00:04:49 · Guillaume Moubeche
[00:04:49] S1: And you mentioned the the 100 customers in one hundred days. When you join a company and you have to line up, let's say, these 100 meetings, how exactly do you, like, decide which customers you're gonna meet? Do you actually, like, put them in some sort of buckets? Let's say, the one that have the largest lifetime value, the one that are just brand new, but are like massive accounts, like how exactly do you decide who you're gonna meet and how do you structure like each interview?
00:05:20 · Dan Rogers
[00:05:20] S0: Yeah, exactly. So I think having a diversity is very important. A diversity of of roles as well because obviously naturally as a as, you know, a CEO, the account teams want you to meet very senior people in the accounts. But if you do that, they may not actually be close to the work. And so you have to, again, over index on creating the right sample that is representative of today and the right sample of where you think you might want to head as well. So a lot of new customers can be very interesting as an example. And then yes, I do tend to ask very similar questions as part of the formula, which is how are we doing and what problems are we solving for you today? You know, on the scorecard, how would you evaluate us? And against what we solve for you today, how could we better solve that? And then what are some of the emerging problems that you see, and how could we dive into those more fully? And do we have a right to solve those? And if we did, what uniquely could we bring to the party that's not already being solved? So something like that, but they tend to... You know, that's the controlled piece, but after about five minutes, they tend they tend to go they tend to go their own direction as well. But I love it when, you know, customers open up their screens and kinda show me what they're doing, show me what they're seeing, show me what other tools they're using. So the more I can get into the work, the better it is for me. Let's kinda back to this iterative porpoising idea. I love going deep. And so as deep as we can go, the better.
00:06:47 · Guillaume Moubeche
[00:06:47] S1: Awesome. And I think like what's great with Asana is like, obviously like, as you mentioned, you need to have a variety of roles and people you discuss with because it's really a product that can be used by most teams. Can you maybe like explain how the vision of the product evolved from being like a software for teams and towards now like more AI agents, and how do you see the future actually of AI agents?
00:07:15 · Dan Rogers
[00:07:15] S0: Yeah. Well, if you start back in the beginning eighteen years ago, we solved a simple problem that was fairly ubiquitous, which is when you get more than two people together in a room and you get them to work on something that nowadays we would call a long horizon project, so something that takes a lot of time, then you have a coordination challenge. At some point, you're going to need to figure out who is doing what by when and how we're gonna get it done. And so you end up trying to create some version of what we would call a pyramid of clarity, which is, okay. This is the goal we're trying to do. Break it up into these projects. These are the tasks. Let's assign them in this way against this. And suddenly, you need a way of manifesting that pyramid of clarity. This is the original Asana. And this is why we're ubiquitous today. 300,000 customers, 85 of the Fortune five hundred. Because it turns out coordination is a real problem. And so you could actually... Today, I don't think it would be an exaggeration to say any team that is working on a long horizon complex multidisciplinary project is probably using Asana or maybe some variant of, you know, new entrant, a type of Asana. But the change that there is afoot is that the modern team is no longer just humans. So it's not two people in a room. It's two people and five agents in the room. But it turns out that the agents also need coordinating. As you work individually with a, you know, some chatbot, it's great. They provide amazing answers to lots of things. That's not quite the same as coordinating that work inside a workflow. But the modern team is going to need to take all of those outputs from LLMs and integrate that deeply into how they work. So Asana is fast becoming the operating system for human agent teams. If you think about maybe the models, the models you can think of them as providing answers, providing quality text, providing documents, providing code. That's not the same as coordinating action across teams. So the models themselves become quite interchangeable. Some people say the models might even be commoditizing. What happens on top is you need two things. You need context, which is really a good sense of, again, who's doing what? Which systems do we need to call on? What needs to happen next? How have we done this in the past?
00:09:40 · Dan Rogers, Guillaume Moubeche
Are there some rules of the road that we need to be following? Are there some guidelines that we need to be following? Is there some governance that we need to be following? And then what I would call the harness, which is really actually making that work happen, pushing that work out to the right people, taking action, involving those people, and then the right moments making sure that they can prove things and that it's fully governable, and in the end, ultimately a repeatable process. So this is where you need something like Asana, which is the human agent operating system, which is really that enterprise ledger, that strata for humans and agents to collaborate off of off of the same living plan. [00:10:24] S1: So your view is down the line to kind of have like agents perform like as many tasks as possible and get all the context from Asana while always like keeping a human in the loop to validate like that the work has been done properly, that the project is going forward, etcetera? [00:10:44] S0: Yeah. That's a very nice description. Maybe I'll make it even more real. So today, as an example, we have a thing called teammates. These are our agent characters. We have 30 pre built teammates. They perform a particular package set of actions that you have scoped and defined. With AgenTic Work Management, which is really where our platform is headed today, we launched it today actually. So it's very, you know, lucky to [00:11:08] S1: be Congrats. Conversation.
00:11:10 · Dan Rogers
[00:11:10] S0: Where with AgenTic Work Management, these teammates make themselves available to you as you're working in Asana. So it might say, hey. I noticed you're working on a product launch. I'm the product launch assistant. Would you like me to help? Hey. I've noticed you're working on some pricing thing. Do want me to help? I... I'm like a pricing analyst. Or, hey. I noticed you're doing some company competitive research. Well, I'm your competitive research assistant. So we have 30 of these people agents they make themselves known to. But what's beautiful about Asana is because we have this multiplayer coordination, any of those agents can interact with many team members. Many team members can improve them, correct them, and say, actually, no. No. These are our competitors. Please refer to this knowledge base. Now that competitive analyst that's been maybe informed by two team members can be used by 30 other team members, and it knows who the competitors are. And as it's coordinating actions, it's doing so again within Asana. It's literally taking tasks, performing them, writing it back to Asana, letting the humans know what's happened. So suddenly, this is fully coordinated and fully contexted. And it's live and a living plan because everything it does helps the very next run of that agent. So you'll end up with this tapestry of these agents working deeply within a workflow. It's super important. It's super different than how to think about some AI bolt on feature or even interacting with a chatbot. This is real enterprise productivity. It is kind of a game changer from where we've been. Where we've been in this evolution was single player interaction with models. Good, you know, helpful from a personal perspective. This is not how any work happens. The work is that myriad of handovers, the myriad of crossovers, the myriad of little things that need to happen to keep things moving. So for example, maybe you wanna make a change to some software and maybe you have like a billing system and you want it to do some new calculation. Well, it turns out you probably need to inform your customers. It turns out it's gonna affect the way you recognize your revenue, you're gonna involve your finance team. You've got some engineers that need to work on it.