進一步提升營運效率，並在整個系統中大規模利用複雜的醫療保健數據。

A leading Japanese heavy industry manufacturer chose Teradata for his on-prem data platform as it transforms to a data-driven manufacturing entity and improves operational efficiency. Our Central European financial services company, we committed to us through a seven-year partnership with Teradata as a service on AWS. This enhances security provides uninterrupted operations through disaster recovery systems that match production, supports monthly innovation testing, and meet stringent data sovereignty requirements.

日本一家領先的重工業製造商選擇 Teradata 作為其本地數據平台，以轉型為數據驅動型製造企業並提高營運效率。我們是一家位於中歐的金融服務公司，我們與 Teradata 建立了為期七年的合作關係，Teradata 以 AWS 服務的形式為我們提供服務。這增強了安全性，透過與生產相符的災難復原系統提供不間斷的運營，支援每月創新測試，並滿足嚴格的資料主權要求。

We recently held our annual customer event named possible. It was three days of high energy with our people, partners and customers speaking of what they are doing now with data and analytics and what they are looking ahead to do with AI and agentic AI.

我們最近舉辦了名為「可能」的年度客戶活動。三天來，我們的員工、合作夥伴和客戶都熱情高漲，暢談了他們目前如何利用數據和分析，以及他們未來如何利用人工智慧和智慧人工智慧。

It was our pleasure to recognize VodafoneThree, Ooredoo, and Sicredi at the conference for demonstrating exceptional creativity, technical excellence, and business impact through the use of AI on the Teradata AI and Knowledge platform.

我們很高興在大會上表彰 VodafoneThree、Ooredoo 和 Sicredi，他們透過在 Teradata AI 和知識平台上使用 AI，展現了卓越的創造力、技術實力和商業影響力。

VodafoneThree in the UK was recognized for deploying an AI-supported broad detection framework by leveraging AI to detect and medicate fraud that has strengthened customer trust, improve regulatory compliance, and enhanced operational resilience.

英國 VodafoneThree 因部署人工智慧支援的廣泛檢測框架而受到認可，該框架利用人工智慧來檢測和處理詐欺行為，從而增強了客戶信任、提高了監管合規性並增強了營運彈性。

Ooredoo Qatar, a leading dollar-based telco earned this award for its advanced analytical capabilities and AI-powered customer engagement strategy. This strategy is built on Teradata VantageCloud and ClearScape Analytics, which were integrated with, and run on, GCP native services.

卡達領先的美元電信公司 Ooredoo 憑藉其先進的分析能力和人工智慧驅動的客戶互動策略贏得了該獎項。該策略基於 Teradata VantageCloud 和 ClearScape Analytics，它們與 GCP 原生服務整合並在其上運行。

Sicredi, Brazil's largest financial cooperative was honored for its innovative use of Clearscape Analytics and our cloud platform to transform credit risk management as well as support sustainability initiatives. Most recently, Sicredi has also begun developing an AI agent to support provision analysis under Brazilian banking regulations, further strengthening its governance and risk management capabilities.

巴西最大的金融合作社 Sicredi 因其創新地使用 Clearscape Analytics 和我們的雲端平台來轉變信用風險管理並支持永續發展舉措而受到表彰。最近，Sicredi 也開始開發人工智慧代理，以支援巴西銀行業監管下的撥備分析，進一步加強其治理和風險管理能力。

We also held our first agent builder workshop at the portable event. This hands-on workshop was oversubscribed and packed with customers keen to build AI agents on Teradata. We're in the process of launching an online agent builder experience to help accelerate the development and deployment of autonomous, contextually intelligent AI agents. It will be available from our website in the coming weeks. We held our Annual Partner Forum concurrently with the portable event, and we had strong year-on-year growth in partner participation.

我們也在該移動活動上舉辦了首屆代理商建設者研討會。本次實作研討會報名人數遠超預期，擠滿了渴望在 Teradata 上建立 AI 代理的客戶。我們正在推出線上代理建構器體驗，以協助加速自主、情境智慧人工智慧代理的開發和部署。未來幾週內，該產品將在我們的網站上提供下載。我們在舉辦便攜式活動的同時舉辦了年度合作夥伴論壇，合作夥伴的參與人數較去年同期成長強勁。

Companies that will win in the agentic AI future will be the ones that create the most trusted, interoperable foundation that less every other AI innovation flourish. We believe that's our role in the ecosystem. We strive to be the trusted data foundation that makes everyone else's AI work better with the governance layer that led companies experiment safely.

在人工智慧主導的未來時代，能夠贏得勝利的公司將是那些創造最值得信賴、最具互通性的基礎架構的公司，而其他所有人工智慧創新都將因此蓬勃發展。我們認為這就是我們在生態系統中扮演的角色。我們致力於成為值得信賴的數據基礎，透過治理層讓其他所有人的 AI 更好地發揮作用，從而引導企業安全地進行實驗。

We're partnering across all layers in the ecosystem, and we have strong partner co-sell activity in the third quarter, validating the strength in our ecosystem in identifying and nurturing new opportunities. While at our event, I hosted a forsade chat with one of our partners, ServiceNow.

我們正在與生態系統中的各個層面進行合作，第三季度我們與合作夥伴開展了強有力的聯合銷售活動，這驗證了我們生態系統在識別和培育新機會方面的實力。在活動期間，我與我們的合作夥伴 ServiceNow 進行了一次面對面的交流。

We discussed how together, we can power autonomous operations at scale by combining our enterprise-grade analytics with ServiceNow's workflow engine. Our platforms work together to enable seamless integration, governance and automation.

我們探討如何透過將我們的企業級分析與 ServiceNow 的工作流引擎結合，共同實現大規模的自主營運。我們的平台協同工作，實現無縫整合、治理和自動化。

We're collaborating to help customers realize the full potential of the data delivering intelligence and automation at enterprise scale. This is how we enable AI native transformation for our customers, empowering organizations to break down silos, unlock real-time intelligence, and transform every part of their business.

我們正在攜手合作，幫助客戶充分發揮數據的潛力，在企業規模上實現智慧和自動化。我們透過這種方式為客戶實現 AI 原生轉型，賦能組織打破資訊孤島，釋放即時智能，並轉變其業務的各個方面。

By combining deep analytics, trusted data and intelligent workflow automation, we're enabling organizations to move from passive data collection to active agentic operations, delivering real-time insights, proactive engagement, and measurable business value. Exciting stuff, and that was just one of the leading partners that participated with us.

透過結合深度分析、可信任資料和智慧工作流程自動化，我們正在協助組織從被動資料收集轉向主動代理操作，從而提供即時洞察、主動參與和可衡量的業務價值。真是令人興奮，而這僅僅是與我們合作的眾多主要合作夥伴之一。

We also hosted a number of industry analysts and a comment from Constellation Research summarize our focus on helping provide context to AI, noting that we believe there is no AI without context. That context isn't just data. It's the metadata, business logic, and domain know-how that make AI decisions relevant and reliable.

我們也接待了許多產業分析師，Constellation Research 的一則評論總結了我們致力於幫助人工智慧提供背景資訊的重點，指出我們認為沒有背景資訊就沒有人工智慧。這種背景資訊不僅僅是數據。正是元資料、業務邏輯和領域知識，使得人工智慧的決策具有相關性和可靠性。

Without business context, even the best algorithms can't deliver the accuracy or explainability needed in real-world regulated environments. They also recognize that we are turning our decades of decision analysis experience into domain and industry knowledge model to give AI agents real context and that our context intelligence framework captures how industries actually operate, so organizations don't have to start from scratch as we help teams build agents faster with enterprise-grade performance, governance, and trust already built in.

如果沒有業務背景，即使是最好的演算法也無法在現實世界的監管環境中提供所需的準確性或可解釋性。他們也意識到，我們將數十年的決策分析經驗轉化為領域和行業知識模型，為人工智能代理提供真實的背景信息，並且我們的背景智能框架能夠捕捉行業的實際運作方式，因此組織無需從零開始，我們就能幫助團隊更快地構建代理，並內置企業級性能、治理和信任。

Our hybrid capabilities are resonating in our customer base with interest in our recent product introductions, AI factory, MCP server, an agent builder, giving us further conviction that we offer a unique value proposition.

我們的混合能力在我們的客戶群中引起了共鳴，他們對我們最近推出的產品（AI 工廠、MCP 伺服器、代理建構器）表現出了濃厚的興趣，這讓我們更加確信，我們提供了獨特的價值主張。

We provide the flexibility to have consistent data, compute models, workloads, outcomes, and experiences across a hybrid environment. We have full confidence in total ARR and are affirming our outlook for 2025. In our recent discussions with customers, we have seen how the Teradata Knowledge platform is ideally suited for AI workloads. AI is always on with ever-increasing agents driving massive complex query volumes. That's Teradata's sweet spot.

我們提供了靈活性，使混合環境中的資料、計算模型、工作負載、結果和體驗保持一致。我們對年度經常性收入總額充滿信心，並重申我們對 2025 年的展望。在最近與客戶的交流中，我們發現 Teradata Knowledge 平台非常適合 AI 工作負載。人工智慧始終處於運作狀態，越來越多的智慧體正在處理大量的複雜查詢。這正是 Teradata 的優勢所在。

Our ARR mix may vary as we see customers evaluating between cloud and on-prem for where to deploy the workloads as they build for their AI-enabled future. Regardless of the deployment options they choose, customers can rely on Teradata to run agentic AI at scale and provide the context needed for trusted results. Thank you very much.

隨著客戶在建構人工智慧未來時，會評估在雲端還是本地部署工作負載，因此我們的年度經常性收入 (ARR) 組成可能會有所不同。無論客戶選擇何種部署方案，他們都可以依靠 Teradata 大規模運行智能體 AI，並提供獲得可信任結果所需的上下文資訊。非常感謝。

Now I'll turn the call over to John.

現在我把電話交給約翰。

Thank you, Steve, and good afternoon, everyone. I'm pleased with the progress we are making this year as we've demonstrated a return to consistent execution with our third quarter in a row of meeting or exceeding our guidance metrics. And perhaps as importantly, we expect that trend to continue in Q4 as we are reiterating our guidance for the full year.

謝謝你，史蒂夫，大家下午好。我對我們今年的進展感到滿意，因為我們已經連續三個季度達到或超過預期指標，證明我們恢復了穩定的執行力。或許同樣重要的是，我們預計這一趨勢將在第四季度繼續，我們也重申了我們對全年業績的預期。

Looking at a few of the highlights for the third quarter. Total ARR growth was ahead of expectations, representing the second consecutive quarter of a return to positive growth. We exceeded the top end of our total revenue and recurring revenue guidance. We improved gross margin sequentially from Q2. We delivered considerable upside on our non-GAAP earnings per share and we increased free cash flow on a year-over-year basis for Q3 and the year-to-date.

讓我們來看看第三季的一些精彩時刻。總年度經常性收入成長超出預期，這是連續第二季恢復正成長。我們的總收入和經常性收入都超過了預期上限。從第二季開始，我們的毛利率較上季有所提高。我們的非GAAP每股收益實現了相當大的成長，第三季和年初至今的自由現金流均較去年同期成長。

Finally, as Steve commented, we are building a solid foundation this year to deliver continued financial improvement next year. In terms of our detailed financial results for the third quarter, total ARR grew 1% as reported and flat in constant currency. This was our second consecutive quarter of a return to total ARR growth and this was driven by better retention and expansions in the quarter.

最後，正如史蒂夫所說，我們今年正在打下堅實的基礎，以便明年繼續實現財務上的改善。就我們第三季的詳細財務業績而言，按報告匯率計算，總 ARR 成長了 1%，以固定匯率計算則持平。這是我們連續第二季恢復 ARR 總成長，這主要得益於本季客戶留存率和業務擴張的改善。

At the beginning of the year, our target was to get back to positive total ARR growth by Q4, and we are pleased to be several quarters ahead of schedule. Cloud ARR grew 11% on an as-reported and constant currency basis, and the cloud net expansion rate was 109%.

年初時，我們的目標是到第四季度恢復正的年度經常性收入成長，我們很高興提前幾季實現了這一目標。以報告和固定匯率計算，雲端 ARR 成長了 11%，雲端淨擴張率為 109%。

As discussed on our Q2 earnings call, we expected Q3 cloud ARR growth to be below our guidance range for the year due to the pull forward of a few deals last quarter. Total revenue was $416 million, down 5% year over year as reported and 6% in constant currency, which was 1 point above the high end of our outlook due to higher recurring revenue.

正如我們在第二季財報電話會議上所討論的，由於上季一些交易提前完成，我們預計第三季雲端業務 ARR 成長將低於我們全年的預期範圍。總收入為 4.16 億美元，按報告匯率計算年減 5%，以固定匯率計算年減 6%，由於經常性收入較高，比我們預期的上限高出 1 個百分點。

Recurring revenue was $366 million, down 2% year over year as reported and 3% in constant currency, which was 1 point above the high end of our outlook. Recurring revenue as a percentage of total revenue was 88%, up from 85% in Q3 last year.

經常性收入為 3.66 億美元，以報告匯率計算年減 2%，以固定匯率計算年減 3%，比我們預期的上限高出 1 個百分點。經常性收入佔總收入的比例為 88%，高於去年第三季的 85%。

Services revenue was $47 million, which was consistent with our recent performance. We are seeing a transition in our services business this year as the team is moving from migration projects to delivering AI services, which we believe will provide improved performance next year.

服務收入為 4700 萬美元，與我們近期的業績一致。今年，我們的服務業務正在經歷轉型，團隊正從遷移專案轉向提供人工智慧服務，我們相信這將在明年帶來更好的業績。

Looking at profitability and free cash flow. Please note that I will be referencing non-GAAP numbers for expenses and margins, and a full reconciliation to GAAP results is provided in our press release. For the third quarter, total gross margin was 62.3%, which was up 70 basis points year over year.

著眼於獲利能力和自由現金流。請注意，我將引用非GAAP資料來計算費用和利潤率，完整的GAAP結果調節表已在我們的新聞稿中提供。第三季總毛利率為 62.3%，較去年同期成長 70 個基點。

On a sequential basis, total gross margin was up 400 basis points, driven by improvements in both recurring and services gross margins. Recurring revenue gross margin was 68.9%, up 140 basis points sequentially. On services gross margin, we took actions last quarter to align our cost with current revenue and we made substantial improvement in non-GAAP gross margin from negative 2% in Q2 to positive 8.5% in Q3.

與上一季相比，總毛利率上升了 400 個基點，這主要得益於經常性收入和服務毛利率的增加。經常性收入毛利率為 68.9%，季增 140 個基點。在服務毛利率方面，我們上個季度採取了措施，使我們的成本與當前收入保持一致，並且我們大幅提高了非GAAP毛利率，從第二季度的-2%提高到第三季度的8.5%。

Operating margin for Q3 was 23.6% and which was up 110 basis points year over year and up 720 basis points sequentially. Overall, we are seeing improving margins as a result of cost efficiency actions we started last year. Non-GAAP diluted earnings per share were $0.72, exceeding the top end of our outlook range by $0.17. The outperformance was driven by higher recurring revenue and lower expense.

第三季營業利益率為 23.6%，較去年同期成長 110 個基點，較上季成長 720 個基點。總體而言，由於我們去年開始實施的成本效率措施，利潤率正在提高。非GAAP稀釋後每股收益為0.72美元，比我們預期範圍的上限高出0.17美元。業績超預期主要得益於經常性收入增加和支出減少。

We generated $88 million of free cash flow in the quarter, which was up 28% on a year over year basis and provides us with increased confidence in our full year outlook. And finally, in the third quarter, we repurchased approximately $30 million of our stock or 1.4 million shares. We continue to target returning 50% of our free cash flow to shareholders in the form of share repurchases this year.

本季我們產生了 8,800 萬美元的自由現金流，年增 28%，這讓我們對全年前景更有信心。最後，在第三季度，我們回購了約 3,000 萬美元的股票，即 140 萬股。我們今年仍將以股票回購的形式，把50%的自由現金流回饋給股東。

Turning to our outlook for the remainder of the year. For the fourth quarter of 2025, we expect recurring revenue to be in the range of minus 1% to minus 3% year over year on a constant currency basis. We expect total revenue to be in the range of minus 2% to minus 4% year over year on a constant currency basis. And we expect non-GAAP diluted earnings per share to be in the range of $0.53 to $0.57.

接下來展望今年剩餘時間的情況。我們預計，2025 年第四季經常性收入以固定匯率計算將年減 1% 至 3%。我們預計以固定匯率計算，總收入將年減 2% 至 4%。我們預計非GAAP稀釋後每股盈餘將在0.53美元至0.57美元之間。

For fiscal '25, we reiterate our previous guidance for total ARR growth, and we are maintaining our range for cloud ARR growth. We have confidence in our total ARR target and continue to see a path to our cloud ARR range for the year.

對於 2025 財年，我們重申先前對總 ARR 成長的預期，並維持對雲端 ARR 成長的預期範圍。我們對全年年度經常性收入目標充滿信心，並繼續看到今年雲端業務年度經常性收入目標的路徑。

However, there are a handful of deals where customers are still assessing deployment options, which could have an impact on the mix between cloud and on-premise subscription ARR. We also reiterate our previous guidance for recurring revenue and total revenue. Given the guidance ranges that we provided for Q4, we anticipate recurring revenue and total revenue to be at the midpoint of our fiscal '25 ranges.

然而，仍有一些交易的客戶正在評估部署方案，這可能會對雲端和本地訂閱 ARR 的組合產生影響。我們也重申了先前對經常性收入和總收入的預期。根據我們對第四季度的預期範圍，我們預計經常性收入和總收入將達到我們 2025 財年預期範圍的中點。

On free cash flow, due to our strong performance year-to-date, we are narrowing the range to the top end of our initial outlook and now expect free cash flow to be in the range of $260 million to $280 million. Finally, we are raising our non-GAAP earnings per share guidance to a range of $2.38 to $2.42, reflecting our strong performance in Q3.

關於自由現金流，由於我們今年迄今為止的強勁表現，我們將預期範圍縮小到最初預期的上限，現在預計自由現金流將在 2.6 億美元至 2.8 億美元之間。最後，我們將非GAAP每股盈餘預期上調至2.38美元至2.42美元，以反映我們在第三季的強勁表現。

Based on foreign exchange rates at the end of September, we anticipate 1 to 2 points of benefit to our Q4 '25 revenue. For the full year, we do not anticipate any material currency impact. Finally, we expect the non-GAAP tax rate to be approximately 23.1% and the weighted average shares outstanding to be 96.1 million for the full year.

根據 9 月底的外匯匯率，我們預計 2025 年第四季的營收將受益 1 到 2 個百分點。我們預計全年不會受到任何重大匯率影響。最後，我們預期全年非GAAP稅率約為23.1%，加權平均流通股數為9,610萬股。

Again, please refer to our Q3 earnings presentation on our Investor Relations website for a complete list of our 2025 outlook ranges.

再次提醒，請參閱我們投資者關係網站上的第三季財報，以取得我們 2025 年展望範圍的完整清單。

In closing, we are taking actions that we believe will ultimately drive shareholder value. The first important steps were to: one, return total ARR growth to positive territory; two, focus on cost efficiencies; three, drive consistency in the business; and four, stabilize free cash flow -- all of which are on track to achieve this year.

最後，我們正在採取我們認為最終將提升股東價值的行動。首要步驟是：一、使年度經常性收入總成長轉正；二、注重成本效益；三、推動業務的穩定性；四、穩定自由現金流－所有這些目標都有望在今年實現。

As we start to focus on the objectives for next year, we are prioritizing our investments to capitalize on the substantial opportunity ahead for Teradata as a leading AI and knowledge platform for the autonomous enterprise.

隨著我們開始關註明年的目標，我們將優先考慮投資，以充分利用 Teradata 作為領先的自主企業人工智慧和知識平台所面臨的巨大機會。

We believe that these investments, combined with the continued optimization of our business, will enable us to deliver profitable growth and higher free cash flow. Thank you all for your time today.

我們相信，這些投資加上我們業務的持續優化，將使我們能夠實現獲利成長和更高的自由現金流。感謝各位今天抽出時間。

Now let's open up the call for questions.

現在開始接受提問。

(Operator Instructions) Erik Woodring, Morgan Stanley.

（操作說明）摩根士丹利 Erik Woodring。

Great. Really nice to see the earnings and free cash flow upside this quarter. I think this is the first time since you began disclosing cloud ARR that we've seen sequentials be negative intra calendar year for cloud ARR. I know you mentioned that it would dip below the target range this quarter, but I guess I'd look at the 11% and say it felt a bit below maybe where you would have expected sequentials. But maybe you could just elaborate on how the quarter transpired for Cloud ARR, when and where we see that net expansion rate bottom.

偉大的。本季獲利和自由現金流均有所成長，真是令人欣喜。我認為這是自從你們開始揭露雲端 ARR 以來，我們第一次看到雲端 ARR 在日曆年內出現季負成長。我知道你提到本季會低於目標範圍，但我認為 11% 的降幅感覺有點低於你對環比的預期。但或許您可以詳細說明一下本季雲端 ARR 的情況，以及我們何時何地看到淨擴張率觸底。

And maybe why we just aren't derisking 4Q a bit, just given some of your commentary around customers assessing where they're going to be deploying with Teradata? And then a quick follow-up.

或許正是因為您之前提到客戶正在評估他們將在哪些地方部署 Teradata，所以我們才沒有稍微降低第四季度的風險？然後是一個簡短的後續問題。

Yes. Thanks, Erik. So I think on cloud, we did perform to our expectations. As we said in our Q2 earnings, we did expect that linearity to be below the full year outlook.

是的。謝謝你，埃里克。所以我覺得在雲端方面，我們的表現達到了預期。正如我們在第二季財報中所說，我們預期線性成長將低於全年預期。

I think as we look at the market, we're not seeing -- we're no longer seeing that kind of a headlong rush to the cloud. It's much more of a nuanced decision of how our customers can accelerate the time to value for the AI workloads that they're deploying in their environment.

我認為，從市場來看，我們已經不再看到那種一頭栽進雲端的趨勢了。這其實是一個更細緻的決策，即如何幫助我們的客戶加快在其環境中部署的 AI 工作負載的價值實現速度。

And we're continuing to see that pattern of customers using our hybrid capabilities. I think I've said in the past that a good proportion of our cloud customers have deployments both on-prem and in the cloud waters, and they can decide across that massive data estate that they run from a Teradata perspective, where to run that workload.

我們持續看到客戶使用我們混合功能的這種趨勢。我想我以前說過，我們相當一部分雲端客戶在本地和雲端都有部署，他們可以從 Teradata 的角度決定，在他們運行的龐大資料資產中，在哪裡運行工作負載。

And I think that's nicely evidenced actually by the fact that our total ARR growth is ahead of schedule overall, we expect it to return to growth in the fourth quarter. So being ahead of schedule from that perspective and demonstrating that we're growing overall with our customers is a good achievement as we've executed through the year.

我認為這很好地證明了這一點，因為我們的總 ARR 成長總體上超前於預期，我們預計它將在第四季度恢復成長。因此，從這個角度來看，我們提前完成了計劃，並證明了我們與客戶的整體成長，這是我們在過去一年中取得的良好成就。

We do start to see our net expansion rate starting to consolidate. And I think as we look at the overall results for the year in terms of our guidance, we were pretty confident in our total ARR growth. And as we look to 2026, we continue to see a path for continuing ARR growth in 2026.

我們確實開始看到淨擴張率趨於穩定。我認為，就我們今年的業績預期而言，我們對年度經常性收入 (ARR) 的成長相當有信心。展望 2026 年，我們仍然看到 2026 年 ARR 將繼續成長的路徑。

Okay. And then just a quick follow-up. Your comment on -- I think you used the term meaningful free cash flow growth into 2026 as to me was the most confident I've heard you sound on free cash flow looking forward in a while. Can you maybe just unpack where this confidence comes from? I'm sure it has to do with ARR growth and some of your OpEx initiatives.

好的。然後還有一個簡短的後續問題。您對「到 2026 年實現有意義的自由現金流成長」的評論——我認為您使用了「到 2026 年實現有意義的自由現金流增長」這一表述——是我一段時間以來聽到您對未來自由現金流最有信心的表態。能否具體說說您的自信源自於何處？我相信這與年度經常性收入成長以及你們的一些營運支出舉措有關。

But just -- I know you're not going to guide to 2026, but any way you can help us think about when you talk about meaningful free cash flow growth, kind of what you're trying to tell us between the lines.

但是——我知道您不會預測到 2026 年，但是當您談到有意義的自由現金流增長時，您能否幫助我們思考一下，您究竟想透過哪些言外之意告訴我們什麼？

Yes. Thanks, Erik. I think you hit the nail on the head with the 2 points, but I'll just ask John to add any more color.

是的。謝謝你，埃里克。我覺得你這兩點說得很有道理，不過我還是請約翰補充一些細節吧。

Yes, I think that's exactly right. I mean I think if you look at how this year is progressing, we've done a nice job on free cash flow relative to where we were at this point last year. And I think we're doing the right things and really focusing on this is a key driver for us, certainly getting total ARR back to growth territory has had a positive impact. And then the cost actions that we've taken last year and this year as well are also supporting that number. And so we feel like we're putting the right pieces in place to continue that improvement next year.

是的，我覺得完全正確。我的意思是，如果你看看今年的進展情況，與去年同期相比，我們在自由現金流方面做得相當不錯。我認為我們正在做正確的事情，真正專注於此是我們的關鍵驅動力，當然，讓年度經常性收入總額恢復成長已經產生了積極的影響。此外，我們去年和今年採取的成本控制措施也印證了這個數字。因此，我們感覺我們正在為明年繼續取得進展做好充分的準備。

Radi Sultan, UBS.

Radi Sultan，瑞銀集團。

First for Steve, last quarter, we talked about -- I think it was roughly a third of pipeline, including an AI component. So I guess, first, like how did that track this quarter? You mentioned the agent offerings, MCP server, like is there any area in particular within the AI portfolio moving the needle? And any way to think about how these AI discussions more broadly are impacting competitive win rates?

首先是史蒂夫，上個季度我們談到了——我認為大約佔管道的三分之一，其中包括人工智慧組件。所以我想，首先，這季的情況如何？您提到了代理產品、MCP 伺服器，那麼在人工智慧產品組合中，是否有哪個領域正在取得顯著進展？那麼，我們該如何更廣泛地思考這些關於人工智慧的討論會對競技比賽的勝率產生怎樣的影響呢？

Yes. Thanks for the question, Radi. Yes, we're continuing to see that the AI influence pipeline increase saw increases we went through Q3, which is really great to see. We have supported that with a fantastic set of innovation and the leases from a product perspective.

是的。謝謝你的提問，Radi。是的，我們持續看到人工智慧影響力管道的成長，這一成長在第三季度有所增加，這真是個好消息。從產品角度來看，我們透過一系列出色的創新和租賃方案為此提供了支持。

Our new Chief Product Officer, Sumeet, is making a real difference there. And in terms of -- we're measuring our innovation releases in terms of time from concept to press release.

我們的新任首席產品長蘇米特正在那裡發揮真正的作用。就衡量創新成果的發布而言——我們以從概念到新聞稿發布所需的時間來衡量。

And I think as evidenced by the discussions we had with our customers at our most recent marketing event, they're really seeing those innovations as something that Teradata can provide in a holistic way to enable them to deploy agentic AI workloads, whether it's from our enterprise vector store capabilities, our MCP server, our agent builder capabilities or model ops where we can include language model capabilities.

我認為，正如我們在最近的行銷活動中與客戶的討論所表明的那樣，他們確實認為 Teradata 可以以整體的方式提供這些創新，使他們能夠部署代理 AI 工作負載，無論是來自我們的企業向量存儲功能、MCP 伺服器、代理構建器功能還是模型操作（我們可以包含語言模型功能）。

So all of these, I think, are coming together. And one of our customers actually, I think, said it best where they said, Teradata is one provider in this area who is really putting it all together. But I think the most interesting thing, Radi, from a technology perspective is that we are seeing that the Teradata technology platform is really built for these AI workloads.

所以我覺得，所有這些因素都匯聚到了一起。我們的一位客戶說得最好，他說，Teradata 是這個領域真正將所有要素整合在一起的供應商之一。但我認為，從技術角度來看，Radi，最有趣的是，我們看到 Teradata 技術平台確實是為這些 AI 工作負載而建構的。

When you think about an always-on AI agent, essentially begin to execute thousands of queries and complex queries. So really large volumes of queries executing concurrently inside an environment with different types of workload our architecture and site Teradata or massively parallel architecture combined with our workload management and query optimization allows our customers to run those types of queries and the AI agents that they have developed to run those queries more effectively and efficiently than anybody else.

當你想到一個始終在線的人工智慧代理時，實際上就是要開始執行成千上萬個查詢和複雜查詢。因此，在具有不同類型工作負載的環境中並發執行大量查詢，我們的架構和站點 Teradata 或大規模並行架構結合我們的工作負載管理和查詢優化，使我們的客戶能夠比其他任何人都更有效地運行這些類型的查詢，並且他們開發的 AI 代理能夠更高效地運行這些查詢。

Awesome. And then second for John, you've been in the seat a couple of quarters now strung together a couple of nice quarters. I guess is it fair to think that your approach to guidance being relatively consistent into join? And maybe are there any leading indicators or KPIs that you're looking at that give you confidence in the outlook, especially around Q4?

驚人的。其次是約翰，你已經坐在這個位置上幾個季度了，並且連續贏了幾個不錯的季度。我想，認為您在指導方面採取相對一致的做法是否合理呢？您是否專注於某些領先指標或關鍵績效指標，以增強您對前景的信心，尤其是在第四季？

Yes. Thanks for the question. And I would say from an overall standpoint, I guess, in terms of guidance and our philosophy on it, we try to call it as we see it. And so we take a look at our forecast. We -- we do have a number of KPIs that we'll look at from a pipeline to our expenses to the revenue model, et cetera.

是的。謝謝你的提問。從整體來看，就指導原則和我們的理念而言，我們盡量實事求是。所以，我們來看看預測結果。我們——我們確實有很多關鍵績效指標需要關注，從銷售管道到支出，再到收入模式等等。

There's a whole bunch of metrics that we'll take a look at and roll all of that up. When you talk about Q4 in particular, you're now getting down to the last few months and we're literally going deal by deal. And so we've got that kind of granularity in terms of how we ultimately roll up the forecast and then our resulting guidance from it.

我們會查看一大堆指標，並將所有這些指標匯總起來。特別是談到第四季時，現在已經進入最後幾個月，我們真的只能一筆一筆地完成交易了。因此，我們在最終匯總預測以及由此得出的指導意見方面，具有這種精細程度。

Yitchuin Wong, Citi.

黃義泉，花旗銀行。

Steve, maybe I'll start with you, great to see everyone. I want to just follow on what Radi was asking, like around the competitive age, like the agentic AI strategy with autonomous out as possible and then agent builders like 3D position Teradata like directly against some of this road map of your larger hyperscaler platform and then even compared to the Databricks and Snowflake.

史蒂夫，或許我先從你開始吧，很高興見到大家。我想接著Radi剛才的問題繼續討論，比如在競爭激烈的時代，像智能AI戰略那樣盡可能實現自主輸出，然後像3D定位Teradata這樣的智能體構建器，直接與你們的大型超大規模平台路線圖進行對比，甚至與Databricks和Snowflake進行比較。

Can you kind of help us understand what is really the longer-term durability competitive advantage that you see that Teradata can compete in the space? And is it more the hybrid cloud environment that you've been talking about and then the small enterprise side the IP within your decades of experience, maybe we can start there?

您能否幫我們理解一下，您認為 Teradata 在這個領域真正具有的長期耐用性競爭優勢是什麼？您一直談論的是混合雲環境，而您憑藉幾十年的經驗，在小型企業方面擁有豐富的智慧財產權，我們或許可以從這裡開始？

Yes. I think -- thanks for the question, YC. I think fundamentally, what senses our part is actually our technology mode that we have already. As the set of patented capabilities that allow us to execute these workloads in the most effective and efficient way. And we can do that both on-prem and in the cloud.

是的。我想——謝謝你的提問，YC。我認為從根本上講，真正影響我們自身的是我們已經擁有的技術模式。作為一系列專利技術，我們能夠以最有效、最有效率的方式執行這些工作負載。我們既可以在本地部署，也可以在雲端部署。

So really being able to deliver that hybrid environment to customers is clearly a differentiator for us. We announced earlier in the year our AI factory, which essentially combines a lot of capabilities together. We partnered with NVIDIA in terms of the development of that AI factory. That gives us a fantastic base for future on-prem capabilities.

因此，能夠為客戶提供這種混合環境顯然是我們的差異化優勢。今年早些時候，我們發布了人工智慧工廠，它本質上是將多種功能整合在一起的。我們與英偉達合作開發了這款人工智慧工廠。這為我們未來建置本機部署功能奠定了良好的基礎。

We're looking forward to the next release of our Teradata technology platform, which will have GPUs built right into the platform and in terms of executing that workload. But our customers are already using their Teradata on-prem platform actually operate and execute AI workloads today in a very reliable way. And we're seeing it both in a real hybrid context, so both on-prem and in the cloud.

我們期待 Teradata 技術平台的下一個版本，該平台將內建 GPU，並能更好地執行工作負載。但我們的客戶已經在使用他們的 Teradata 本地平台，以非常可靠的方式運行和執行 AI 工作負載。我們在真正的混合環境中都看到了這一點，即既在本地部署，也在雲端部署。

At the end of the day, we see this as being a battle of the query engine, and we believe that our query engine is the best query engine to deliver AI type workloads and to be that true knowledge platform in terms of building enterprise context for our customers.

歸根結底，我們認為這是一場查詢引擎之戰，我們相信我們的查詢引擎是交付 AI 類型工作負載的最佳查詢引擎，並且能夠成為構建企業級上下文的真正知識平台，為我們的客戶服務。

And that's built on all of the capabilities and solutions that we've developed over the last 40 years for our customers, we understand the domains around customer experience or supply chain management, and we understand the debate across all of the industries.

而這一切都建立在我們過去 40 年來為客戶開發的所有能力和解決方案之上，我們了解客戶體驗或供應鏈管理等領域，我們也了解所有行業的爭論。

So all of these things combined together to give us our unique position, and we are really excited about getting that message out to the marketplace and demonstrating that to customers on a day-to-day basis.

所有這些因素結合起來，使我們擁有了獨特的地位，我們非常高興能夠將這一訊息傳遞給市場，並每天向客戶展示這一點。

Understood. Maybe one for John here. You talked about like the much improved service gross margin to positive have during the quarter. It's great to see an inflection there. Could you kind of help us double quick on some of these actions?

明白了。或許約翰可以試試這個。您提到本季服務毛利率大幅提高並轉為正值。很高興看到情況出現了轉折。您能否幫我們加快一些操作速度？

I know we heard talk about use the team is starting to leverage more FTE or maybe even AI FTE here within the sales motion, especially with these new AI use cases. Is this something that expected to go forward that could help drive better margin here or even efficiency driving like AI, leveraging AI within the company that you touched on a little bit during possible as well?

我知道我們聽說團隊正在開始利用更多的 FTE，甚至可能是 AI FTE 來進行銷售，尤其是在這些新的 AI 應用案例中。這是預期會持續發展，並有助於提高利潤率，甚至提高效率（例如，像人工智慧一樣，在公司內部利用人工智慧，就像您剛才稍微提到的那樣）的事情嗎？

Yes. So I think a couple of different questions in there. I think in terms of the services business overall, to be perfectly honest, a lot of that is just rightsizing the organization for the current revenue stream that we've got there.

是的。所以我覺得這裡麵包含幾個不同的問題。坦白說，我認為就整個服務業務而言，很多時候只是為了適應我們目前的收入來源而調整組織規模。

We saw some headwinds this year due to higher migration activity in the prior year. And so for us, in the first couple of quarters, we were a little bit behind in that from a cost structure standpoint, and we fixed that in the second quarter, and we saw a nice rebound in Q3, and we think we've got some room to go in Q4. And so I think, again, there, it's just aligning the costs with the anticipated revenue.

由於去年的遷徙活動較為頻繁，我們今年遇到了一些不利因素。因此，在前幾個季度，從成本結構的角度來看，我們在這方面有點落後，我們在第二季度糾正了這個問題，並在第三季度看到了不錯的反彈，我們認為在第四季度還有一些提升空間。所以我覺得，歸根究底，就是要讓成本與預期收入一致。

In terms of your broader question around margins overall and some of the things that we're doing with AI from an internal standpoint, there's actually quite a bit. And I won't do it justice, but I would encourage you to check out some of the presentations at our possible conference.

至於你提出的關於整體利潤率以及我們從內部角度利用人工智慧所做的一些事情的更廣泛的問題，實際上有很多方面值得探討。我無法全面介紹它，但我鼓勵您去看看我們可能舉辦的會議上的一些演講。

We had one that talked specifically about some of the things that we're doing internally. And there's a whole work stream around this that's really touching all parts of the business from cost of revenue through the operating lines.

我們有一個專門討論我們內部正在做的一些事情的會議。圍繞著這一點，我們展開了一整套工作流程，它真正觸及了業務的各個方面，從收入成本到營運線。

I'll just add to that, YC. I think from an AI services perspective, we're seeing customers have a real appetite to deploy real solutions. So with the launch of our customer intelligence framework and also backing that up with low consulting expertise, folks that can actually implement AI solutions inside our customers we're really pivoting our consulting and services capability to delever and something that we see is supply-constrained marketplace in terms of folks that actually know how to deploy the solutions inside our customer base.

我再補充一點，YC。我認為從人工智慧服務的角度來看，我們看到客戶確實渴望部署真正的解決方案。因此，隨著我們推出客戶智慧框架，並輔以低諮詢專業知識，以及能夠真正為客戶實施人工智慧解決方案的人員，我們正在真正調整我們的諮詢和服務能力以降低槓桿率。我們看到，在真正知道如何在客戶群中部署解決方案的人員方面，市場供應受到限制。

So our most recent press release in the last couple of weeks around AI services and the capabilities that we have to help enable our customers in this market is super exciting. And obviously, of course, working alongside our partners to deliver those capabilities to the market is super important for us.

因此，我們最近幾週發布的關於人工智慧服務以及我們為幫助客戶在這個市場中取得成功所具備的能力的新聞稿非常令人興奮。當然，與我們的合作夥伴攜手將這些能力推向市場對我們來說至關重要。

Yes. Hopefully, the traction continues.

是的。希望這種勢頭能夠持續下去。

Chirag Ved, Evercore.

Chirag Ved，Evercore。

Following up on one of the prior questions here. I was wondering whether you could speak to the underlying trajectory of progress on-prem at this point over the next couple of quarters even qualitatively.

這是對之前某個問題的後續解答。我想知道您能否就未來幾季本地部署的潛在發展軌跡，即使是定性的方面，談談情況。

Should we index more on the on-prem side of the business when we're looking out or and perhaps moderating cloud growth? And then any comments you might have on the associated margin and pricing implications, if you could share that?

我們在展望未來或可能放緩雲端成長時，是否應該更加重視本地部署業務？那麼，如果您能分享一下您對相關利潤率和定價影響的看法，那就太好了？

Yes, I'll start and maybe John can make some comments. So I think -- Chirag, thanks for the query. I think we are definitely seeing that our on-prem is stabilized, and we're seeing a rate of change and improvement and that's due to -- from an on-prem perspective, both retention and expansion of those on-prem environments.

好的，我先開始，也許約翰可以提些意見。所以我想——Chirag，謝謝你的提問。我認為我們已經看到本地部署環境趨於穩定，並且正在發生變化和改進，這從本地部署的角度來看，是由於本地部署環境的保留和擴展。

On that, we certainly are happy with our retention rates. It's in line with enterprise software overall. But the fact is we'll take growth wherever we see it, right? And we're well positioned to take advantage of growth in this hybrid environment that some of our customers have got.

在這方面，我們對客戶留存率非常滿意。它與企業軟體的整體發展趨勢一致。但事實是，無論在哪裡看到成長，我們都會抓住機會，對吧？我們已做好充分準備，可以利用部分客戶所處的混合環境所帶來的成長機會。

But we're also really well placed to take advantage of on-prem growth for data sovereignty requirements or where the data gravity is on-prem. But we also see the fact that we can grow in the cloud successfully with our customers.

但我們也完全有能力利用本地部署的成長來滿足資料主權要求，或當資料重心在本地時。但我們也看到，我們可以與客戶一起在雲端成功發展。

We're seeing our expansion rates in the cloud pick up as we've gone through this year in comparison to some of our other years. And we expect that to continue from an expansion perspective. And so I think we've -- we're well placed to take advantage of the opportunity that's in front of us, and we'll see that growth in terms of that hybrid platform that we offer to the market.

與往年相比，今年我們在雲端的業務擴張速度明顯加快。從擴張的角度來看，我們預計這種情況還會持續下去。所以我認為我們已經——我們已經做好充分準備，抓住擺在我們面前的機會，我們將看到我們向市場提供的混合平台實現成長。

Okay. Got it. That's really helpful. Maybe just one more. Great to see more of a focus on AI services, living consulting really speaks to the importance and percolation of this technology.

好的。知道了。這真的很有幫助。或許就再來一個。很高興看到更多對人工智慧服務的關注，而生活諮詢服務則真正體現了這項技術的重要性和普及性。

Looking ahead, do you see consulting revenue stabilizing a bit at this point, driven by the focus on delivering AI services? Or is this still a category that you're involved with, but starting to or continuing to shift over to your partner ecosystem?

展望未來，您認為在人工智慧服務交付的推動下，諮詢收入是否會趨於穩定？或者，這仍然是您參與的一個類別，但正在開始或繼續轉移到您的合作夥伴生態系統？

Yes. I think we've -- at our cool, we're a technology company. We're about ARR growth. We do consulting and services to support technology ARR growth and clearly, the margin profile for that is where we want to operate. We've created that headroom as we've discussed, in terms of creating that space for our partners to operate successfully with us.

是的。我認為我們——說到底，我們是一家科技公司。我們的目標是實現年度經常性收入成長。我們提供諮詢和服務，以支援技術 ARR 成長，顯然，我們希望在該領域的利潤率達到預期水準。正如我們討論的那樣，我們已經創造了這樣的空間，為我們的合作夥伴與我們成功合作創造了空間。

But I think every great technology organization needs a great consulting and services capability to support that technology value proposition. And it's great to be able to see our consulting and services organization pivot towards these AI services.

但我認為，每個偉大的科技公司都需要強大的諮詢和服務能力來支持其技術價值主張。很高興看到我們的諮詢和服務機構轉型發展人工智慧服務。

So the relevance in the marketplace can increase and it can help existing customer base and new customers that we come across deploy these AI solutions, and we see it actually as a great competitive differentiator.

因此，其在市場上的相關性可以提高，可以幫助現有客戶群和我們遇到的新客戶部署這些人工智慧解決方案，我們認為這實際上是一個巨大的競爭優勢。

We've got a go-to-market motion now that supports a forward deployed engineering model to get POCs and to our customers. But our consulting and services teams and their partners are going to ensure that they take those POCs from that proof of concept into reality and into production.

我們現在有了一個市場推廣策略，支援前沿部署的工程模型，以便將概念驗證產品交付給我們的客戶。但是我們的諮詢和服務團隊及其合作夥伴將確保將這些概念驗證轉化為現實並投入生產。

And we've already seen success with the customers in terms of taking real problems and business domains that they have and turning into production-ready capabilities. So really [tame] to value from an AI perspective is super important, and we see our AI services capabilities is something that's going to support that.

我們已經看到，在將客戶遇到的實際問題和業務領域轉化為可投入生產的能力方面，我們取得了成功。所以，從人工智慧的角度來看，真正實現價值是至關重要的，我們認為我們的人工智慧服務能力將為此提供支援。

(Operator Instructions) Matthew Hedberg, RBC Capital Markets.

（操作員說明）Matthew Hedberg，RBC Capital Markets。

This is Mike Richards on for Matt. Maybe just double clicking on that dynamic where customers are assessing the deployment often. Just curious, is that a result of the announcement of the hardware refresh next year, where maybe some customers are seeing the transformation you're bringing to the on-prem offering and now it's a bigger decision of whether or not to stay or move to the cloud and then just any early feedback you've gotten on that decision to have the big refresh?

這裡是麥克理查茲，替馬特報道。或許只要雙擊一下客戶經常評估部署情況的那個動態因素。我只是好奇，這是否與明年硬體更新的公告有關？或許有些客戶看到了你們對本地部署產品帶來的變革，現在他們需要做出一個更重要的決定：是繼續使用本地部署還是遷移到雲端？你們是否已經收到了一些關於此次重大更新決定的早期回饋？

Thanks, Mike, for the question. No, I would say it's got anything to do with the technology platform that we're coming out with next. I think the technology that we've haven't in place today is actually enabling some of these decisions, both in terms of things like the AI factory, which are available today on the technology stack that we have.

謝謝你的提問，麥克。不，我認為這與我們即將推出的技術平台沒有任何關係。我認為我們目前還沒有採用的技術實際上正在促成這些決策，無論是像人工智慧工廠這樣的東西，這些東西在我們現有的技術堆疊上已經可以實現。

It's actually given our customers exactly what they want. They want the choice of deployment. We want to be able to choose where they put the workloads. And we offer our customers a workload first deployment model.

它確實給了我們的客戶他們想要的一切。他們想要自主選擇部署方式。我們希望能夠選擇他們把工作負載放在哪裡。我們為客戶提供工作負載優先的部署模式。

So they can choose whether they want to run the workload in the cloud or whether they want to run it on-prem, and so that's the decision making that our customers are going through. And the fact that we offer those technology capabilities in that hybrid environment is essentially giving our customers the choice of deployment.

因此，他們可以選擇是在雲端運行工作負載，還是在本地運行工作負載，這就是我們的客戶正在經歷的決策過程。我們能夠在混合環境中提供這些技術能力，這實際上給了我們的客戶部署方式的選擇權。

Raimo Lenschow, Barclays.

雷莫·倫肖，巴克萊銀行。

Congrats from me as well on a great quarter. The quick question, Steve, more for you. If you think about the debate of where AI gets that data from there is kind of a big debate kind of is it coming out of the operational data stores and Oracle, et cetera, is making no more out of like the data warehouses like you guys or more out of the data lakes. Can you speak to that, how you see that playing out? Or is that different use cases will have like a different data foundation?

我也要祝賀你們本季取得了優異的成績。史蒂夫，這個問題很簡單，更適合你。如果你思考一下人工智慧從哪裡獲取數據的爭論，你會發現這是一個很大的爭論，即它是來自運營數據存儲，而 Oracle 等公司是否像你們一樣更多地從數據倉庫中獲取數據，還是更多地從數據湖中獲取數據。您能談談您對此的看法嗎？您認為事情會如何發展？或者說，不同的使用場景會有不同的數據基礎？

Raimo, you answered the question right at the very end, I think we are actually seeing customers want to get the best out of their data no matter where it sits. That's why we love QueryGrid as our technology to be able to combine all of these different data stores together.

Raimo，你最後回答得非常到位，我認為我們實際上看到客戶希望無論資料儲存在哪裡，都能充分利用他們的資料。這就是為什麼我們喜歡使用 QueryGrid 技術，因為它能夠將所有這些不同的資料儲存組合在一起。

So no matter where the data is in the ecosystem, they can take that in a highly governed, reliable way and combine it together, whether it's coming from the data lake or whether it's coming from an enterprise data warehouse, and they can feed that into a language model in a very trusted environment.

因此，無論資料位於生態系統的哪個位置，他們都可以以高度可控、可靠的方式獲取資料並將其組合在一起，無論資料來自資料湖還是企業資料倉儲，他們都可以將其輸入到非常值得信賴的環境中的語言模型中。

And that's what we're really delivering and offering to our customers. So I think this is all about -- if you think about AI solutions, they have to be trusted. They have to be ethical. You have to be able to track back through it, and they have to run efficiently and effectively. And that's where the Teradata platform enables our customers to do by combining all of those data sources together.

而這正是我們真正為客戶提供和交付的。所以我覺得關鍵在於——如果你考慮人工智慧解決方案，它們必須值得信賴。他們必須恪守道德準則。你必須能夠追溯整個過程，而且它們必須有效率地運作。Teradata 平台正是透過將所有這些資料來源整合在一起，使我們的客戶能夠做到這一點。

Derrick Wood, TD Cowen.

德里克·伍德，TD Cowen。

This is for you, John. You guys had nice outperformance on recurring revenue in Q3. But now for Q4, we had been kind of assuming low single-digit growth implied from your guide last quarter to now low single-digit decline.

這是給你的，約翰。你們第三季的經常性收入表現非常出色。但現在，對於第四季度，我們原本預計會像您上個季度的業績指引那樣實現個位數低成長，而現在卻預期會下降個位數低。

So was there any kind of pull forward of deals from Q4 to Q3? Or what would you call out on the change in the Q4 growth assumptions? And if I could just squeeze one other in on the cloud having kind of dropped to 109.

那麼，第四季到第三季之間是否有任何提前成交的交易呢？或者，您會對第四季度成長假設的變化提出哪些批評意見？如果我能再擠出一點空間，把另一個人放到雲端，因為人數已經降到 109了。

Just remind us what the main drags of this number are? And any color on kind of when and where this could start to stabilize and perhaps move back up?

請您提醒我們一下，這個數字的主要拖累因素是什麼？那麼，對於這種情況何時何地可能開始趨於穩定並可能回升，目前有什麼看法嗎？

Yes. Thanks, Derrick. So on the recurring revenue side of things, I think our guidance for the year has actually been fairly consistent on the recurring revenue piece. We did have some variability, if you look quarter to quarter, and that comes from the upfront portion of the on-premise subscriptions.

是的。謝謝你，德里克。因此，就經常性收入而言，我認為我們對今年的經常性收入預期實際上相當一致。如果按季度來看，我們確實存在一些波動，這主要是由於本地訂閱的預付款部分造成的。

And so depending on the mix of that in any given quarter, you might have more upfront revenue which would otherwise throw off your expected linearity.

因此，根據每個季度的具體情況，你可能會獲得更多的前期收入，否則這將打亂你預期的線性成長。

In terms of the net expansion rate, we have seen some consolidation on that. If you look at what we've done historically and even for this year, we're still on track for the same. About 50% of our expansion rate is coming from migration activity and the remainder is coming from expansions with existing customers.

就淨擴張率而言，我們看到這方面出現了一些鞏固。回顧我們過去所做的工作，甚至包括今年的工作，我們仍然朝著同樣的目標前進。我們約 50% 的擴張速度來自遷移活動，其餘則來自現有客戶的擴張。

And so we see that continuing into Q4 right now, you're seeing those rates consolidate and so the net expansion rate is pretty close to what you're seeing for the cloud ARR growth overall.

因此，我們看到這種情況延續到了第四季度，這些成長率正在趨於穩定，因此淨成長率與雲端 ARR 整體成長非常接近。

Wamsi Mohan, Bank of America.

Wamsi Mohan，美國銀行。

Yes. I think, Steve, you mentioned sort of cost takeout helping free cash flow into next year. Can you help us maybe think about just the absolute sort of OpEx trajectory going from here into '26, how are you thinking about the route of progression from here? And if I could, just I know federal is not really very large for you guys, but are you seeing any impact at all from the government shutdown?

是的。史蒂夫，我想你剛剛提到削減成本有助於明年的自由現金流。您能否幫我們思考一下從現在到 2026 年的營運支出發展軌跡，您是如何看待未來的發展路線的？如果可以的話，我想問一下，我知道聯邦政府對你們來說規模不大，但是你們是否感受到政府停擺的影響？

Yes, I'll take the first -- the last question, first Wamsi. No, we're not seeing any impact to our revenues as a result of the federal shutdown. And then just from an OpEx perspective, clearly, we've taken some fairly major restructuring activities through the year and a lot of them in the kind of June time frame and then into the September time frame. So we are expecting full year of impacts and benefits to essentially -- ultimately our free cash flow position as we move into 2026. So we are expecting that to amplify.

是的，我先回答第一個問題——最後一個問題，Wamsi。不，聯邦政府停擺並未對我們的收入造成任何影響。從營運支出角度來看，顯然，我們今年進行了一些相當大的重整活動，其中許多活動都集中在 6 月到 9 月這段時間。因此，我們預計全年都會產生影響和帶來收益，最終將對我們的自由現金流狀況產生正面影響，直至 2026 年。所以我們預計這種情況會加劇。

And I think in relation to one of the other questions, John head it on the head, we're expecting that free cash flow growth to come from both our ARR growth expectations for '26 and also the operational efficiency effectiveness, productivity measures we've executed in 2025.

我認為，關於另一個問題，約翰，我們預計自由現金流成長將來自我們對 2026 年 ARR 成長的預期，以及我們在 2025 年實施的營運效率、效益和生產力措施。

Patrick Walravens, Citizens.

派崔克‧沃爾拉文斯，市民。

Great. John, this ones for you, too. And I know this was a good quarter, but divide your free cash flow by the revenue, you get like 21%. Not putting a time frame on it, I think where can net free cash flow margins go.

偉大的。約翰，這首歌也獻給你。我知道這個季度業績不錯，但用自由現金流除以收入，你只能得到大約 21%。我不打算設定時間期限，但我認為淨自由現金流利潤率會達到什麼水準。

Yes. No, it's a good question. I think it's somewhat related to what Steve just commented on in terms of the operating leverage. And if you look -- and just step back and look at what we did this year, with revenue headwinds, we don't guide on operating margin specifically. But I think if you do the math in reverse engineer, you're going to mind that the operating margin has to be pretty flat and comparable with where we were last year.

是的。不，這是個好問題。我認為這與史蒂夫剛才提到的經營槓桿問題有些關係。如果你仔細看看——退後一步，看看我們今年在收入逆風的情況下所做的事情，我們並沒有具體說明營業利潤率。但我認為，如果你反向推算一下，你會發現營業利潤率必須保持平穩，與去年的水平相當。

So that means in the face of revenue headwinds, we're still able to capture that margin percentage. And we've done some things from an operational standpoint and a cost efficiency standpoint that will continue to benefit us next year. And so I won't give you a number today. But suffice to say that we've done some things this year that we think set us up well for next year from a margin and a cash flow standpoint.

所以這意味著，即使面臨營收逆風，我們仍然能夠獲得這樣的利潤率。從營運和成本效益的角度來看，我們已經採取了一些措施，這些措施將在明年繼續使我們受益。所以今天我就不給你具體數字了。但總而言之，我們今年做了一些事情，我們認為這些事情從利潤率和現金流的角度來看，將為明年奠定良好的基礎。

Thanks, Pat, for the question. And thanks, everyone, for joining us today. We are absolutely committed to show what the AI future holds for our customers and what our differentiated platform and capabilities can deliver. As we continue our focus on execution, we're really confident in our outlook, and we are looking forward to updating you all next quarter. Thank you very much.

謝謝你的提問，帕特。感謝各位今天蒞臨本節目。我們致力於向客戶展示人工智慧的未來將如何發展，以及我們差異化的平台和能力能帶來什麼。我們將繼續專注執行，對前景充滿信心，並期待下季向大家報告最新進展。非常感謝。

And operator, you can end the call.

接線員，您可以結束通話了。

Thank you. This concludes today's conference call. You may now all disconnect your lines.

謝謝。今天的電話會議到此結束。現在你們都可以斷開線路了。