I would now like to hand the call over to Eva Leung, Head of Investor Relations. Please go ahead. 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 2027 full-year guidance, strategic plans, our market position and growth opportunities, and our capital allocation strategy, including our stock repurchase program, among other items. 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 to differ materially from those set forth in such statements. 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. Reconciliation 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 investors.asana.com. With that, I'd like to turn the call over to Dan. We delivered a solid second quarter, exceeding our expectations on both revenue and profitability. We've continued improvement in the underlying health of the business. There's three things I want to point out this quarter. First, the business continued to get healthier. Growth is accelerating, retention is improving again, and we saw 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. Customers adopting AI Studio and AI Teammates are engaging more deeply, retaining better, and expanding faster than the broader customer base. We believe this gives us an early validation of our opportunity to build meaningful consumption and outcome-orientated revenue stream. Third, we're acting on the learnings by bringing AI Teammates, AI Studio, and Asana Dash together as a core part of the Asana experience through our Agentic Work Management product. We want our customers to experience these capabilities early and naturally as part of how they work every day, rather than as separate AI products that they have to discover and purchase. We are going to be bringing that same orchestrated execution across humans, agents, and systems with Asana Client Management, Asana Service Management, and our Command products. Let us have a look at this quarter. Improving health of our core business validates our strategy. It gives us confidence in the investments we are 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. 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, that is those spending over $100,000 or more, improved to 98% from 96%. That improvement is being driven by broader multi-product adoption within the largest customers, creating additional paths for expansion. The technology sector delivered a second consecutive quarter of year-over-year growth. While growth remains modest, we are 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 that 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. In fact, we added new customers across a range of industries this quarter, including one of the largest telecommunications operators in the U.S., a large insurance operator in the U.S., a Big Four professional services firm, one of the world's leading law firms, and an iconic American luxury jewelry brand. We also saw encouraging acceleration in the U.S., 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 at our tech customers, which are concentrated in the U.S., strong adoption of our AI products, and acceleration in new logo acquisition. Internationally, Darktrace and a leading U.K.-based financial services company were notable new logo wins for our EMEA team. 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. While still early, we are seeing encouraging validation of the opportunity to build meaningful consumption and outcome-orientated 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 that 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. More than 25% of our $100,000+ customers have now purchased AI Studio or AI Teammates. This has been a key contributor to the NRR expansion we are seeing in 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 multi-million dollar agreement with a Fortune 500 media company spanning AI Studio and AI Teammates, with our AI products representing almost half of the total contract value. Most particularly important is the role our AI products played in the expansion. 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 the smaller footprint, resulting in a modest overall expansion, with also the additional upside potential if consumption grows over time. They are already seeing measurable value. In fact, in one creative marketing workflow, AI Teammates have already reduced the content operations 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. We are 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. It also runs the dynamic intake and triage across their 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 has cut lead time for raw requests to active project by 60%. It has 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. Washmen, a U.A.E.-based textile care business, is an early example of AI Teammates running an operation end to end. They are using AI Teammates to agentify their customer support and returns process. When a garment comes in, one teammate researches its retail value, a second reviews the care plan for risk, a third checks it against every past claim, and a 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. These kind of results reinforce our belief that our AI products create the greatest value when they are deeply embedded in business-critical workflows, with a shared context that enables people and agents to coordinate and execute together towards outcomes. This principle is the heart of what we are bringing to market in mid-September with Agentic Work Management. Let us take a look at Agentic Work Management. Let me explain what we mean here, because this is real meaningful evolution of our product, not simply a label on traditional work management. 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 close 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 with the same context, the same goals, and the same governance. AWM brings three things into every paid package tier. First, AI Teammates, including more than 30 pre-built teammates for marketing operations and IT. These are pre-approved and ready to work and pre-trained with no prompt engineering required. Second, AI Studio, so that any team can build no-code workflow automations for intake, routing, approvals, and status. Third, Asana Dash. This is your AI Chief of Staff that knows a person's goals and priorities, pulls decisions out of meetings, emails, and chat, and surfaces what needs their attention and keeps them that one step ahead. 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. They'll start with AI Teammates, Asana 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. This is deliberate. We want customers to experience the full value of Asana early. Similarly, the full allotment of requests 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 increased consumption and stronger seat retention expansion over time. We chose requests as the unit of consumption because we want our AI pricing to be customer-friendly, simple, and predictable. 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. Behind the scenes, Asana's going to select and optimize the appropriate AI model. That complexity should be ours to manage, not the customer's. 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 a business. Asana Client Management applies the same orchestrated execution to client delivery, Asana 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 that people, systems, and agents need. 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. 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. Nearly a third of our customers today are already doing some form of client delivery or running a professional services team today. They often run client delivery in Asana while managing the rest of the client relationship across disconnected systems, communication and email, 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 adds 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 that sits alongside the actual work. 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. 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, email, or a portal before they even reach a human. 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 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 handoff, the release plans, the traceability. 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 a 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, 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 the many agents and tools modern engineering teams use. Its open platform design lets developers bring SpaceXAI into a broader orchestration layer without being locked into a closed system. 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 a company already runs on, while Asana provides the plan, the shared context, and the people around that execution. Importantly, it gives us a more complete solution to enterprise AI transformation initiatives we are increasingly seeing from our IT and AI transformation buyers. In that motion, we have 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 Stack AI together for larger, more complex enterprise workflows. In closing, taken together, we are 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. 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. With that, I will turn it over to Aziz to take you through the quarter and the outlook. 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. 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. 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 multi-product adoption with AI Studio and AI Teammates increasingly creating an expansion vector at renewal. As Dan discussed, that allows us to expand with customers in ways that are less dependent 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. That pressure comes as several of our underlying growth acceleration levers are improving. NRR continues to strengthen, we are experiencing strong momentum with our AI products, our U.S. business has accelerated, and technology vertical has now returned to year-over-year growth for two consecutive quarters. It also explains the gap in our net retention. Core and our $100,000+ 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 above 100% really comes down to three levers. First, gross retention improvement in the core and enterprise base. Second, seat multi-product and consumption expansion in those same cohorts. Lastly, improving ICP mix and driving stronger retention and expansion in the sub-$5,000 customer base. 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 FY 2028. Improving the growth in NRR within our sub-$5,000 customer base is centered on two areas. First, we are focusing our acquisition spend on the customer sizes, industries, and use cases with the strongest fit and highest lifetime value potential. 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. AWM and ACM launch in self-serve in mid-September, bringing AI Teammates directly to our large PLG install base while expanding 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. Now moving to profitability, where I will 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. This decline reflects three primary factors. First, higher AI infrastructure and compute costs attributes to one-time scaling and development cost 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. 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. 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 roadmap in Asana's history without a commensurate increase in R&D spend. 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 were stronger than the reported growth rates suggest. This is due to the comparison against the large multi-year 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. 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 change is 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. 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've reflected that transition in our guidance. There are two dynamics affecting revenue recognition as we transition towards consumption. First, going forward, all new AI Teammate sales will be consumption-based, with revenue recognized as customer requests are consumed rather than ratably over the contract term. 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. As customers ramp consumption over time, this shifts a portion of revenue recognition into future periods. The shift of new AI Teammate 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. In addition, this transition also creates approximately 150 basis points of gross margin pressure across Q3 and Q4, reflecting both costs incurred a head of associated consumption-based revenue recognition and the growing mix of AI products, which currently carry 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. 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 cost through efficiencies and productivity gains elsewhere in the cost base while continuing to deliver margin expansion ahead of our expectations. Note, this is all while absorbing approximately one percentage point of incremental operating expense as a percentage of revenue from the StackAI acquisition, as we discussed last quarter. Second, the PLG headwind we 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 the current PLG trends persist through the balance of the year and incorporates no recovery in FY 2027 from the initiatives I discussed earlier. 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 Stack AI, we now expect AI products to represent approximately 20% of net 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. No attribution is being made from the AWM packaging change. Fourth, we continue to assume minimal FY 2027 revenue contribution from Client Management, Service Management, and Command. Given enterprise sales cycles and deployment timelines, we expect their financial contribution to become more meaningful as a key growth driver in FY 2028. Finally, Q3 includes approximately $3 million of incremental AWM and agentic application launch investment consistent with what we discussed last quarter. 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 am giving includes all of the assumptions I mentioned above. For Q3 fiscal 2027, we expect revenue of $217 million- $219 million, representing 8% to 9% growth year-over-year. This includes a $700,000 headwind to revenue from our AWM packaging transition. We expect non-GAAP operating income of $18 million- $19 million, representing an operating margin of 8%- 9%. In addition, we expect non-GAAP net income per share of $0.08 per share, 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- $863.5 million, representing growth of 9% year-over-year at the midpoint of the guidance. The full year revenue guide 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 includes 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 growth in constant currency, which is consistent with what we shared last quarter. We expect non-GAAP operating income of $84.5 million- $86.5 million, representing an operating margin of approximately 10%. 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. With that, operator, we are now ready for questions. Thank you. As a reminder, to ask a question, you will need to press star one one on your telephone. To remove yourself from the queue, you may press star one one again. You will be limited to one question to allow everyone the opportunity to participate. Please stand by while we compile the Q&A roster. Our first question comes from the line of Patrick Walravens of Citizens. Your line is open, Patrick. Oh, great. Thank you. 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? Maybe you could share a simple example of a teammate surfacing to help the user. Yeah. Thanks, Pat. 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, Agentic Work Management. 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. We spent really the last, I'd say, six 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. What we found is, the more we can bring that directly into their experience, the better. You'll see in September some, I'd say, innovative ideas on how we create this amazing experience. Innovation number one is our AI Chief of Staff, Dash, as you ask it questions and interact with it will suggest the right teammates to help you complete your execution of that task. Number teo is through in-product nudges. It will actually recognize the type of task that you are trying to complete and suggest one of the pre-built, pre-skilled teammates that can help you. 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. It's no coincidence that we built these 30 pre-built teammates. Those are exactly the kinds of work that our customers are doing today. That matching will happen. 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. This is all in response of the idea that, yet today, one of the biggest hurdles of the agentic enterprise is actually the discovery of the agents, being able to find the right ones that will work for you. Thank you. Our next question comes from the line of Steve Enders of Citi. Please go ahead, Steve. 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. I guess, one, better understand the PLG headwind dynamics and what exactly maybe changed there in the guidance here versus last quarter. Then, I guess, with the headwinds we are 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? Yeah, thanks. Just to frame it up, I would say, looking forward, two things we are really excited about, one that is a work in progress. What are we excited about? The first is, as you see, we are now manifesting our vision as the human agent operating system. We have got so much good stuff ahead of Agentic Work Management. Then you see all of these other buyer-specific products of Asana Client Management, Asana Service Management, Command, and StackAI. So really five new products that we are excited about. Number two is you saw the up-market strength, and you saw that this quarter manifest as an increase in NRR across our $5000 cohort, across our $100,000+ cohort, getting up to 98% now. AI adoption across the board, whether that is to every customer, which was, you saw, say, 25% of our net new ARR is now coming from AI products. Then also for those large customers. In fact, over 25% of our greater than $100,000 customers have AI attached. So real excitement there. Then the work in progress is PLG. The dynamics changed a little bit. The things that we are now, I would 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. What is new is the top of the funnel. It can get very clogged up with, I would say, maybe 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 are our ICP. You'll see us focus a lot more of our efforts, a lot more of our marketing dollars on our ICP. 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. A lot more customers will have a lot richer and deeper experience early as they get used to Asana. Steve, just to add on to Dan's points, we're really encouraged what we're seeing about up-market. Just another KPI I'll call out is just the growth in RPO and CRPO. If you actually back out the large customer renewal, multiyear renewal we had in Q2 2026 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. That's really the best proxy for up-market and enterprise growth. We're seeing really strong traction there. Also with our $100,000+ customer cohort, that accelerated to 16% year-over-year on a customer count basis from 12% last year. Importantly, we're driving this up-market strength with efficiency. Our sales and marketing spend has been roughly flat over two quarters, so we're seeing stronger sales efficiency there. As you think about how that up-market strength is manifesting in our consolidated growth and our guidance, as Dan called out, the PLG piece is really masking that. We had called that a two-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. That ARR impact gets greater each quarter, where in Q3, it's about a percent, and in Q4, it grows to about a percentage and a half. That's underlying our guidance. 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. I think you asked whether that'll grow or have a bigger headwind going forward. It won't. It'll actually have the biggest headwind in Q3, Q4, and then normalize thereafter. If you take those two things into 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 that we didn't foresee in the last couple of quarters. So in absence of the $1.2 million impact from the transition from CWM to AWM, we would've rolled the full beat and then some. Just putting into context how we're thinking about the guide, and just to reinforce, these new products that were coming out, super excited, but we have not factored any contribution from them in our FY 2027 guidance. Thank you. Our next question comes from the line of Billy Fitzsimmons, Piper Sandler. Your line is open, Billy. 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, Asana Service Management, Asana Client Management. These products obviously expand your TAM, but in some cases, you're competing against new vendors. Dan, love if you could kind of talk about what is Asana's right to win in these spaces. 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? As these, appreciated that last point there. To be crystal clear, it sounds like potentially if 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? Yeah. I'll just start off for Dan, guys. That's fair to say. Yeah. 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. The platform itself promises instant productivity for any of the agents that run on it. Why? 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 forward of email and Slack. It promises the ability to control and manage the enterprise risk of those agents because every agent is auditable. If you think about that, now apply that to those new products. We already know a lot about these workflows. It turns out we've served IT teams, 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. 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. I'll give you kind of one example, and maybe we do this for Asana Service Management. Asana Service Management on Asana looks like a request might come in through a single portal, or it might come in through Slack. We'll understand the context of that request because we have this rich data. 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. Then when we actually make a resolution, the resolution isn't just trapped in email, as an example, but part of the Work Graph itself. Now, when the next request comes in, we know exactly how that in turn was solved in the last time. This is a kind of dynamic learning system that's all baked off this orchestrated execution platform. That's our right to win. 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. Thank you. Our next question comes from the line of Elizabeth Porter of Morgan Stanley. Please go ahead, Elizabeth. 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. What is the impact to your efficiency to be able to deliver AI 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? Thank you. Yeah. Thanks. I'd say, look, this is a growing competency, and we're getting rather good at it. I would say the piece that we've gotten rather good at over the last, let's say, six months to a year, is figuring out which types of tasks should go to which types of model. 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. 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 as the unit that we're charging customers on. So that we can deal with that request most efficiently, both in terms of the efficacy of the outcome for them, but also the cost delivered. 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 us, I'd say, getting a much better gross margin profile from that. Yeah. 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. You have seen that while it is having 150 basis points impact into COGS in the second half because we are investing ahead of the benefits, we have been able to rationalize other places in the cost base to still deliver the margin expansion above our expectations. That was an important determinant of how broad we were going to go, and how broad we were 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. Thank you. Our next question comes from the line of Jackson Ader of KeyBanc. Your line is open, Jackson. Great. Thanks, guys. The question I had was about the seeding the market in AWM and trying to reduce the friction for AI adoption across your three AI products. I am just curious, what friction are you hoping to alleviate by going to this kind of embedded packaging? Was price a hurdle? Is there so much noise from every software vendor or AI vendor that people did not necessarily know what they could access via Asana? What is it that you are hoping to alleviate by embedding this in everybody's package? Thank you. My short answer would be yes, and then I'll expand on that a little bit. There's a great productivity gap in AI, which is individuals have seen massive improvements in their productivity by interacting with chat agents. They've 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? 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. What are we trying to solve? It's really that. It's how do we embed AI more deeply into the workflows that actually matter to our customers. Yes. There's a discovery part to that. We want to make sure that the agents are eminently discoverable. But also that 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. That's literally why we call them AI Teammates. They are things that multiple people can interact with and improve upon, and they interact with humans in the loop every time. 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 them multiplayer basically means everyone gets to take part, everyone gets to make them better over time. 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. The benefits kind of compound, and that's often what's missing in some of this single-player chat interaction today. Our next question comes from the line of Rob Oliver of Baird. Your question, please, Rob. Great. Thanks, guys. Good afternoon. Thanks for taking my question. With 25% of net new ARR now coming from AI Teammates and AI Studio, it really, I think, underscores the case you guys have laid out for now being the right time to transition here to AWM. I am curious, you talked about, and Aziz, you mentioned in detail, appreciate all the detail, some of the impact on rev rec as the move to consumption happens. You guys also mentioned 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. Thanks. I will try and describe some of the philosophy here. Our customers want predictability in pricing, but they also want things to tie as closely as possible to the value that they are achieving. Predictability definitely comes through a, let us call it like a subscription type model. But in order to tie to value, we need to more and more tie to the outcomes that they were delivering together. That is really where the hybrid model comes in. How should we tie our pricing to value? We have decided that the unit that we are going to anchor on is requests. We have seen, of course, other companies with other 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 said we want to obfuscate all of that. We think request is the most customer-friendly possible unit. Why? Because it is 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. We think it is a very natural idea that is honestly as customer-friendly as we could imagine. The hybrid model is essentially a predictable piece that really does scale up and down with the size of your organization. And then also a knowable piece, which is how many requests do you want this system to deliver to you the outcomes of? We think that is the right customer-friendly mix. And then on the forecasting, I will be honest, our forecasting position on this in a year from now will be better than it is today, so that we have taken some prudence in how we have built this AI product target, raising it from 15% to 20%. The seeding should accelerate adoption and usage, but it can push out the timing of incremental paid consumption. So we have factored that in in how we have designed the 20%, and we will 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 are impacting and influencing the seed renewal and seat expansion. It is not part of that AI metric, but it is an influence and an attribute of seeding that we look to drive over time. Thank you. Our next question comes from the line of Taylor McGinnis of UBS. Please go ahead, Taylor. Yeah. Hi. Thanks for taking my question. Given that it sounds like up-market has been pretty strong and the weaknesses 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? Just as we think about the 150 basis points of impact to 4Q revenue, does that mean that in FY 2028 you'll see a similar headwind of 150 basis points, or how should we think about that as we look beyond this year? Yeah. To answer kind of is it getting worse in Q2, Q3? It is, but not materially. 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. 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. 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 in Studio, 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. As we called out, the real inhibitor right now to getting to 100%+ NRR we're seeing is in that self-serve cohort, which is concentrated in less than $5000. 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, and our in-quarter is trending towards 100, and really what's driving down the consolidated is that sub $5000. So we don't expect this headwind to persist in the same level in FY 2028. Thank you. Our next question comes from the line of Rishi Jaluria of RBC. Your line is open, Rishi. Hi, team. This is [Josh] standing in for Rishi. You guys mentioned looking to improve the sub-$5,000 customer base. I just wanted to sort of dig into that a little bit. I was curious around how you're balancing developing the product to appeal to a broader audience, and sort of being out of the box for given a customer size, while also balancing the specialization that comes with verticalization. Just a little bit more context around that would be great. Thank you. 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, if you're a large business, you will want to have pre-built agents that are working alongside you. Which ones you pick from that menu of 30 will depend, of course, how thoroughly you've built out those departments, because these are essentially packaged up agents that are pre-built for your department. So if you have a well-hewn, let's say, campaign department, then you're going to absolutely love the campaign orchestration agent. If you have a well-hewn 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. Then I'd say things like Asana Service Management. If you have, 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 really 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 where small businesses and large businesses come to know us through that digital engagement. Thank you. I would now like to turn the conference back to management for closing remarks. 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 will also have our Marquee Work Innovation Summit in New York on October 14th. 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. This concludes today's conference call. Thank you for participating. You may now disconnect.