Asana, Inc. (ASAN) 2027 Q2 法說會逐字稿

Thank you for standing by and welcome to Asana's second quarter fiscal year 2027 earnings conference call. (Operator Instructions) I would now like to hand the call over to Eva Leung, Investor Relations. Please go ahead.

感謝您耐心等候，歡迎參加 Asana 2027 會計年度第二季財報電話會議。(接線員指示) 現在我想把電話交給投資人關係部的 Eva Leung。請開始。

Good afternoon, and thank you for joining us on today's conference call to discuss the financial results for Asana's second quarter fiscal year 2027. With me on today's call are Dan Rogers, our Chief Executive Officer; and Aziz Megji, our Chief Financial Officer. Today's call will include forward-looking statements, including statements regarding the expected release and benefits of our product offerings and our expectations for revenue to be generated by those offerings, our retention and expansion opportunities, our expectations for our financial outlook, including our fiscal year '27, full year guidance, strategic plans, our market position and growth opportunities, and our capital allocation strategy, including our stock repurchase program, among other items.

各位下午好，感謝您參加今天的電話會議，與我們一同討論 Asana 2027 會計年度第二季的財務結果。今天與我一同出席的有我們的執行長 Dan Rogers，以及我們的財務長 Aziz Megji。今天的電話會議將包含前瞻性陳述，包括關於我們產品供應預期推出與效益，以及我們預期這些產品將帶來的營收、我們的留存與擴張機會、我們對財務展望的預期（包括 2027 會計年度全年指引）、策略計畫、市場地位與成長機會，以及我們的資本配置策略（包括股票回購計畫）等其他事項。

Forward-looking statements, including risks, uncertainties and assumptions may cause our actual results to be materially different from those expressed or implied by the forward-looking statements. Please refer to our filings with the SEC, including our Annual Report on Form 10-K and our most recent quarterly report on Form 10-Q for additional information on risks, uncertainties and assumptions that may cause actual results differ materially from those set forth in such statements.

前瞻性陳述所涉及的風險、不確定性與假設，可能導致我們的實際結果與前瞻性陳述中明示或暗示的結果存在重大差異。請參閱我們向美國證券交易委員會（SEC）提交的文件，包括 Form 10-K 年度報告以及最近一期 Form 10-Q 季度報告，以取得關於可能導致實際結果與該等陳述所載內容出現重大差異之風險、不確定性與假設的更多資訊。

In addition, during today's call, we will discuss non-GAAP financial measures. These non-GAAP financial measures are in addition to and not a substitute for or superior to measures of financial performance prepared in accordance with GAAP. Reconciliations between GAAP and non-GAAP financial measures and a discussion of the limitations of using non-GAAP measures versus the closest GAAP equivalents are available in our earnings release, which is posted on our Investor Relations website at investor.asana.com. With that, I would like to turn the call over to Dan.

此外，在今天的電話會議中，我們將討論非 GAAP 財務衡量指標。這些非 GAAP 財務衡量指標是對依 GAAP 編製之財務績效衡量指標的補充，並非替代，也不優於 GAAP 指標。GAAP 與非 GAAP 財務衡量指標之間的調節表，以及使用非 GAAP 指標相較於最接近之 GAAP 等值指標的限制說明，已載於我們的財報新聞稿，並發布於投資人關係網站 investor.asana.com。接下來，我想把電話交給 Dan。

We delivered a solid second quarter, exceeding our expectations on both revenue and profitability with continued improvement in the underlying health of the business. There's three things I want to point out this quarter. First, the business continues to get healthier. Growth is accelerating, retention is improving again, and we saw a broad-based strength across industries and geographies. Second, while still early, our AI products are creating a new growth and expansion vector beyond our traditional seat-based model.

我們交出穩健的第二季成績，營收與獲利能力皆超出預期，且業務基本面健康度持續改善。本季我想強調三點。第一，業務持續變得更健康。成長正在加速，留存再次改善，我們也看到各產業與各地區呈現廣泛的強勁表現。第二，雖然仍在早期階段，我們的 AI 產品正在在傳統以席位為基礎的模式之外，創造新的成長與擴張動能。

Customers adopting AI Studio and AI Teammates are engaging more deeply, retaining better, and expanding faster than the broader customer base. And we believe this gives us an early validation of our opportunity to build meaningful consumption and outcome-oriented revenue stream. Third, we're acting on the learnings by bringing AI Teammates, AI Studio, and Dash together as a core part of the Asana experience through our Agentic Work Management product.

採用 AI Studio 與 AI Teammates 的客戶互動更深入、留存更佳，且擴張速度快於整體客戶群。我們相信，這為我們打造具意義的「按使用量」與「以成果為導向」的營收來源提供了早期驗證。第三，我們正把這些學習轉化為行動，透過我們的 Agentic Work Management 產品，將 AI Teammates、AI Studio 與 Dash 整合為 Asana 體驗的核心部分。

We want our customers to experience these capabilities early and naturally as part of how they work every day rather than a separate AI products that they have to discover and purchase. And we're going to be bringing that same orchestrated execution across humans, agents, and systems with Asana Client Management, Asana Service Management and our Command products.

我們希望客戶能夠及早且自然地在日常工作方式中體驗這些能力，而不是把它們視為需要另外去發現與購買的獨立 AI 產品。我們也將把同樣的「協同編排式」執行能力，延伸到 Asana Client Management、Asana Service Management 以及我們的 Command 產品，實現人員、代理（agents）與系統之間的整合協作。

So let's have a look at this quarter. Improving health of our core business validates our strategy. It gives us confidence in the investments we're making to drive future growth. Revenue was $216.4 million, up 10% year-over-year and above the high end of our guidance. Reported net retention improved in every cohort we report.

接著我們來看看本季表現。核心業務健康度的改善驗證了我們的策略。這也讓我們對推動未來成長所做的投資更有信心。營收為 2.164 億美元，年增 10%，高於指引區間上緣。我們所揭露的各個客群（cohort）之淨留存率皆有所改善。

Overall NRR improved to 97% from 96%. In quarter net retention improved for the fifth consecutive quarter. Core customers NRR improved to 98% and our largest customers, those spending over $100,000 or more improved to 98% from 96%. That improvement is being driven by broader multiproduct adoption within the largest customers creating additional pass-through expansion. The technology sector delivered a second consecutive quarter of year-over-year growth.

整體 NRR 由 96% 提升至 97%。單季淨留存率已連續第五季改善。核心客戶 NRR 提升至 98%；而我們最大的客戶（年支出超過 10 萬美元者）NRR 由 96% 提升至 98%。這項改善主要來自最大型客戶更廣泛採用多產品，帶動額外的「隨附式」擴張。科技產業連續第二季實現年對年成長。

Now while growth remains modest, we're encouraged by the continued acceleration in this vertical. That growth included another expansion with a leading AI lab this quarter, adding seats, in addition to the expansion with AI Teammates that we mentioned last quarter as well as a global streaming service to both expanded seats and added AI Studio. Outside of tech, the story has been consistent for more than a year. Non-tech continues to grow faster than the company's overall growth.

雖然成長仍屬溫和，但我們對該垂直領域持續加速感到鼓舞。本季成長包含與一家領先的 AI 實驗室再度擴張、增加席位；此外，還包括我們上季提到的 AI Teammates 擴張，以及一家全球串流服務業者同時擴增席位並新增採用 AI Studio。在科技業之外，過去一年多來的趨勢一致。非科技產業的成長持續快於公司整體成長。

In fact, we added new customers across a range of industries this quarter, including one of the largest telecommunications operators in the US, a large insurance operator in the US, a big four professional services firm, one of the world's leading law firms, and an iconic American luxury jewelry brand.

事實上，本季我們在多個產業新增客戶，包括美國最大的電信營運商之一、美國一家大型保險業者、四大（Big Four）專業服務事務所之一、全球領先的律師事務所之一，以及一家具代表性的美國奢華珠寶品牌。

We also saw encouraging acceleration in the US, where revenue grew 10% year-over-year in Q2, returning to double-digit growth for the first time in over two years. This growth acceleration is attributed to improvement in both bookings and retention in our tech customers, which are concentrated in the US, strong adoption of our AI products, and acceleration in new logo acquisition. Internationally, Darktrace and a leading UK based financial service company were notable new logo wins for our EMEA team, and Delivery Hero expanded its relationship with Asana, including our AI products. Looking now at our AI product momentum. Momentum across our AI products continued to build this quarter.

我們也看到美國市場出現令人鼓舞的加速成長：第二季美國營收年增 10%，為兩年多來首次回到雙位數成長。這波成長加速歸因於我們科技客戶（主要集中在美國）的訂單（bookings）與留存改善、AI 產品的強勁採用，以及新客戶（new logo）獲取加速。在國際市場方面，Darktrace 與一家英國領先的金融服務公司是我們 EMEA 團隊的重要新客戶；同時 Delivery Hero 也擴大了與 Asana 的合作關係，包含採用我們的 AI 產品。接著看我們 AI 產品的動能。本季我們 AI 產品的動能持續增強。

And while still early, we're seeing an encouraging validation of the opportunity to build meaningful consumption and outcome-oriented growth and expansion revenue streams alongside our traditional seat-based model. AI Studio and AI Teammates, in fact, drove about 25% of our net new ARR, up from 17% last quarter. This is above our 15% full year target, which we set in March. We find customers that are adopting our AI products, engage more deeply, retain better, and expand faster than the broader customer base. This shows up most clearly in our largest accounts.

雖然仍在早期階段，但我們看到令人鼓舞的驗證：在傳統以席位為基礎的模式之外，我們有機會建立具意義的「按使用量」與「以成果為導向」的成長與擴張營收來源。事實上，AI Studio 與 AI Teammates 約貢獻我們淨新增 ARR 的 25%，高於上季的 17%。這高於我們在 3 月設定的全年 15% 目標。我們發現，採用 AI 產品的客戶互動更深入、留存更佳，且擴張速度快於整體客戶群。這在我們最大型的客戶帳戶中表現得最為明顯。

More than 25% of our $100,000-plus customers have now purchased AI Studio or AI Teammates. This has been a key contributor to the NRR expansion we're seeing up market. Also seeing clear evidence that AI products can mitigate seat-based pressure while creating new expansion opportunities tied to usage and outcomes. This quarter, we signed our largest AI expansion deal in Asana's history, a three year multimillion-dollar agreement with a Fortune 500 media company, spanning AI Studio and AI Teammates with AI products representing almost half of the total contract value. What's particularly important is the role our AI product played in the expansion.

目前，年支出超過 10 萬美元的客戶中，已有超過 25% 購買了 AI Studio 或 AI Teammates。這是我們在高端市場（up market）所看到 NRR 擴張的重要貢獻因素。我們也看到明確證據顯示，AI 產品能在創造與使用量與成果連動的新擴張機會的同時，緩解以席位為基礎的壓力。本季我們簽下 Asana 歷史上最大的 AI 擴張交易：與一家《財富》500 大媒體公司簽訂為期三年的數百萬美元合約，涵蓋 AI Studio 與 AI Teammates，AI 產品約占合約總價值的近一半。特別重要的是，我們的 AI 產品在此次擴張中所扮演的角色。

The customer is operating with a smaller workforce which historically would have resulted in a seat contraction. Instead, the investment in AI Studio and AI Teammates more than offset a smaller footprint, resulting in a modest overall expansion with also the additional upside potential of consumption growth over time. And they're already seeing measurable value.

該客戶在較小的人力規模下運作，過去這通常會導致席位縮減。然而，對 AI Studio 與 AI Teammates 的投資不僅抵銷了較小的席位規模，還帶來整體小幅擴張，並且隨著時間推移，仍有按使用量成長的額外上行空間。而且他們已經看到可量化的價值。

In fact, in one creative marketing workflow, AI Teammates have already reduced the content operation cycle time by 30%. This is an important example of how our AI products are creating new growth vectors beyond seats, allowing us to expand with customers, based increasingly on the work and outcomes delivered through Asana rather than changes in headcount.

例如，在一個創意行銷工作流程中，AI Teammates 已將內容營運的週期時間縮短 30%。這是一個重要例子，說明我們的 AI 產品正在在席位之外創造新的成長動能，使我們能夠與客戶擴張合作，且越來越以透過 Asana 交付的工作與成果為基礎，而非以人力編制變動為依據。

We're seeing customers move beyond individual use cases to make Asana a core part of their broader agentic enterprise strategy, coordinating humans and AI across the workflows that run their businesses. Asana is becoming the operating system for human agent teams for them. Let me share a couple of examples of what that looks like in practice. Indeed is a great example of how enterprises are using our AI products together to remove manual coordination at global scale. The world's number one job site deployed AI Studio to automate project discovery and the technical scoping for its analytics teams.

我們看到客戶正從單一使用情境走向把 Asana 納入其更廣泛的代理型企業（agentic enterprise）策略核心，在支撐其業務運作的各項工作流程中協調人類與 AI。對他們而言，Asana 正成為人類代理團隊的作業系統。我分享幾個實務上的例子，說明這在實際運作中是什麼樣子。Indeed 是企業如何把我們的 AI 產品搭配使用、在全球規模移除人工協調工作的絕佳例子。這個全球第一的求職網站部署了 AI Studio，為其分析團隊自動化專案發掘與技術範疇界定。

It also runs the dynamic intake and triage across the 70-person in-house creative agency, which operates in more than 60 countries and 28 languages. Annually, that work reclaims more than 1,400 hours of senior level time. It's cut lead time from raw request to active project by 60%. It's reduced manual ticket management by more than 40% for the creative team and delivers roughly $300,000 in savings and unlocked capacity. Indeed is also piloting AI Teammates as an autonomous brand auditor, matching localized content to global brand guidelines across dozens of languages.

它也在一個 70 人的內部創意代理團隊中執行動態受理與分流（intake and triage）；該團隊在 60 多個國家、28 種語言中運作。每年，這項工作可回收超過 1,400 小時的資深層級工時。它將從原始需求到啟動專案的前置時間縮短了 60%。它為創意團隊將人工工單管理降低了 40% 以上，並帶來約 30 萬美元的節省與釋放的產能。Indeed 也正在試點將 AI Teammates 作為自主品牌稽核員，跨數十種語言把在地化內容與全球品牌規範進行比對。

Washmen, a UAE-based textile care business is another example of AI Teammates running an operation end-to-end using AI Teammates to agentify their customer support and returns process. So when a garment comes in, one teammate researches its retail value. The second reviews the care plan for risk. Third, checks it against every past claim, and the fourth handles compensation and drafts the customer message. A person steps in only when a teammate escalates, the result is 90% faster claim resolution, taking it from three days down to six hours.

Washmen 這家總部位於阿聯的紡織品護理企業，是另一個 AI Teammates 端到端運行作業的例子；他們使用 AI Teammates 將其客服與退貨流程代理化（agentify）。因此當一件衣物送進來時，第一位 Teammate 會研究其零售價值。第二位會審查護理方案的風險。第三位會把它與過往每一筆理賠紀錄比對，第四位則處理補償並撰寫給客戶的訊息。只有在某位 Teammate 升級（escalate）時人才會介入；結果是理賠結案速度快了 90%，從三天縮短到六小時。

These kind of results reinforce our belief that our AI products create the greatest value when they're being embedded in business-critical workflows with a shared context that enables people and agents to coordinate and execute together towards outcomes. This principle is at the heart of what we're bringing to market in mid-September with Agentic Work Management. So let's take a look at Agentic Work Management. Let me explain what we mean here because this is a real meaningful evolution of our product. Not simply label on traditional work management.

這類成果強化了我們的信念：當我們的 AI 產品被嵌入到攸關業務的關鍵工作流程中，並在共享情境下讓人與代理能夠協調與共同執行、朝向成果前進時，才能創造最大價值。這項原則正是我們在 9 月中旬以「Agentic Work Management」推向市場的核心。那我們來看看 Agentic Work Management。我先說明我們在這裡的意思，因為這是我們產品一次真正且有意義的演進。而不只是替傳統工作管理貼上一個標籤。

Individuals have experienced significant productivity gains from AI, but most organizations haven't yet translated that into the productivity gains at the enterprise level. AI often sits outside the workflows that run the business, requiring people to find the right agent, provide the right context, and bring the output back into the work. With AWM, we closed that gap by putting people and agents and systems on the same plan. Historically, customers use Asana to coordinate work between people, to provide visibility into those tasks. With AWM, they can orchestrate execution across people and agents in the same context, same goals, and the same governance.

個人已從 AI 獲得顯著的生產力提升，但多數組織尚未把這些提升轉化為企業層級的生產力增長。AI 往往位於支撐企業運作的工作流程之外，要求人們去找到合適的代理、提供正確的情境，並把輸出再帶回工作中。透過 AWM，我們把這個落差補上，讓人、代理與系統在同一個計畫上運作。過去，客戶使用 Asana 來協調人與人之間的工作，並提供對這些任務的可視性。有了 AWM，他們可以在相同情境、相同目標與相同治理（governance）下，協同編排人與代理的執行。

AWM brings three things into every paid package tier. First, AI Teammates, including more than 30 prebuilt teammates for marketing, operations and IT. These are preapproved and ready to work and pretrained with no prompt engineering required. Second, AI Studio, so that any team can build no-code workflow automations for intake, routing, approvals, and status. And third, Asana Dash, this is your AI chief of staff that knows a person's goals and priorities, pulls decisions out of meetings, e-mails and chat, and surfaces what needs their attention and keeps them that one step ahead.

AWM 會把三項能力帶入每一個付費方案層級。第一，AI Teammates，包括超過 30 個為行銷、營運與 IT 預先建置的 Teammate。這些都已預先核准、可立即投入工作，並已預先訓練完成，不需要提示工程（prompt engineering）。第二，AI Studio，讓任何團隊都能建立無程式碼的工作流程自動化，用於受理、派送、核准與狀態更新。第三，Asana Dash，這是你的 AI 幕僚長（chief of staff），了解個人的目標與優先順序，從會議、電子郵件與聊天中萃取決策，並提示需要關注的事項，讓你始終領先一步。

So what does this mean for customers when AWM comes to market later this month? Well, beginning mid-September, all our new logos, self-service customers, and sales-led renewals will be moving to AWM. And they'll start with AI Teammates, AI Dash built directly into their package tier. This includes an allotment of Teammates and Dash requests. Most importantly, rather than trying to find the right agent, the Teammates will surface themselves based on what a customer is trying to accomplish.

那麼，當 AWM 在本月稍晚上市時，對客戶意味著什麼？從 9 月中旬開始，我們所有的新客戶（new logos）、自助式客戶，以及由銷售主導的續約，都將轉移到 AWM。他們將從直接內建於其方案層級中的 AI Teammates 與 AI Dash 開始使用。其中包含一定額度的 Teammates 與 Dash 請求（requests）。最重要的是，客戶不必再嘗試尋找合適的代理；Teammates 會依據客戶想完成的事情主動浮現。

This is deliberate. We want customers to experience the full value of Asana early. Similarly, the full allotment of request is designed to let customers put our AI products to work in their mission-critical workflows from day one. By simplifying the purchase decision, we can get more customers to first value faster and create a natural path from demonstrated outcomes to deeper AI adoption to increase consumption and stronger seat retention and expansion over time. We chose requests as the unit of consumption because we want our AI pricing to be customer-friendly, simple, and predictable.

這是刻意為之。我們希望客戶能在早期就體驗到 Asana 的完整價值。同樣地，完整的請求額度設計，是為了讓客戶從第一天起就能把我們的 AI 產品投入其關鍵任務工作流程。透過簡化採購決策，我們能讓更多客戶更快達到首次價值（first value），並建立一條自然路徑：從可驗證的成果到更深度的 AI 採用，進而提升使用量（consumption），並在時間推進下帶來更強的席位留存與擴張。我們選擇以「請求」作為消耗單位，是因為我們希望 AI 定價對客戶友善、簡單且可預測。

A request gives a customer a clear understanding of what they're buying with a consistent price per request, speed limits, usage visibility, and alerts. And behind the scenes, Asana is going to select and optimize the appropriate model. That complexity should be ours to manage, not the customer's. So AWM is how we bring the operating system for human agent teams to customers today. People and agents running those cross-functional work that runs the business.

「請求」能讓客戶清楚理解自己購買的是什麼：一致的每次請求價格、速率限制、使用可視性與提醒。而在幕後，Asana 會選擇並最佳化合適的模型。這種複雜度應由我們來管理，而不是客戶。因此，AWM 是我們今天把「人類代理團隊的作業系統」帶給客戶的方式。讓人與代理共同運行那些支撐企業運作的跨職能工作。

Asana Client Management applies the same orchestrated execution to client delivery, service management to service delivery, and Command to product development, same platform, different kinds of work. We're not entering these markets with point solutions. Each is a purpose-built application built on top of the enterprise work graph that our customers are already running on. So each starts with that same shared context, memory and governance, the people, systems and agents need.

Asana Client Management 將同樣的協同編排式執行應用於客戶交付；Service Management 應用於服務交付；Command 應用於產品開發——同一平台，不同類型的工作。我們不是以單點解決方案（point solutions）進入這些市場。每一項都是建立在企業工作圖譜（enterprise work graph）之上的目的型應用，而我們的客戶已經在其上運行。因此，每一項都從相同的共享情境、記憶與治理開始——這些是人、系統與代理所需要的。

And the AI Teammates and automation a customer builds in one application carry into those others under the same permissions and audit trail. Each of these new products represents a large adjacent market and new buying center. So let's take a look at them. Starting with Client Management, The promise here is simple. The complete client workflow coordinated across clients, account teams, delivery teams, AI, files, approvals, budgets, and projects.

而且，客戶在某一個應用中建立的 AI Teammates 與自動化，會在相同權限與稽核軌跡（audit trail）下延伸到其他應用。這些新產品各自代表一個龐大的相鄰市場與新的採購決策中心（buying center）。那我們來看看它們。先從 Client Management 開始，這裡的承諾很簡單。完整的客戶工作流程，跨客戶、客戶經理團隊、交付團隊、AI、檔案、核准、預算與專案進行協調。

Nearly one third of our customers today are already doing some form of client delivery or running a professional services team today. But they often run client delivery in Asana while managing the rest of the client relationship across disconnected systems communication and e-mail, statements of work and approvals elsewhere and resourcing and spreadsheet. That makes it really difficult for them to maintain a single view of client health, project profitability and team capacity. ACM brings those pieces together. It has a branded client portal for requests, reviews and approvals, AI Teammates that draft statements of work, client-ready assets, and status updates, and time and budget tracking sits alongside actual work.

目前我們將近三分之一的客戶，已在進行某種形式的客戶交付，或正在運作專業服務團隊。但他們往往在 Asana 中執行客戶交付，同時卻在彼此斷裂的系統中管理其餘客戶關係：溝通與電子郵件、工作說明書（statements of work）與核准在其他地方，資源配置則在試算表中。這使得他們很難維持對客戶健康度、專案獲利能力與團隊產能的單一視圖。ACM 把這些拼圖整合在一起。它提供具品牌化的客戶入口網站，用於需求提交、審查與核准；AI Teammates 可起草工作說明書、可直接交付給客戶的資產與狀態更新；並且時間與預算追蹤與實際工作並列呈現。

Client Management is in early access right now. Next, let's have a look at Asana Service Management. Traditional service management was built to route a ticket to a person and track it to resolution. Well, AI has changed that model, enterprises increasingly want service teams to resolve requests automatically, not simply route them faster. Asana Service Management is one AI-native service platform for IT, HR, facilities and legal with 24/7 agents that can resolve routine requests through Slack, e-mail, or a portal before they even reach a human.

Client Management 目前正處於早期存取（early access）。接著，我們來看看 Asana Service Management。傳統的服務管理是為了把工單派給某個人，並追蹤直到結案。然而，AI 已改變了這個模型；企業愈來愈希望服務團隊能自動解決請求，而不只是更快地轉派。Asana Service Management 是一個 AI 原生的服務平台，面向 IT、人資、設施與法務，提供 24/7 的代理，可透過 Slack、電子郵件或入口網站解決例行請求，甚至在它們觸及人類之前就完成處理。

Service Management builds on that with one front door for every department, a self-learning knowledge base that gets more accurate with every resolved case, and agentic resolution that moves Asana from a place where service work is tracked to a place where it's actually resolved. ASM is in early access now with strong feedback from IT design partners, particularly around the self-learning knowledge base. Finally, looking at the Asana Command. As we know, AI has made code generation dramatically faster, but the coordination around that code hasn't kept pace. The spec, the handoffs, the release plans, the traceability.

服務管理在此基礎上更進一步，為每個部門提供單一入口、一個會隨著每次案件解決而愈加精準的自我學習知識庫，以及能將 Asana 從「追蹤服務工作」提升到「實際解決問題」的代理式解決能力。ASM 目前已進入早期存取，並獲得 IT 設計合作夥伴的強烈正面回饋，尤其是在自我學習知識庫方面。最後，來看 Asana Command。如我們所知，AI 讓程式碼生成大幅加速，但圍繞程式碼的協作並未跟上。規格、交接、發佈計畫、可追溯性。

Increasingly, that's where the bottleneck now sits. Coding agents need more than the ability to generate code, they need context, a shared plan, and the decision history they can trust. Command provides that planning and orchestration layer built on the same enterprise work graph that already supports product and engineering planning teams today. That's the promise, ship faster with humans and agents in sync. We designed Command as an open platform from day one.

越來越多時候，瓶頸就卡在這裡。寫程式的代理不只需要生成程式碼的能力，還需要情境脈絡、共享計畫，以及他們能信任的決策歷史。Command 提供了這個規劃與編排層，建立在同一個企業工作圖譜之上，而該圖譜今天已支援產品與工程規劃團隊。這就是承諾：讓人類與代理同步協作，更快交付。我們從第一天起就將 Command 設計為開放平台。

So customers can orchestrate the agents and tools, they choose rather than being locked into any one proprietary agent ecosystem. As SpaceXAI described it: Command is a novel approach to a difficult problem. Coordinating work across many agents and tools modern engineering teams use. Its open platform design lets developers bring SpaceXAI into a broader orchestration there without being locked into a closed system.

因此客戶可以編排他們選擇的代理與工具，而不是被鎖定在任何單一的專有代理生態系中。正如 SpaceXAI 所描述：Command 以新穎方式解決一個困難問題。也就是協調現代工程團隊所使用的眾多代理與工具之間的工作。其開放平台設計讓開發者能將 SpaceXAI 納入更廣泛的編排之中，而不會被鎖在封閉系統裡。

Later this year, Command will also integrate deeply with OpenAI's Codex. This will bring parallelized cloud-hosted coding agents natively into how work gets planned, assigned and shipped. Command, reaches early access later this month. Turning now to StackAI. StackAI is about turning your business processes into governed agentic workflows in minutes. Reading, writing, and executing across all the systems that the company already runs on. While Asana provides the plan, the shared context, and the people around that execution.

今年稍晚，Command 也將與 OpenAI 的 Codex 進行深度整合。這將把可平行化、雲端託管的寫程式代理，原生帶入工作如何被規劃、指派與交付的流程中。Command 將於本月稍晚進入早期存取。接著談 StackAI。StackAI 的目標是把你的業務流程在幾分鐘內轉換為具治理的代理式工作流程。能在公司既有運行的所有系統之間進行讀取、寫入與執行。而 Asana 則提供計畫、共享情境脈絡，以及圍繞執行的人員協作。

Importantly, it gives us a more complete solution to enterprise AI transformation initiatives we're increasingly seeing from our IT and AI transformation buyers. And in that motion, we've already seen early wins including one of Australia's largest retailers. We believe these engagements are early validation of the opportunity to bring Asana and StackAI together for larger, more complex enterprise workflows. In closing, taken together, we're expanding Asana in two dimensions. AWM gives us a path to drive deeper product adoption across our customer base and create meaningful long-term consumption growth alongside seats.

重要的是，這讓我們能為企業 AI 轉型計畫提供更完整的解決方案，而這類需求正日益由我們的 IT 與 AI 轉型採購方提出。在這個推進動能中，我們已看到早期成果，包括澳洲最大的零售商之一。我們相信，這些合作是早期驗證：將 Asana 與 StackAI 結合，可支援更大型、更複雜的企業工作流程。總結來說，整體而言，我們正從兩個維度擴展 Asana。AWM 讓我們能在客戶群中推動更深的產品採用，並在席位之外，創造有意義的長期用量成長。

While our new applications expand the workflow users and buying centers we can serve, all of it is running on the same architecture and advances our strategy to become the operating system for human agent teams.

同時，我們的新應用擴大了我們可服務的工作流程使用者與採購中心；而所有這些都運行在同一套架構之上，並推進我們成為人類—代理團隊作業系統的策略。

With that, I'll turn it over to Aziz, to take you through the quarter and the outlook.

接下來我把時間交給 Aziz，帶各位回顧本季表現與展望。

Thanks, Dan. Let me start with the quarter. Q2 revenue was $216.4 million, up 10% year-over-year, an acceleration from Q1 and above the high end of our guidance. StackAI contributed approximately 50 basis points to reported growth which was in line with the expectation we shared last quarter. Currency impact was immaterial this quarter.

謝謝，Dan。我先從本季開始。第二季營收為 2.164 億美元，年增 10%，較第一季加速，且高於我們指引區間的上緣。StackAI 對已揭露的成長貢獻約 50 個基點，符合我們上季分享的預期。本季匯率影響不顯著。

We have 26,778 Core customers which we define as customers spending $5,000 or more on an annualized basis. Revenues from Core customers grew 11% year-over-year, and this cohort represented 77% of our revenues in Q2. We now have 890 customers spending $100,000 or more on an annualized basis. This represents a growth rate of 16-percentage-points year-over-year. As a reminder, these cohorts are measured using annualized GAAP revenue during the quarter and therefore, can be affected by the number of days in the quarter.

我們有 26,778 家 Core 客戶，我們將其定義為年化支出達 5,000 美元或以上的客戶。Core 客戶營收年增 11%，且該族群在第二季占我們營收的 77%。目前有 890 家客戶年化支出達 10 萬美元或以上。這代表年增 16 個百分點的成長幅度。提醒一下，這些族群是以當季的年化 GAAP 營收衡量，因此可能會受到季度天數多寡影響。

Our dollar-based net retention increased on every cohort we report. Our overall dollar-based net retention was 97%. Core customer NRR was 98%, and among customers spending $100,000 or more NRR was 98%. As a reminder, our NRR is a trailing four quarter average and therefore, a lagging indicator of more recent trends. This improvement is being driven by the continued strength in gross retention, healthier seat expansion within our largest enterprise customers, and broader multiproduct adoption, AI Studio and AI Teammates increasingly creating an expansion vector at renewal.

我們所揭露的每個客群，其以美元計的淨留存率（NRR）皆有所提升。整體以美元計的淨留存率為 97%。Core 客戶 NRR 為 98%，年化支出達 10 萬美元或以上的客戶 NRR 亦為 98%。提醒一下，我們的 NRR 是過去四個季度的平均值，因此是反映較近期趨勢的落後指標。這項改善主要來自總留存率持續強勁、我們最大型企業客戶的席位擴張更健康，以及更廣泛的多產品採用；AI Studio 與 AI Teammates 也日益在續約時創造擴張動能。

As Dan discussed, that allows us to expand with customers in ways that are less dependant on seat growth alone. Turning to self-serve. The PLG headwind we discussed last quarter builds throughout the year. The impact of lower PLG bookings compounds into the revenue base each quarter. So the drag on reported revenue growth increases even if the underlying self-serve trend does not deteriorate further.

如 Dan 所提到，這讓我們能以不那麼依賴單純席位成長的方式，與客戶擴張合作。接著談自助式（self-serve）。我們上季提到的 PLG 逆風會在全年逐步累積。較低的 PLG 訂單會在每一季持續滾入營收基礎，形成複利式影響。因此，即使自助式的基本趨勢沒有進一步惡化，已揭露的營收成長仍會承受更大的拖累。

That pressure comes as several of our underlying growth acceleration levers are improving. NRR continues to strengthen. We're experiencing strong momentum with our AI products. Our US business has accelerated and technology vertical has now returned to year-over-year growth for two consecutive quarters.

在這樣的壓力下，我們多項底層的成長加速槓桿正在改善。NRR 持續走強。我們的 AI 產品動能強勁。我們的美國業務已加速成長，而科技垂直領域也已連續兩季恢復年對年成長。

It also explains the gap in our net retention. Core and our $100,000-plus cohort are both at 98%, while company-wide NRR is 97%. That differential sits in the sub-$5,000 cohort which is concentrated in self-serve and skews towards customers outside our ideal customer profile. Getting company-wide NRR back to above 100% really comes down to three levers. First, gross retention improvement in the core and enterprise space; second, seat multiproduct and consumption expansion in those same cohorts; and lastly, improving ICP mix and driving stronger retention and expansion in the sub-$5,000 customer base.

這也解釋了我們淨留存率之間的差距。Core 與年化支出 10 萬美元以上的客群皆為 98%，而全公司 NRR 為 97%。差異主要來自年化支出低於 5,000 美元的客群，該族群集中於自助式，且客戶輪廓較偏離我們的理想客戶輪廓（ICP）。要讓全公司 NRR 回到 100% 以上，關鍵在三個槓桿。第一，改善 Core 與企業市場的總留存率；第二，在相同客群中推動席位、多產品與用量的擴張；最後，改善 ICP 組合，並在年化支出低於 5,000 美元的客戶基礎中推動更強的留存與擴張。

The first two are already starting to show benefits, and you see that reflected in our Q2 KPIs and financial results. The third remains a key focus area and we expect the investments we are making there to contribute to improving NRR in FY28, improving the growth in NRR within our sub-$5,000 customer base is centered on two areas. First, we're focusing our acquisition spend on the customer sizes, industries and use cases with the strongest fit and highest lifetime value potential.

前兩項已開始帶來效益，並反映在我們第二季的 KPI 與財務結果中。第三項仍是關鍵聚焦領域，我們預期在該方面的投資將於 FY28 促進 NRR 改善；而要提升年化支出低於 5,000 美元客群的 NRR 成長，主要聚焦兩個方向。第一，我們將獲客支出聚焦在最契合、終身價值（LTV）潛力最高的客戶規模、產業與使用情境。

That includes becoming more targeted and verticalized with industry-specific team templates, AI Teammates and use cases designed to improve conversion and retention. Second, we are increasing the surface area through which these customers can expand with us.

這包括更精準且更垂直化的策略：提供產業別團隊範本、AI Teammates，以及旨在提升轉換與留存的使用情境。第二，我們正在擴大這些客戶可與我們進行擴張的接觸面。

AWM and ACM launched in self-serve in mid-September, bringing AI Teammates directly to our large PLG installed base while extending Asana into new workflows and use cases. We believe this creates a new vector to get deeper into critical workflows and expand these relationships beyond seats, which we feel will improve retention over time.

AWM 與 ACM 已於 9 月中旬在自助式渠道上線，將 AI Teammates 直接帶給我們龐大的 PLG 已安裝客群，同時把 Asana 延伸到新的工作流程與使用情境。我們相信，這將創造一個新向量，讓我們更深入關鍵工作流程，並在席位之外擴大這些合作關係，進而隨時間推移改善留存。

Now moving to profitability, where I'll be discussing non-GAAP results and year-over-year comparisons. We delivered a 10% non-GAAP operating margin in Q2, expanding approximately 300 basis points year-over-year while continuing to make significant investments in our AI products and agentic applications and the go-to-market capabilities to scale them. Our gross margin was 87%, which was down approximately 120 basis points from last quarter.

接著談獲利能力，我將說明非 GAAP 結果與年對年比較。我們第二季非 GAAP 營業利益率為 10%，年對年擴張約 300 個基點；同時我們仍持續對 AI 產品與代理式應用，以及可規模化推進的市場進入（go-to-market）能力進行重大投資。我們的毛利率為 87%，較上季下降約 120 個基點。

This decline reflects three primary factors: first, higher AI infrastructure and compute costs attributed to onetime scaling and development costs for our new products, which accounted for approximately 80 basis points of the change. Second, the addition of StackAI, which has a lower gross margin profile, given its subscale accounted for approximately 30 basis points of the change. And third, the remainder of the gross margin impact reflects the mix shift from seats to our AI products. R&D expenses were $50.7 million or 23% of revenue. Sales and marketing expenses were $88.2 million or 41% of revenue.

這一下滑反映三項主要因素：第一，較高的 AI 基礎設施與運算成本，主要歸因於我們新產品一次性的擴容與開發成本，約占變動的 80 個基點。第二，併入 StackAI；由於其規模尚小、毛利率結構較低，約占變動的 30 個基點。第三，毛利率影響的其餘部分反映從席位轉向我們 AI 產品的組合轉移。研發費用為 5,070 萬美元，占營收 23%。銷售與行銷費用為 8,820 萬美元，占營收 41%。

G&A expenses were $28 million or 13% of revenue. Net income was $23.8 million or $0.10 per share on a diluted basis. We have kept our overall expense base relatively flat while adding capacity in lower-cost regions such as Poland and using AI products to increase productivity and expand capacity across our teams. We're seeing that most acutely in R&D, where AI is enabling our teams to deliver the most robust product road map in Asana's history without a commensurate increase in R&D spend.

一般及行政（G&A）費用為 2,800 萬美元，占營收 13%。淨利為 2,380 萬美元，稀釋後每股盈餘為 0.10 美元。在將產能增加至波蘭等低成本地區，並使用 AI 產品提升生產力、擴大全團隊產能的同時，我們整體費用基礎仍維持相對持平。我們在研發方面感受最為明顯：AI 讓團隊在研發支出未相應增加的情況下，交付 Asana 史上最強健的產品路線圖。

The combination of a more efficient talent footprint and AI-driven productivity gives us the capacity to continue investing behind our highest growth opportunities while driving operating leverage over time. Moving on to the balance sheet and cash flow. At the end of Q2, cash, cash equivalents, and marketable securities were approximately $340 million. Our remaining performance obligations, or RPO, was $522 million, and current RPO grew 10% year-over-year. This represents 81% of total RPO and will be recognized over the next 12 months. The underlying RPO trends was stronger than the reported growth rates suggest.

更高效率的人才佈局與 AI 驅動的生產力相結合，使我們有能力持續投資於最高成長機會，同時隨時間推進帶動營運槓桿。接下來談資產負債表與現金流。截至第二季末，現金、約當現金及有價證券約為 3.40 億美元。我們的剩餘履約義務（RPO）為 5.22 億美元，其中當期 RPO 年增 10%。這占總 RPO 的 81%，並將在未來 12 個月內認列。RPO 的基本趨勢比表面成長率所顯示的更強勁。

This is due to the comparison against the large multiyear contract we signed in Q2 of last year. Excluding that contract, current RPO growth accelerated to approximately 11% from 8% last quarter while total RPO growth accelerated to approximately 12% year-over-year growth versus 7% year-over-year growth last quarter. Our total ending Q2 deferred revenue was $350.7 million, up 12% year-over-year. Adjusted free cash flow was $42.3 million or 20% on a margin basis. Note free cash flow benefited this quarter by approximately $5 million from stronger collections than expected.

這是因為與去年第二季簽署的一筆大型多年期合約相比的基期因素所致。排除該合約後，當期 RPO 成長由上季的約 8% 加速至約 11%；同時總 RPO 年增也由上季的 7% 加速至約 12%。第二季期末遞延收入總額為 3.507 億美元，年增 12%。調整後自由現金流為 4,230 萬美元，按利潤率計為 20%。請注意，本季自由現金流因回款強於預期而受益約 500 萬美元。

Before I turn to guidance, I want to connect the product strategy Dan described to the evolution of our financial model. In mid-September, we are including a base level of AI Teammates and Dash requests in the AWM tiers without changing tier pricing. This changes both for new and existing customers. We're seeding that usage deliberately, investing to drive adoption first, with the expectation that stronger retention, seat expansion, increasing consumption follow over time.

在我轉向財測指引之前，我想把 Dan 所描述的產品策略與我們財務模型的演進連結起來。在 9 月中旬，我們將在 AWM 各層級中納入基本額度的 AI Teammates 與 Dash 請求量，而不調整層級定價。這項變更同時適用於新客戶與既有客戶。我們會有意識地先播種這些使用量，先投資以推動採用，並預期更強的留存、席位擴張與消耗量提升將隨時間逐步跟進。

Underpinning this shift, we have made significant investment in our monetization infrastructure and in-product experience, enabling AI native capabilities such as usage metering, overages, and consumption-based billing at scale. Let me walk through how we reflected that transition in our guidance. There are two dynamics affecting revenue recognition as we transition towards consumption. First, going forward, all new AI Teammates sales will be consumption-based with revenue recognized as customer requests are consumed rather than ratably over the contract term.

支撐這項轉變的是，我們已在變現基礎設施與產品內體驗上進行重大投資，使我們能以規模化方式提供 AI 原生能力，例如用量計量、超額用量（overages）以及按消耗計費。我來說明我們如何在指引中反映這項轉型。在我們轉向消耗計費的過程中，有兩個動態會影響收入認列。第一，未來所有新的 AI Teammates 銷售都將採用消耗計費，收入將在客戶消耗請求時認列，而非在合約期間按期平均認列。

Because customers have flexibility in the timing of their consumption, this also introduces greater variability in the timing of revenue recognition. Second, as we transition our core packaging from CWM to AWM and embed our AI products into the core subscription, a portion of subscription value that historically would have been recognized ratably is now allocated to AI consumption and recognized as that capacity is consumed.

由於客戶在消耗時間點上具有彈性，這也使收入認列的時間點出現更大的波動性。第二，當我們將核心方案從 CWM 轉為 AWM，並把 AI 產品嵌入核心訂閱中時，過去會按期平均認列的一部分訂閱價值，現在會分攤至 AI 消耗，並在該產能被消耗時認列。

As customers ramp consumption over time, this shifts a portion of revenue recognition into future periods. The shift of new AI Teammates sales from ratable to consumption-based recognition, along with the AWM packaging changes creates a $1.2 million revenue timing impact in the second half. This is roughly split between Q3 and Q4. Note this is just a timing impact. It does not change anything in customer economics, has no impact on ARR, bookings, billings, deferred revenue, RPO, or cash flow.

隨著客戶的消耗量隨時間爬升，這會把一部分收入認列推遲到未來期間。新的 AI Teammates 銷售由按期平均認列改為按消耗認列，加上 AWM 方案包裝的變更，將在下半年造成 120 萬美元的收入時點影響。該影響約在第三季與第四季各占一半。請注意，這僅是時點影響。這不會改變任何客戶經濟性，亦不影響 ARR、訂單（bookings）、開票（billings）、遞延收入、RPO 或現金流。

In addition, this transition also creates approximately 150 basis points of gross margin pressure across Q3 and Q4, reflecting both costs incurred ahead of associated consumption-based revenue recognition and the growing mix of AI products, which currently carried lower contribution margins than our seat-based business. Importantly, we're making these investments deliberately to seed AI usage and drive deeper utilization of the platform with expected benefits to retention and expansion occurring over subsequent renewal periods.

此外，這項轉型也會在第三季與第四季帶來約 150 個基點的毛利率壓力，反映（1）在相關的按消耗收入認列之前先行發生的成本，以及（2）AI 產品占比提高；目前 AI 產品的貢獻毛利率低於我們以席位為基礎的業務。重要的是，我們是有意識地進行這些投資，以播種 AI 使用並推動平台更深度的使用，預期對留存與擴張的效益將在後續續約期間逐步顯現。

As a result, we expect gross margin to be in the mid-80s exiting the year. We've already seen meaningful reductions in the cost of delivering our AI products through optimization and routing. And we expect those efficiencies to continue as we scale. Importantly, as you'll see in our operating margin guidance, we've been able to absorb the remaining increased costs through efficiencies and productivity gains elsewhere in the cost base while continuing to deliver margin expansion ahead of our expectations.

因此，我們預期在年底時毛利率將落在 80% 中段水準。透過最佳化與路由調整，我們已看到交付 AI 產品成本的顯著下降。並且我們預期隨著規模擴大，這些效率將持續改善。重要的是，正如你們將在營業利益率指引中看到的，我們已能透過成本結構其他部分的效率與生產力提升來吸收剩餘的成本增加，同時仍能實現超出我們預期的利潤率擴張。

Note, this is all while absorbing approximately 1-percentage-point of incremental operating expense as a percentage of revenue from the StackAI acquisition as we discussed last quarter. Second, the PLG headwind discussed earlier continues to weigh on the second half revenue growth profile. We estimate approximately 100 basis points of pressure to revenue growth in Q3, which increases to 150 basis points of pressure in Q4. Our outlook assumes that current PLG trends persist through the balance of the year and incorporates no recovery in FY27 from the initiatives I discussed earlier.

請注意，這一切是在吸收我們上季所討論的 StackAI 併購所帶來、約占營收 1 個百分點的增量營運費用的同時完成的。第二，先前討論的 PLG 逆風仍持續對下半年營收成長輪廓造成壓力。我們估計第三季營收成長將承受約 100 個基點的壓力，第四季則增加至 150 個基點。我們的展望假設目前的 PLG 趨勢將持續至年底，且未將我先前提到的各項措施在 FY27 帶來的任何復甦納入。

Third, AI Studio and AI Teammates represented about 25% of net new ARR in the quarter or closer to 22%, excluding the large deal. Including StackAI, we now expect AI products to represent approximately 20% of that new ARR for the full year, which is up from approximately 15% of net new ARR, which we discussed in March. We're deliberately prudent with this target because seeding every customer with AI Teammates and Dash starting in mid-December may delay some consumption package purchases by a matter of months. This metric captures only new consumption and capacity package purchases, not the requests and credits included within the AWM tiers.

第三，本季 AI Studio 與 AI Teammates 約占淨新增 ARR 的 25%，若排除該筆大型交易則更接近 22%。納入 StackAI 後，我們現在預期 AI 產品在全年淨新增 ARR 中約占 20%，高於我們在 3 月討論的約 15%。我們對此目標刻意保持審慎，因為自 12 月中旬起為每位客戶播種 AI Teammates 與 Dash，可能會使部分消耗型套裝的購買延後數個月。此指標僅涵蓋新的消耗與產能套裝購買，不包含 AWM 各層級內含的請求量與點數（credits）。

No attribution is being made from the AWM packaging change. Fourth, we continue to assume minimal FY27 revenue contribution from Client Management, Service Management and Command. Given enterprise sales cycles and deployment time lines, we expect the financial contribution to become more meaningful as a key growth driver in FY28. Finally, Q3 includes approximately $3 million of incremental AWM and agentic application launch investment consistent with what we discussed last quarter.

AWM 方案包裝變更不做任何歸因。第四，我們仍假設 FY27 來自 Client Management、Service Management 與 Command 的營收貢獻極小。考量企業銷售週期與部署時程，我們預期其財務貢獻將在 FY28 作為關鍵成長動能時變得更為顯著。最後，第三季包含約 300 萬美元的增量 AWM 與代理式（agentic）應用程式上市投資，與我們上季討論一致。

That investment is concentrated in global brand and marketing and AI go-to-market activities around our September launches. We expect that spend to normalize following the launch with sequential operating margin expansion returning in Q4. Now moving to guidance. The guidance I'm giving includes all the assumptions I mentioned above. For Q3 fiscal 2027, we expect revenue of $217 million to $219 million, representing 8% to 9% growth year-over-year. This includes a $700,000 headwind to revenue from our AWM packaging transition.

該投資主要集中於全球品牌與行銷，以及圍繞 9 月發布的 AI 上市（go-to-market）活動。我們預期該支出在發布後將回歸常態，並在第四季恢復季對季的營業利益率擴張。現在進入指引。我所提供的指引包含上述所有假設。就 FY2027 第三季而言，我們預期營收為 2.17 億至 2.19 億美元，年增 8% 至 9%。其中包含因 AWM 方案包裝轉型而對營收造成的 70 萬美元逆風。

We expect non-GAAP operating income of $18 million to $19 million, representing an operating margin of 8% to 9%. In addition, we expect non-GAAP net income per share of $8 -- $0.08, assuming diluted weighted average shares outstanding of approximately 236 million shares. For the full fiscal year 2027, we expect revenue to be in the range of $858.5 million to $863.5 million, representing growth of 9% year-over-year at the midpoint of the guidance.

我們預期非 GAAP 營業利益為 1,800 萬至 1,900 萬美元，對應營業利益率 8% 至 9%。此外，在稀釋後加權平均流通股數約 2.36 億股的假設下，我們預期非 GAAP 每股淨利為 0.08 美元。就 2027 財政年度全年而言，我們預期營收介於 8.585 億至 8.635 億美元之間，以指引中點計算，年增 9%。

The full year revenue reflects the outperformance from our Q2 results and the expected contribution from StackAI of approximately 50 basis points to growth, same as last quarter. In addition, as mentioned above, it included a $1.2 million headwind to revenue from our transition to AWM and consumption. We expect an approximately 20 basis points tailwind to our full year revenue in constant currency which is consistent with what we shared last quarter.

全年營收反映了我們第二季業績的超預期表現，以及 StackAI 對成長的預期貢獻約 50 個基點，與上季相同。此外，如上所述，其中包含因我們轉向 AWM 與用量計費而對營收造成的 120 萬美元逆風。我們預期以固定匯率計算，全年營收將有約 20 個基點的順風，與我們上季分享的內容一致。

We expect non-GAAP operating income of $84.5 million to $86.5 million, representing an operating margin of approximately 10%. And we expect non-GAAP net income per share of $0.37, assuming diluted weighted average shares outstanding of approximately 239 million shares. As we look ahead, AWM brings our AI products to our broader customer base, creating new expansion opportunities as adoption and consumption grow. We're investing ahead of those benefits while maintaining our margin commitments, creating the foundation for stronger growth and operating leverage over time.

我們預期非 GAAP 營業利益為 8,450 萬至 8,650 萬美元，對應約 10% 的營業利益率。在稀釋後加權平均流通股數約 2.39 億股的假設下，我們預期非 GAAP 每股淨利為 0.37 美元。展望未來，AWM 將我們的 AI 產品帶給更廣泛的客戶群，隨著採用與用量成長，創造新的擴張機會。我們在維持利潤率承諾的同時，提前投入以迎接這些效益，為未來更強勁的成長與營運槓桿奠定基礎。

With that, operator, we are now ready for questions.

接下來，接線員，我們現在準備開始提問。

(Operator Instructions)

（接線員指示）

Patrick Walravens, Citizens.

Patrick Walravens，Citizens。

Oh, great. Thank you. And Dan, congratulations on all the progress on the product side around Agentic Work Management. There was one thing in your prepared remarks that stuck out to me, and I would love to hear more about it. You said, rather than having to find the right agent, the right teammate can surface based on what the customer is trying to accomplish. That sounds like a very good idea to me. How is that going to work and maybe you could share a simple example of a teammate surfacing to help the user?

喔，太好了。謝謝。Dan，也恭喜你們在 Agentic Work Management 相關產品面取得的所有進展。你在事先準備的談話中有一點讓我印象深刻，我很想多聽一些。你說，不必去找對的 agent，而是可以根據客戶想完成的事情，讓合適的 teammate 自動浮現。我覺得這是個非常好的想法。這會如何運作？也許你可以分享一個簡單例子，說明某個 teammate 如何浮現來協助使用者？

Yeah, thanks, Pat. And you're right, that is a good idea. And we think it's a bit of a game changer. Just to kind of level set on AWM, so Agentic Work Management. So this is the evolution of collaborative work management. The big idea here is that we think humans and agents are going to be working together and coordinating together to drive orchestrated execution. And we spent really the last, I'd say, 6 months figuring out how we want AI Studio, AI Teammates and our new AI chief of staff that we call Dash to appear to our customers. And what we found is, the more we can bring that directly into their experience, the better.

好的，謝謝你，Pat。你說得對，這確實是個好主意。我們也認為這會帶來相當大的改變。先簡單對齊一下 AWM，也就是 Agentic Work Management。這是協作式工作管理的演進。核心概念是，我們認為人類與 agents 將會一起工作、一起協調，以推動編排式的執行。過去大概 6 個月，我們一直在思考要如何讓 AI Studio、AI Teammates，以及我們稱為 Dash 的新 AI 幕僚長呈現在客戶面前。我們發現，越能把這些能力直接帶進他們的使用體驗，效果就越好。

So you'll see in September some, I'd say, innovative ideas on how we create this amazing experience. So innovation number one is our AI chief of staff, Dash, as you ask it questions and interact with it, it will suggest the right teammates to help you complete your execution of that task. Number two is through input nudges. We'll actually recognize the type of task that you are trying to complete and suggest one of the prebuilt pre-skilled teammates that can help you.

所以你會在 9 月看到一些我認為相當創新的做法，來打造這種令人驚豔的體驗。第一項創新是我們的 AI 幕僚長 Dash：當你向它提問並與它互動時，它會建議合適的 teammates，協助你完成該任務的執行。第二項是透過輸入提示（input nudges）。我們會辨識你正在嘗試完成的任務類型，並建議一位預先建置、具備預先技能的 teammate 來協助你。

And that's because, of course, we've got all of this great work graph history, so we know exactly what kind of work you're trying to do. And it's no coincidence that we've built these 30 prebuilt teammates, those are exactly the kinds of work that our customers are doing today.

這是因為我們當然擁有所有這些很棒的 work graph 歷史資料，所以我們能精準知道你想做的是哪一類工作。而我們打造這 30 個預先建置的 teammates 也絕非巧合；它們正是我們客戶今天正在做的那些工作類型。

So that matching will happen. And then finally, if you want at the administrative level to do what we call a work graph analyzer, you can actually do that across all of your work and the admin can easily see which teammates could be most useful to help. And this is all in response to the idea that -- today, one of the biggest hurdles of agentic enterprise is actually the discovery of the agents being able to find the right ones that will work for you.

因此，這種配對會發生。最後，如果你想在管理層級使用我們所稱的 work graph analyzer，你可以針對所有工作進行分析，管理員也能輕鬆看出哪些 teammates 最有用、最能提供協助。這一切都是為了回應一個現況：在 agentic enterprise 裡，當前最大的障礙之一，其實是 agent 的「發現」問題——也就是要能找到適合你的那些 agent。

Steve Enders, Citi.

Steve Enders，Citi。

Okay, great. Thanks for taking the questions here. I guess I want to dig in a little bit more just in terms of the factors being included in the guidance outlook on the revenue side, in particular. And I guess, one, better understand the PLG headwind dynamics and I guess, what exactly maybe change there in the guidance here versus last quarter? And then I guess with the headwinds that we're talking about on the packaging side as well, just how should we think about that continuing, I guess, beyond Q4 and going into next year for the potential impact that these factors could have here?

好的，很棒。謝謝讓我在這裡提問。我想更深入了解一下，特別是在營收面向的指引展望中，納入了哪些因素。第一，我想更清楚理解 PLG 逆風的動態，以及相較於上季，這次指引中究竟有哪些變化？第二，關於我們也在談的包裝（packaging）面向逆風，我們應該如何看待它在第四季之後、進入明年仍會持續的情況，以及這些因素可能帶來的影響？

Yeah, thanks. So just to frame that up, I'd say, looking forward, two things we're really excited about, one is work-in-progress. So what are we excited about? The first is, as you see, we are now manifesting our vision as the human-agent operating system.

好的，謝謝。我先做個框架整理：展望未來，我們真正感到興奮的有兩件事，其中一件是仍在進行中的工作（work-in-progress）。那我們興奮的是什麼？第一，如你所見，我們現在正在把我們作為「人類—agent 作業系統」的願景具體呈現出來。

We've got so much good stuff ahead of Agent Work Management. And then you see all of these other buyer-specific products of Asana Client Management, Asana Service Management, Command, and Stack. So really five new products that we're excited about. Number two is you saw the upmarket strength, and you saw that this quarter, manifest as an increase in NRR across our $5,000 cohort across our $100,000-plus cohort getting up to 98% now. and AI adoption across the board, whether that's to every customer, which was -- you saw us say, 25% of our net new ARR is now coming from AI products.

在 Agent Work Management 方面，我們接下來還有非常多很棒的內容。同時你也看到其他針對不同買方角色的產品：Asana Client Management、Asana Service Management、Command，以及 Stack。所以總共是五個我們很興奮的新產品。第二，你也看到我們在中大型客戶市場（upmarket）的強勁表現；而這一季也反映在 NRR 的提升上——在 5,000 美元客群、以及 10 萬美元以上客群中，NRR 現在提升到 98%。此外，AI 的採用也全面提升，不論是面向所有客戶——你也聽到我們提到，目前我們新增 ARR 中有 25% 來自 AI 產品。

And then also for those large customers, in fact, over 25% of our greater than $100,000 customers have AI attached. So real excitement there, and then the work-in-progress is PLG. The dynamics changed a little bit. The thing that we're now, I'd say, encouraged by is customers do still want to engage digitally, a digital discovery, digital playing, and many customers want to fully use and consume in a digital engagement. That remains true.

另外，對於那些大型客戶，事實上，超過 25% 的 10 萬美元以上客戶已經加購（attached）AI。所以我們對此非常振奮，而仍在進行中的工作則是 PLG。其動態有些改變。我們現在感到鼓舞的是：客戶仍然希望以數位方式互動——數位探索、數位試用，而許多客戶也希望在數位互動中完整使用並以用量方式消費。這點仍然成立。

What is new is the top of the funnel can get very clogged up with, I'd say, heavy tire kickers. And the best thing that we can do is focus all our efforts in making sure that the customers that are coming in and actually paying are the right customers for us that they're our ICP. So you'll see us focus a lot more of our efforts, a lot more of our marketing dollars on our ICP.

新的情況是，漏斗頂端可能會被大量「只是來看看」的重度試用者（heavy tire kickers）塞得很擁擠。我們能做的最好事情，就是把所有努力聚焦在確保進來並實際付費的客戶，是適合我們的正確客戶，也就是我們的 ICP。因此你會看到我們把更多努力、更多行銷預算投入在我們的 ICP 上。

And as they do so as we get the right ICP into our funnel, because of that product strength, we now have so much more to delight those customers with. AWM will be in our PLG funnel. ACM will be in our PLG funnel. And so a lot more customers will have a lot richer and deeper experience early as they get used to Asana.

而當我們把正確的 ICP 帶進漏斗後，憑藉產品的強勁實力，我們現在有更多方式能讓這些客戶感到驚喜。AWM 會進入我們的 PLG 漏斗。ACM 也會進入我們的 PLG 漏斗。因此，更多客戶在一開始熟悉 Asana 的早期階段，就能獲得更豐富、更深入的體驗。

And Steve, just to add on to Dan's point, we're really encouraged what we're seeing about upmarket, just another KPI I'll call out is just the growth in RPO and cRPO. So if you actually back out the large customer renewal, multiyear renewal we had in Q2 '26 of RPO and cRPO. RPO accelerated from 7% year-over-year growth last quarter to 12% this quarter and cRPO from 8% to 11% this quarter. And that's really the best proxy for upmarket and enterprise growth. So we're seeing really strong traction there.

Steve，再補充 Dan 的觀點，我們對於在高端市場（upmarket）看到的情況感到非常振奮；另外我想特別點出的一個 KPI 是 RPO 與 cRPO 的成長。因此，如果你把我們在 2026 會計年度第二季（Q2 '26）一筆大型客戶續約、且為多年期續約所帶來的 RPO 與 cRPO 影響扣除掉。RPO 的年增率從上一季的 7% 加速到本季的 12%，而 cRPO 也從 8% 提升到本季的 11%。而這其實是衡量高端市場與企業級成長的最佳替代指標。所以我們在那裡看到非常強勁的動能。

Also with our $100,000-plus customer cohort, that accelerated to 16% year-over-year on a customer count basis from 12% last year. And importantly, we're driving this upmarket strength with efficiency. Our sales and marketing spend has been really flat over two quarters. So we're seeing stronger sales efficiency there. So as you think about how that upmarket strength is manifesting in our consolidated growth and our guidance.

另外，在我們年支出 10 萬美元以上的客戶族群方面，以客戶數量計算的年增率加速至 16%，高於去年同期的 12%。而且重要的是，我們是在效率提升的情況下推動這股高端市場的強勁表現。我們的銷售與行銷支出在連續兩季都幾乎持平。因此我們看到更高的銷售效率。所以當你思考這股高端市場的強勁表現如何反映在我們整體合併成長與財測指引上時。

As Dan called out, the PLG piece is really masking that. So we called out a 2-point headwind to ARR back in March. That actually gap has widened a bit. And the impact of the Q4 headwind, the Q1 headwind and now again in Q2 on revenue growth compounds each quarter, so that ARR impact gets greater each quarter, where in Q3, it's about 1%. And in Q4, it grows to about 1.5 percentage.

如 Dan 所提到的，PLG（產品導向成長）這一塊其實在很大程度上掩蓋了上述表現。我們在 3 月時曾指出 ARR 會有 2 個百分點的逆風。而那個落差其實又擴大了一些。此外，第四季的逆風、第一季的逆風，以及現在第二季再度出現的逆風，對營收成長的影響會在每一季疊加，因此 ARR 的影響會逐季變大；到第三季約為 1%。到第四季則增加到約 1.5 個百分點。

So that's underlying our guidance. And then you add the packaging transition to AWM, having about a $1.2 million impact in the second half or 30 basis points. That's just timing, and a lot of that is just created because it's the first quarter we're moving to that. It will normalize and should normalize in Q4 and subsequent in 2028, and we'll get that timing impact back in subsequent quarters. So I think you asked whether that will grow or have a bigger headwind going forward.

這些因素構成了我們指引的基礎。接著再加上包裝方案轉換至 AWM，在下半年約有 120 萬美元的影響，約 30 個基點。這主要只是時點問題，其中很大一部分是因為這是我們首次轉換到該方案的第一個季度所造成的。它會在第四季以及 2028 年之後的季度回歸正常、也應該會正常化，而我們也會在後續季度把這個時點影響拿回來。所以我想你問的是，這個影響未來是否會擴大或形成更大的逆風。

It won't. It actually have the biggest headwind in Q3, Q4 and then normalize thereafter. So if you take those two things in account and you think about our guide, especially with the $1.2 million, we beat Q2 by about $2.4 million. We raised $1.5 million. We had this $1.2 million impact we didn't foresee in the last couple of quarters.

不會。它其實會在第三季、第四季達到最大的逆風，之後就會正常化。因此，如果把這兩件事都納入考量，再來看我們的指引，尤其是那 120 萬美元的影響，我們第二季實際表現比指引高出約 240 萬美元。我們上調了 150 萬美元。而我們在過去幾季並未預見到這 120 萬美元的影響。

So in absence of the $1.2 million impact from the transition from CWM to AWM, we would have rolled the full beat and then some. So just putting into context how we're thinking about the guide. And just to reinforce these new products that were coming out of super excited, but we have not factored any contribution from them in our FY27 guidance.

因此，若不考慮從 CWM 轉換到 AWM 所帶來的 120 萬美元影響，我們本來會把第二季的超預期表現全部延續到後續指引，甚至更多。所以這是我們思考指引時的背景脈絡。另外也再次強調，我們對即將推出的這些新產品非常興奮，但在 FY27（2027 會計年度）指引中，我們尚未把它們的任何貢獻納入。

Billy Fitzsimmons, Piper Sandler.

Billy Fitzsimmons，Piper Sandler。

Hey guys, thanks for taking the question. I think great segue here. In terms of, Dan, a lot of new products rolling out in the second half, Command by Asana, Service Management, Asana Client Management. These products obviously expands your TAM. But in some cases, you're competing against new vendors. So Dan, I love that you could kind of talk about what is Asana's right to win in these spaces.

嗨，各位，謝謝讓我提問。我覺得這裡銜接得很好。就 Dan 而言，下半年會推出很多新產品，包括 Command by Asana、Service Management、Asana Client Management。這些產品顯然會擴大你們的 TAM（總可服務市場）。但在某些情況下，你們也會與新的供應商競爭。所以 Dan，我很希望你能談談 Asana 在這些領域的致勝理由（right to win）是什麼。

And you touched on this a little bit, but what has to be done from a go-to-market standpoint as these products go GA to kind of get them out to customers. And then as these -- I appreciate that last point there. So to be crystal clear, it sounds like potentially of adoption for these new products is better than expected. It could be a source of upside in the back half. Is that fair to say?

你剛剛也稍微提到一點，但從 go-to-market（上市/商業化）角度來看，當這些產品進入 GA（正式可用）時，需要做哪些事情才能把它們推向客戶。另外，關於你剛才最後那點，我很感謝你提到。所以為了完全釐清，聽起來這些新產品的採用情況可能比預期更好。它們可能會成為下半年業績上行的來源。這樣說公平嗎？

Yes, I'll start off. Dan goes, that's fair to say.

是的，我先開始。Dan 說，這樣說是公平的。

Yes. So -- thanks for the question. Returning to your first piece about new products, new TAMs and what's our right to win in those areas. I guess the first piece to think about is, I wouldn't think of them as just single products. This is a platform. The platform is an orchestrated execution across every team. And the platform itself has many of these differentiators built-in, really orientating around the work graph. So the platform itself promises instant productivity for any of the agents that run on it. Why?

是的。所以——謝謝你的問題。回到你第一個部分：新產品、新 TAM，以及我們在那些領域的致勝理由。我想第一點是，不要把它們只當作單一產品。這是一個平台。這個平台是在每個團隊之間進行協同編排的執行（orchestrated execution）。而平台本身內建了許多差異化能力，核心是圍繞 work graph（工作圖譜）。因此，平台本身承諾能讓在其上運行的任何代理（agents）立即提升生產力。為什麼？

Because we can quickly recognize all the relevant work, we can recognize who work needs to get routed to. It also promises increased velocity because there are a lot less handoffs if you know exactly who's supposed to get it next. There's no back and forth of e-mail and Slack. And then it promises the ability to control and manage the enterprise risk of those agents because every agent is auditable. So if you think about that, now apply that to those new products, so we already know a lot about these workflows. It turns out we've served IT teams.

因為我們能快速辨識所有相關工作，也能辨識工作需要被分派（route）給誰。它也承諾更高的速度，因為如果你清楚知道下一步應該交給誰，交接（handoffs）就會少很多。不需要在電子郵件和 Slack 之間來回溝通。此外，它也承諾能控制並管理這些代理所帶來的企業風險，因為每個代理都是可稽核的（auditable）。所以把這些概念套用到那些新產品上，我們其實已經對這些工作流程非常了解。事實上，我們已經服務過 IT 團隊。

We've served R&D teams. We've served HR teams. We've served client delivery teams. We know exactly what tasks and work is and what the workflow looks like. So we get to bring an agentified solution to those workflows now based on all of the deep, rich data we have on how those workflows actually travel.

我們服務過研發（R&D）團隊。我們服務過人資（HR）團隊。我們服務過客戶交付（client delivery）團隊。我們非常清楚任務與工作是什麼，以及工作流程長什麼樣子。因此，我們現在可以基於我們掌握的深度且豐富的資料——也就是這些工作流程實際如何流轉——把「代理化」（agentified）的解決方案帶進這些工作流程。

So I'll give you kind of one example. Let me do this for Asana Service Management. So Asana Service Management on Asana looks like a request might come in through a single portal or it might come in through Slack. Well, we'll understand the context of that request because we have this rich data. So instantly, we're now able to do one of two things, either a, resolve it instantly using AI, or b, route it to exactly the right person that we know is capable of dealing with that, with all of the full project context and history.

我給你一個例子。以 Asana Service Management 來說。在 Asana 上的 Asana Service Management，一個請求可能透過單一入口網站進來，也可能透過 Slack 進來。我們會因為擁有這些豐富資料而理解該請求的脈絡。因此，我們可以立刻做兩件事之一：a，用 AI 立即解決；或 b，把它分派給我們知道最能處理該問題的正確人選，並附上完整的專案脈絡與歷史。

Then when we actually make a resolution the resolution isn't just trapped in e-mail as an example, but part of the work graph itself. And now when the next request comes in, we know exactly how that in turn was solved in the last time. So this is a kind of dynamic learning system that's all baked off this orchestrated execution platform. So that's our right to win. And so what does that lead us to? Yes, sometimes we'll be working alongside some of those point solutions. And sometimes, our customers may want to consolidate their spend on Asana.

接著，當我們真正做出解決方案時，這個解決方案不會像例子中的那樣只被困在電子郵件裡，而是成為 work graph 本身的一部分。而當下一個請求進來時，我們就能清楚知道上一次是如何解決的。所以這是一個動態學習系統，全部都建立在這個協同編排的執行平台之上。這就是我們的致勝理由。那這會帶來什麼結果？是的，有時我們會與某些點狀解決方案（point solutions）並行合作。而有時，我們的客戶可能希望把他們的支出整併到 Asana 上。

Elizabeth Porter, Morgan Stanley.

Elizabeth Porter，Morgan Stanley。

Great. Thank you so much for the question. I wanted to follow up on your comment about Asana being able to select and optimize the appropriate AI models for customers and you guys taking on that complexity as opposed to pushing it down. So what is the impact to your efficiency to be able to deliver AI and more cost effectively. Is this something where you could start to see greater savings that benefit the margin or more likely pass through in order to drive more share and usage within AI?

很好。非常感謝你的提問。我想跟進你剛才提到 Asana 能夠為客戶選擇並最佳化合適的 AI 模型，而你們選擇承擔這種複雜度、而不是把它下放給客戶的評論。那麼，這對你們的效率有何影響，能否讓你們更有效率、也更具成本效益地交付 AI。這是否意味著你們可能開始看到更大的節省、進而有利於毛利率；或更可能是把節省讓利出去，以在 AI 領域推動更高的市占與使用量？

Yeah, thanks. I'd say, look, this is a growing competency and we're getting rather good at it. And I would say the piece that we've gotten rather good at over the last, say, six months to a year is figuring out which types of tasks should go to which types of model. And so something that may come in as a, I'd say, a generic request or a net new task type, we're doing pretty good categorization now of passing that out into the right model, to both solve for quality and cost optimization.

是的，謝謝。我會說，這是一項正在成長的核心能力，而我們也做得相當不錯。我會說，過去大概六個月到一年間，我們特別擅長的一塊，是判斷哪些類型的任務應該交給哪一類模型。因此，對於一些看起來像是一般性需求、或全新的任務類型，我們現在在分類上做得很好，能把它分派到正確的模型上，同時兼顧品質與成本最佳化。

And so this will in turn lead to a much better gross margin profile as we're able to deal with that request, and we'll talk about request in a second is the unit that we're charging customers on so that we can deal with that request most efficiently, both in terms of efficacy of the outcome for them but also the cost delivered. And so yes, in the beginning, I'd say the gross margin burden, we've taken that a lot on our shoulders. But over time, you'll be able to see, I'd say, getting a much better gross margin profile from that.

因此，當我們能夠以最有效率的方式處理該需求時，這反過來會帶來更好的毛利率結構；我們稍後也會談到「需求（request）」——這是我們向客戶計費的單位——讓我們能以最高效率處理該需求，既確保對客戶而言結果有效，也把交付成本降到最低。所以是的，一開始我會說，毛利率的負擔我們確實承擔了很大一部分。但隨著時間推進，你們會看到我們在這方面的毛利率結構會明顯改善。

And just to add, as we were determining the scope of the AWM launch, whether this would be new customers only or taking it to specific segments or bringing it to the full entire base like we are. The progress we have made reducing the cost of delivering our AI products particularly through the model matching and routing that Dan just mentioned, gave us confidence that we could go to the broader base while keeping the cost of that rollout manageable and mitigatable.

再補充一下，當我們在決定 AWM 上線的範圍時——是只針對新客戶、或推向特定客群、或像現在這樣推向整個客戶基礎——我們在降低 AI 產品交付成本方面取得的進展，特別是 Dan 剛提到的模型配對與路由，讓我們有信心能推向更廣泛的客戶群，同時把這次推廣的成本控制在可管理、可緩解的範圍內。

And you've seen that while it's having a 150 basis points impact into COGS in the second half because we're investing ahead of the benefits, we've been able to rationalize other places in the cost base to still deliver the margin expansion above our expectations. And so that was an important determinant of how broad we were going to go, and how broad we're going to go allows us to spark that adoption and that flywheel of adoption leading to better seat dynamics leading to consumption much sooner and much broader.

你們也看到，雖然因為我們在效益顯現前先行投入，導致下半年在銷貨成本（COGS）上有 150 個基點的影響，但我們也能在成本結構的其他部分做合理化調整，仍然交出超出預期的利潤率擴張。因此，這是我們決定要推多廣的一個重要因素；而推得越廣，就越能更快、更大範圍地點燃採用，形成採用的飛輪效應，進而帶動更好的席位（seat）動態，並更早、更廣泛地帶動用量（consumption）。

Jackson Ader, KeyBanc.

Jackson Ader，KeyBanc。

Great. Thanks, guys. The question I had was about the seeding the market in AWM and kind of trying to reduce the friction for AI adoption across your three AI products. I'm just curious, like what friction are you hoping to alleviate by going to this kind of embedded packaging, was like price a hurdle? Is there so much noise from every software vendor or AI vendor that like people didn't necessarily know what they could access via Asana? Like what is it that you're hoping to alleviate by embedding this in everybody's package? Thank you.

很好。謝謝各位。我想問的是關於在 AWM 上「播種」市場，以及試圖降低你們三項 AI 產品在採用上的摩擦。我很好奇，你們希望透過這種內嵌式的套裝方案來消除哪些摩擦？例如價格是否是一個門檻？還是因為每個軟體供應商或 AI 供應商都在發聲，噪音太多，導致大家不一定知道透過 Asana 能使用到什麼？你們把它內嵌到每個人的方案裡，究竟希望解決的是什麼？謝謝。

My short answer would be yes, and then I'll expand on that a little bit. So there's a great productivity gap in AI, which is, individuals have seen massive improvements in the productivity by interacting with chat agents. They become much more productive in code generation, much more productive in document generation. But oftentimes, enterprises haven't been able to translate that into real productivity. Why?

我的簡短回答是：是的；我再多展開說一點。AI 存在一個很大的生產力落差：個人透過與聊天代理互動，已經看到生產力大幅提升。他們在程式碼生成上更有效率，在文件生成上也更有效率。但很多時候，企業卻無法把這些轉化為真正的生產力。為什麼？

It's because the AI is not actually part of their core workflow. It's not part of what teams do every day as teams. It's not part of the handoff process between teams. It's not part of the, let's say, coordination that's required to actually get work done in an enterprise. So what are we trying to solve?

因為 AI 並沒有真正成為他們核心工作流程的一部分。它不是團隊每天以團隊形式在做的事情的一部分。它不是團隊之間交接流程的一部分。它也不是企業要真正把工作做完所需的那種協調的一部分。所以我們要解決什麼？

It's really that. It's how do we embed AI more deeply into the workflows that actually matter to our customers. So yes, there's a discovery part to that. We want to make sure that the agents are imminently discoverable. But also, anything that the agents do actually operates within the context of a team that they are actors within the same work pattern as your humans.

就是這個。我們要做的是，如何把 AI 更深度地嵌入到對客戶真正重要的工作流程中。所以是的，其中有一部分是「發現」：我們希望確保這些代理是非常容易被發現、容易找到的。但同時，代理所做的任何事情，都必須在團隊的脈絡中運作——它們是在同一種工作模式中行動的角色，與人類一起協作。

And so that's literally why we call them teammates. They are things that multiple people can interact with and improve upon and to interact with humans in the loop every time. So these are going to be much more deeply embedded in your day-to-day work. And because they're so discoverable, we think the cost of discovery has gone down, but also your ability to try these things out has also gone down. And that ability to keep the multiplayer basically means everyone gets to take part, everyone gets to make them better over time.

因此我們才會把它們稱為「隊友（teammates）」。它們是可以讓多人互動、共同改進的東西，而且每一次都有人類在迴路中參與互動。所以它們會更深地嵌入你日常的工作。而因為它們非常容易被發現，我們認為「發現成本」降低了，同時你嘗試這些功能的門檻也降低了。而這種多人協作的能力，基本上意味著每個人都能參與、也都能隨時間讓它們變得更好。

And when you add the work graph to it, you get this nice additional benefit, which is all of the work that you do to make your agents better, all the work you do to make your workflows better, make the very next run once again better in turn. And so benefits kind of compound and that's often what's missing in some of the single-player chat interaction today.

再加上工作圖譜（work graph），你會得到一個很好的額外好處：你為了讓代理變得更好所做的所有工作、你為了讓工作流程變得更好所做的所有工作，都會讓下一次執行又變得更好。因此效益會以複利方式累積，而這往往是今天一些單人式聊天互動所缺少的。

Rob Oliver, Baird.

Rob Oliver，Baird。

Great. Thanks, guys. Good afternoon. Thanks for taking my question. With 25% of net new ARR now coming from Teammates and Studio and would -- really, I think, underscores the case you guys have laid out for now, now being the right time to kind of transition here to AWM. I'm curious, you talked about and Aziz, you mentioned in detail, I appreciate all the detail, some of the impact on rev rec as the move consumption happens. You guys also mentioned in the prepared remarks, outcome-based pricing. And I would love to get some more color on how outcome plays into your thoughts and expectations about AWM as it ramps and how that potentially influences your ability to forecast the business?

很好。謝謝各位。午安。謝謝讓我提問。目前淨新增 ARR 的 25% 來自 Teammates 和 Studio，我認為這確實凸顯了你們先前所提出的論點：現在正是轉向 AWM 的合適時機。我想請教，你們談到、而且 Aziz 也很詳細地提到（我很感謝這些細節），在用量計費（consumption）轉換發生時，對收入認列（rev rec）的影響。你們在事先準備的講稿中也提到「以成果為基礎的定價」。我希望能更了解「成果」如何影響你們對 AWM 隨著規模提升時的想法與預期，以及這可能如何影響你們預測業務的能力？

Yeah, I'll try and describe some of the philosophy here. So our customers want predictability in pricing, but they also want things to tie as closely as possible to the value that they're achieving. Predictability, definitely comes to a, let's call it, like a subscription-type model. But in order to tie to value, yes, we need to more and more tied to the outcomes that they were delivering together. And so that's really where the hybrid model come in.

好的，我試著說明一下這裡的理念。我們的客戶希望定價具有可預測性，但他們也希望盡可能與他們所獲得的價值緊密連結。可預測性，確實比較符合我們所稱的訂閱型模式。但要與價值連結，是的，我們需要越來越多地與他們共同交付的成果（outcomes）綁在一起。這也正是混合模式（hybrid model）發揮作用的地方。

So how should we tie our pricing to value. Well, we've decided that the unit that we're going to anchor on is requests. We've seen, of course, other companies with their endeavors around tokens or around credits or putting the burden on the customer themselves to choose the model and do model optimization. We, kind of, say we want to -- all of that. We think request is the most customer-friendly possible unit.

那我們應該如何把定價與價值連結？我們決定要錨定的計價單位是「需求（requests）」。當然我們也看到其他公司在代幣（tokens）、點數（credits）等方面的嘗試，或是把選擇模型與模型最佳化的負擔放在客戶自己身上。我們某種程度上是說，我們希望把這些都承擔起來。我們認為「需求」是對客戶最友善的計價單位。

Why? Because it's literally how you interact with Asana. You will ask it or your teammate to do something or help with something and then fulfill that request. And so we think it's a very natural idea that is, honestly, as customer-friendly as we could imagine. So the hybrid model is essentially a predictable piece that really does scale up and down with the size of the organization. And then also a knowable piece, which is how many requests do you want this system to deliver to you the outcomes of. So yes, we think that's the right customer-friendly mix.

為什麼？因為這就是你與 Asana 互動的方式。你會請它或你的隊友做某件事或協助某件事，然後再完成那個請求。因此我們認為這是一個非常自然的概念，坦白說，也是我們所能想像中最以客戶為中心的做法。所以混合模式本質上包含一個可預測的部分，會隨著組織規模而真正地上下調整。另外還有一個可衡量的部分，也就是你希望這個系統為你交付多少個請求的成果。所以是的，我們認為這就是對客戶最友善的正確組合。

Yes, and then on the forecasting, I'll be honest, our forecasting position on this in a year from now will be better than it is today. So that we've taken some prudence in how we've built this AI product target, raising it from 15% to 20%. The seeding should accelerate adoption in users, but it can push out the timing of incremental paid consumption. So we factored that in and how we have designed the 20%.

是的，然後關於預測，我坦白說，我們一年後在這方面的預測能力會比今天更好。因此我們在建立這個 AI 產品目標時採取了一些審慎作法，將其從 15% 提高到 20%。這種「播種」應該會加速使用者採用，但也可能把增量的付費使用時點往後推。所以我們把這點納入考量，並據此設計了 20% 的目標。

And we'll learn a lot more post launching in a couple of weeks about how customers are adopting how fast the seeded credits are leading to expansion. And then upon renewal, how they're impacting and influencing the seat renewal and seat expansion, which is a it's not part of that AI metric, but it's an influence and an attribute of seeding that we look to drive over time.

而且在幾週後上線之後，我們會學到更多：客戶採用的速度如何、播種的點數如何帶動擴張。以及在續約時，它們如何影響並帶動席位續約與席位擴張；這雖然不屬於那個 AI 指標的一部分，但它是我們希望隨時間推動的播種效應與屬性。

Thank you.

謝謝。

Taylor McGinnis, UBS.

Taylor McGinnis，瑞銀（UBS）。

Yeah. Hi. Thanks for taking my question. So given that it sounds like up market has been pretty strong and the weakness is in the PLG motion. I'd love to ask you a question on that and what you're seeing in terms of top-of-funnel activity there. Were those demand trends stable? Or have they become more challenging in 2Q and 3Q? And just as we think about the 150 basis points of impact of 4Q revenue, does that mean that in FY28, you'll see a similar headwind of 150 basis points? Or how should we think about that as we look beyond this year?

是的。嗨。謝謝讓我提問。所以聽起來上移市場（up market）表現相當強，而疲弱點在 PLG 推動模式。我想就這點問一下，想了解你們在那邊看到的漏斗頂端（top-of-funnel）活動情況。那些需求趨勢是穩定的嗎？還是說在第二季和第三季變得更具挑戰？另外，談到第四季營收受到 150 個基點的影響，這是否意味著在 FY28 你們也會看到類似的 150 個基點逆風？或者我們在展望今年之後時，應該如何看待這件事？

Yes. So the impact -- so to answer, kind of, is it getting worse in Q2, Q3? It is, but not materially. So I think we called out the 2 points of ARR headwind back in March when we reported Q4. That's gotten a little bit worse, but more to the tune of about 50 basis points.

是的。所以影響——回答你「在第二季、第三季是否變得更糟？」這個問題：是有變糟，但不算顯著。我想我們在 3 月公布第四季時就提到 ARR 有 2 個百分點的逆風。那之後確實又稍微變差，但大概是再差了約 50 個基點的程度。

The impact we called out on revenue is really from Q4, Q1, Q2. We don't expect and have not factored in Q3 and Q4 to further deteriorate from what we saw in Q2. And all the efforts that Dan outlined in terms of driving the right top of funnel, not just the volume but the ICP mix, whether it be the size the industry of the customer. We see that the right ICP drives the right LTV. And then with new products and additional surface areas to procure ACM, AWM, the expansion opportunities with Teammates and Studio.

我們提到對營收的影響，主要是來自第四季、第一季、第二季。我們不預期、也沒有把第三季和第四季會比我們在第二季看到的情況進一步惡化納入假設。而且 Dan 所概述的所有努力，都是為了帶動正確的漏斗頂端，不只是量，也包括 ICP 組合——無論是客戶規模或所屬產業。我們看到正確的 ICP 會帶來正確的 LTV。再加上新產品與更多可採購的觸點，例如 ACM、AWM，以及 Teammates 和 Studio 的擴張機會。

It just amplifies that. So now you have a higher LTV customer with more to buy. It just creates better ACV and expansion outcomes and retention. So and as we called out, the real inhibitor right now to getting to 100% plus NRR we're seeing is in that self-serve cohort, which is concentrated in less than $5,000. And if you look at, our Core is at 98%, if you kind of back in what that means on in-quarter based on the improvement, our in-quarter is trending towards 100%. And really what's driving down the consolidated is that sub-$5,000. So we don't expect this headwind to persist in the same level in FY28.

這只會把效果放大。所以你現在有一個 LTV 更高、可購買項目更多的客戶。這會帶來更好的 ACV、擴張成果與留存。因此，正如我們提到的，目前阻礙我們回到 100% 以上 NRR 的主要因素，是自助式（self-serve）族群，而這部分集中在低於 5,000 美元的客群。如果你看我們的 Core 是 98%，若把這代表的單季內（in-quarter）改善倒推回去，我們的單季內趨勢正朝 100% 走。真正拉低整體合併數字的是那個低於 5,000 美元的部分。所以我們不預期這股逆風會在 FY28 以同樣程度持續。

Rishi Jaluria, RBC.

Rishi Jaluria，RBC。

Hi, team. This is Josh standing in for Rishi. You guys mentioned looking to improve the sub-$5,000 customer base. And I just wanted to sort of dig into that a little bit. I was curious around how you're balancing developing the product to be -- to appeal to a broader audience and sort of being out of the box for giving a customer size while also balancing the specialization that comes with verticalization. And just a little bit more context around that would be great. Thank you.

嗨，各位團隊。我是 Josh，代替 Rishi 發問。你們提到希望改善低於 5,000 美元的客戶基礎。我想再深入了解一下。我很好奇你們如何在「把產品打造得——能吸引更廣泛受眾、開箱即用以適配不同規模客戶」與「隨著垂直化而來的專業化」之間取得平衡。如果能多提供一些背景脈絡會很有幫助。謝謝。

Yeah, well, I'll say all of our five new products serve really every segment rather well. And it's about how deeply you adopt it and which kinds of workflows you will use against them. So if you take Agentic Work Management as an idea. Well, it turns out, if you're a small business, you're a large business, you will want to have pre-built agents that are working alongside you.

是的，我想先說，我們五個新產品其實對每個細分市場都相當適用。差別在於你採用的深度，以及你會用哪些工作流程來搭配它們。以 Agentic Work Management 這個概念來說。事實上，不論你是小型企業或大型企業，你都會希望有預先建置的代理（agents）在旁協作。

Which ones you pick from that menu of 30 will depend, of course, how thorough you've built out those departments because these are essentially like packaged up agents that are prebuilt for your department. And so if you have a well-tuned, let's say, campaign department, then you're going to absolutely love the campaign orchestration agent.

你會從那份 30 個選項的清單中挑哪些，當然取決於你把那些部門建置得多完整，因為這些本質上就像是為你的部門預先打造、打包好的代理。所以如果你有一個調校得很好的、比方說行銷活動部門，那你一定會非常喜歡行銷活動編排（campaign orchestration）代理。

If you have a well-tuned launch process, you're going to love the launch agent. But similarly, if you're a small business and potentially you want to improve your reporting, then maybe you're going to use the reporting agent. So I don't think the size of the company or really how they engage with us is going to gate how much they love these products.

如果你有一個調校得很好的上市（launch）流程，你會喜歡上市代理。但同樣地，如果你是小型企業，可能想改善報表，那你也許會使用報表代理。所以我不認為公司規模，或他們與我們互動的方式，會限制他們有多喜歡這些產品。

And then I'd say things like a Asana Service Management, if you have a, let's say, a large service department or you have a lot of manual service requests. Clearly, you're going to get a lot more value from that than if those departments are maybe immature or haven't started yet. So I'd say all of our products really serve all of those segments, and that's really part of the strength of Asana is, we have a great digital discovery, digital trial, digital experience and both small businesses and large businesses come to know us through that digital engagement.

另外我會說，像 Asana Service Management 這類產品，如果你有一個很大的服務部門，或你有很多人工的服務請求。很明顯，你會從中獲得更多價值；相較之下，如果那些部門可能還不成熟或尚未開始，那價值就會少一些。所以我會說，我們所有產品其實都能服務所有這些細分市場，而這也正是 Asana 的優勢之一：我們有很好的數位探索、數位試用、數位體驗；小型企業與大型企業都會透過這種數位互動來認識我們。

Thank you.

謝謝。

Thank you. I would now like to turn the conference back to management for closing remarks.

謝謝。現在我想把會議交回管理團隊作結語。

Hi. Thank you, everyone, for joining the call today. We are on the road attending the Citi and Piper Sandler conference in the coming weeks, and we'll also have a marquee Work Innovation Summit in New York on October 14. Hope to see you all there. As always, if you have any questions, please reach out to me at ir@asana.com. Thank you very much.

嗨。謝謝各位今天參加電話會議。接下來幾週我們會在路上參加 Citi 與 Piper Sandler 的研討會，我們也將在 10 月 14 日於紐約舉辦旗艦 Work Innovation Summit。希望到時能見到各位。一如往常，如果你有任何問題，請透過 ir@asana.com 與我聯繫。非常感謝。

This concludes today's conference call. Thank you for participating. You may now.

今天的電話會議到此結束。感謝各位參與。你現在可以。