00:03:17 · Guillaume Moubeche
[00:03:17] S1: And to your point, I I don't remember who was saying that to Jeff Bezos, but it's like, you have enough good ideas to ruin the business or something like this. Sometimes you need to focus and... Yeah.
00:03:29 · Dan Rogers
[00:03:29] S0: So I think the enemy of great in this example would be coming in with a with a playbook and coming in with answers. I think it's much healthier to come in with questions. And then as you have hypothesis, you should be very skeptical of yourself as well, of the hypothesis that it... You know, if you jump to something too soon that maybe you need to be a lot more precise, maybe you need to be a lot more sharp before you you go live with it. In McKinsey, they call it iterative porpoising, which I I do like as an idea, which is the idea. You know a porpoise, it's like a dolphin. It goes in and out the water. I think you know you have to take these kind of... Maybe you have an idea about how AI is gonna transform the world. It's great that you have a big macro level view and then you need to come in and kind of say, will this really work? How should we shape this? I'm thinking of doing it this way, and then you get some more feedback and you come out and in. But that's definitely the process that you have to go through, very fluid in your kind of your thought process. And you have to control your mind, I think, very well because you may have been successful, you know, for the past decade and that is, you know, as you say that that can be... Those great ideas can ruin something that's already great. So you have to be very self controlled on what your inputs are and how you're formulating those outputs.
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?