The Exchanges

Q If I wanted to build, let's say you hadn't built creative director, I wanted to build creative director. How do I sort of onboard that type of experience in Asana?

A Absolutely. So the way you would do that is, uh, you would like type in a little prompt that says, I want to build an AI teammate that is, uh, that is a creative director. Uh, uh, the builder, uh, chat AI agent will come back to you and be like, Hey, so what kind of, um, you know, projects and portfolios do you want this creative director to have access to? Because let's say you'd hired a real human creative director onto your team, you'd probably have an onboarding plan for them, right? You'd probably tell them to, uh, read these documents about the company's mission and values and, uh, look at these projects and tasks for how the creative team has worked in the past. So you provided with that context.

AI assessment note: “you would like type in a little prompt that says, I want to build”

Q So just talk to me a little bit about what you think this looks like in an ideal world. Like, this all works out. How does that change people's lives?

A It changes people's lives by taking away the busy work and not just doing that in a way where you're getting a ton more content that is mid, that's average, but you're getting a ton more content that is highly optimized and specialized for the way in which you work, in the way in which your company has set its mission and values, the way in which your company runs, and what your company level goals are. So that should be elevating the, the ability for the company to hit its outcome metrics. It's true, like, Key results versus, oh, we shipped a lot of code, but we actually didn't manage to sell the product, or we shipped like five new campaigns, but they were undifferentiated. So getting to the level of differentiation, getting the key results, elevating every human team member to becoming a tastemaker, those are the outcomes that I'm driving for.

AI assessment note: “It changes people's lives by taking away the busy work”

Q this progression where it was basically like you start with OpenAI because that's the most common one. Then you make your products interoperable so you can use any model within them. And then you graduate to open source so you can customize it more. So there have been advances on the customization front from the frontier labs. That have allowed you to build on top of them versus open source?

A That's a good question. What's happened to that? Um, so again, like, uh, our sort of maximalist thinking is that the frontier labs are going to keep innovating in the level of reasoning and capabilities of their models, and so trying to, uh, create these customizations or adding our own token weights Is not a good idea and is a waste of R&D resources at this particular point in time because of the rate of innovation that we're seeing. Like, why not just trust that they will keep innovating in, in their space with the funding they have and the quality of research talent that they have, uh, and then just basically evaluate which of the ones work best for our use case. Um, my perspective might change over time, but like in this day and age, I'm seeing like enough velocity coming out of them That it doesn't make sense to try and create, like, a separate path where I might just fall behind.

AI assessment note: “trying to, uh, create these customizations or adding our own token weights Is not a good idea”

Q First, just a definition question. What is a work graph?

A So, uh, think about the way in which Asana is defined today. Uh, you can create projects. You can have a team of people who have access to the project. Within the project, there are tasks or work items that individuals complete. When a task is completed, the project moves forward. The project could be part of a broader set of projects called a portfolio. There could be a company level goal for it. So this is the, the data model behind Asana. So Asana is powered by this sort of concept of helping teams get work done, helping teams coordinate, helping teams find this, um, this clarity from chaos. And there's an underlying data model backing it, which we call the work graph. And that's what has powered our human to human coordination features over the years. And that's what we're opening up and we have opened up. With our new Asana AI teammates launch so that AI agents can leverage that work graph for some of these, like, key outcomes I'm talking about, where they get the right business context, where they can learn from human beings and how human beings have completed tasks in the past, uh, where they can create this concept called shared memory, where let's say if Alex, you use one of the AI teammates and, you know, you tell it that, oh, this particular campaign brief or the risk you've found within this launch plan is incorrect. As long as I also have access to that AI teammate…

AI assessment note: “there's an underlying data model backing it, which we call the work graph”

Q takes, it allows people to take software that's built for the masses, built to scale, and sort of customize it in a way to their own interests and, um, and build the set of tools or work within the set of tools that specifically attack their use case and customize software. Do you see that as something that, you know, you can embrace or do you, do you fight that?

A Oh, um, I think that's something we're totally embracing. So again, like, as Asana, we've been thinking hard about our strategy, about where we differentiate, and what value do we provide to enterprises. The value you brought to the enterprises is the fact that we've been thinking so hard about this coordination tax for so many years, like over a decade. We've been thinking about how do we ensure that there's this pyramid of clarity that you get to by using the work craft which defines tasks and projects and portfolios That clearly ladder up to company level goals and mission. And that framework is something that we've provably demonstrated works really well for training AI agents and ensuring that AI agents have the right level of business context, as well as enterprise read memory to go get work done. And so again, like, uh, we want to make sure that Asana is fit for purpose and works in the best possible way for every enterprise out there. And customization, where you can bring your own agent or we can customize one of our pre-built AI teammates, that is a hundred percent part of our strategy. We want to make sure that you're having to do as little thinking about how you want to use Asana and leverage us to get your work done, achieve your mission and vision.

AI assessment note: “I think that's something we're totally embracing.”

Q How do you plan for that? I mean, you sort of, you can only integrate the capabilities that exist today within these models, but you see some of the advancement on the horizon, and is it like we will rebuild it when the model is more capable?

A Well, uh, the good news is the way in which Asana works, uh, the real value we're providing, again, is with the enterprise-weight context and the shared memory. And so that becomes instantly more valuable as the reasoning model gets better. So the things that be, like, the things that we provide as differentiated value, those are things that don't need rebuilding because that's, like, input context. So that's, like, one interesting angle. The second thing is, uh, we're like maximalists in our perspective, which is, hey, if the model can't do something today, like, let's say I'm making this way, it actually can, it can, uh, Opus can go ahead and create really good HTML previews of what your, uh, updated website should look like if the campaign launches. So you can tell one of these agents to create a mock-up, but it can do that. But let's say it didn't do it right now. Uh, our assumptions from an R&D perspective is that, uh, whatever, uh, skill the models lack today, they will have in the future. And so to assume that that is coming and to see, like, how would we support the right frameworks and the right human experiences around that, uh, should it be there? Uh, and that level of, like, maximalist thinking is, is good in this day and age because you're seeing the advances come every, like, four to six weeks.

AI assessment note: “those are things that don't need rebuilding because that's, like, input context”