高知特 (CTSH) 2026 Q2 法說會逐字稿

Ladies and gentlemen, welcome to the Cognizant Technology Solutions second quarter 2026 earnings conference call. (Operator Instructions)

各位女士、先生，歡迎參加 Cognizant Technology Solutions 2026 年第二季財報電話會議。(操作員指示)

I would now like to turn the conference over to Mr. Tyler Scott, Senior Vice President, Investor Relations. Please go ahead.

現在我想把會議交給投資人關係資深副總裁 Tyler Scott 先生。請開始。

Thank you, operator, and good morning, everyone. Welcome to Cognizant's second quarter 2026 earnings call. I am joined today by Ravi Kumar, Chief Executive Officer; and Jatin Dalal, our Chief Financial Officer.

謝謝，操作員，各位早安。歡迎參加 Cognizant 2026 年第二季財報電話會議。今天與我一同出席的有執行長 Ravi Kumar，以及財務長 Jatin Dalal。

By now, you should have received a copy of the earnings release and investor supplement. If you have not, copies are available on our website, cognizant.com. Before we begin, I would like to remind you that some of the comments made on today's call and some of the responses to your questions may contain forward-looking statements. These statements are subject to the risks and uncertainties as described in the company's earnings release and other filings with the SEC.

此時各位應已收到財報新聞稿與投資人補充資料。若尚未收到，可至我們的網站 cognizant.com 取得。在開始之前，我想提醒各位，今天電話會議中的部分評論以及對各位提問的部分回覆，可能包含前瞻性陳述。這些陳述受公司財報新聞稿及向美國證券交易委員會（SEC）提交之其他文件中所述風險與不確定性影響。

Additionally, during our call today, we will reference certain non-GAAP financial measures that we believe provide useful information for our investors. Reconciliations of non-GAAP financial measures where appropriate to the corresponding GAAP measures can be found in the company's earnings release and other filings with the SEC.

此外，在今天的電話會議中，我們將引用若干非 GAAP 財務衡量指標，我們相信這些指標能為投資人提供有用資訊。在適用情況下，非 GAAP 財務衡量指標與相對應 GAAP 指標之調節表，可於公司財報新聞稿及向 SEC 提交之其他文件中查閱。

With that, over to you, Ravi.

接下來交給你，Ravi。

Thank you, Tyler. Good morning, everyone. Thank you for joining us. We delivered a solid second quarter with organic revenue growth at the high end of our expectations and year-over-year adjusted operating margin expansion. Nearly all our healthy sequential growth was driven by our organic business. We accelerated our evolution as an AI builder by building new capabilities, launching new platforms, and deploying frontier talent as we begin to unlock entirely new business categories and client value pools.

謝謝你，Tyler。各位早安。感謝各位加入我們。我們交出穩健的第二季成績：有機營收成長達到我們預期區間的高端，且調整後營業利益率較去年同期擴張。我們幾乎所有健康的季增成長都來自有機業務。隨著我們開始解鎖全新的業務類別與客戶價值池，我們透過建立新能力、推出新平台並部署前沿人才，加速向 AI 建構者（AI builder）的演進。

Looking at the quarter's highlights. Revenue grew 4.1% year over year in constant currency, led by strong performance in North America as large deals signed over the past year moved into full execution. Financial Services grew nearly 12% year over year in constant currency, its second consecutive quarter of 10%-plus growth.

回顧本季重點。以固定匯率計，營收年增 4.1%，主要由北美的強勁表現帶動，過去一年簽署的大型交易進入全面執行階段。以固定匯率計，金融服務業務年增近 12%，連續第二季達到 10% 以上成長。

Trailing 12 months bookings increased 5%. We signed seven large deals, each with TCV of more than $100 million, including three new logos. As we expanded adjusted operating margins year over year for the sixth straight quarter, demonstrating continued profitable revenue growth.

過去 12 個月的訂單（bookings）成長 5%。我們簽下七筆大型交易，每筆合約總價值（TCV）均超過 1 億美元，其中包含三個新客戶（new logos）。我們連續第六季實現調整後營業利益率年增擴張，展現持續的獲利性營收成長。

From an AI indicators perspective, our revenue and adjusted operating income per associate increased 4.6% and 7.1%, respectively. Starting this quarter, we are excluding trainees who are not fully deployed for both the current and the comparable prior periods. Over 40% of our software development is now AI-assisted. We have over 8,000 AI engagements.

從 AI 指標角度來看，我們每位員工的營收與調整後營業利益分別成長 4.6% 與 7.1%。自本季起，我們在本期與可比前期期間，均排除尚未完全投入專案的受訓人員（trainees）。目前我們超過 40% 的軟體開發已由 AI 協助。我們已有超過 8,000 個 AI 專案（engagements）。

And we view strength in Financial Services, which include some of the world's most technically sophisticated companies as a leading indicator for other industries. Our research reveals that financial services is well ahead with AI initiatives and advanced AI adoption. We are helping clients tackle significant technology debt by using AI to compress modernization timelines that are shifting towards outcome-based pricing. We also now see financial services clients leveraging AI for growth imperatives with new discretionary spend cycles.

我們也將金融服務的強勢表現視為其他產業的領先指標，因為該領域包含全球技術最為成熟的一些公司。我們的研究顯示，金融服務在 AI 計畫與進階 AI 採用方面明顯領先。我們正協助客戶運用 AI 壓縮現代化改造時程，以處理龐大的技術債，且定價模式正轉向以成果為基礎（outcome-based pricing）。我們也看到金融服務客戶正運用 AI 推動成長要務，並進入新的可自由支配支出週期。

We completed our previously announced acquisition of Astreya, a global IT managed services provider with deep expertise in data center infrastructure, enterprise networks, digital workplace services, and AI first managed operations. Momentum is already building. Astreya will continue its work with Google to deliver services across its corporate engineering environment, including global IT ops, workplace collaboration infrastructure, and platform services.

我們完成了先前公告的 Astreya 收購案。Astreya 是全球 IT 託管服務供應商，在資料中心基礎設施、企業網路、數位工作場所服務，以及 AI 優先（AI first）的託管營運方面具備深厚專長。動能已開始形成。Astreya 將持續與 Google 合作，在其企業工程環境中提供服務，包括全球 IT 營運、工作場所協作基礎設施與平台服務。

We delivered these results against a cautious demand environment while growing at the top of our peer group. While we expect that caution to persist in the near term, AI is driving fundamental change in our industry that we believe creates a significant long-term growth opportunities.

我們在審慎的需求環境下仍交出這些成果，且成長表現位居同業群的前段。雖然我們預期短期內這種審慎態度仍將持續，但 AI 正在推動產業的根本性變革，我們相信這將帶來顯著的長期成長機會。

The key question is why growing AI capability has not yet translated into more enterprise value. Our research shows two-thirds of the Global 2000 have not yet realized measurable AI productivity gains, one in four have paused AI deployments and billions of dollars in potential value remain unrealized.

關鍵問題在於：為何 AI 能力的提升尚未轉化為更多的企業價值。我們的研究顯示，全球 2000 大企業中有三分之二尚未實現可衡量的 AI 生產力提升；每四家就有一家暫停 AI 部署；仍有數十億美元的潛在價值尚未實現。

The opportunity to address this gap is enormous. We estimate the $1 trillion system integration market can expand into $5 trillion to $6 trillion enterprise operations market with $4.5 trillion of operational labor exposed to AI. Services firms are structurally positioned to capture this opportunity. As models proliferate and inference costs decline, models stop being the differentiator and value shifts to the applied layer, which is context governance and business processes. This layer addresses how each enterprise applies AI, what it learns from it, how effectively it retains, reuses, and compounds that learning, and how well it protects the proprietary intelligence that constitutes its alpha.

彌補這一落差的機會極為龐大。我們估計，規模約 1 兆美元的系統整合市場，可擴展為 5 兆至 6 兆美元的企業營運市場，其中約 4.5 兆美元的營運勞動力成本暴露於 AI 影響之下。服務型公司在結構上具備掌握此機會的有利位置。隨著模型大量湧現且推論成本下降，模型本身不再是差異化因素，價值將轉移至應用層，也就是情境治理（context governance）與業務流程。此層解決每家企業如何應用 AI、從中學到什麼、如何有效保留、重用並累積（compounds）這些學習，以及如何保護構成其超額報酬（alpha）的專有智慧。

That is why we launched the Cognizant AI Delivery Operating System, a continuously learning delivery system that combines human expertise, organizational knowledge, client context, and AI intelligence. It has three pillars: our engineering harness, which coaches engineers in real time; our business operations harness, which feeds best practices into a shared organizational system; and our Intelligence Spine, which connects intelligence across physical and edge environments. All of this is supported by our context engineering capabilities, which is the ability to assemble an enterprise's work graphs, guardrails, and tribal knowledge.

因此，我們推出 Cognizant AI Delivery Operating System，這是一套持續學習的交付系統，結合人類專業、組織知識、客戶情境與 AI 智慧。它有三大支柱：工程加速器（engineering harness），可即時指導工程師；業務營運加速器（business operations harness），將最佳實務輸入共享的組織系統；以及 Intelligence Spine，連結跨實體與邊緣環境的智慧。上述一切皆由我們的情境工程（context engineering）能力支撐，也就是組裝企業工作圖譜（work graphs）、護欄（guardrails）與部落知識（tribal knowledge）的能力。

Working with Cisco, we are using Context Fabric to build a digital signature of the account management role forming the foundation for an account manager's digital twin and broader agentification solutions. This AI solution with a digital twin at its core holds the potential to streamline daily operations and support on time in full complete deliveries, significantly improving customer satisfaction.

我們與 Cisco 合作，運用 Context Fabric 建立客戶經理角色的數位簽章，作為客戶經理數位分身（digital twin）以及更廣泛代理化（agentification）解決方案的基礎。這項以數位分身為核心的 AI 解決方案，具備簡化日常營運並支援準時且完整交付（on time in full complete deliveries）的潛力，可顯著提升客戶滿意度。

For a large North American bank, we piloted a solution for their fraud dispute management and KYC operational workflows. Context was engineered through a custom solution that combines static knowledge from customer and operations interactions and documents with dynamic business context sourced through enterprise application APIs. The fraud dispute management multi-agent pilot solution has demonstrated the potential to reduce manual effort by more than 50%, while the KYC process can significantly improve decision efficacy, reducing the risk of fines and fees.

針對一家北美大型銀行，我們為其詐欺爭議管理與 KYC 營運工作流程試點一套解決方案。情境透過客製化方案進行工程化建構：將來自客戶與營運互動及文件的靜態知識，與透過企業應用程式 API 取得的動態業務情境相結合。詐欺爭議管理的多代理（multi-agent）試點方案已展現將人工投入降低逾 50% 的潛力；同時，KYC 流程可顯著提升決策效能，降低罰款與費用風險。

Last quarter, I described how AI is reinforcing our industry's first principles and driving four significant shifts. First, becoming an AI builder rather than a traditional systems integrator, owning the full stack required to design bespoke AI systems. Second, rebuilding our talent model by shifting from a traditional pyramid towards interdisciplinary teams working at the intersection of domain operations and technology.

上一季我曾說明，AI 正在強化我們產業的第一性原理，並推動四項重大轉變。第一，成為 AI 建構者，而非傳統系統整合商，掌握設計客製化 AI 系統所需的完整技術堆疊。第二，重塑我們的人才模式，從傳統金字塔結構轉向跨領域團隊，在產業營運與科技的交會處協作。

Third, shifting our economics from labor to outcomes. Our mix of fixed price and transaction-based work has grown for three consecutive years, creating a more durable business. And fourth, moving from delivering projects to underwriting results.

第三，將我們的經濟模式從勞動力轉向成果。我們的固定價格與交易計價工作占比已連續三年成長，打造更具韌性的業務。第四，從交付專案轉向為成果承擔責任（underwriting results）。

Let me share our progress across these four shifts, starting with the first one, becoming an AI builder by strengthening our proprietary IP and ecosystem. This year, we launched a dedicated AI Market Unit, an elite team of business designers, industry strategists and frontier engineers focusing on converting AI investments into realized value. We're already seeing early traction.

讓我分享我們在這四項轉變上的進展，先從第一項開始：透過強化我們的專有智慧財產（IP）與生態系，成為 AI 建造者。今年，我們成立了專責的 AI 市場事業單位（AI Market Unit），由一支菁英團隊組成，包含商業設計師、產業策略師與前沿工程師，專注於將 AI 投資轉化為可實現的價值。我們已經看到初步的市場牽引力。

For example, a large payer client chose us to help build an agentic development practice for its biggest division through pods of frontier engineers and AI agents. We cut manual effort by 60% for a midsized payer while improving the claims throughput. And we compress AI development cycles from months to days for a leading European online fashion retailer, advancing agentic workflows across supply chain inventory returns and customer experience.

例如，一家大型支付方客戶選擇我們，透過由前沿工程師與 AI 代理組成的 pods，協助其最大事業部建立代理式（agentic）開發實務。我們在提升理賠處理吞吐量的同時，為一家中型支付方將人工投入降低了 60%。此外，我們也為一家領先的歐洲線上時尚零售商將 AI 開發週期從數月壓縮到數天，推進供應鏈庫存、退貨與客戶體驗等環節的代理式工作流程。

On the partnership front, we established a dedicated Gemini Enterprise practice as a Google Cloud Diamond Partner, and we joined OpenAI's Daybreak consortium. We also expanded our partnership with Anthropic, becoming one of the small number of Global Premier Partners in the Claude Partner Network.

在合作夥伴方面，作為 Google Cloud 菁英級（Diamond）合作夥伴，我們建立了專責的 Gemini Enterprise 實務團隊，並加入 OpenAI 的 Daybreak 聯盟。我們也擴大與 Anthropic 的合作，成為 Claude Partner Network 中少數的全球頂級合作夥伴（Global Premier Partners）之一。

An example of this partnership at work is Travelport, where we partnered with Anthropic on a strategic AI transformation aimed at modernizing Travelport's software development and embedding AI across Travelport's travel retailing and distribution platforms.

這項合作的實際案例之一是 Travelport：我們與 Anthropic 合作推動策略性 AI 轉型，目標是現代化 Travelport 的軟體開發，並在其旅遊零售與分銷平台中全面嵌入 AI。

And with A+E Global Media, we partnered with Snowflake to deploy custom intelligent agents that transform complex advertising operations and legal workflows. By automating document validation and enabling natural language queries, we helped accelerate decision-making and significantly improved time-intensive processes.

此外，與 A+E Global Media 的合作中，我們與 Snowflake 攜手部署客製化智慧代理，改造複雜的廣告營運與法務工作流程。透過自動化文件驗證並支援自然語言查詢，我們協助加速決策，並大幅改善耗時的流程。

For our second shift, we are rearchitecting our talent model into an AI augmented early career talent led by more senior player coaches. We introduced two new certified roles, frontier certified engineers who audit workflows and build intelligent agents and frontier business operators who manage blended human digital teams to deliver outcomes. We plan to scale this Cognizant Forward team to 5,000 frontier certified engineers and 10,000 frontier business operators. We currently have 10,000 Claude-certified architects, the most of any organization globally, and we power one of the largest pools of Codex and Gemini enterprise-trained badges.

第二項轉變方面，我們正將人才模型重新架構為「AI 增強」的早期職涯人才，由更資深的「球員教練」（player coaches）帶領。我們推出兩個新的認證職務：前沿認證工程師（frontier certified engineers），負責稽核工作流程並建置智慧代理；以及前沿業務營運人員（frontier business operators），負責管理人機混合的數位團隊以交付成果。我們計畫將 Cognizant Forward 團隊擴大至 5,000 名前沿認證工程師與 10,000 名前沿業務營運人員。目前我們擁有 10,000 名 Claude 認證架構師，為全球任何組織之最，並且支援最大規模之一的 Codex 與 Gemini 企業訓練徽章人才庫。

Last quarter, we introduced Cognizant Skillspring, an AI-native platform that embeds agent-driven tutoring directly into daily workflows and gives associates real-time visibility into their AI proficiency and token usage. It is gaining significant momentum with our associates as learning time has doubled and AI usage has tripled. We have also opened this platform to early prospective clients.

上季我們推出 Cognizant Skillspring，這是一個 AI 原生平台，將代理驅動的輔導直接嵌入日常工作流程，並讓員工即時掌握自身 AI 熟練度與 token 使用量。該平台在員工間快速累積動能：學習時間加倍、AI 使用量成長三倍。我們也已將此平台開放給早期潛在客戶。

Our third and fourth shifts move Cognizant from a labor-based model to an agentic and platform-enabled model and from delivering outcomes to underwriting results. This is why we established a new AI Products and Platform Group earlier this year to unify Cognizant's proprietary offerings and scale innovation across the portfolio.

第三與第四項轉變，將 Cognizant 從以人力為基礎的模式，轉向代理式與平台賦能的模式，並從「交付成果」進一步走向「承保結果」（underwriting results）。因此，我們在今年稍早成立新的 AI 產品與平台事業群（AI Products and Platform Group），以整合 Cognizant 的專有產品並在整體產品組合中擴大創新規模。

Our platform strategy has two dimensions: first, our engineering platforms, which provide the foundation for everything we build, including accelerators, agent frameworks and AI engineering tools that power our AI-native software development life cycle and agents development life cycle. They are increasingly powered by their own agentic workforce. Together, they compress the software cycle, improve productivity, and accelerate business outcomes for clients.

我們的平台策略有兩個面向：第一是工程平台，為我們打造的一切提供基礎，包括加速器、代理框架與 AI 工程工具，支撐我們的 AI 原生軟體開發生命週期與代理開發生命週期。這些平台也日益由其自身的代理式勞動力所驅動。整體而言，它們能壓縮軟體週期、提升生產力，並加速客戶的業務成果。

Second, and building on that foundation, our business platforms combine technology, data, AI, and deep industry expertise to create differentiated client value, purpose-built for the industries we serve, they embed industry-trained agents directly into critical workflows.

第二，在此基礎之上，我們的業務平台結合技術、資料、AI 與深厚的產業專業，創造差異化的客戶價值；這些平台依我們服務的產業量身打造，將受產業訓練的代理直接嵌入關鍵工作流程。

TriZetto is the strongest proof point of our platform strategy. Our healthcare platform business generates more than $1.1 billion in annual revenue and through the first half of 2026 grew faster than the overall company while delivering substantially higher margins. What began as a software product has evolved into a broad health care platform ecosystem.

TriZetto 是我們平台策略最有力的佐證。我們的醫療健康平台業務每年創造超過 11 億美元營收，且在 2026 年上半年成長速度快於公司整體，同時帶來顯著更高的利潤率。最初作為一項軟體產品，如今已演進為廣泛的醫療健康平台生態系。

TriZetto demonstrates how platforms can drive deep client relationships, create recurring revenue streams, and deliver growth and profitability that exceeds traditional services. It's a blueprint for how we intend to scale platform-led growth across other industries.

TriZetto 展示了平台如何推動更深的客戶關係、創造經常性收入來源，並帶來超越傳統服務的成長與獲利能力。這也是我們計畫在其他產業擴大平台驅動成長的藍圖。

In healthcare claims, we built a pioneering auto adjudication solution that uses large language models to digitize adjudication knowledge and rules, enabling agentic AI to analyze claims, apply complex business rules, and reach decisions with human validation wherever it's needed. It positions us to take a share in this large, high-volume category.

在醫療理賠領域，我們打造了開創性的自動裁決（auto adjudication）解決方案，運用大型語言模型將裁決知識與規則數位化，使代理式 AI 能分析理賠、套用複雜的業務規則，並在需要時由人工進行驗證後做出決策。這使我們有能力在這個龐大且高交易量的類別中取得市占。

Other platform-led modernization wins include a global claims and risk administration leader. Leveraging Neuro AI Flowsource and our 3Cloud-enhanced Microsoft expertise, we signed a five-year agreement to accelerate processing times and upgrade core infrastructure.

其他以平台帶動的現代化勝利還包括一家全球理賠與風險管理領導者。運用 Neuro AI Flowsource 與我們經 3Cloud 強化的 Microsoft 專業能力，我們簽署了一份為期五年的協議，以加速處理時間並升級核心基礎架構。

And Cotality, a global data and analytics company has deployed Cognizant's neuro business process workflow across multiple business operations processes. It resulted in over 40% improvement in research TAT, turnaround times and faster and higher quality resolution of their customers.

此外，全球資料與分析公司 Cotality 已在多項業務營運流程中部署 Cognizant 的 neuro 業務流程工作流。其成果是研究 TAT（周轉時間）提升超過 40%，周轉時間縮短，並更快且更高品質地解決其客戶問題。

We are also moving beyond delivery to underwriting results. We signed a major engagement with a leading insurance brokerage, committing to more than 50% productivity improvement over five years through AI and operating model redesign. We won on the strength of our domain expertise, reimagining the core workflows and accelerated delivery using AI tools from our partner ecosystem.

我們也正從「交付」進一步邁向「承保結果」。我們與一家領先的保險經紀公司簽署重大合作案，承諾在五年內透過 AI 與營運模式重設，實現超過 50% 的生產力提升。我們憑藉產業領域專業勝出，重新構想核心工作流程，並運用合作夥伴生態系的 AI 工具加速交付。

As we execute these four shifts, our AI builder model expands where we create value across three categories. First, our traditional work done dramatically more productively; second, old things in new ways; and third, entirely new things that didn't exist before AI.

在推進這四項轉變的同時，我們的 AI 建造者模式也擴展到三類價值創造。第一，以大幅更高的生產力完成傳統工作；第二，用新方式做舊事；第三，打造在 AI 出現之前根本不存在的全新事物。

First, our traditional work done more productively. This includes autonomous software engineering or Vector 1 work, which over the past two years has driven both consolidation and productivity-led engagements. A great example of our success in this area is Novartis, which selected Cognizant earlier this year for a five-year engagement to transform its global IT operations. Building on a relationship that spans more than 20 years, we expect to leverage our Neuro AI platform to create a unified AI-powered operating model that combines automation, full stack observability and agentic capabilities. This is where our AI builder strategy is aimed at helping clients move from labor-intensive operations to intelligent self-service and increasingly autonomous technology environments.

第一，以更高生產力完成傳統工作。這包括自主軟體工程（autonomous software engineering），或稱 Vector 1 工作，在過去兩年推動了整併與以生產力為導向的合作案。我們在此領域成功的絕佳例子是諾華（Novartis），其於今年稍早選擇 Cognizant 展開為期五年的合作，以轉型其全球 IT 營運。在超過 20 年的合作關係基礎上，我們預期運用 Neuro AI 平台打造統一的 AI 驅動營運模式，結合自動化、全堆疊可觀測性（full stack observability）與代理式能力。這正是我們 AI 建造者策略的目標：協助客戶從勞力密集的營運，轉向智慧自助服務與日益自主的技術環境。

Second, old things in new ways. Here, we see a significant pipeline across secure AI services, mainframe modernization, SAP S/4HANA migration and SaaS reimagination, long-standing enterprise challenges that AI can now solve far faster at a fraction of the cost. Cybersecurity is a clear example. AI is turning security from a cost center into a remediation opportunity as machines expose vulnerabilities at unprecedented speed. We are positioned for this moment by combining frontier models with a 5,000-person security practice and all the leading frontier program partners, including CrowdStrike, Palo Alto Networks, Zscaler, Anthropic, OpenAI, Microsoft, and Red Hat. We see a strong pipeline forming across these partnerships.

第二，用新方式做舊事。在此，我們看到在安全 AI 服務、大型主機現代化、SAP S/4HANA 遷移與 SaaS 重塑等領域有顯著的案源管線；這些長期的企業挑戰，如今 AI 能以更快速度、以更低成本的一小部分加以解決。資安就是明確的例子。AI 正以空前速度揭露弱點，使資安從成本中心轉變為修復機會。我們透過結合前沿模型、5,000 人的資安實務團隊，以及所有主要前沿計畫合作夥伴（包括 CrowdStrike、Palo Alto Networks、Zscaler、Anthropic、OpenAI、Microsoft 與 Red Hat），已為此時刻做好準備。我們看到這些合作關係正形成強勁的案源管線。

Third, entirely new things made possible by AI, including context engineering, reinvention of business flows, agentification of business operations and physical AI. With physical AI, as intelligence begins to govern physical environments, we believe a new domain of autonomous operations will open. We launched our sovereign physical AI Platform-as-a-Service to position Cognizant ahead of this iPhone moment for robotics and infrastructure. This builds directly on a capability we have built over the past decade. More than 10,000 of our associates have trained AI and machine learning models for the world's largest technology companies.

第三，AI 所帶來的全新可能性，包括情境工程、商業流程再造、業務營運的代理化，以及實體 AI。隨著智慧開始治理實體環境，我們相信將開啟一個全新的自主營運領域。我們推出主權實體 AI 的 Platform-as-a-Service（平台即服務），以使 Cognizant 在機器人與基礎設施的「iPhone 時刻」到來前搶占先機。這直接建立在我們過去十年打造的能力之上。我們已有超過 10,000 名同仁為全球最大的科技公司訓練 AI 與機器學習模型。

We are now repurposing that expertise for the enterprise through our AI model training and data services. For example, for a global automotive manufacturer, we train models on the company's products, technical data, and visual content to achieve accuracy generic models cannot match, automate complex processes and unlock value from knowledge the company already owned. Public sector is emerging as a meaningful business as our organic and inorganic investments gain traction. In Q2, Cognizant Government Solutions, built on our Belcan acquisition, secured a landmark engagement with the state of Iowa to modernize its IT infrastructure.

我們目前正透過 AI 模型訓練與資料服務，將這些專業能力重新運用於企業客戶。例如，針對一家全球汽車製造商，我們以該公司的產品、技術資料與視覺內容訓練模型，以達到通用模型無法匹敵的準確度，自動化複雜流程，並從公司既有的知識資產中釋放價值。隨著我們的有機與無機投資逐步見效，公共部門正成為一項具意義的業務。在第二季，建立於我們收購 Belcan 基礎上的 Cognizant Government Solutions，與愛荷華州簽下具里程碑意義的合作案，以現代化其 IT 基礎設施。

We have a growing pipeline across defense, federal, and state government, including AI infrastructure and citizen experience, while TriZetto expands the opportunity to health agencies, including our work supporting the Department of Veterans Affairs in partnership with Signature Performance. This builds on our long-lasting UK public sector practice.

我們在國防、聯邦與州政府領域的商機管線持續成長，涵蓋 AI 基礎設施與公民體驗；同時，TriZetto 也將機會擴展至衛生機構，包括我們與 Signature Performance 合作、支援美國退伍軍人事務部的工作。這也建立在我們長期深耕的英國公共部門實務之上。

For example, with the Home Office, we developed and support the foundational data platform behind the UK's migration and border systems. In Q2, we won expanded home office work across software engineering, testing, delivery, and managed services for critical case working systems, improving case worker productivity and reducing manual intervention.

例如，針對英國內政部（Home Office），我們開發並支援英國移民與邊境系統背後的基礎資料平台。在第二季，我們贏得內政部擴大合作，涵蓋軟體工程、測試、交付與託管服務，用於關鍵案件處理系統，提升承辦人員生產力並降低人工介入。

And for His Majesty's Revenue and Customs, we recently won an additional work to help configure low-code services in support of build and DevOps functions that is valued at more than [$215 million] over the life of the deal, including option years.

此外，針對英國皇家稅務與海關總署（His Majesty's Revenue and Customs），我們近期再度贏得一項額外工作，協助配置低程式碼服務以支援建置與 DevOps 職能；該案在合約期間（含選擇延長年度）總價值超過 [$215 million]。

Our AI builder strategy is also gaining traction outside the US. For example, a global pharma company in Europe selected Cognizant as a sole partner to build and scale its enterprise data, AI, and Agentic AI capability through a three-year agreement covering 68 projects initially. As the client's official AI builder, Cognizant will translate their agentic AI vision into a production-grade, governed enterprise platform spanning all business domains globally.

我們的 AI builder（AI 建置者）策略在美國以外也持續獲得動能。例如，歐洲一家全球製藥公司選擇 Cognizant 作為唯一合作夥伴，透過一份為期三年的協議（初期涵蓋 68 個專案）建置並擴展其企業級資料、AI 與 Agentic AI 能力。作為客戶的官方 AI 建置者，Cognizant 將把其 agentic AI 願景落地為可投入生產、具治理機制的企業平台，並在全球範圍內涵蓋所有業務領域。

And Cognizant helped a large European bank to agentify its mortgage processes by building a mortgage operations agent, which brings multiple specialized agents together to support complex decision-making, analyze business rule outcomes, proposing remediation paths, and generating clear and actionable insights.

此外，Cognizant 也協助一家大型歐洲銀行將其房貸流程代理化，建置房貸營運代理（mortgage operations agent），把多個專業代理整合在一起，以支援複雜決策、分析業務規則結果、提出補救路徑，並產生清晰且可執行的洞見。

To conclude, we are in the midst of a profound transformation with a clear vision for the industry's future and confidence in the expansive AI-led opportunity ahead. Our actions, deploying interdisciplinary talent, shifting to outcome-based platforms and opening new value pools are designed to drive sustainable growth. As we redefine Cognizant, we remain focused on our growth -- on our goals of delivering top-tier growth, consistent margin expansion, and EPS growth ahead of revenue.

總結而言，我們正處於一場深刻的轉型之中，對產業未來有清晰願景，並對前方由 AI 驅動的廣闊機會充滿信心。我們的行動——部署跨領域人才、轉向以成果為導向的平台，以及開啟新的價值池——旨在推動可持續的成長。在我們重新定義 Cognizant 的同時，我們仍聚焦於成長——致力於實現一流的成長、持續的利潤率擴張，以及 EPS 成長快於營收成長的目標。

Thank you to our associates, clients, and shareholders for your continued dedication, partnership, and trust.

感謝我們的同仁、客戶與股東持續的投入、合作與信任。

With that, I'll turn the call over to Jatin.

接下來，我把電話會議交給 Jatin。

Thank you, Ravi, and thank you all for joining us. We are pleased with our second quarter performance, highlighted by industry-leading growth and steady adjusted operating margin expansion. We achieved these results while continuing to invest, including in the completed acquisition of Astreya, new frontier skilling initiatives, expanded partnership and our AI labs and platform-led offerings. We also deployed more than $1.1 billion through share repurchases, reflecting our conviction in the long-term opportunity AI creates for Cognizant and our critical role as an AI builder. While market conditions remain complex, we have continued to deliver on our commitments while investing in and evolving our business for the future.

謝謝你，Ravi，也感謝各位加入我們。我們對第二季表現感到滿意，亮點包括領先同業的成長，以及穩健的調整後營業利益率擴張。在持續投資的同時，我們也達成了這些成果，包括完成收購 Astreya、推動新前沿技能培訓計畫、擴大合作夥伴關係，以及強化我們的 AI 實驗室與平台導向產品。我們也透過股票回購投入超過 11 億美元，反映我們對 AI 為 Cognizant 帶來長期機會的信念，以及我們作為 AI 建置者的關鍵角色。儘管市場環境仍然複雜，我們在投資並演進業務以迎向未來的同時，仍持續履行我們的承諾。

Now moving on to the details of the quarter. In Q2, revenue grew 4.1% year over year in constant currency to $5.5 billion. Our sequential organic growth was at the high end of our expectations. Year-over-year performance was driven by volume growth, increase in third-party product revenue associated with our integrated offering strategy and inorganic revenue from our investment in 3Cloud.

接著說明本季細節。第二季營收以固定匯率計算年增 4.1%，達 55 億美元。我們的連續季有機成長位於預期區間的高端。年對年表現主要由量的成長所帶動，並受惠於與我們整合式產品策略相關的第三方產品營收增加，以及我們投資 3Cloud 所帶來的無機營收。

From a geographic perspective, growth was once again driven by North America. And from a services perspective, our BPO practice once again led growth, while demand remains strong for data and cybersecurity driven by AI adoption. We are also seeing strong growth from industry-specific AI-led transformation in Financial Services and Life Sciences.

從地理區域來看，成長再次由北美帶動。從服務面來看，我們的 BPO 業務再次領先成長；同時，在 AI 採用推動下，資料與資安需求仍然強勁。我們也看到金融服務與生命科學領域，由產業特定、AI 驅動的轉型帶來強勁成長。

By segment, Financial Services again led with healthy growth across banking, capital markets, and insurance clients. Growth is also being driven by strong performance in the UK public sector. We are seeing legacy modernization programs accelerate as clients advance their AI journeys to address significant technology debt. This is also reflected in sustained bookings momentum.

按事業部門來看，金融服務再次領先，在銀行、資本市場與保險客戶方面皆呈現健康成長。成長也受到英國公共部門強勁表現的帶動。隨著客戶推進其 AI 旅程以處理龐大的技術負債，我們看到既有系統現代化計畫正在加速。這也反映在持續的訂單動能上。

Health Sciences was stable. Demand remains cautious and cost-driven, with clients prioritizing vendor consolidation, legacy modernization, and compliance while discretionary spend faces tight scrutiny. It must demonstrate a clear ROI. As Ravi mentioned, TriZetto had a strong quarter.

健康科學業務維持穩定。需求仍偏審慎且以成本為導向，客戶優先推動供應商整併、既有系統現代化與合規，同時對可自由支配支出進行嚴格審視。相關投資必須展現明確的投資報酬率（ROI）。如 Ravi 所提，TriZetto 本季表現強勁。

Products and Resources was steady. While clients in retail, consumer goods, and travel and hospitality continue to navigate pressure from geopolitical uncertainty, supply chain disruptions and elevated oil prices, we are seeing momentum in manufacturing, logistics, energy, and utilities, where physical AI and smart manufacturing are creating compelling opportunities for us.

產品與資源業務表現平穩。儘管零售、消費品以及旅遊與餐旅客戶仍在因地緣政治不確定性、供應鏈中斷與高油價而承受壓力，我們在製造、物流、能源與公用事業領域看到動能；在這些領域，實體 AI 與智慧製造正為我們創造具吸引力的機會。

Within Communications, Media, and Technology, demand among comms and media customers is muted, consistent with last quarter. With technology customers, demand remains strong, driven by AI native engineering, digital operations, data, and cloud services. As Ravi noted, we are already seeing momentum with Astreya, and we are confident our joint capabilities can generate attractive growth synergies in the years ahead, driven by AI infrastructure build-out.

在通訊、媒體與科技領域，通訊與媒體客戶的需求依舊低迷，與上季一致。在科技客戶方面，需求仍然強勁，主要由 AI 原生工程、數位營運、資料與雲端服務所帶動。如 Ravi 所述，我們已在 Astreya 方面看到動能，並相信雙方的聯合能力可在未來數年創造具吸引力的成長綜效，並由 AI 基礎設施建置所驅動。

Turning to bookings. We delivered another strong quarter of large deal bookings, signing seven deals each with TCV of more than $100 million, including three new logos. On a trailing 12-month basis, bookings grew 5% and represented a book-to-bill of 1.3 times. Annual contract value decreased modestly, reflecting the impact of lengthening contract duration due to a greater mix of large deals. We are pleased with the growth we have delivered in new and expansion bookings, which grew in the mid-teens in the first half of the year.

接著談訂單。我們再度交出大型交易訂單強勁的一季，簽下 7 筆合約總價值（TCV）超過 1 億美元的交易，其中包含 3 個新客戶。以過去 12 個月滾動基礎計算，訂單成長 5%，接單出貨比（book-to-bill）為 1.3 倍。年度合約價值（ACV）小幅下降，反映因大型交易占比提高而導致合約期限拉長的影響。我們對新簽與擴約訂單所帶來的成長感到滿意；今年上半年其成長率達到十幾個百分點的中段水準。

Moving to margins. During the quarter, we incurred approximately $84 million in costs related to the Project Leap program we announced last quarter. In addition, as a result of India Labor Code and subsequent regulations notified by the Indian government in Q2, we recorded $81 million onetime benefit for a partial reversal of the India defined contribution obligation liability we had originally recorded in 2019. Excluding these impacts, second quarter adjusted operating margin was 16%, up 40 basis points year over year.

接著看利潤率。本季我們因上季宣布的 Project Leap 計畫而產生約 8,400 萬美元的相關成本。此外，由於印度《勞動法典》及印度政府於第二季公告的後續法規，我們就原於 2019 年認列的印度確定提撥義務負債，因部分回轉而一次性認列 8,100 萬美元利益。排除上述影響後，第二季調整後營業利益率為 16%，年增 40 個基點。

Operational efficiency and favorable currency movements more than offset higher third-party costs and compensation costs as well as the impact of our recently completed acquisitions.

營運效率提升與有利的匯率變動，已足以抵銷較高的第三方成本與薪酬成本，以及我們近期完成收購所帶來的影響。

Now to details of EPS, cash flow, and capital allocation. Second quarter adjusted EPS was $1.37, up 5% year over year, driven by revenue growth, margin expansion, and lower share count. EPS was negatively impacted by a higher interest expense associated with $1 billion we borrowed under our revolving credit facility to fund the Astreya acquisition and share purchase activity in the quarter.

接下來說明每股盈餘（EPS）、現金流與資本配置的細節。第二季調整後EPS為1.37美元，年增5%，主要受營收成長、利潤率擴張以及流通股數下降所帶動。EPS受到較高利息費用的不利影響，該利息費用與我們為資助Astreya收購案及本季股票回購活動，而自循環信用額度借入10億美元相關。

DSO was 88 days, up 5 days year over year, primarily driven by a change in the business mix. This factor also led to a corresponding increase in payables and therefore, the impact was neutral to cash flow.

應收帳款週轉天數（DSO）為88天，較去年同期增加5天，主要由於業務組合變化所致。此因素亦導致應付帳款相應增加，因此對現金流的影響為中性。

Second quarter free cash flow was $459 million, bringing year-to-date free cash flow to $652 million. During the second quarter, we deployed $1.1 billion on share repurchases and bought back over 22 million shares at an average price of approximately $51 per share. This includes $500 million accelerated share repurchase program announced in May.

第二季自由現金流為4.59億美元，使年初至今自由現金流達到6.52億美元。第二季期間，我們投入11億美元進行股票回購，以每股約51美元的平均價格回購超過2,200萬股。其中包含5月宣布的5億美元加速股票回購計畫。

Year-to-date, we have returned $1.9 billion to shareholders through share repurchases and dividends and remain on pace to return about $2.6 billion. This represents more than 10% of our current market cap. We have also deployed $1.3 billion on acquisitions aligned with our AI builder strategy. Finally, we ended the quarter with cash and short-term investments of $1.1 billion.

年初至今，我們透過股票回購與股利向股東返還19億美元，並仍按計畫可望返還約26億美元。這相當於我們目前市值的10%以上。我們也投入13億美元進行與AI Builder策略一致的收購。最後，本季期末我們持有現金與短期投資11億美元。

Turning to guidance. For the third quarter, we expect revenue to grow 3.8% to 5.3% year over year in constant currency. This includes approximately 200 basis points from our recently completed acquisitions. As we discussed on our last earnings call, our prior guidance range contemplated an improved discretionary spending environment at the midpoint. Instead, macro uncertainty has remained elevated.

接著談展望。第三季我們預期以固定匯率計算，營收年增3.8%至5.3%。其中約有200個基點來自我們近期完成的收購。如同我們在上次法說會所提到，我們先前的指引區間在中位數假設可選性支出環境改善。然而，宏觀不確定性仍然偏高。

We have, therefore, revised our full year revenue guidance range to 4% to 5.5% growth in constant currency. This includes 150 basis points of inorganic growth, unchanged from our prior expectations, but similar to last quarter, our M&A pipeline remains active. And we are focused on executing with discipline on opportunities aligned with our AI Builder strategy.

因此，我們將全年營收指引區間修正為以固定匯率計算成長4%至5.5%。其中包含150個基點的非有機成長，與先前預期一致；但與上季相同，我們的併購管線仍然活躍。我們也專注以紀律執行與AI Builder策略一致的機會。

While discretionary spending has remained pressured, we have maintained good traction on large deals, which we will expect to continue to ramp in the back half of the year. Our revised guidance range assumes the discretionary spending environment remains stable at the midpoint, while the high end contemplates an improvement in short-cycle revenue in the fourth quarter.

儘管可選性支出仍承壓，我們在大型交易上仍維持良好動能，並預期這些交易將在下半年持續放量。我們修正後的指引區間在中位數假設可選性支出環境維持穩定，而高端則假設第四季短週期營收有所改善。

There are no changes to our Project Leap cost estimates or expected savings, and we continue to expect the program will run through the remainder of the year.

我們的Project Leap成本估計或預期節省金額沒有變動，並仍預期該計畫將持續至今年剩餘期間。

Our adjusted operating margin guidance is unchanged at 16% to 16.2%, representing 20 to 40 basis points of year-over-year expansion. Our free cash flow conversion guidance for the year remains 90% to 100% of net income. Full year tax rate is now expected to be towards the low end of our prior guidance range of 25% to 26%. Based on our current expectations, we expect our third quarter rate to be above the high end of the full year range. We now expect full year weighted average diluted share count of approximately 460 million, down from our prior estimates due to the pace of repurchases in Q2.

我們的調整後營業利益率指引維持不變，為16%至16.2%，代表年增擴張20至40個基點。我們全年自由現金流轉換率指引仍為淨利的90%至100%。全年稅率目前預期將落在先前25%至26%指引區間的低端。依據目前預期，我們第三季稅率將高於全年區間的高端。我們目前預期全年加權平均稀釋後流通股數約為4.60億股，因第二季回購速度而較先前估計下修。

Interest expense has also increased modestly, reflecting a lower cash balance and the drawdown of our revolver this quarter. As a reminder, the previously disclosed enactment of the Indian Labor Code reforms in 2025 has resulted in a higher run rate of other expenses.

利息費用亦小幅上升，反映現金餘額較低以及本季動用循環信用額度。提醒一下，先前已揭露的印度《勞動法典》改革於2025年生效，已導致其他費用的常態化水準提高。

We expect this below-the-line cost related to our India defined benefit plan will be around $10 million per quarter for the foreseeable future, consistent with the first half 2026 run rate. This is in line with our estimates in our initial guidance in February, but we are highlighting it to support your modeling. Our EPS guidance has increased to $5.70 to $5.82, representing 8% to 10% growth versus 7% to 9% growth previously.

我們預期與印度確定給付退休金計畫相關的這項線下成本，在可預見的未來每季約為1,000萬美元，與2026年上半年常態化水準一致。這與我們2月初始指引中的估計一致，但我們特別提出以利各位建模。我們的EPS指引上調至5.70至5.82美元，代表成長8%至10%，高於先前的7%至9%。

Finally, we continue to make progress and advance on our evaluation of a potential primary offering and secondary listing in India. We are working in close collaboration with external stakeholders and regulators.

最後，我們持續推進並深化對於在印度進行潛在首次公開發行（primary offering）與第二上市（secondary listing）的評估。我們正與外部利害關係人及監管機關密切合作。

We will make a decision on this once we have visibility of the revised regulatory framework. We are pleased with the progress made to date and remain committed to acting in the best interest of our shareholders. We will provide updates as appropriate.

待我們對修訂後的監管框架有更清楚的能見度後，將就此作出決定。我們對目前取得的進展感到滿意，並仍致力於以股東最佳利益行事。我們將在適當時機提供更新。

With that, we will open the call for your questions.

接下來我們開放提問。

(Operator Instructions) Maggie Nolan, William Blair.

（接線員指示）Maggie Nolan，William Blair。

I'm hoping you can give us a little bit more commentary on the business momentum in the context of bookings growth compared to last quarter as well as that second half ramp-up that you had previously expected from large deals. Maybe update us on how those signings and ramps are progressing and how it now shapes your second half expectations.

我希望你們能就業務動能提供更多評論，特別是在訂單（bookings）成長相較上季的表現，以及你們先前預期由大型交易帶動的下半年放量。也請更新這些簽約與放量的進展，以及這如何影響你們目前對下半年的預期。

We've continued to have good bookings momentum. Last quarter, we did 22% bookings growth. TTM this quarter has been 5%. If you take the first half, it is 6%. It's a tough compare also because we had two mega deals last year. And last year, we grew by almost 18% in quarter two last year. So keeping all this in context, I think we have done pretty well on bookings, and I actually feel very confident about bookings for the rest of the year as well.

我們的訂單動能持續良好。上季我們的訂單成長為22%。本季過去十二個月（TTM）為5%。若看上半年則為6%。這也是一個艱難的比較，因為去年我們有兩筆超大型交易。而且去年第二季我們成長接近18%。綜合以上背景來看，我認為我們在訂單方面表現相當不錯，而且我對今年剩餘期間的訂單也非常有信心。

Now one of the nuances which we are excited about in our bookings momentum is Financial Services is really running hot. I mean, you've seen in Q4, we had 9% growth, 9%-plus. In Q1, we had 10%-plus and now 12%. So Financial Services has literally -- has overwhelming increase in bookings in the first half, and I expect that to remain very strong in the second half.

另外一個我們對訂單動能感到振奮的細節是，金融服務業務確實非常火熱。你們在Q4看到我們成長9%，9%以上。Q1為10%以上，現在是12%。因此金融服務在上半年訂單確實大幅增加，我預期下半年仍會非常強勁。

We did seven large deals, three new logos. We are starting to see activation of $50 million to $100 million deals, which have significantly improved. I mean, if you take those two large deals out and compare from last year, $50 million to $100 million deals have gone through a massive bump, $25 million to $50 million deals have gone through a massive bump. Percentage of new business has bumped up by 10% in the first half in comparison -- 10%-plus in comparison to the mix, which is also good because it kind of translates to new revenue -- incrementally new revenue for the second half.

我們完成了七筆大型交易，其中三個為新客戶（new logos）。我們開始看到5,000萬至1億美元規模的交易啟動，且情況明顯改善。我的意思是，如果把去年那兩筆大型交易排除後再比較，5,000萬至1億美元的交易出現大幅躍升，2,500萬至5,000萬美元的交易也出現大幅躍升。上半年新業務占比相較於組合提升了10%以上，這也很好，因為它某種程度上會轉化為下半年新增營收——增量的新增營收。

So we've had a pretty good step-up change in our bookings momentum. When we entered the year in 2025, we were at $27 billion TTM, then we got to $28 billion TTM. And in the last two quarters, we are at $29 billion TTM. So we are starting to move up. And bookings are going to be a little bumpy between quarters. But if you look at the aggregate numbers and you look at TTM and you look at the tail velocity of the last two quarters, we feel super excited about the second half as well.

因此，我們的預訂動能出現了相當不錯的躍升式變化。當我們在 2025 年進入年度時，我們的 TTM 為 270 億美元，接著提升到 280 億美元 TTM。而在最近兩個季度，我們達到 290 億美元 TTM。所以我們開始往上走。而預訂在季度之間可能會有些起伏。但如果你看整體數字、看 TTM，並看最近兩個季度的尾端增速，我們對下半年同樣感到非常振奮。

And then on the BPO business, you've seen good traction there. Can you talk a little bit more about where you're seeing that traction from an end market perspective? Is it really your vertical expertise that's helping there? Or is it more the partnerships that you outlined with some of the model providers and others that are important in this space? What's driving the success there? And how can you perpetuate it?

接著談到 BPO 業務，你們已經看到那裡有不錯的進展。你能否再多談一些，從終端市場的角度，你們看到的動能主要來自哪裡？真的是你們的垂直產業專業能力在發揮作用嗎？還是說，更重要的是你們所提到的、與部分模型供應商及其他夥伴的合作關係，對這個領域更關鍵？那裡的成功是由什麼驅動的？以及你們要如何延續它？

Great question. In fact, BPO has always been a blockbuster service line for Cognizant over the last three years. We continue to lead on industry vertical BPO over the last few years. In fact, even when I came on board in 2023, the BPO organization was called Intuitive Operations. So it had embedded itself with data and automation and machine learning then and now AI-led BPO.

問得很好。事實上，在過去三年裡，BPO 一直是 Cognizant 的爆款服務線。過去幾年我們持續在產業垂直 BPO 領域保持領先。其實，即使我在 2023 年加入時，BPO 組織當時稱為 Intuitive Operations。因此它當時就已把資料、自動化與機器學習嵌入其中，而現在則是 AI 驅動的 BPO。

Maggie, I've actually mentioned this in my remarks as well as in all my commentary in the last one year, the expansive opportunity of system integration services goes from a $1 trillion market where we build software systems for companies to embedding technology, which is AI technology, agentic into business operations of firms. And that is going to move our market from $1 trillion to $5 trillion to $6 trillion, and it is actually much, much more expansive than ever before, and it kind of brings data, technology and process altogether.

Maggie，我其實也在我的發言以及過去一年所有評論中提到過：系統整合服務的龐大機會，正從一個約 1 兆美元、我們為企業建置軟體系統的市場，擴展到把技術——也就是 AI 技術、具代理能力（agentic）的技術——嵌入企業的營運之中。這將把我們的市場從 1 兆美元推升到 5 兆到 6 兆美元，而且實際上比以往任何時候都更為廣闊，並且把資料、技術與流程整合在一起。

So we are very excited about the BPO business with the strength of our model company partnerships where we cannot just apply it for software engineering, but apply it for business operations, vertical and horizontal and also platformize that business. I mean, our TriZetto business is running at a higher velocity than the rest of the company and the BPaaS business underneath it, which is healthcare operations is equally running with the same velocity. So we want to replicate the platforms, AI-led agentic business operations for companies.

因此，我們對 BPO 業務非常興奮，尤其是憑藉我們與模型公司的合作夥伴關係，我們不僅能把它用於軟體工程，也能用於業務營運——涵蓋垂直與水平面——並且把該業務平台化。我的意思是，我們的 TriZetto 業務運行速度高於公司其他部分，而其下的 BPaaS 業務（也就是醫療保健營運）同樣以相同的速度在運行。所以我們希望複製這些平台，為企業打造 AI 驅動、具代理能力的業務營運。

Just to give you an example, we have a blueprint for F&A, frontier-led F&A. We have a blueprint for frontier-led customer operations. So we've kind of started to put that in the mix, which effectively means we can embed digital labor and human labor and deliver outcomes through frontier operators, as we call -- as we coined it. It's a new archetype of role and deliver those services to our clients. So I'm pretty upbeat about the future of our business process operations and agentic-led business process operations.

舉例來說，我們已經有一套針對 F&A 的藍圖，也就是 frontier-led 的 F&A。我們也有一套針對 frontier-led 客戶營運的藍圖。因此我們已開始把這些納入組合，這實際上意味著我們可以嵌入數位勞動與人力勞動，並透過我們所稱——我們自己命名的——frontier operators 來交付成果。這是一種全新的角色原型，並把這些服務交付給客戶。所以我對我們業務流程營運，以及由代理式（agentic）驅動的業務流程營運的未來相當樂觀。

We also have a training capability now, which is the AI data training services. Historically, we did it for the big -- for the Magnificent Seven companies. Now we are transitioning that capability to AI-led into the Global 2000 because if intelligence is not going to be drawn centrally and if enterprises are going to build distributed intelligence, they're going to have their own specialized models. We think we have a unique service to attach to it. We have 10,000-plus associates who work on data training services. That's a part of our BPO organization.

我們現在也具備一項訓練能力，也就是 AI 資料訓練服務。過去我們主要為大型——為「七巨頭」（Magnificent Seven）公司提供這項服務。現在我們正把這項能力轉向以 AI 驅動、服務全球 2000 大企業，因為如果智慧不再由中央集中提供，而企業要建立分散式智慧，它們就會擁有各自的專用模型。我們認為我們有一項獨特的服務可以與之配套。我們有超過 10,000 名員工從事資料訓練服務。這是我們 BPO 組織的一部分。

Jim Schneider, Goldman Sachs.

Jim Schneider，高盛。

Ravi, I think relative to your comments about corporate one out of four sort of pausing their AI progress because of cost or return issues, can you maybe talk about more tactically? I mean, you've talked about how Cognizant can address that opportunity, but can you maybe talk about more tactically what customers are doing then? If they pause, what is their sort of immediate action? Are they going back to sort of more traditional implementation work or outsourcing work? Or are they just sort of pausing until they can get a better handle on the scenario?

Ravi，針對你提到大約四分之一的企業因成本或回報問題而暫停其 AI 進展，你能否更具體地談談？我的意思是，你談過 Cognizant 如何把握這個機會，但你能否更戰術性地談談客戶接下來在做什麼？如果他們暫停，他們的即時行動是什麼？他們會回到較傳統的導入工作或外包工作嗎？還是他們只是先暫停，直到能更清楚掌握情境？

And how long would you think it would be on an average engagement before you can really see for Cognizant, a big uptick at customers like that?

以及你認為平均而言，在這類專案中需要多久，Cognizant 才能在這些客戶身上真正看到明顯的上升動能？

Jim, great question again. Thank you. Look, the first chapter of AI adoption was broad-based, open-ended experimental and this is a magical -- the technology was magical. So everybody tried to use it in a way that they could find some magic coming out of outcomes. As you productionize this, which is the second chapter, you're going to go very nuanced, you want to start to focus on not token consumption, but token economics, and you're going to start to optimize where you use advanced reasoning and where you don't use advanced reasoning.

Jim，又是一個很好的問題。謝謝。你看，AI 採用的第一章是廣泛、開放式的實驗，這項技術很神奇——所以每個人都嘗試以各種方式使用它，希望能從結果中找到一些「魔法」。當你把它產品化（productionize），也就是第二章，你會變得非常細緻，你會開始關注的不是 token 消耗，而是 token 經濟學，並開始最佳化在哪些地方使用進階推理、哪些地方不使用進階推理。

So the step back from clients is to say, wait a minute, I'm spending a lot of money on tokens. I'm spending a lot of money on the entire AI stack. Am I getting the value? And if I'm not, let me revisit how to optimize it and get value out of it. The capabilities out there, I've said this, the production value is way below and the bridges to that production value is assembling context, assembling tribal knowledge, setting the guardrails, grounding the technology into the heterogeneity of an enterprise.

因此，客戶的退一步是說：等一下，我在 token 上花了很多錢。我在整個 AI 技術堆疊上也花了很多錢。我有得到價值嗎？如果沒有，那就讓我重新檢視如何最佳化並從中取得價值。我一直說，現有能力的「生產價值」遠低於應有水準，而通往該生產價值的橋樑在於：彙整情境、彙整部落知識（tribal knowledge）、設定護欄，並把技術落地到企業的異質性環境中。

And we have started to believe now that we have a role to play, a big role to play in that process, starting from building the harnesses where you can capture the context so that when you do the transactions on a regular basis, you can create repeatability, model routing, which means depending on the kind of task, you could use an open weight model, you could use a costly/expensive closed frontier model or you could use a cheaper closed frontier model or you could -- you may not use a model.

而我們現在開始相信，我們在這個過程中有角色可扮演，而且是很大的角色：從建立能捕捉情境的「工具框架」（harnesses）開始，讓你在日常進行交易時能建立可重複性；以及模型路由（model routing），也就是依任務類型，你可以使用開放權重模型（open weight model）、使用昂貴的封閉式前沿模型（closed frontier model），或使用較便宜的封閉式前沿模型，或者你也可能——不使用模型。

And then creating learning loops between human effort and machine effort so that you could integrate human and machine effort together, which means we have a methodology called basis in our consulting organization, which allows us to reinvent and reimagine those processes.

接著在人力與機器之間建立學習迴圈，讓你能把人力與機器的努力整合在一起；也就是說，我們在顧問組織中有一套稱為 basis 的方法論，使我們能重新發明並重新想像這些流程。

So if you put all of this together, there's a lot of heavy lift needed before you can actually productionize it and get value. And we are building platforms, and we're building services underneath it. Our clients are coming back to us for a variety of things, starting from productivity, which is related to software engineering, which was historically for the last two years, very mainstream.

因此，把這些放在一起看，在你真正能把它產品化並取得價值之前，需要做大量的重工。而我們正在打造平台，也在其下建立各項服務。我們的客戶正因各種需求回到我們這裡，從生產力開始——這與軟體工程相關，而在過去兩年裡，這一直是非常主流的。

Now going back to my previous response, they're coming back to us on business operations. I mean, business operations is where the future is because you want to embed this technology into business operations if $6 trillion of AI ramp on the infrastructure is going to happen in the next three years, $15 trillion to $20 trillion has to come out of value from enterprise businesses. And that's not going to come from system building, through AI-first software engineering, but it will actually come from embedding it into business operations.

回到我先前的回覆，他們現在回到我們這裡談的是業務營運。我的意思是，業務營運才是未來所在，因為如果未來三年在基礎設施上將出現 6 兆美元的 AI 加速投入，你就必須把這項技術嵌入到業務營運中，企業端必須釋放出 15 兆到 20 兆美元的價值。而這不會來自於系統建置、或透過 AI 優先（AI-first）的軟體工程；它實際上會來自把 AI 嵌入到業務營運之中。

So clients are using it for using it for -- using us for getting the frontier capacity, the frontier engineering and frontier operator capacity, platforms, harnesses, context engineering, reimagining the workflows. And some of our clients are starting to ask us to deliver an AI-infused rate card, which means you embed the pretraining costs and you embed the inference costs into that process. Software engineering is very mature. Business operations is actually evolving now. And we have a third harness for physical AI, which we are preparing, which we think will be the future as we go forward.

所以客戶正在使用它——也就是使用我們——來取得前沿算力、前沿工程與前沿營運能力，以及平台、harness（工具框架）、情境工程（context engineering），並重新想像工作流程。而我們的一些客戶開始要求我們提供「AI 注入式」的費率卡（rate card），也就是把預訓練成本與推論成本嵌入到該流程之中。軟體工程非常成熟。業務營運其實正在演進。此外，我們還在準備第三個用於實體 AI 的 harness，我們認為這將是未來的方向。

So that's the broad story. So the ability to build that bridge is what will drive companies like us to add value in the process.

這就是整體的故事。因此，能夠搭起這座橋樑的能力，將驅動像我們這樣的公司在這個過程中創造價值。

And then maybe as a follow-up, a financial question sort of maybe for Jatin. Can you maybe talk broadly to sort of your overall hiring and headcount plan in relation to gross margins. I saw headcount ticked down a little bit sequentially. I'm assuming a lot of that was just Project Leap and some efficiencies there. But maybe talk about your hiring plans over the next two or three quarters. And to what extent you expect to be able to hold gross margins at or above the current level?

接著也許作為追問，一個財務問題，可能是問 Jatin。你能否大致談談你們整體的招募與人力（headcount）規劃，與毛利率之間的關係？我看到人力規模在環比上略有下降。我猜其中很大一部分只是 Project Leap 以及相關效率提升。但能否談談未來兩到三個季度的招募計畫？以及你們預期在多大程度上能把毛利率維持在目前水準或更高？

Sure, Jim. So as you rightly observed, we have flattish headcount between quarter one and quarter two. We continue to add the recent college graduates to the company, as you indicated in the past, and we have made good progress by the end of first half. And we remain on track to get to approximately 20,000 by the end of the year. The Project Leap will -- is also underway.

當然可以，Jim。如你所觀察到的，我們第一季到第二季的人力規模大致持平。我們持續招募近期的大學畢業生加入公司，正如你過去提到的，我們在上半年末已取得不錯的進展。我們仍按計畫在年底前達到約 20,000 人。Project Leap 也正在推進中。

And as a result, you will see a certain amount of headcount reduction. So on the balance, we expect that the headcount should remain range bound versus an increase. We -- and that's how we are budgeting or we are planning for rest of the year.

因此，你會看到一定程度的人力縮減。整體而言，我們預期人力規模將維持在一個區間內波動，而不是持續增加。我們——這也是我們在今年剩餘期間的預算編列與規劃方式。

So long as gross margin is concerned, you have, I'm sure, noticed the improvement that we were able to execute between quarter one and quarter two, which is roughly 60 basis points. We are still trending a little lower than last year. And we will continue to work on it during the course of the year. And I do hope that we continue to show an improvement in that number as quarters progress.

至於毛利率，我相信你已注意到我們在第一季到第二季之間實現了改善，約 60 個基點。我們的水準仍略低於去年。我們會在今年過程中持續努力改善。我也希望隨著季度推進，我們能持續在這個數字上展現改善。

Jamie Friedman, Susquehanna International.

Jamie Friedman，Susquehanna International。

Good results here. I wanted to ask about the linearity of the remainder of the year. And Jatin, the sequential assumption on the Q4. It looks like if you're at or just above the midpoint on the Q3, you could be flat to slightly down in the Q4 sequentially. But there is some M&A in there. So if you could help us think about how you're thinking about the sequential Q4 in particular, on an organic basis, that would be helpful.

這次的結果不錯。我想問一下今年剩餘期間的線性（linearity）表現。以及 Jatin，對第四季的環比假設。看起來如果你們第三季落在指引中位數或略高，第四季環比可能持平到小幅下滑。但其中包含一些併購（M&A）。所以如果你能幫我們理解，特別是以有機（organic）口徑來看，你們如何思考第四季的環比走勢，會很有幫助。

Sure. So we have modeled it based on the trends that we see every year, what we have super imposed this year are a couple of variables. One is the larger new and expansion percentage of bookings that we have seen from the beginning of this year. We also have seen the ramp-up of the deals, which are in transition phase now and which will move to more to billable volumes in quarter three, quarter four.

好的。我們是依據每年所看到的趨勢來建模；而今年我們疊加了幾個變數。第一個是今年年初以來，我們看到新增與擴張訂單（bookings）占比更高。我們也看到一些交易正在爬坡，這些交易目前處於轉換（transition）階段，並將在第三季、第四季更多轉為可計費量（billable volumes）。

And finally, we do have a view on furloughs. As you know, the furloughs typically are represented largely by banking and financial services, and that is continuing to be very robust this year. So we have assumed a slightly lower proportion of furloughs coming in quarter four. So the assumption is a slightly superior sequential growth in quarter four compared to what we have seen traditionally in quarter four, which is typically negative because of the bill days impact and furloughs.

最後，我們也對休假停工（furloughs）有一個判斷。如你所知，休假停工通常主要來自銀行與金融服務業，而今年該領域仍然非常強勁。因此我們假設第四季的休假停工占比會略低。所以我們的假設是：第四季的環比成長會比傳統第四季略好；傳統上第四季通常為負成長，因為計費天數影響與休假停工。

Perfect. And then, Ravi, I just want to ask about Products and Resource. It's been a couple of years now since Belcan closed. You had a ton of inorganic in the period of comparison in the Q3. So at a higher level, though, how is Products and Resources performing relative to what you had expected when you closed the deal?

很好。另外，Ravi，我想問一下「產品與資源」（Products and Resource）。Belcan 併購案完成至今也有幾年了。在第三季的比較基期中，你們有大量的非有機（inorganic）因素。但從更高層次來看，「產品與資源」的表現相較於你們在交易完成時的預期如何？

Yeah. So that's a great question. In fact, one of my endeavors is to go beyond Financial Services and Healthcare and create more diversity in our portfolio. So we are very pleased with the performance in Products and Resources over the last few quarters. We've got some good traction, new logos.

是的。這是個很好的問題。事實上，我的其中一項努力是超越金融服務與醫療保健，讓我們的產品組合更具多元性。因此，我們對過去幾季「產品與資源」的表現非常滿意。我們取得了一些不錯的進展，也拿下了新的客戶標誌（new logos）。

You've seen a section on my earnings around Belcan and its tailwind with other public sector opportunities we have won using the Belcan engine. That is a great add to our portfolio mix. We are starting to see significant traction with our clients on physical AI, which I spoke about. We have built a harness around it. It's called the Intelligence Spine. And we have just now hired a new leader for oil and gas.

你也在我的財報說明中看到一段談到 Belcan，以及我們如何運用 Belcan 引擎，在其他我們贏得的公共部門機會中形成順風（tailwind）。這對我們的產品組合結構是一個很好的補強。我們也開始在客戶端看到實體 AI（physical AI）的顯著動能，我先前也提到過。我們已圍繞它建立了一個 harness。它叫做 Intelligence Spine（智慧脊柱）。而且我們剛剛也為油氣（oil and gas）領域聘請了一位新的領導者。

So we are continuing to make good progress on diversifying our portfolio and Products and Resources is one of the important areas to do. I actually believe the AI opportunity will actually be in -- you will see a leapfrog of digital enhancement on physical things in Products and Resources, and you will equally see low-margin businesses, which is what I mean by it is our clients, our enterprise clients, they're going to use AI to unlock more value than high-margin businesses just because of the productivity opportunity there. So Products and Resources is going to be one of our high investment zones in the future, and we'll continue to invest to make it a very important portfolio for Cognizant.

因此，我們在推動產品組合多元化方面持續取得良好進展，而「產品與資源」是其中一個重要領域。我其實相信 AI 的機會將會在——你會看到在「產品與資源」領域，AI 會讓實體事物的數位強化出現跳躍式提升；同時你也會看到低毛利業務——我的意思是，我們的客戶、我們的企業客戶，將會用 AI 在低毛利業務中解鎖比高毛利業務更多的價值，原因在於那裡的生產力提升空間更大。因此，「產品與資源」將是我們未來的高投資區域之一，我們會持續投入，讓它成為 Cognizant 非常重要的產品組合板塊。

Darrin Peller, Wolfe Research.

Darrin Peller，Wolfe Research。

All right. Can you just touch on how you'd assess the competitive dynamics in the market right now, just especially for the larger deals, how important is pricing in these discussions right now? And then just when you when you're having these discussions with customers, what do they look like when large deals come up for renewal, just productivity savings they're demanding now versus prior?

好的。你能否談談你如何評估目前市場的競爭態勢，特別是針對較大型的交易；在這些討論中，定價現在有多重要？另外，當你們與客戶進行這些討論、且大型合約面臨續約時，對話通常是什麼樣子？他們現在要求的生產力節省（productivity savings）相較於以前有何不同？

Yeah. So look, we have done productivity-led large deals over the last three years. We have outperformed on our margin performance versus what we originally assumed. We have -- we've done pretty well in winning more than our peers. And that has led to a large deal momentum over the last two years.

是的。所以你看，過去三年我們做了以生產力驅動的大型交易。我們的利潤率表現超出最初的假設。我們——我們在贏得交易方面做得相當不錯，勝過許多同業。而這也帶動了過去兩年大型交易的動能。

Now we have progressively gone into newer things, which is my second and third swim lane, which is doing old things in new ways, which is primarily, say, I do a mainframe migration using frontier models or I do SAP HANA migrations or I do a vulnerability remediation coming out of security or SaaS reimagination. All those are starting to become large deals.

現在我們逐步進入更新的領域，也就是我第二與第三條泳道：用新方式做舊事。主要例如，使用前沿模型進行主機（mainframe）遷移，或做 SAP HANA 遷移，或從資安出發做弱點修補，或進行 SaaS 的重新想像。這些都開始成為大型交易。

And new things in new ways, which is primarily using AI to do things which we didn't do before for growth imperatives or smaller deals because they come more modular. So the mix of deals has changed as we progressed on this process.

以及用新方式做新事，主要是運用 AI 去做我們以前沒做過的事情，以滿足成長要務；或是因為更模組化而形成較小型的交易。因此，隨著我們推進這個流程，交易組合也發生了變化。

And on -- specifically on productivity, look, unlike in the past where you had circuit breakers and how much you could do on linear productivity because labor costs are not as nonlinear. Now you have a level of nonlinearity because you could pass on that productivity to clients and outreach what you have actually committed to clients and keep some for yourselves. And that's why our margin profile on all our large deals, both $50 million above and $100 million above, is actually trending much better than what we originally signed the contracts for.

至於——特別是在生產力方面，你看，不同於過去你有「斷路器」以及在線性生產力上能做到多少的限制，因為人力成本並沒有那麼非線性。現在你有一定程度的非線性，因為你可以把那個生產力讓利給客戶，交付超出你實際對客戶承諾的內容，並且把一部分留給自己。這就是為什麼我們所有大型交易（不論是 5,000 萬美元以上或 1 億美元以上）的利潤率輪廓，實際上都比我們最初簽約時的水準趨勢更好。

So as long as we stay ahead on AI-led productivity for software engineering and business process operations, and we keep staying ahead of it, we can pass on the productivity, stay competitive in the market and still be margin accretive for ourselves. And that flywheel is working very nicely for us. And that's why we're continuing to win large deals, which are productivity-led. We will start to see that move from software engineering to business process operations where the span -- where the expansive opportunity is going to be much, much more.

所以只要我們在 AI 驅動的軟體工程與業務流程營運（BPO）生產力上保持領先，並持續領先，我們就能把生產力讓利出去、在市場上保持競爭力，同時對我們自身仍具利潤增厚效果。而這個飛輪對我們運轉得非常好。這也是為什麼我們持續贏得以生產力驅動的大型交易。我們將開始看到這種動能從軟體工程轉向業務流程營運，因為那裡的機會廣度——也就是可擴張的機會——會大得多、多得多。

All right. That's helpful. Maybe just a quick follow-up would be around what the path looks forward for scaling the frontier certified workforce that you described earlier. Just what degree will this come from new hires versus existing? And then just how are you going to keep differentiating as other companies try to develop front our workforces also?

好的。這很有幫助。也許再快速追問一下：關於你先前提到的、擴大前沿認證（frontier certified）人才隊伍的未來擴張路徑，看起來會是什麼樣子？其中有多少會來自新招募、又有多少來自既有人員？另外，當其他公司也嘗試打造他們的前沿人才隊伍時，你們要如何持續保持差異化？

We're doing it at scale. We are hiring at the bottom of the pyramid from outside and we're doing it at scale, building bridges from inside. Just look at where we are. Earlier this week, we announced we have the largest pool of Claude-certified architects on the planet. We have 10,000 plus.

我們正在以規模化方式推進。我們在金字塔底部從外部招募，並以規模化方式進行，同時也從內部搭橋培養。你只要看看我們目前的狀況。本週稍早，我們宣布我們擁有全球最大的 Claude 認證架構師人才庫。我們有超過 10,000 名。

We just finished the hackathon today with open AI in India, and we have 10,000 associates getting badges. We're doing a similar exercise with Gemini. We are also doing a lot of work with open weight models. So at scale, bending the cost curve and having the context of businesses to deploy the talent and get value out of it is what will drive companies like ours to be on the cutting edge.

我們今天剛在印度與 OpenAI 完成黑客松（hackathon），有 10,000 名員工取得徽章。我們也在與 Gemini 做類似的活動。我們也在開放權重模型（open weight models）上做了大量工作。因此，以規模化方式去壓低成本曲線，並且具備業務情境來部署人才、從中取得價值，將驅動像我們這樣的公司站在最前沿。

You need a combination of things. You need to know how to reinvent the flows. You need to know how to audit the flows, integrate the agentic work into the business flows. You need to know the context, and you need to do it at scale at a lower cost, bending the cost curve. And that's what we're doing.

你需要多種要素的組合。你需要知道如何重新發明流程。你需要知道如何稽核流程，把代理式（agentic）的工作整合進業務流程。你需要理解情境，並且要以更低成本、以規模化方式去做，讓成本曲線下彎。而這正是我們在做的事。

So I'm pretty confident that we will have the largest pool of certified frontier engineers and frontier operators, which is a new archetype of a role we have established. Frontier engineers is about engineering agentic tech into business flows. Frontier operators is about operating those flows, which has digital and human labor together.

所以我相當有信心，我們將擁有最大的前沿認證工程師與前沿操作員（frontier operators）人才庫；這是我們建立的一種新的角色原型。前沿工程師是把代理式技術工程化地嵌入業務流程。前沿操作員則是負責營運那些流程，讓數位勞動與人力勞動共同運作。

And doing -- bending the cost curve is what we have done for the last 30 years at scale, and that's what we are continuing to do. So it's a combination of building a pipe from outside, building a pipe at the bottom of the pyramid, early carriers and building bridges from inside.

而讓成本曲線下彎——以規模化方式做到這點——是我們過去 30 年一直在做的事，我們也會持續做下去。所以這是外部建立管道、在金字塔底部建立管道（早期職涯），以及從內部搭橋的組合。

Tien-Tsin Huang, JPMorgan.

黃天勤（Tien-Tsin Huang），摩根大通（JPMorgan）。

I just want to ask around Financial Services. That was up double digits. It's growing at a premium over the other sectors. In the past, we've looked at that sector as a maybe a leading indicator of another subsectors would follow. Do you see that potentially being the case here? Should we be encouraged that that could be the case? Or is there something unique that maybe is a little bit different in terms of their willingness to adopt some of these AI-driven projects?

我想問一下金融服務業。那個部門是雙位數成長。它的成長溢價高於其他產業。過去我們曾把該產業視為其他次產業可能跟進的領先指標。你認為這次也可能是這樣嗎？我們是否應該因此感到鼓舞，覺得可能會如此？或者在他們採用這些 AI 驅動專案的意願上，有沒有什麼獨特之處、使得情況有些不同？

Absolutely, Tien-Tsin. Thank you. Look, Financial Services has always been a pioneering industry, high on technology spend, they create asymmetry using technology, and they're on the cutting edge on AI. In fact, Financial Services, we have -- we are probably the number one company on growth in our peer group. All of last year, we did higher single digits.

當然，Tien-Tsin。謝謝。你看，金融服務一直是先行者產業，科技支出高，他們用科技創造不對稱優勢，而且在 AI 上也走在最前沿。事實上，在金融服務領域，我們——我們大概是同業群中成長表現第一的公司。去年一整年，我們做到較高的個位數成長。

At the end of quarter four, we got to 9%. Quarter one, we got to 10%. Quarter two, we are now at 12%. And Financial Services is activated on all three swim lanes, starting from consolidation, productivity, sharing the productivity to modernization of landscapes using AI and refactoring landscapes, which is my second swim lane to the third swim lane, building new things using AI and generating growth imperatives.

到第四季末，我們達到 9%。第一季達到 10%。第二季我們現在是 12%。而金融服務在三條泳道上都已啟動：從整併、提升生產力、把生產力分享出去，到用 AI 進行版圖現代化與版圖重構（這是我的第二條泳道），再到第三條泳道——用 AI 建立新事物並創造成長要務。

So I am actually super optimistic that financial services will lead the path and other industries will follow. And other industries will, sometimes, leapfrog as well. I mean, I now start to see that in industrial clients who are looking at the miss they had in the digital revolution to leapfrog directly into physical AI. So you are absolutely right. I think it's a leading indicator to what's going to come.

所以我其實非常樂觀，金融服務將引領道路，其他產業會跟進。而其他產業有時也會出現跳躍式超車。我的意思是，我現在開始在工業客戶身上看到這點：他們希望彌補在數位革命中錯失的機會，直接跳躍到實體 AI（physical AI）。所以你完全說得對。我認為這是一個對未來走向的領先指標。

And if you look at our quarter four exit rate, just as a company, it's also powered by Financial Services. We are super excited about the fact that we are exiting -- if you take the midpoint of our guidance range, we are exiting on a high, and we will be on the winner's circle.

而如果你看我們第四季的退出成長率（exit rate），就公司整體而言，也同樣是由金融服務所驅動。我們對於這件事非常興奮：我們正在以強勁態勢退出——如果取我們財測區間的中點，我們是以高點退出，我們將站上勝利者的行列。

Good. And then maybe for you, Ravi and Jatin. Just thinking about -- I have to ask question on tokeconomics, if you don't mind, just thinking about that, any update with respect to cost usage, what you're hearing from your client base. Any update there? I know you talked a lot about that. That's your AI event, but I'd love to hear the update if there any?

很好。接著也許想請教你，Ravi 和 Jatin。我得問一個關於 token 經濟學（tokeconomics） 的問題，如果你不介意的話：就成本使用情況而言，從你們的客戶群那邊聽到什麼最新狀況？這方面有任何更新嗎？我知道你們談過很多。那是你們的 AI 活動，但如果有任何更新我很想聽聽。

I think it's continuing to be the hot topic. The conversation has moved from consumption of tokens to optimizing token usage, not using tokens where not needed, using open weight where needed, building specialized models using open weight as the base, making a difference between using expensive closed frontier models to not expensive closed frontier models, capturing the learning and creating a learning loop and building an alpha around it.

我認為這仍將是熱門話題。討論已從代幣消耗轉向最佳化代幣使用：在不需要的地方不使用代幣、在需要的地方使用開放權重、以開放權重作為基礎建立專門化模型、區分使用昂貴的封閉式前沿模型與不昂貴的封閉式前沿模型、捕捉學習並建立學習迴圈，並在其上建立 alpha。

So I mean, all of these have become so much a hot topic of discussion, especially customers who are doing business operations. I mean, software engineering is more mature now. Business operations is not as mature. So this is going to be a hot topic of discussion for the next 12 months, and it will actually lead to more and more work and more and more services for companies like us. And it also means bundling pretraining and inference costs along with our services.

所以我的意思是，所有這些都已成為非常熱門的討論話題，尤其是正在做業務營運的客戶。我的意思是，軟體工程現在更成熟了。業務營運則沒那麼成熟。因此，這將是未來 12 個月的熱門討論話題，並且實際上會為像我們這樣的公司帶來越來越多的工作與越來越多的服務。這也意味著要把預訓練與推論成本與我們的服務一起打包。

So we will -- we now have arrangements with all three frontier model companies to bundle those services and bundle the inference and bundle the pretraining costs, which means we will have to build that craft and that craft is an important craft to build it because it will then mean that the input factor is not just going to be human effort. It's going to be human effort, platforms, software, frontier services, all bundled for an output, which is not effort-based but outcome-based.

因此我們將——我們現在已與三家前沿模型公司都達成安排，把那些服務打包，並把推論與預訓練成本一併打包；這意味著我們必須建立那套工藝，而建立那套工藝很重要，因為這將意味著投入要素不再只是人力投入。它將是人力投入、平台、軟體、前沿服務等全部打包，以交付一個輸出——不是以投入工時為基礎，而是以成果為基礎。

Bryan Bergin, TD Cowen.

Bryan Bergin，TD Cowen。

I'm curious if you can share any kind of rough mix of the managed services business that already incorporates Gen AI-led efficiencies. And I'm just trying to understand the balance of the multiyear book that still needs to go through a cycle of renewals so that we can kind of better project a potential crossover point when you see a potential acceleration from AI activity that can more than offset that existing base compression and other factors.

我想了解你是否能分享一下：在託管服務業務中，已經納入由生成式 AI 帶動的效率提升的大致占比。我只是想理解：仍需經過一輪續約週期的多年期訂單存量之間的平衡，這樣我們就能更好地推估一個可能的交叉點——也就是你看到 AI 活動可能加速、且足以抵消既有基礎的壓縮與其他因素的時間點。

Yeah. I think it's -- as you know, our revenue has roughly two components, time and material and fixed price and fixed prices, both are now 50-50. So our view is that time and material is continually every time we renew it and that short-cycle business, typically six to nine months, sometimes 12 to 15 months, but never more than 2 years or 2.5 years. So that's a short-cycle business. So that's continually embedding in itself, even on a managed services basis, the benefit of AI into itself.

是的。我認為——如你所知，我們的營收大致有兩個組成：按工時與材料（T&M）以及固定價格；目前兩者各占 50%。因此我們的看法是，按工時與材料的業務每次續約時——而且那是短週期業務，通常 6 到 9 個月，有時 12 到 15 個月，但從不超過 2 年或 2.5 年。所以那是短週期業務。因此它會持續把 AI 的效益嵌入自身，即使是在託管服務的基礎上也是如此。

On the remaining 50%, which is fixed price book of business, typically, the contract lines are between 24 and 36 months on an average. Of course, there could be some which are five to seven years and some could be shorter. But typically, on an average, between 24 to 36 months. If we believe that we have started this journey of embedding AI into our solution more actively from beginning of last year, which is 2025, we are roughly halfway into it, and we have probably another half to go.

在其餘 50%（也就是固定價格的業務訂單）方面，通常合約期限平均在 24 到 36 個月之間。當然，也可能有一些是 5 到 7 年，也可能有一些更短。但一般平均是 24 到 36 個月。如果我們相信自去年年初（也就是 2025 年）開始，更積極地把 AI 嵌入我們的解決方案，那麼我們大約已走到一半，可能還有另一半要完成。

Okay. That's very helpful. My follow-up is on Project Leap. So just any further details, how much of the plan have you actioned thus far? Any kind of in-year savings from the program that you realized here in 2Q?

好的。這非常有幫助。我的追問是關於 Project Leap。所以想請問是否有更多細節：到目前為止你們已執行了計畫的多少？在第二季，你們是否已實現任何當年度的節省？

And just anything important for us to consider as far as the pacing of kind of cost and savings yield as you go through 3Q and 4Q?

另外，就你們在第三季與第四季推進時，關於成本與節省產出節奏方面，有沒有什麼我們需要特別考量的重要事項？

Sure, Bryan. So we continue to execute the program. We have taken approximately $84 million of cost in quarter two, of which $50 million, $55 million is from -- related with employee severance and remaining is from -- related with facilities and software and some of that. As we have -- as you know, we have baked in the savings from the program as part of our guidance range, and we believe we are executing well towards that goal. I think you should continue to see the rest of the year, evenly split from Project Leap execution between quarter three and quarter four as we move forward.

當然，Bryan。我們持續執行該計畫。我們在第二季承擔了約 8,400 萬美元的成本，其中 5,000 萬到 5,500 萬美元來自——與員工資遣相關，其餘則來自——與設施、軟體以及其中一些項目相關。如你所知，我們已將該計畫帶來的節省納入我們的財測區間之中，我們相信我們正朝著該目標良好執行。我認為在今年剩餘時間，你應該會看到 Project Leap 的執行在第三季與第四季之間大致平均分攤，隨著我們持續推進。

And we get full year benefit next year. And it also reshapes the cost of technology deployment in the market. To a large extent, this is about margins, but it's equally about growth, can we get more growth using a baseline where productivity is shared with the clients.

而且我們明年會取得全年效益。它也會重塑技術在市場部署的成本結構。在很大程度上，這關乎利潤率，但同樣也關乎成長：在生產力與客戶共享的基準之上，我們能否取得更多成長。

At this time, I'd like to turn the floor back over to management for closing comments.

此時，我想把時間交還給管理層作結語。

Thank you so much for joining in today. We are very, very excited about our quarter two earnings, continue to be on the winner circle. We have confidence of staying at the winner circle for the rest of the year and create some nice tail velocity for the next year. And we are excited about the activation of all three swim lanes, productivity, doing old things in new ways, using AI. And as I talk, we are seeing accelerated momentum on new things in using AI, which is primarily driving growth imperatives for enterprises.

非常感謝各位今天參與。我們對第二季的財報表現感到非常、非常振奮，並持續位居勝利者行列。我們有信心在今年剩餘時間持續保持在勝利者行列，並為明年創造良好的尾端動能。我們也對三條泳道的全面啟動感到興奮：生產力、以新方式做舊事、運用 AI。而且就在我發言的同時，我們看到在運用 AI 做新事情方面的動能正在加速，這主要在推動企業的成長要務。

So thank you again for joining the call today.

再次感謝各位今天參與本次電話會議。

Ladies and gentlemen, this concludes today's teleconference for Cognizant's second quarter 2026 earnings call. You may now disconnect or log off the webcast at this time, and enjoy the rest of your day.

各位女士、先生，Cognizant 2026 年第二季財報電話會議到此結束。您現在可以中斷連線或登出網路直播，祝您今天剩餘時間愉快。