Asana, Inc. ($ASAN)

Welcome, and thank you for joining today's Asana Investor Webinar. Here is our forward-looking statements. You can also find it in our published deck after the webinar concludes. Over the next 60 minutes, we have a lot to cover. Let me give you a quick agenda. You'll hear first from our Chief Executive Officer, Dan Rogers, who set the context of why this moment matters, and what Asana has built to meet it. Then our Chief Product Officer, Arnab Bose, will walk you through a live showcase of four new Agenda app we launched last week and customer event in London, the Work Innovation Summit. Our Chief Financial Officer, Aziz Megji will connect the product story to the financial opportunity. Then, we'll open it up for Q&A. Please send in your questions through the Q&A chat box in the Webinar. With that, I'll hand it over to Dan.

Thank you, Eva. So exciting times for Asana. Last week, we had our Work Innovation Summit in London. What I thought I'd do for you all today is talk you a little bit through the strategy that we shared, the differentiation of our platform, and also, as Eva mentioned, our brand new products that we're bringing to market. So let's discuss that. So the first is our strategy. And so all begins here with this idea that the future of work is changing. We're going to want to work faster than ever, plan better than ever, ship more than ever, launch bigger and, in fact, get even more stuff done. And the reality is our personal productivity has indeed increased with AI. Our personal productivity has gone up magnifold were producing more documents, more code in our everyday lives. But as you all know, that hasn't actually translated into productivity in our organizations and for our teams. In fact, some stats show that as little as 5% of organizations, can note any particular improvement in their productivity. So this is the great AI gap, and that is what Asana is going after. Because the way to actually create a productivity is through the workflows. It's agentify these workflows. And if you think about any company, any company is just a collection of these workflows. So when I speak to our customers, they have a long list of potential workflows that they want to agentify. These are workflows that [indiscernible] have been very manual. These are the things that actually affect their productivity to actually slow them down. And so why aren't more companies identifying those workflows? What's been holding them back. And so as I spend time in the field around the world, the first thing I hear is it's very hard to get started. It's hard to discover agents. Most companies don't have a menu of agents that every employee can just pull and pick from. Most companies are worried about producing such a thing, how would they even deliver that to be employees. They also worried that if they do so, that the agents have no framework in which to operate alongside their team members, that those agents won't be necessarily good team players that humans won't be in the loop in the right way. So they don't know how to coordinate with agents. They're also worried about the programming of those agents, who is going to onboard these things? Who's going to ramp them in the right way to make sure they follow our way of doing business? And then finally, if you're CIO or your IT enterprise leader, you're really worried about agent Palooza. You heard about agents running back that don't have the right data controls, security controls or even cost controls. So these are real impediments. And this is where Asana comes in. We announced Asana as the operating system for human agent teams. This is a place that your teams can achieve the real activity and get the real world that they wanted to do with AI and with agents. And this is the place where you can agentify your enterprise. So what is it that allows us to do that? And I used an example here. I'm a tennis fan personally, love -- just finished the watching the French Open, and get to roll right into Wimbledon. So I want to show a fictitious example, is say the Wimbledon merchandise manager wanted to launch a new jacket. What does that look like in a world where humans and agents able to work together? So with Asana, the first thing that happens is we have onboarded ready-to-go teammates. Now if you remember, I said, it's super hard today for people to discover agents and trust that those agents are ready for their enterprise. We have 30 prebuilt agents that we have built based on the patterns of usage that we know from our customers, these are agents that are marketing operation, IT product teams across the board. But we did more than that. When anyone invokes any of the agents, the first thing that they do is they consume the work graph. So if you look here, we imagined Houston, our launch planner, instantly starting to see as someone is writing in a task that they want to launch in jacket, and they need to do so in the next couple of weeks. Our launch plan is going to kick in direction and figure out from all the past tasks past projects, had interactions between people. How did we launch products in the past? Is there a playbook is either codified specifically in a written document or inferred from how we've done this in the past. From that inference, the launch planner gets going. It's actually able to create from day 1 a real project plan pre-populated with the tasks that it's going to take to launch this jacket. So we talked about blocker #1, hard to discover how to get going. Well, our agents are out the box productive, ready to go. So the second piece that we talked about is in many organizations, they don't really have this notion of multiplayer mode. What does multiplayer mode look like? Well, this is the place where a given agent, in this case, a brand consistency auditor that Houston, the other agent involved and recommended. This is to make sure that the design is done exactly on spec. The message has done exactly on spec. Houston recommended that we bring this agency this agent into the fold, brand consistency auditor. What's amazing with Asana is because it's multiplayer from the get-go, we automatically know who needs to program this agent, who needs to set the rules this agent on what it should do, whose work should we look at. And you see here three people interacting with one agent to program and onboard this agent to make sure it follows exactly the specifications. This is an architectural choice that Asana has made that all of our agents are going to be multiplayer. So if you go further, the other differentiation is a thing called shared memory. So what does shared memory look like? Well, shared memory is the promise that each new run of a project is going to be better than the prior one of the project. And what you see here is one of these agents this agent is called Century. Its job is to scan any notifications or any regulations that might affect this particular garment or any garment in general. And it looks like there's some new in this example, labeling requirements. Century picks up this laboring requirement, but it does a little bit more than that. It know that the last time that we had this labeling requirement, we actually had to change the labels out. In fact, we had to do 12 bespoke labels that comply with the EU regulations depending on the region. The Century has now come into this workflow and recommended that we use these new sets of labels in the jacket instead of the alterative labels. This is shared memory. Shared memories every new run of the project is better than the last one because we learned either from what human said or even how the agents interacted and where agents recommended efficiencies for the next run. The final differentiation is this idea of governance. So we said that IT leaders are really worried about agent Palooza. All of our agents are governed. They're governed in terms of data they can access, the commissions that they have and the human approvals that they need to go through. In this example, Tally, which is the production ordering agent wants to order 10,000 units based on forecasting. But when we built Tally, we pre-described that a human will always be in the loop on any orders over 5,000 units, and only Sarah has the permission to do that run. So now that agent within that gallery is fully scoped and fully permissioned for the organization. And within, it actually describes how the human is going to be involved. So this is where our differentiation comes to bear, four franchisors hit the four key blockers, that are inhibiting the agentification of the enterprise today. And as a result, when I presented this with most of the people in the audience said we need to get going. We need to get going tomorrow. And over time, we're going to get even smarter with these teammates. These teammates are going to recommend themselves as you go about your work. Instead of even having to go to a library to discover them, they will be ready and available and make themselves known to you. And FedEx was one of our beta customers, as I described in earnings and FedEx have put agents into their AI Studio workflows. These agents hit both their sales teams, their marketing teams and some of their ordering teams as well. And as a result, they're able to go to market 9x faster. This is human and agents working together in a workflow. Now I am going to walk you through some of the products that we announced. So not only have traded this framework, this operating system for humans and agents to work alongside each other. We actually lit up that framework by launching five net new applications. So if you think about Asana, we really had one asteric application with collaborative work management. While starting today, we are five applications, Agentic Work Management, which is all cross-functional teams, which involves collaborative work management to now include all of those teammates, AI teammates, working alongside as well as the AI system, which we'll show you in a second. So a Service Manager for service teams, so a client management, for any team delivering client work, Command by Asana for any developer teams and with our acquisition, stack AI by Asana. Now we have the ability to create these mission-critical cross-functional workflows across applications in an organization. So to go a little deeper, we are going to have Arnab from here.

Awesome. Thanks so much, Dan. Hi, everyone. I'm Arnab Bose. I'm the Head of Product here at Asana. And I want to pick up in one particular bot where Dan left off with respect to Asana's differentiation, which is the work graph. So have we been investing in this system, which tracks who does what, by when and how, for well over 17 years. And it's this interconnected graph of tasks to projects, to portfolios to company level goals that has clarity around the individuals involved in those particular workcraft objects. And now we've introduced AI agents into that word craft as well. What's really interesting is not only the ability for the worker to connect human beings but to also connect AI agents on that same real-time shared ledger of who does what by when and how. And on top of that, from a platform level, we've been adding in new primitives into the work craft as well. So it's not just restricted to projects and DAS. We now have time sheets and budgets. We've got notes and meetings, we've got calendar items. We've got the ability to synchronize chats and a lot more. And I'm going to pay that off in demos, showcasing how these agentic applications take the power of these differentiators like the work craft, like shared memory, like the human AI experience that we've got within Asana and enterprise grade governance to create real differentiation and new value for our customers. So let's dive a bit deeper into Agentic Work Management, which is the easy button for getting AI productivity into every team. And as Dan said, it's for any cross-functional project. I think of this is the evolution, the AI era of collaborative work management. At Work Innovation Summit London, we talked about several customers who are already on their way in terms of adopting Agentic Work Management. You heard from Dan about FedEx. We had the Chief Digital and Technology Officer of COS, a global retailer on stage, talking about how they have invested in Asana as their standard AI productivity tool to get to 90% faster campaign setup times. We also talked about interesting customers like Watchman and the United Arab Emirates as well as a service in Italy, which is a manufacturing company, we've all started using AIT mets live in production. But I would love to kind of bring this to you to life to you in terms of what are actually -- what is actually available within this agented work management products. Agentic Work Management is a combination of not just collaborative work management, the work graph and teammates, but of four different technology innovations that we've built over the last year. The first is Dash. Asana Dash is your AI Chief of Staff. Asana Dash is a way in which you can interact with Asana's work craft, Dash projects and other elements in a chat-based way. It also has your back no matter what's happened in terms of giving you next best actions. I'll show you how this works in a demo, and it's fully plugged into many different sources of data, not just Asana task and projects, but also your Slack, all your e-mail, also your meetings in your calendar to glean what is the next best action that you need to work on in order to stay in your zone of Genius and get your work done as an individual? So there's a lot of productivity increases that come from investing in an AI chief of staff like Dash. The second is Asana AI Teammates, your prebuilt ready-to-go AI agents. You've seen examples of how they work in Dan section, how they present themselves and suggest to take on work, and how they're prebuilt. And I would love to sort of go a little bit deeper and showcase exactly how Asana Dash Asana AI Teammates work side by side to help you get work done. The third block is Asana's Built-in Automation and AI Studio. So this powers AI-powered cross-functional workflows and helps you schedule to us or trigger actions when events happen within Asana. And finally, we've increased our depth of integration, both inbound into Asana and from Asana into other third-party applications in a massive way. We've got MCP applications available already for Claude and ChatGPT Enterprise. And we've also got plug-ins for Google Gemini and Amazon Quick. And on the integration side, there's a whole host of different important business-critical applications that we are plugging into as well from Asana AI Teammates and Asana Dash. All right. Now I would love to show you how all of this works with the demo. So in this setup, it's implying myself. I've traveled to Warsaw, where we have a very large engineering team and I didn't have WiFi on the flight. So when I land in Warsaw, and I turn on my phone, and it connects to the network, my lock screen is blowing up with notifications. I've got Google Workspace notifications. I've got Asana notifications, I've got Slack notifications. And because we've been out of touch for about 10 hours, it's going to be very hard for me to go through all of this and catch up on the [indiscernible]. But now there is a mode of Zen. You'll notice that there's a notification from Asana saying that my morning briefing is ready. This is a morning briefing from Asana Dash, my AI Chief of Staff. Dash has looked through multiple sources of data, has look through what I have do within Asana today. It's look through my e-mail inbox, it's looked through chat, it look through calendar. And it's noticed that there is an e-mail that's unread in my inbox that's associated with the Dash launch creative production project, and it's a note from a vendor saying that they can no longer support what we ask them to do. They've got dark because they've been double booked. Now in this situation, before AI, before all of the stooling of Agentic Work Management, I would have to maybe call up my team, find out what alternative vendors we could use, look through multiple different databases, but with Dash, I can simply ask Dash that question. And again, because the approved vendor let's distract and available within Asana itself, it can go ahead and glean that information. It has already got access to historical product launches we've done in the past. So it knows what are the vendors we've used in the past and does there's a match of approved vendors versus vendors I've liked and suggests that I should go with Brighton company. And I can simply talk to Dash via chat or voice to text and tell Dash to go and post a decision directly on that task to unblock the team. So I've gone from a complete lack of clarity where my notification stream was blowing up, I might have even missed the fact that the vendor is now no longer going to make it, to being alerted about this critical gap for a project that is directly tied to one of my strategic goals for this quarter to making a decision that unlocks the team that helps them move forward. Now there's one more thing that comes up, which is there's a team, the Asana AI team that's been meeting on a EMEA regional approval for me. And again, this is something that I missed because I was planning my trip. I can simply say approved, and this is going to go ahead and update that workcraft object, update the task and unblock the team and get them off to the races. So super interesting way in which for an executive, for a senior leader how Asana Dash like works on their behalf to keep them in their zone of genius and help unblock their team across multiple sources, not just Asana data, but e-mails, Slack, Teams, calendar and more. Now from the perspective of the team, what happens going forward, is that they're unlocked. And this triggers an Asana AI studio workflow that sets up a bunch of different tasks that will kick off AI Teammates. Now what this screen is showing is AI Teammates normally run on the context of the workcraft and the context of the individual that they provide within the task. But with every single run, they have this concept called shared memory, which makes them better and better going forward. And if Aziz, Dan and I all have access to the same AI Teammate, and we are using them in our tasks and projects, the really interesting thing is that three of us are coaching that AI Teammate made as if they're a member of our executive staff and getting them up to speed. And that improvement in its ability is not restricted just to me or individually, all three of us can get advantage of it. So it's literally like having a new person on the team who you're enabling with all this knowledge, all this context and all this feedback. All right. So what's happening now is now that have unblock the team and have asked to go head and draft that campaign brief. Launch Planner is already working on behalf of everybody in San Francisco. They might have been sleeping because it's 10 hours separate for more so. But it's gone ahead and created that campaign brief looking at historical runs, looking at all of the data in the work graph and assigned it out to Stephanie or Christy to review. Now perhaps Stephanie and Christy are on either the legal team or maybe they're in a different team that's sort of dissociated from marketing, and they don't actively work within Asana, they prefer working out of comments and Google Docs. Now Asana AI Teammates are plugged into Google Docs as well. So when they create that brief and when Stephanie and Christy go ahead and provide feedback, has comments on that dock, this multiplayer experience where multiple human beings are interacting with an AI artifact. All of that can be incorporated by AI Teammates. So AI Teammates work in a multiplayer way, not only inside of Asana, but also on artifacts like Google Docs that are outside of Asana, this is super, super cool. So you noticed that Kirk who is on the Asana AI team asked for that feedback to get incorporated. It's got incorporated and now this particular campaign brief task is is complete and after the races. Now that was a good way of showcasing how Dash can trigger some work that can automatically generate results via AI Teammates and the AI Teammates to work in this multiplayer way. Let's take a look at how Dash can help you with unforeseen issues, something that's a risk to your product plan. What happened to -- this actually happened to us within the R&D team. One of our PMs who's leading AI Teammates, was expecting a baby. The baby came early. Everything is fine. The baby is happy and healthy, but it means we now have to find coverage that we were not expecting. And so you can simply talk to Dash and say, I need a creative brief, I need competitive research, I need a copy because we're getting really, really close to Work Summit Innovation at London. Please help me out. It can analyze the work and start creating sub tasks that can be handed off to AI Teammates. Again, this is going back to what Dan was calling out where we want to take the guesswork for the knowledge worker out of trying to understand how should they bring in AI agents, what AI agents to bring in, what AI agents are approved for use within my company to -- you can simply talk to your AI Chief of Staff. Your AI Chief of Staff has your back. It knows all of the approved AI Teammates that you have access to. These AI Teammates with shared memory and the work craft contacts are constantly being improved by your entire organization, your team and you can hand a task and then go and build that first cut for you. So it's handing off all these tasks, and what's going to happen next is, if you say go, the competitive market researcher will be scanning across both deep web search as well as historical data. The copywriter will be generating copy, the creative spec writer will be drafting specs and could be in Word documents or Google Docs. Your campaign here assets could be created. And so this is showcasing one of our deeper integrations. This is with Figma make, where social assets are created directly within Figma for the Dash launch. I click one back too far. So this is a really interesting veneer that's showcasing orchestration of multiple AI agents. And again, the AI agents being recommended directly by your AI Chief of Staff across the set of agents that individual within your company has access to all within enterprise-grade governance and audibility all within the context of the work craft and all powered by really interesting concepts like shared memory. So now I want to end this demo veneer for agenetic work managed by talking a bit about how Asana's data is available across a variety of other AI applications as well. So in this part of the demo, let's say that Kevin, who is our Chief Revenue Officer, is heavily on the road, and he needs to stay up to date on what R&D is up to, and he needs to know if he can pitch one of our upcoming products like Asana Dash to know on who's a customer in EMEA. He doesn't know for sure if this will be available to me as yet. So he can simply pull up Claude on the phone. Claude is connected directly into Asana. And he can ask Claude, he was the [indiscernible] on Dash. And all of this data that you're seeing on the screen is being pulled directly via Asana's MCP connector. So you can go ahead and pull out status on whether the PRD is complete, what risks are, and if you can actually pitch Dash to Denon during his customer meeting. Now the reason why we would have this is for single player use cases where you've got an executive or somebody who's not directly in the project and they just need to get status updates or things like that out of Asana, we want Asana to be available in all of those canvases as well and not restricted just to things like Asana Dash, Teammates where somebody has to make a conscious decision to dip into the app. Again, we want the entire enterprise to get the value out of the work graph at the value out of what we are able to accomplish with the Asana AI Teammates and on the other AI features. Okay. So that in a nutshell is Asana Agentic Work Management, a completely reimagined way to go ahead and think about collaborative work management. It's now no longer just human being coordinating projects and task. It's human beings and AI agents. It's no longer just projects and tasks, it's chat, it's e-mail, it's documents. And it's this -- it's built on these four fundamental features, Asana Dash, AI Teammates, AI Studio and MCP connectors and apps. Okay. Now that was just one of our five agentic tech applications that we want to talk about today. So I'm going to cover off 3 more, and then hand it back to Dan for Stack. So let's take a look at Asana Service Management, which is our AI native enterprise service management desk for HR, IT, facilities and legal. All right. So a question would be like, hey, what is unique and what's interesting about Asana Service Management. Well, first of all, we are leveraging all of the AI infrastructure work we've built and all the integrations into chat tools, to provide instant AI resolutions in the flow of work. It's a 24/7 AI agent that automatically deflects about 50% of routine requests, and it can be present in Slack or Teams or e-mail or in Asana portal. The second thing that's even more interesting is for questions that actually become tickets that a human being has to go ahead and resolve, we've built a self-learning knowledge base. So the process of resolving that ticket automatically gets qualified as a knowledge-based article that a human being can read or can be leveraged by instant care resolutions. And again, the process is building on the foundational platform work that we've done for things like shed memory that you saw earlier on. One intuitive intake for every team. So there are multiple internal teams that have help as our service desk. It's not just the IT team for break-fix IT issues. It could be a legal team, it could be our finance team. It could be a creative team. This is something we've historically seen a lot of in Asana. There's a lot of Asana customers today, I would say about 20% of our existing enterprise customers where Asana sold into the office of the CIO or the IT team, and they're using us already today side-by-side with ITSM products because Asana is really good at providing this -- the single desk for all of the enterprise service needs. And it's also really good at the last thing, which is when a ticket is no longer a break-fix ticket, but actually has to be a project like you're rolling out an upgraded product or maybe you're moving from one CRM product to another. Anything that's a project is really, really great to track in Asana. We see this use case today where we are already being used side by side. within these IT deployments when requests on to projects. And Asana Service Management would have that seamless integration where you can go from a request to projects in one click. And of course, project management will be part of the package as well. So let's take a quick look at what this would look like for a customer who's deployed. The IT desk or maybe people within these various departments, like services or workplaces or or legal are drowning in a sea of inbound slack messages. And that's happening because nobody particularly wants to go ahead and raise a request via the IT ticketing portal. The historical legacy IT ticketing portals are are places where questions can I go to get stock. It takes a long time to get a response. The IT teams or the help test teams themselves are overloaded. So we have to get to a better way. We have to get away from this legacy enterprise service management, where things are just like sitting in these like queues, and they're costing a lot of human labor to go and get a resolution. So with a solid service management, when that ticket gets raised and the ticket could be raised in a variety of places via your portal, via Slack, via e-mail. The first thing that happens is our AI infrastructure captures it, automatically routes search to an excellent AI agent and tries to go ahead and resolve it without it ever reaching a human being. And this is, again, by all of the AI infrastructure work that we've built for agenetic work management. And we've been deploying Asana service management internally already and we are seeing pretty good AI deflection rates. And we believe by the time August comes around when this is going to be an early access, we can get into that 50% number that we are pushing. So the first thing is, okay, all of these input channels how do you go ahead and automatically deflect them so that they don't become a ticket. They don't even hit a human being. The next thing is for something that actually requires review by a human being. So this legal review for vendors NDA, this is probably something you don't want to get auto deflected right off the bat. But when it gets to a person, what happens is that interaction with the person is codified in Asana's work graph, and we can go ahead and incorporate all of that learning directly back into the finance knowledge base. So the next time a question like that comes up, the Asana AI Teammate can take a first crack at getting to a 90%, 99% good resolution. So the amount of human interaction time even for a ticket that requires even oversight can be driven down massively. So there's a sneak peak, early preview of what we've been doing with Asana Service Management. Again, like this is selling into existing customers that we have as well as growing our base in terms of like new accounts we could target with a specific job to be done. And we've also got a whole host of design partners we've been working with. As I said, we've rolled it out internally and we're seeing really great agent resolution rates. We've also got NYU, Callon Lord, Upkar Leep, Ari and Ricour, as design bottles for Asana Service Management. The second product I want to talk to you about is Asana Client Management. This is all about building lasting client relationships on an AI-native platform that's built for agency work. Okay. So what does this mean? Let's dive a bit deeper into what we've got here. Now something that you may not be aware of is we have a large set of existing Asana customers today who are already using us for agency work. They're using us to track resource management and capacity planning. They're using us to track their project work internally across like different teams within their agency work. But what we haven't provided to date is this branded client portal to complete the journey. Okay. So how do you do intake? How do you have a clean branded client portal so the client can log in and see the status of their projects again, like how do you connect up these newer work craft elements like meetings, how do you connect up these newer features like time sheets and budgets. So all of that new workcraft content as well as all of the new AI platform capabilities are being combined together along with this new branded client portal so we can complete the journey for client work. So unifying all communications, ensuring multiple agencies are productive with AI agents, excluding work across the business with agentic capacity planning, statement of work creation, client-ready asset reduction, status update drafting. So all of these jobs to be done are now connected under one single umbrella. So it's super, super cool. This is also going to be available in the August, September time frame. And it connects the dots not only for these existing customers, I'm using us already for project work within their agency businesses, but also attracts net new customers who might be concerned about, oh, I don't want to bet on multiple products. Asana is great for project management, but I need the client portal. Now we're eliminating all that choice. Like just go with us Asana Client Management and complete your end-to-end job to be done. The final -- sorry, this is a slide that talks about all of the existing professional services teams that are managing client working Asana today, and clarifies like why I am excited about it from a product strategy perspective because there already is clear indication of product market shares, and we know who the ideal customer profile is. All right. Great. So now I want to take a little bit of time to talk about Command by Asana, which from an R&D point of view, is super interesting because it's a way in which the Asana research and development team has been working probably for the last year, where we have gone all in on Asana to track the entire product development life cycle, to trigger coating agents, to trigger code review agents, to do our security and compliance processes. And we've been seeing phenomenal improvements in productivity and cycle time. But because this was a highly customized, we are deploying Asana, it was not something that we were able to bring to bear for our customers in market. And Command by Asana is a prioritization of that end to an experience. So a good question would be like, what is the value prop for Command by Asana? And from a customer's perspective, how should they change now and why should they change to Asana. So a really interesting thing that's happening in market today for engineering games is the rate at which you can generate code is faster than ever before with these really cool coding agents like Codex and Claudeco that are available in market. But the problem that most companies are running into and there's widespread coverage of this where people are running out of AI budget there's some commentary from Microsoft and ServiceNow and Uber about the amount of spend that they have, but they're not really seeing truly improved product delivery in cycle. The reason why this is happening is coding agents are currently really good at plugging into your GitHub repo on your source code and then learning from that. But what's not happening is an agentic way to improve anti product development life cycle, from ideation, to BRD creation to ticket creation, where the ticket has all of the context required to then run the going get effectively to take all of the decisions that happen in those loops where you might decide to change the spec slightly or you might decide to react to bug once you evaluate the first initial PR, none of that gets recodified back into the PRDs of the tickets. And then ultimately, the final product that gets still word is not connected to the rest of your planning and development life cycles around product launches or scheduling downstream activities and so on and so forth. So we want to create that software development factory where you move from ideas to sharing products that your team can sell in the fastest and safest way possible. So ship faster with humans and agents in sync. So there's three different things in there from a feature perspective, cleaner tickets with faster agentic output on repeat, no manual reconciliations of things like sprint planning and capacity planning and always on visibility into risk, drift and dependencies because again, like this is all powered by the work graph and shared memory. So as you do more and more projects within Asana, it can detect when you're running into a risk when there's -- when there might be drift between the PRD and the PR. And then what are all of your cross-team dependencies where -- if you look at the GaN shot of everything required to go deliver your product, how do you reconcile those issues. So those are the three key features that have bought of Command by Asana. And so I'll kind of walk you through a flow over here which is everybody within Asana, we all love to build. We've got product managers who've been building prototypes as well as fixing most of the customer issues. Of course, our dev team is all in on the leader tools like Claude, Cursor and Kodex. And our designers are also like fixing defects and shipping visual updates as well. So how are we doing this? Like what does this Command by Asana way of working? And it's like a totally new way of going in and building product. So we want to take this like process of requirements, plan, gold, coordinate and ship and convert every single step of the process into an agent portfolio. So requirements and planning are scattered across multiple different artifacts today. You might have a live meeting with your team. You might have a word document that's tracking a BRD we want to identify that entire process, again, leveraging the platform components of Asana. When you're coding , again, on the comes out or needing across multiple people for code review or evaluating the secure reviews is something that again happens in a disparate system. And then finally, when you're shipping that product marketing team or that actual executive leadership team within product is disconnected from the systems today. So when they ask a question, let's say, when Dan asked me a question of, hey, when is this thing shipping? That's a very reasonable question. but it often becomes this sort of multipronged access request, like figure out, okay, what is the engineering management 1 team thing? And is the product marketing manager on that project like in sync with them and so on and so forth. So we believe there is a better way, the better rates Command. Command helps you go ahead and author PRDs directly from those meeting minutes and notes. It helps you iterate over those PRDs so that you can go ahead and break them down into tickets that are ready for coating agents like [indiscernible] and ClaudeCo and [indiscernible] pick up with the appropriate amount of context provided by the work craft, we do capacity planning in a way that is highly coordinated in agentic, so you can coordinate across both human and AI agent tasks. And then finally, you can plug all of that in to Asana's project management tooling and things like AI Teammates. So if you ask a simple question like when does the ship, something like the Houston project plan Teammate he made that Dan called out way before it is part of the talk track and instantly response by saying -- respond by saying it's still on track for June 4. All right. So that's a preview into Command by Asana. We've got our R&D teams using it actively today, and I'm super excited about bringing this to market again in that September time frame. With that, over to you, Dan, to talk to us a bit about StackAI.

Thank you, Arnab. So yes, Stack is really the missing piece to the puzzle. We described this on our earnings, and I'll go a little bit deeper here. So this was our acquisition. And it turned out that most of our customers want to identify these workflows and that many of these workflows, the most complex workflows actually span across multiple systems databases, CRM systems, order management systems, you kind of name it. And so what stack has built is this ability to quickly visualize and automate any workflow with no code. You kind of see here a little bit on the right. This is literally the UI. And they can drag and drop any of these workflows that hit multiple systems with prebuilt integrations. They have literally built hundreds of integrations. And they can build net new integrations very quickly. And so what this allows you to do is create rules that kind of move from humans to various other systems and in this case, back to Asana Teammates and Asana agents. And so what you end up with is that ability to visualize and imagine your workflow or what you want -- how you want to automate it and the rules that you want to set in place and which bits you want to identify and which bits you want humans to take care of. So Stack actually as a stand-alone product, it's going to be commercially super viable. It's already a commercially viable product. But it will also feed into all of those other products that Arnab just showcased. As a logical extension how we want to do service management how to do some of your developer workflows. So start comes a product and the capability across many of our other products. And these are all governed. And so they already have met the highest requirements for federal and enterprise customers. So that stack and really I sometimes describe as a missing piece to the puzzle. And let's go to the next slide. So at this point, we'll talk a little bit about our right to win in our differentiation. Again, we cover a little bit on this on earnings. We described this in a much more visual way in sort of the keynote that we just delivered. But just to set a little context here, is kind of the journey that we've been on, our multiproduct journey. So back in June, we launched AI Studio. AI Studio has had a very nice commercial ramp. So monetization really started to take hold. And then as you get towards the December time frame, we start hitting some decent commercial milestones on AI Studio with many customers spending over $100,000 on that capability, launching of AI Teammates. So we go from the beta of our AI Teammates to GA of our AI Teammates. So at this point, we really have a couple of other products in our portfolio. We acquired Stack in June and launched Agentic Work Management. And through Agentic Work Management, AI Teammates and AI Studio will be discovered now in the line of your work. They will recommend themselves as you go about your regular daily tasks. And as we look then a little bit forward to the second half, our new product portfolio, which is all agenetic workflows, all built for human agentic teams is a Agentic Work Management. Command by Asana, Asana Service Management, Asana Client Management and StackAI, all of which allow us to not go just further into other buying centers, but also deeper into the buying centers that we are already in today. So touching our differentiation a little bit. The first is these are all underpinned by our platform. Our platform has four very unique architectural differences, the work graph which we think of as a neural network of the connections between all of these perimeters of people, tasks, projects, this living plan that allows everyone to know who's doing what, when and by everyone, I mean both humans and agents. Our multiplayer mode, which is the ability for humans to train, guide, improve, provide feedback to any of those agents. Our shared memory, which essentially self improves every one of every project. And then finally, our enterprise governance, which means are aged automatically scoped and permissioned in terms of what data it can access, what cost it consume and which approvals it can make on its own and which one humans are going to have to make. So built on top of that platform are five AI capabilities. And these AI capabilities all serve our products is just how we go to market. So these are the five applications that we will go to market with. So this is the new lineup. This is the new Asana. And we're now going to hand over to Aziz, who's going to describe what the financial implications of Asana.

Thanks, Dan. So while we're still early in this journey, the initial proof points of our multiproduct strategy give us confidence we're moving in the right direction. So as we saw last week at our Q1 earnings, even quarter NRR has improved for four consecutive orders and reached 97% in Q1. And a lot of that improvement has to do with the expansion and retention that AI Studio is driving within our base. We also saw our technology customer base returned to year-over-year growth in Q1. So that was after 8 straight quarters of decline and then stabilization in Q4. Our AI product bookings represented 17% of net new ARR in Q1, which was ahead of our FY '27 target of 15%. So our AI products are contributing faster than we had expected. And at the same time, productivity improvements and operating velocity are translating into meaningful margin expansion, demonstrating that we can invest in innovation while improving profitability at some time. So now moving to our pricing model for this multiproduct platform. It's really about meeting customers where they are with flexible monetization models, ranging from seat-based subscription with AI-powered expansion path to request and resolution-based workflow-based and credit-based offerings. The common threads between this is that customers can start with predictable platform subscriptions and expand as AI usage, workflows, agents and outcomes grow. So this approach provides customers with predictable costs and no surprises, while enabling Asana to align monetization with customer value and better matching the outcomes being delivered. So this creates a scalable growth model with greater visibility in the unit economics and margins as AI adoption grows. So I want to just touch upon this reinforcing growth model that we're creating as we've transitioned into a multiproduct company and expanded into new buying centers and TAMs. So historically, Asana was a single product collaborative work management platform, with Growth driven primarily by seed expansion, package upgrades and then adoption concentrated in a handful of buying centers and departments. So over the past year, beginning with AI Studio and Teammates and then significantly amplified by the announcements at the Work Innovation Summit last week, which we showed today, Asana has evolved into a multiproduct platform for human agent teams. We have multiple ways to land customers, expand across workflows and departments and monetize value through both seats and consumption. So that evolution expands our addressable market, increases workflow depth and customer value and creates growth vectors beyond traditional seat expansion. So the result is a reinforcing growth model with more path to land, more opportunities to expand, deeper customer engagement over time and really setting the foundation for durable growth acceleration and long-term value creation. So with that, we'll open it up for questions. Eva?

All right. Thank you. I guess what we -- a lot of investors have in their mind is, why is Asana launching these new agentic apps right now, particular Asana Service Management and Command by Asana. It feels like a change in your multiproduct and adjacent TAM strategy. Can you kind of talk a little bit about that?

Yes, why don't I take that one. It turns out that we had been serving many different buying centers with our horizontal platform already, marketing teams, IT teams, product development, operations teams. And as they got more deeply embedded with our products, they began to describe workflows that they wanted us to build agents that I wanted to bring to bear on their work as these new capabilities make themselves possible. So we began a design partner program with each of these areas to try and figure out what they wanted from us next. And it became obvious that they wanted bespoke applications, bespoke identic workflows and team mix that could operate within their teams. And hence, was born really an incubation mindset of bearing out these products for these particular buying centers for these particular verticals. And so yes, through the design process and really how we launch products, we came to learn exactly what the requirements are and to build all of these of the same work graph foundation, glad you make everything makes sense for the human agent teams.

Thank you, Dan. Another question we have is, how do you win against ServiceNow, Atlassian, Jira and other incumbent with this new agentic apps. Maybe Arnab, maybe you could take that?

Yes, sounds good. So I'll break it down into maybe two different categories. Again, I'll choose Asana Service Management and Command. The framework that we used within the Asana product team is answering the question should the customer change, why should they change now? And why should they change to Asana from whatever they're doing for with the enterprise service management or current R&D processes. So when you take a look at like why should they change within Asana's customer bases, Dan was calling out, we are already seeing about 20% of our existing customers be within the organization because they have a pain point around it that can't be serviced by those ITSM products when they become real products. That was like an existing change that made it existing reason to change that introduced Asana into those companies in the first place. The second thing is with the advent of AI capabilities, there's a massive push to go ahead and drive down internal operational costs. people want to identify their workflows, so they want to change now to something that actually provides that level of 50% ticket deflection, proactive coaching and so on and so forth. Then we only take a look at Asana's differentiation in that area. Well, first of all, not only are we able to deflect the tickets that can be answered by knowledge-based articles with our AI infrastructure and capabilities, where we can build these self-learning knowledge bases, we can connect up human interaction in a way which generate shared memory we can connect a human interaction in a way that can elevate things that are no longer tickets into true projects, a project can work on agentic work management. So there's like three levels of things. We're already in a bunch of accounts today, solving the project management use case, even with our historical products. The second thing is AI is causing every single customer in the world who has an enterprise service management team to reimagine, reevaluate what would it take to make it agentic. The third thing is we're providing this connected agent. We are solving that problem in a way that's not just ticket deflection. That's not just automatic knowledge-based creation, but it's generating end-to-end workflows, even for the most complex tasks. So we believe strongly that we have a red to play and win a substantial amount of that market. The same thing can be applied to Command and the R&D processes. Again, like historical tools are designed around sprint planning, ticket management, bug management for a largely human-driven process. That's human beings getting together. They're planning on capacity. They're doing things like story pointing for those who have been in agile R&D conversations in the past, all of that is gone. Like there's a massive change in the way in which you work because the cost of actually generating the code is going lower and lower every single day. The problem that's happening is how do you set up the right context, so that when that code is generated, the PR that pull request is something you actually want to ship. If you keep going back and forth with PR because you're trying to prompt your way to the best possible outcome. You're going to burn through your tokens really fast, which is why you get all of these reports in the media today about people burning through their budgets as they've invested in AI coding. So again, Command is a really different way of working where you go from ideas to the entire product development life cycle in a fully agentic manner, contributing into work craft assets to create that company brain that makes it run faster and better every single time.

Great. And maybe One question on go-to-market. You're expanding into IT, engineering and professional service buyers. How would your go-to-market model evolve over time?

There will be I'd say three themes. One is new buying centers, which means that we'll need to learn some new languages for those buying centers, and we have the ability to do a lot more cross-selling within our accounts. The second is the criticality of the workflows that we'll be going after. So we'll be able to, I'd say, go up in some of these accounts because we really are going to attach yourself to much more business-critical workflows that are the core for these enterprises. And then finally, because we'll be moving to some more consumption and outcome type of meters, adoption will become even more important, making sure that we hold customers' hands to get those workflows lit up in the first place. So those are the ways in which I can see as evolving.

Great. And maybe just one last question for the group. If AI models continue to improve, why doesn't the value [indiscernible], OpenAI, Microsoft or another platform company instead of Asana?

Yes. Well, as we -- or actually Arnab, you go ahead then I'll go.

Yes, sounds good. So I mean, again, let's take a look at the advances that have happened in the models, even in calendar year 2025. They've got an exponentially better. But when you look at research reports from Goldman Sachs or McKinsey, actual productivity improvement within a company's outcome or job to be done, there's been no improvement. And the reason for this is the reasoning models are amazing at going through to us and sort of taking the context that they have and iterating through it. But -- most of the joints now lie in all the scaffolding around it, the context, the shared memory, the human interaction, like how does a human being reason about the which these results are coming out. And we've already seen that data play out in 2025. And so our thesis is that, okay, even if the reasoning models get better, like the rest of the scaffolding has nothing to do with AI. Like it's actually hard data about contact scraps, it's hard data about the human and the multiple human in the loop processes to get human beings to rock and understand what's going on and so on and so forth. So Dan, let me know...

Thank you. That's good.

All right. That concludes our Q&A session. Thank you, Dan, Arnab and Aziz for your time today, and thank you for everyone who joined us. We've covered a lot today, so we hope you leave with a clear picture of what Asana is building, and what we're positioned to lead this category and how the financial model support this durable value create over time. For any follow-up questions, please reach out to me directly at ir.asana.com. We look forward to continuing this conversation. Thank you.